Dataflow API . projects . locations . jobs

close()

Close httplib2 connections.

create(projectId, location, body=None, replaceJobId=None, view=None, x__xgafv=None)

Creates a Cloud Dataflow job. To create a job, we recommend using `projects.locations.jobs.create` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.create` is not recommended, as your job will always start in `us-central1`. Do not enter confidential information when you supply string values using the API.

Args:
  projectId: string, The ID of the Cloud Platform project that the job belongs to. (required)
  location: string, The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job. (required)
  body: object, The request body.
    The object takes the form of:

{ # Defines a job to be run by the Cloud Dataflow service. Do not enter confidential information when you supply string values using the API.
  "clientRequestId": "A String", # The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.
  "createTime": "A String", # The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.
  "createdFromSnapshotId": "A String", # If this is specified, the job's initial state is populated from the given snapshot.
  "currentState": "A String", # The current state of the job. Jobs are created in the `JOB_STATE_STOPPED` state unless otherwise specified. A job in the `JOB_STATE_RUNNING` state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field might be mutated by the Dataflow service; callers cannot mutate it.
  "currentStateTime": "A String", # The timestamp associated with the current state.
  "environment": { # Describes the environment in which a Dataflow Job runs. # Optional. The environment for the job.
    "clusterManagerApiService": "A String", # The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".
    "dataset": "A String", # Optional. The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}
    "debugOptions": { # Describes any options that have an effect on the debugging of pipelines. # Optional. Any debugging options to be supplied to the job.
      "dataSampling": { # Configuration options for sampling elements. # Configuration options for sampling elements from a running pipeline.
        "behaviors": [ # List of given sampling behaviors to enable. For example, specifying behaviors = [ALWAYS_ON] samples in-flight elements but does not sample exceptions. Can be used to specify multiple behaviors like, behaviors = [ALWAYS_ON, EXCEPTIONS] for specifying periodic sampling and exception sampling. If DISABLED is in the list, then sampling will be disabled and ignore the other given behaviors. Ordering does not matter.
          "A String",
        ],
      },
      "enableHotKeyLogging": True or False, # Optional. When true, enables the logging of the literal hot key to the user's Cloud Logging.
    },
    "experiments": [ # The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.
      "A String",
    ],
    "flexResourceSchedulingGoal": "A String", # Optional. Which Flexible Resource Scheduling mode to run in.
    "internalExperiments": { # Experimental settings.
      "a_key": "", # Properties of the object. Contains field @type with type URL.
    },
    "sdkPipelineOptions": { # The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.
      "a_key": "", # Properties of the object.
    },
    "serviceAccountEmail": "A String", # Optional. Identity to run virtual machines as. Defaults to the default account.
    "serviceKmsKeyName": "A String", # Optional. If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY
    "serviceOptions": [ # Optional. The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).
      "A String",
    ],
    "shuffleMode": "A String", # Output only. The shuffle mode used for the job.
    "streamingMode": "A String", # Optional. Specifies the Streaming Engine message processing guarantees. Reduces cost and latency but might result in duplicate messages committed to storage. Designed to run simple mapping streaming ETL jobs at the lowest cost. For example, Change Data Capture (CDC) to BigQuery is a canonical use case. For more information, see [Set the pipeline streaming mode](https://cloud.google.com/dataflow/docs/guides/streaming-modes).
    "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
    "useStreamingEngineResourceBasedBilling": True or False, # Output only. Whether the job uses the Streaming Engine resource-based billing model.
    "userAgent": { # A description of the process that generated the request.
      "a_key": "", # Properties of the object.
    },
    "version": { # A structure describing which components and their versions of the service are required in order to run the job.
      "a_key": "", # Properties of the object.
    },
    "workerPools": [ # The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.
      { # Describes one particular pool of Cloud Dataflow workers to be instantiated by the Cloud Dataflow service in order to perform the computations required by a job. Note that a workflow job may use multiple pools, in order to match the various computational requirements of the various stages of the job.
        "autoscalingSettings": { # Settings for WorkerPool autoscaling. # Settings for autoscaling of this WorkerPool.
          "algorithm": "A String", # The algorithm to use for autoscaling.
          "maxNumWorkers": 42, # The maximum number of workers to cap scaling at.
        },
        "dataDisks": [ # Data disks that are used by a VM in this workflow.
          { # Describes the data disk used by a workflow job.
            "diskType": "A String", # Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard
            "mountPoint": "A String", # Directory in a VM where disk is mounted.
            "sizeGb": 42, # Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
          },
        ],
        "defaultPackageSet": "A String", # The default package set to install. This allows the service to select a default set of packages which are useful to worker harnesses written in a particular language.
        "diskSizeGb": 42, # Size of root disk for VMs, in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
        "diskSourceImage": "A String", # Fully qualified source image for disks.
        "diskType": "A String", # Type of root disk for VMs. If empty or unspecified, the service will attempt to choose a reasonable default.
        "ipConfiguration": "A String", # Configuration for VM IPs.
        "kind": "A String", # The kind of the worker pool; currently only `harness` and `shuffle` are supported.
        "machineType": "A String", # Machine type (e.g. "n1-standard-1"). If empty or unspecified, the service will attempt to choose a reasonable default.
        "metadata": { # Metadata to set on the Google Compute Engine VMs.
          "a_key": "A String",
        },
        "network": "A String", # Network to which VMs will be assigned. If empty or unspecified, the service will use the network "default".
        "numThreadsPerWorker": 42, # The number of threads per worker harness. If empty or unspecified, the service will choose a number of threads (according to the number of cores on the selected machine type for batch, or 1 by convention for streaming).
        "numWorkers": 42, # Number of Google Compute Engine workers in this pool needed to execute the job. If zero or unspecified, the service will attempt to choose a reasonable default.
        "onHostMaintenance": "A String", # The action to take on host maintenance, as defined by the Google Compute Engine API.
        "packages": [ # Packages to be installed on workers.
          { # The packages that must be installed in order for a worker to run the steps of the Cloud Dataflow job that will be assigned to its worker pool. This is the mechanism by which the Cloud Dataflow SDK causes code to be loaded onto the workers. For example, the Cloud Dataflow Java SDK might use this to install jars containing the user's code and all of the various dependencies (libraries, data files, etc.) required in order for that code to run.
            "location": "A String", # The resource to read the package from. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket} bucket.storage.googleapis.com/
            "name": "A String", # The name of the package.
          },
        ],
        "poolArgs": { # Extra arguments for this worker pool.
          "a_key": "", # Properties of the object. Contains field @type with type URL.
        },
        "sdkHarnessContainerImages": [ # Set of SDK harness containers needed to execute this pipeline. This will only be set in the Fn API path. For non-cross-language pipelines this should have only one entry. Cross-language pipelines will have two or more entries.
          { # Defines an SDK harness container for executing Dataflow pipelines.
            "capabilities": [ # The set of capabilities enumerated in the above Environment proto. See also [beam_runner_api.proto](https://github.com/apache/beam/blob/master/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/beam_runner_api.proto)
              "A String",
            ],
            "containerImage": "A String", # A docker container image that resides in Google Container Registry.
            "environmentId": "A String", # Environment ID for the Beam runner API proto Environment that corresponds to the current SDK Harness.
            "useSingleCorePerContainer": True or False, # If true, recommends the Dataflow service to use only one core per SDK container instance with this image. If false (or unset) recommends using more than one core per SDK container instance with this image for efficiency. Note that Dataflow service may choose to override this property if needed.
          },
        ],
        "subnetwork": "A String", # Subnetwork to which VMs will be assigned, if desired. Expected to be of the form "regions/REGION/subnetworks/SUBNETWORK".
        "taskrunnerSettings": { # Taskrunner configuration settings. # Settings passed through to Google Compute Engine workers when using the standard Dataflow task runner. Users should ignore this field.
          "alsologtostderr": True or False, # Whether to also send taskrunner log info to stderr.
          "baseTaskDir": "A String", # The location on the worker for task-specific subdirectories.
          "baseUrl": "A String", # The base URL for the taskrunner to use when accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
          "commandlinesFileName": "A String", # The file to store preprocessing commands in.
          "continueOnException": True or False, # Whether to continue taskrunner if an exception is hit.
          "dataflowApiVersion": "A String", # The API version of endpoint, e.g. "v1b3"
          "harnessCommand": "A String", # The command to launch the worker harness.
          "languageHint": "A String", # The suggested backend language.
          "logDir": "A String", # The directory on the VM to store logs.
          "logToSerialconsole": True or False, # Whether to send taskrunner log info to Google Compute Engine VM serial console.
          "logUploadLocation": "A String", # Indicates where to put logs. If this is not specified, the logs will not be uploaded. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
          "oauthScopes": [ # The OAuth2 scopes to be requested by the taskrunner in order to access the Cloud Dataflow API.
            "A String",
          ],
          "parallelWorkerSettings": { # Provides data to pass through to the worker harness. # The settings to pass to the parallel worker harness.
            "baseUrl": "A String", # The base URL for accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
            "reportingEnabled": True or False, # Whether to send work progress updates to the service.
            "servicePath": "A String", # The Cloud Dataflow service path relative to the root URL, for example, "dataflow/v1b3/projects".
            "shuffleServicePath": "A String", # The Shuffle service path relative to the root URL, for example, "shuffle/v1beta1".
            "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
            "workerId": "A String", # The ID of the worker running this pipeline.
          },
          "streamingWorkerMainClass": "A String", # The streaming worker main class name.
          "taskGroup": "A String", # The UNIX group ID on the worker VM to use for tasks launched by taskrunner; e.g. "wheel".
          "taskUser": "A String", # The UNIX user ID on the worker VM to use for tasks launched by taskrunner; e.g. "root".
          "tempStoragePrefix": "A String", # The prefix of the resources the taskrunner should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
          "vmId": "A String", # The ID string of the VM.
          "workflowFileName": "A String", # The file to store the workflow in.
        },
        "teardownPolicy": "A String", # Sets the policy for determining when to turndown worker pool. Allowed values are: `TEARDOWN_ALWAYS`, `TEARDOWN_ON_SUCCESS`, and `TEARDOWN_NEVER`. `TEARDOWN_ALWAYS` means workers are always torn down regardless of whether the job succeeds. `TEARDOWN_ON_SUCCESS` means workers are torn down if the job succeeds. `TEARDOWN_NEVER` means the workers are never torn down. If the workers are not torn down by the service, they will continue to run and use Google Compute Engine VM resources in the user's project until they are explicitly terminated by the user. Because of this, Google recommends using the `TEARDOWN_ALWAYS` policy except for small, manually supervised test jobs. If unknown or unspecified, the service will attempt to choose a reasonable default.
        "workerHarnessContainerImage": "A String", # Required. Docker container image that executes the Cloud Dataflow worker harness, residing in Google Container Registry. Deprecated for the Fn API path. Use sdk_harness_container_images instead.
        "zone": "A String", # Zone to run the worker pools in. If empty or unspecified, the service will attempt to choose a reasonable default.
      },
    ],
    "workerRegion": "A String", # Optional. The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.
    "workerZone": "A String", # Optional. The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.
  },
  "executionInfo": { # Additional information about how a Cloud Dataflow job will be executed that isn't contained in the submitted job. # Deprecated.
    "stages": { # A mapping from each stage to the information about that stage.
      "a_key": { # Contains information about how a particular google.dataflow.v1beta3.Step will be executed.
        "stepName": [ # The steps associated with the execution stage. Note that stages may have several steps, and that a given step might be run by more than one stage.
          "A String",
        ],
      },
    },
  },
  "id": "A String", # The unique ID of this job. This field is set by the Dataflow service when the job is created, and is immutable for the life of the job.
  "jobMetadata": { # Metadata available primarily for filtering jobs. Will be included in the ListJob response and Job SUMMARY view. # This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.
    "bigTableDetails": [ # Identification of a Cloud Bigtable source used in the Dataflow job.
      { # Metadata for a Cloud Bigtable connector used by the job.
        "instanceId": "A String", # InstanceId accessed in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
        "tableId": "A String", # TableId accessed in the connection.
      },
    ],
    "bigqueryDetails": [ # Identification of a BigQuery source used in the Dataflow job.
      { # Metadata for a BigQuery connector used by the job.
        "dataset": "A String", # Dataset accessed in the connection.
        "projectId": "A String", # Project accessed in the connection.
        "query": "A String", # Query used to access data in the connection.
        "table": "A String", # Table accessed in the connection.
      },
    ],
    "datastoreDetails": [ # Identification of a Datastore source used in the Dataflow job.
      { # Metadata for a Datastore connector used by the job.
        "namespace": "A String", # Namespace used in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
      },
    ],
    "fileDetails": [ # Identification of a File source used in the Dataflow job.
      { # Metadata for a File connector used by the job.
        "filePattern": "A String", # File Pattern used to access files by the connector.
      },
    ],
    "pubsubDetails": [ # Identification of a Pub/Sub source used in the Dataflow job.
      { # Metadata for a Pub/Sub connector used by the job.
        "subscription": "A String", # Subscription used in the connection.
        "topic": "A String", # Topic accessed in the connection.
      },
    ],
    "sdkVersion": { # The version of the SDK used to run the job. # The SDK version used to run the job.
      "bugs": [ # Output only. Known bugs found in this SDK version.
        { # A bug found in the Dataflow SDK.
          "severity": "A String", # Output only. How severe the SDK bug is.
          "type": "A String", # Output only. Describes the impact of this SDK bug.
          "uri": "A String", # Output only. Link to more information on the bug.
        },
      ],
      "sdkSupportStatus": "A String", # The support status for this SDK version.
      "version": "A String", # The version of the SDK used to run the job.
      "versionDisplayName": "A String", # A readable string describing the version of the SDK.
    },
    "spannerDetails": [ # Identification of a Spanner source used in the Dataflow job.
      { # Metadata for a Spanner connector used by the job.
        "databaseId": "A String", # DatabaseId accessed in the connection.
        "instanceId": "A String", # InstanceId accessed in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
      },
    ],
    "userDisplayProperties": { # List of display properties to help UI filter jobs.
      "a_key": "A String",
    },
  },
  "labels": { # User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.
    "a_key": "A String",
  },
  "location": "A String", # Optional. The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.
  "name": "A String", # Optional. The user-specified Dataflow job name. Only one active job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a job with the same name as an active job that already exists, the attempt returns the existing job. The name must match the regular expression `[a-z]([-a-z0-9]{0,1022}[a-z0-9])?`
  "pipelineDescription": { # A descriptive representation of submitted pipeline as well as the executed form. This data is provided by the Dataflow service for ease of visualizing the pipeline and interpreting Dataflow provided metrics. # Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.
    "displayData": [ # Pipeline level display data.
      { # Data provided with a pipeline or transform to provide descriptive info.
        "boolValue": True or False, # Contains value if the data is of a boolean type.
        "durationValue": "A String", # Contains value if the data is of duration type.
        "floatValue": 3.14, # Contains value if the data is of float type.
        "int64Value": "A String", # Contains value if the data is of int64 type.
        "javaClassValue": "A String", # Contains value if the data is of java class type.
        "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
        "label": "A String", # An optional label to display in a dax UI for the element.
        "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
        "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
        "strValue": "A String", # Contains value if the data is of string type.
        "timestampValue": "A String", # Contains value if the data is of timestamp type.
        "url": "A String", # An optional full URL.
      },
    ],
    "executionPipelineStage": [ # Description of each stage of execution of the pipeline.
      { # Description of the composing transforms, names/ids, and input/outputs of a stage of execution. Some composing transforms and sources may have been generated by the Dataflow service during execution planning.
        "componentSource": [ # Collections produced and consumed by component transforms of this stage.
          { # Description of an interstitial value between transforms in an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "userName": "A String", # Human-readable name for this transform; may be user or system generated.
          },
        ],
        "componentTransform": [ # Transforms that comprise this execution stage.
          { # Description of a transform executed as part of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransform": "A String", # User name for the original user transform with which this transform is most closely associated.
            "userName": "A String", # Human-readable name for this transform; may be user or system generated.
          },
        ],
        "id": "A String", # Dataflow service generated id for this stage.
        "inputSource": [ # Input sources for this stage.
          { # Description of an input or output of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "sizeBytes": "A String", # Size of the source, if measurable.
            "userName": "A String", # Human-readable name for this source; may be user or system generated.
          },
        ],
        "kind": "A String", # Type of transform this stage is executing.
        "name": "A String", # Dataflow service generated name for this stage.
        "outputSource": [ # Output sources for this stage.
          { # Description of an input or output of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "sizeBytes": "A String", # Size of the source, if measurable.
            "userName": "A String", # Human-readable name for this source; may be user or system generated.
          },
        ],
        "prerequisiteStage": [ # Other stages that must complete before this stage can run.
          "A String",
        ],
      },
    ],
    "originalPipelineTransform": [ # Description of each transform in the pipeline and collections between them.
      { # Description of the type, names/ids, and input/outputs for a transform.
        "displayData": [ # Transform-specific display data.
          { # Data provided with a pipeline or transform to provide descriptive info.
            "boolValue": True or False, # Contains value if the data is of a boolean type.
            "durationValue": "A String", # Contains value if the data is of duration type.
            "floatValue": 3.14, # Contains value if the data is of float type.
            "int64Value": "A String", # Contains value if the data is of int64 type.
            "javaClassValue": "A String", # Contains value if the data is of java class type.
            "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
            "label": "A String", # An optional label to display in a dax UI for the element.
            "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
            "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
            "strValue": "A String", # Contains value if the data is of string type.
            "timestampValue": "A String", # Contains value if the data is of timestamp type.
            "url": "A String", # An optional full URL.
          },
        ],
        "id": "A String", # SDK generated id of this transform instance.
        "inputCollectionName": [ # User names for all collection inputs to this transform.
          "A String",
        ],
        "kind": "A String", # Type of transform.
        "name": "A String", # User provided name for this transform instance.
        "outputCollectionName": [ # User names for all collection outputs to this transform.
          "A String",
        ],
      },
    ],
    "stepNamesHash": "A String", # A hash value of the submitted pipeline portable graph step names if exists.
  },
  "projectId": "A String", # The ID of the Google Cloud project that the job belongs to.
  "replaceJobId": "A String", # If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a `CreateJobRequest`, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.
  "replacedByJobId": "A String", # If another job is an update of this job (and thus, this job is in `JOB_STATE_UPDATED`), this field contains the ID of that job.
  "requestedState": "A String", # The job's requested state. Applies to `UpdateJob` requests. Set `requested_state` with `UpdateJob` requests to switch between the states `JOB_STATE_STOPPED` and `JOB_STATE_RUNNING`. You can also use `UpdateJob` requests to change a job's state from `JOB_STATE_RUNNING` to `JOB_STATE_CANCELLED`, `JOB_STATE_DONE`, or `JOB_STATE_DRAINED`. These states irrevocably terminate the job if it hasn't already reached a terminal state. This field has no effect on `CreateJob` requests.
  "runtimeUpdatableParams": { # Additional job parameters that can only be updated during runtime using the projects.jobs.update method. These fields have no effect when specified during job creation. # This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.
    "maxNumWorkers": 42, # The maximum number of workers to cap autoscaling at. This field is currently only supported for Streaming Engine jobs.
    "minNumWorkers": 42, # The minimum number of workers to scale down to. This field is currently only supported for Streaming Engine jobs.
    "workerUtilizationHint": 3.14, # Target worker utilization, compared against the aggregate utilization of the worker pool by autoscaler, to determine upscaling and downscaling when absent other constraints such as backlog. For more information, see [Update an existing pipeline](https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline).
  },
  "satisfiesPzi": True or False, # Output only. Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
  "satisfiesPzs": True or False, # Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
  "serviceResources": { # Resources used by the Dataflow Service to run the job. # Output only. Resources used by the Dataflow Service to run the job.
    "zones": [ # Output only. List of Cloud Zones being used by the Dataflow Service for this job. Example: us-central1-c
      "A String",
    ],
  },
  "stageStates": [ # This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.
    { # A message describing the state of a particular execution stage.
      "currentStateTime": "A String", # The time at which the stage transitioned to this state.
      "executionStageName": "A String", # The name of the execution stage.
      "executionStageState": "A String", # Executions stage states allow the same set of values as JobState.
    },
  ],
  "startTime": "A String", # The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.
  "steps": [ # Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.
    { # Defines a particular step within a Cloud Dataflow job. A job consists of multiple steps, each of which performs some specific operation as part of the overall job. Data is typically passed from one step to another as part of the job. **Note:** The properties of this object are not stable and might change. Here's an example of a sequence of steps which together implement a Map-Reduce job: * Read a collection of data from some source, parsing the collection's elements. * Validate the elements. * Apply a user-defined function to map each element to some value and extract an element-specific key value. * Group elements with the same key into a single element with that key, transforming a multiply-keyed collection into a uniquely-keyed collection. * Write the elements out to some data sink. Note that the Cloud Dataflow service may be used to run many different types of jobs, not just Map-Reduce.
      "kind": "A String", # The kind of step in the Cloud Dataflow job.
      "name": "A String", # The name that identifies the step. This must be unique for each step with respect to all other steps in the Cloud Dataflow job.
      "properties": { # Named properties associated with the step. Each kind of predefined step has its own required set of properties. Must be provided on Create. Only retrieved with JOB_VIEW_ALL.
        "a_key": "", # Properties of the object.
      },
    },
  ],
  "stepsLocation": "A String", # The Cloud Storage location where the steps are stored.
  "tempFiles": [ # A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
    "A String",
  ],
  "transformNameMapping": { # Optional. The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.
    "a_key": "A String",
  },
  "type": "A String", # Optional. The type of Dataflow job.
}

  replaceJobId: string, Deprecated. This field is now in the Job message.
  view: string, The level of information requested in response.
    Allowed values
      JOB_VIEW_UNKNOWN - The job view to return isn't specified, or is unknown. Responses will contain at least the `JOB_VIEW_SUMMARY` information, and may contain additional information.
      JOB_VIEW_SUMMARY - Request summary information only: Project ID, Job ID, job name, job type, job status, start/end time, and Cloud SDK version details.
      JOB_VIEW_ALL - Request all information available for this job. When the job is in `JOB_STATE_PENDING`, the job has been created but is not yet running, and not all job information is available. For complete job information, wait until the job in is `JOB_STATE_RUNNING`. For more information, see [JobState](https://cloud.google.com/dataflow/docs/reference/rest/v1b3/projects.jobs#jobstate).
      JOB_VIEW_DESCRIPTION - Request summary info and limited job description data for steps, labels and environment.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Defines a job to be run by the Cloud Dataflow service. Do not enter confidential information when you supply string values using the API.
  "clientRequestId": "A String", # The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.
  "createTime": "A String", # The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.
  "createdFromSnapshotId": "A String", # If this is specified, the job's initial state is populated from the given snapshot.
  "currentState": "A String", # The current state of the job. Jobs are created in the `JOB_STATE_STOPPED` state unless otherwise specified. A job in the `JOB_STATE_RUNNING` state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field might be mutated by the Dataflow service; callers cannot mutate it.
  "currentStateTime": "A String", # The timestamp associated with the current state.
  "environment": { # Describes the environment in which a Dataflow Job runs. # Optional. The environment for the job.
    "clusterManagerApiService": "A String", # The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".
    "dataset": "A String", # Optional. The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}
    "debugOptions": { # Describes any options that have an effect on the debugging of pipelines. # Optional. Any debugging options to be supplied to the job.
      "dataSampling": { # Configuration options for sampling elements. # Configuration options for sampling elements from a running pipeline.
        "behaviors": [ # List of given sampling behaviors to enable. For example, specifying behaviors = [ALWAYS_ON] samples in-flight elements but does not sample exceptions. Can be used to specify multiple behaviors like, behaviors = [ALWAYS_ON, EXCEPTIONS] for specifying periodic sampling and exception sampling. If DISABLED is in the list, then sampling will be disabled and ignore the other given behaviors. Ordering does not matter.
          "A String",
        ],
      },
      "enableHotKeyLogging": True or False, # Optional. When true, enables the logging of the literal hot key to the user's Cloud Logging.
    },
    "experiments": [ # The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.
      "A String",
    ],
    "flexResourceSchedulingGoal": "A String", # Optional. Which Flexible Resource Scheduling mode to run in.
    "internalExperiments": { # Experimental settings.
      "a_key": "", # Properties of the object. Contains field @type with type URL.
    },
    "sdkPipelineOptions": { # The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.
      "a_key": "", # Properties of the object.
    },
    "serviceAccountEmail": "A String", # Optional. Identity to run virtual machines as. Defaults to the default account.
    "serviceKmsKeyName": "A String", # Optional. If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY
    "serviceOptions": [ # Optional. The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).
      "A String",
    ],
    "shuffleMode": "A String", # Output only. The shuffle mode used for the job.
    "streamingMode": "A String", # Optional. Specifies the Streaming Engine message processing guarantees. Reduces cost and latency but might result in duplicate messages committed to storage. Designed to run simple mapping streaming ETL jobs at the lowest cost. For example, Change Data Capture (CDC) to BigQuery is a canonical use case. For more information, see [Set the pipeline streaming mode](https://cloud.google.com/dataflow/docs/guides/streaming-modes).
    "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
    "useStreamingEngineResourceBasedBilling": True or False, # Output only. Whether the job uses the Streaming Engine resource-based billing model.
    "userAgent": { # A description of the process that generated the request.
      "a_key": "", # Properties of the object.
    },
    "version": { # A structure describing which components and their versions of the service are required in order to run the job.
      "a_key": "", # Properties of the object.
    },
    "workerPools": [ # The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.
      { # Describes one particular pool of Cloud Dataflow workers to be instantiated by the Cloud Dataflow service in order to perform the computations required by a job. Note that a workflow job may use multiple pools, in order to match the various computational requirements of the various stages of the job.
        "autoscalingSettings": { # Settings for WorkerPool autoscaling. # Settings for autoscaling of this WorkerPool.
          "algorithm": "A String", # The algorithm to use for autoscaling.
          "maxNumWorkers": 42, # The maximum number of workers to cap scaling at.
        },
        "dataDisks": [ # Data disks that are used by a VM in this workflow.
          { # Describes the data disk used by a workflow job.
            "diskType": "A String", # Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard
            "mountPoint": "A String", # Directory in a VM where disk is mounted.
            "sizeGb": 42, # Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
          },
        ],
        "defaultPackageSet": "A String", # The default package set to install. This allows the service to select a default set of packages which are useful to worker harnesses written in a particular language.
        "diskSizeGb": 42, # Size of root disk for VMs, in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
        "diskSourceImage": "A String", # Fully qualified source image for disks.
        "diskType": "A String", # Type of root disk for VMs. If empty or unspecified, the service will attempt to choose a reasonable default.
        "ipConfiguration": "A String", # Configuration for VM IPs.
        "kind": "A String", # The kind of the worker pool; currently only `harness` and `shuffle` are supported.
        "machineType": "A String", # Machine type (e.g. "n1-standard-1"). If empty or unspecified, the service will attempt to choose a reasonable default.
        "metadata": { # Metadata to set on the Google Compute Engine VMs.
          "a_key": "A String",
        },
        "network": "A String", # Network to which VMs will be assigned. If empty or unspecified, the service will use the network "default".
        "numThreadsPerWorker": 42, # The number of threads per worker harness. If empty or unspecified, the service will choose a number of threads (according to the number of cores on the selected machine type for batch, or 1 by convention for streaming).
        "numWorkers": 42, # Number of Google Compute Engine workers in this pool needed to execute the job. If zero or unspecified, the service will attempt to choose a reasonable default.
        "onHostMaintenance": "A String", # The action to take on host maintenance, as defined by the Google Compute Engine API.
        "packages": [ # Packages to be installed on workers.
          { # The packages that must be installed in order for a worker to run the steps of the Cloud Dataflow job that will be assigned to its worker pool. This is the mechanism by which the Cloud Dataflow SDK causes code to be loaded onto the workers. For example, the Cloud Dataflow Java SDK might use this to install jars containing the user's code and all of the various dependencies (libraries, data files, etc.) required in order for that code to run.
            "location": "A String", # The resource to read the package from. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket} bucket.storage.googleapis.com/
            "name": "A String", # The name of the package.
          },
        ],
        "poolArgs": { # Extra arguments for this worker pool.
          "a_key": "", # Properties of the object. Contains field @type with type URL.
        },
        "sdkHarnessContainerImages": [ # Set of SDK harness containers needed to execute this pipeline. This will only be set in the Fn API path. For non-cross-language pipelines this should have only one entry. Cross-language pipelines will have two or more entries.
          { # Defines an SDK harness container for executing Dataflow pipelines.
            "capabilities": [ # The set of capabilities enumerated in the above Environment proto. See also [beam_runner_api.proto](https://github.com/apache/beam/blob/master/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/beam_runner_api.proto)
              "A String",
            ],
            "containerImage": "A String", # A docker container image that resides in Google Container Registry.
            "environmentId": "A String", # Environment ID for the Beam runner API proto Environment that corresponds to the current SDK Harness.
            "useSingleCorePerContainer": True or False, # If true, recommends the Dataflow service to use only one core per SDK container instance with this image. If false (or unset) recommends using more than one core per SDK container instance with this image for efficiency. Note that Dataflow service may choose to override this property if needed.
          },
        ],
        "subnetwork": "A String", # Subnetwork to which VMs will be assigned, if desired. Expected to be of the form "regions/REGION/subnetworks/SUBNETWORK".
        "taskrunnerSettings": { # Taskrunner configuration settings. # Settings passed through to Google Compute Engine workers when using the standard Dataflow task runner. Users should ignore this field.
          "alsologtostderr": True or False, # Whether to also send taskrunner log info to stderr.
          "baseTaskDir": "A String", # The location on the worker for task-specific subdirectories.
          "baseUrl": "A String", # The base URL for the taskrunner to use when accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
          "commandlinesFileName": "A String", # The file to store preprocessing commands in.
          "continueOnException": True or False, # Whether to continue taskrunner if an exception is hit.
          "dataflowApiVersion": "A String", # The API version of endpoint, e.g. "v1b3"
          "harnessCommand": "A String", # The command to launch the worker harness.
          "languageHint": "A String", # The suggested backend language.
          "logDir": "A String", # The directory on the VM to store logs.
          "logToSerialconsole": True or False, # Whether to send taskrunner log info to Google Compute Engine VM serial console.
          "logUploadLocation": "A String", # Indicates where to put logs. If this is not specified, the logs will not be uploaded. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
          "oauthScopes": [ # The OAuth2 scopes to be requested by the taskrunner in order to access the Cloud Dataflow API.
            "A String",
          ],
          "parallelWorkerSettings": { # Provides data to pass through to the worker harness. # The settings to pass to the parallel worker harness.
            "baseUrl": "A String", # The base URL for accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
            "reportingEnabled": True or False, # Whether to send work progress updates to the service.
            "servicePath": "A String", # The Cloud Dataflow service path relative to the root URL, for example, "dataflow/v1b3/projects".
            "shuffleServicePath": "A String", # The Shuffle service path relative to the root URL, for example, "shuffle/v1beta1".
            "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
            "workerId": "A String", # The ID of the worker running this pipeline.
          },
          "streamingWorkerMainClass": "A String", # The streaming worker main class name.
          "taskGroup": "A String", # The UNIX group ID on the worker VM to use for tasks launched by taskrunner; e.g. "wheel".
          "taskUser": "A String", # The UNIX user ID on the worker VM to use for tasks launched by taskrunner; e.g. "root".
          "tempStoragePrefix": "A String", # The prefix of the resources the taskrunner should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
          "vmId": "A String", # The ID string of the VM.
          "workflowFileName": "A String", # The file to store the workflow in.
        },
        "teardownPolicy": "A String", # Sets the policy for determining when to turndown worker pool. Allowed values are: `TEARDOWN_ALWAYS`, `TEARDOWN_ON_SUCCESS`, and `TEARDOWN_NEVER`. `TEARDOWN_ALWAYS` means workers are always torn down regardless of whether the job succeeds. `TEARDOWN_ON_SUCCESS` means workers are torn down if the job succeeds. `TEARDOWN_NEVER` means the workers are never torn down. If the workers are not torn down by the service, they will continue to run and use Google Compute Engine VM resources in the user's project until they are explicitly terminated by the user. Because of this, Google recommends using the `TEARDOWN_ALWAYS` policy except for small, manually supervised test jobs. If unknown or unspecified, the service will attempt to choose a reasonable default.
        "workerHarnessContainerImage": "A String", # Required. Docker container image that executes the Cloud Dataflow worker harness, residing in Google Container Registry. Deprecated for the Fn API path. Use sdk_harness_container_images instead.
        "zone": "A String", # Zone to run the worker pools in. If empty or unspecified, the service will attempt to choose a reasonable default.
      },
    ],
    "workerRegion": "A String", # Optional. The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.
    "workerZone": "A String", # Optional. The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.
  },
  "executionInfo": { # Additional information about how a Cloud Dataflow job will be executed that isn't contained in the submitted job. # Deprecated.
    "stages": { # A mapping from each stage to the information about that stage.
      "a_key": { # Contains information about how a particular google.dataflow.v1beta3.Step will be executed.
        "stepName": [ # The steps associated with the execution stage. Note that stages may have several steps, and that a given step might be run by more than one stage.
          "A String",
        ],
      },
    },
  },
  "id": "A String", # The unique ID of this job. This field is set by the Dataflow service when the job is created, and is immutable for the life of the job.
  "jobMetadata": { # Metadata available primarily for filtering jobs. Will be included in the ListJob response and Job SUMMARY view. # This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.
    "bigTableDetails": [ # Identification of a Cloud Bigtable source used in the Dataflow job.
      { # Metadata for a Cloud Bigtable connector used by the job.
        "instanceId": "A String", # InstanceId accessed in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
        "tableId": "A String", # TableId accessed in the connection.
      },
    ],
    "bigqueryDetails": [ # Identification of a BigQuery source used in the Dataflow job.
      { # Metadata for a BigQuery connector used by the job.
        "dataset": "A String", # Dataset accessed in the connection.
        "projectId": "A String", # Project accessed in the connection.
        "query": "A String", # Query used to access data in the connection.
        "table": "A String", # Table accessed in the connection.
      },
    ],
    "datastoreDetails": [ # Identification of a Datastore source used in the Dataflow job.
      { # Metadata for a Datastore connector used by the job.
        "namespace": "A String", # Namespace used in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
      },
    ],
    "fileDetails": [ # Identification of a File source used in the Dataflow job.
      { # Metadata for a File connector used by the job.
        "filePattern": "A String", # File Pattern used to access files by the connector.
      },
    ],
    "pubsubDetails": [ # Identification of a Pub/Sub source used in the Dataflow job.
      { # Metadata for a Pub/Sub connector used by the job.
        "subscription": "A String", # Subscription used in the connection.
        "topic": "A String", # Topic accessed in the connection.
      },
    ],
    "sdkVersion": { # The version of the SDK used to run the job. # The SDK version used to run the job.
      "bugs": [ # Output only. Known bugs found in this SDK version.
        { # A bug found in the Dataflow SDK.
          "severity": "A String", # Output only. How severe the SDK bug is.
          "type": "A String", # Output only. Describes the impact of this SDK bug.
          "uri": "A String", # Output only. Link to more information on the bug.
        },
      ],
      "sdkSupportStatus": "A String", # The support status for this SDK version.
      "version": "A String", # The version of the SDK used to run the job.
      "versionDisplayName": "A String", # A readable string describing the version of the SDK.
    },
    "spannerDetails": [ # Identification of a Spanner source used in the Dataflow job.
      { # Metadata for a Spanner connector used by the job.
        "databaseId": "A String", # DatabaseId accessed in the connection.
        "instanceId": "A String", # InstanceId accessed in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
      },
    ],
    "userDisplayProperties": { # List of display properties to help UI filter jobs.
      "a_key": "A String",
    },
  },
  "labels": { # User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.
    "a_key": "A String",
  },
  "location": "A String", # Optional. The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.
  "name": "A String", # Optional. The user-specified Dataflow job name. Only one active job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a job with the same name as an active job that already exists, the attempt returns the existing job. The name must match the regular expression `[a-z]([-a-z0-9]{0,1022}[a-z0-9])?`
  "pipelineDescription": { # A descriptive representation of submitted pipeline as well as the executed form. This data is provided by the Dataflow service for ease of visualizing the pipeline and interpreting Dataflow provided metrics. # Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.
    "displayData": [ # Pipeline level display data.
      { # Data provided with a pipeline or transform to provide descriptive info.
        "boolValue": True or False, # Contains value if the data is of a boolean type.
        "durationValue": "A String", # Contains value if the data is of duration type.
        "floatValue": 3.14, # Contains value if the data is of float type.
        "int64Value": "A String", # Contains value if the data is of int64 type.
        "javaClassValue": "A String", # Contains value if the data is of java class type.
        "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
        "label": "A String", # An optional label to display in a dax UI for the element.
        "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
        "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
        "strValue": "A String", # Contains value if the data is of string type.
        "timestampValue": "A String", # Contains value if the data is of timestamp type.
        "url": "A String", # An optional full URL.
      },
    ],
    "executionPipelineStage": [ # Description of each stage of execution of the pipeline.
      { # Description of the composing transforms, names/ids, and input/outputs of a stage of execution. Some composing transforms and sources may have been generated by the Dataflow service during execution planning.
        "componentSource": [ # Collections produced and consumed by component transforms of this stage.
          { # Description of an interstitial value between transforms in an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "userName": "A String", # Human-readable name for this transform; may be user or system generated.
          },
        ],
        "componentTransform": [ # Transforms that comprise this execution stage.
          { # Description of a transform executed as part of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransform": "A String", # User name for the original user transform with which this transform is most closely associated.
            "userName": "A String", # Human-readable name for this transform; may be user or system generated.
          },
        ],
        "id": "A String", # Dataflow service generated id for this stage.
        "inputSource": [ # Input sources for this stage.
          { # Description of an input or output of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "sizeBytes": "A String", # Size of the source, if measurable.
            "userName": "A String", # Human-readable name for this source; may be user or system generated.
          },
        ],
        "kind": "A String", # Type of transform this stage is executing.
        "name": "A String", # Dataflow service generated name for this stage.
        "outputSource": [ # Output sources for this stage.
          { # Description of an input or output of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "sizeBytes": "A String", # Size of the source, if measurable.
            "userName": "A String", # Human-readable name for this source; may be user or system generated.
          },
        ],
        "prerequisiteStage": [ # Other stages that must complete before this stage can run.
          "A String",
        ],
      },
    ],
    "originalPipelineTransform": [ # Description of each transform in the pipeline and collections between them.
      { # Description of the type, names/ids, and input/outputs for a transform.
        "displayData": [ # Transform-specific display data.
          { # Data provided with a pipeline or transform to provide descriptive info.
            "boolValue": True or False, # Contains value if the data is of a boolean type.
            "durationValue": "A String", # Contains value if the data is of duration type.
            "floatValue": 3.14, # Contains value if the data is of float type.
            "int64Value": "A String", # Contains value if the data is of int64 type.
            "javaClassValue": "A String", # Contains value if the data is of java class type.
            "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
            "label": "A String", # An optional label to display in a dax UI for the element.
            "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
            "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
            "strValue": "A String", # Contains value if the data is of string type.
            "timestampValue": "A String", # Contains value if the data is of timestamp type.
            "url": "A String", # An optional full URL.
          },
        ],
        "id": "A String", # SDK generated id of this transform instance.
        "inputCollectionName": [ # User names for all collection inputs to this transform.
          "A String",
        ],
        "kind": "A String", # Type of transform.
        "name": "A String", # User provided name for this transform instance.
        "outputCollectionName": [ # User names for all collection outputs to this transform.
          "A String",
        ],
      },
    ],
    "stepNamesHash": "A String", # A hash value of the submitted pipeline portable graph step names if exists.
  },
  "projectId": "A String", # The ID of the Google Cloud project that the job belongs to.
  "replaceJobId": "A String", # If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a `CreateJobRequest`, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.
  "replacedByJobId": "A String", # If another job is an update of this job (and thus, this job is in `JOB_STATE_UPDATED`), this field contains the ID of that job.
  "requestedState": "A String", # The job's requested state. Applies to `UpdateJob` requests. Set `requested_state` with `UpdateJob` requests to switch between the states `JOB_STATE_STOPPED` and `JOB_STATE_RUNNING`. You can also use `UpdateJob` requests to change a job's state from `JOB_STATE_RUNNING` to `JOB_STATE_CANCELLED`, `JOB_STATE_DONE`, or `JOB_STATE_DRAINED`. These states irrevocably terminate the job if it hasn't already reached a terminal state. This field has no effect on `CreateJob` requests.
  "runtimeUpdatableParams": { # Additional job parameters that can only be updated during runtime using the projects.jobs.update method. These fields have no effect when specified during job creation. # This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.
    "maxNumWorkers": 42, # The maximum number of workers to cap autoscaling at. This field is currently only supported for Streaming Engine jobs.
    "minNumWorkers": 42, # The minimum number of workers to scale down to. This field is currently only supported for Streaming Engine jobs.
    "workerUtilizationHint": 3.14, # Target worker utilization, compared against the aggregate utilization of the worker pool by autoscaler, to determine upscaling and downscaling when absent other constraints such as backlog. For more information, see [Update an existing pipeline](https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline).
  },
  "satisfiesPzi": True or False, # Output only. Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
  "satisfiesPzs": True or False, # Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
  "serviceResources": { # Resources used by the Dataflow Service to run the job. # Output only. Resources used by the Dataflow Service to run the job.
    "zones": [ # Output only. List of Cloud Zones being used by the Dataflow Service for this job. Example: us-central1-c
      "A String",
    ],
  },
  "stageStates": [ # This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.
    { # A message describing the state of a particular execution stage.
      "currentStateTime": "A String", # The time at which the stage transitioned to this state.
      "executionStageName": "A String", # The name of the execution stage.
      "executionStageState": "A String", # Executions stage states allow the same set of values as JobState.
    },
  ],
  "startTime": "A String", # The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.
  "steps": [ # Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.
    { # Defines a particular step within a Cloud Dataflow job. A job consists of multiple steps, each of which performs some specific operation as part of the overall job. Data is typically passed from one step to another as part of the job. **Note:** The properties of this object are not stable and might change. Here's an example of a sequence of steps which together implement a Map-Reduce job: * Read a collection of data from some source, parsing the collection's elements. * Validate the elements. * Apply a user-defined function to map each element to some value and extract an element-specific key value. * Group elements with the same key into a single element with that key, transforming a multiply-keyed collection into a uniquely-keyed collection. * Write the elements out to some data sink. Note that the Cloud Dataflow service may be used to run many different types of jobs, not just Map-Reduce.
      "kind": "A String", # The kind of step in the Cloud Dataflow job.
      "name": "A String", # The name that identifies the step. This must be unique for each step with respect to all other steps in the Cloud Dataflow job.
      "properties": { # Named properties associated with the step. Each kind of predefined step has its own required set of properties. Must be provided on Create. Only retrieved with JOB_VIEW_ALL.
        "a_key": "", # Properties of the object.
      },
    },
  ],
  "stepsLocation": "A String", # The Cloud Storage location where the steps are stored.
  "tempFiles": [ # A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
    "A String",
  ],
  "transformNameMapping": { # Optional. The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.
    "a_key": "A String",
  },
  "type": "A String", # Optional. The type of Dataflow job.
}

get(projectId, location, jobId, view=None, x__xgafv=None)

Gets the state of the specified Cloud Dataflow job. To get the state of a job, we recommend using `projects.locations.jobs.get` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.get` is not recommended, as you can only get the state of jobs that are running in `us-central1`.

Args:
  projectId: string, The ID of the Cloud Platform project that the job belongs to. (required)
  location: string, The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job. (required)
  jobId: string, The job ID. (required)
  view: string, The level of information requested in response.
    Allowed values
      JOB_VIEW_UNKNOWN - The job view to return isn't specified, or is unknown. Responses will contain at least the `JOB_VIEW_SUMMARY` information, and may contain additional information.
      JOB_VIEW_SUMMARY - Request summary information only: Project ID, Job ID, job name, job type, job status, start/end time, and Cloud SDK version details.
      JOB_VIEW_ALL - Request all information available for this job. When the job is in `JOB_STATE_PENDING`, the job has been created but is not yet running, and not all job information is available. For complete job information, wait until the job in is `JOB_STATE_RUNNING`. For more information, see [JobState](https://cloud.google.com/dataflow/docs/reference/rest/v1b3/projects.jobs#jobstate).
      JOB_VIEW_DESCRIPTION - Request summary info and limited job description data for steps, labels and environment.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Defines a job to be run by the Cloud Dataflow service. Do not enter confidential information when you supply string values using the API.
  "clientRequestId": "A String", # The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.
  "createTime": "A String", # The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.
  "createdFromSnapshotId": "A String", # If this is specified, the job's initial state is populated from the given snapshot.
  "currentState": "A String", # The current state of the job. Jobs are created in the `JOB_STATE_STOPPED` state unless otherwise specified. A job in the `JOB_STATE_RUNNING` state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field might be mutated by the Dataflow service; callers cannot mutate it.
  "currentStateTime": "A String", # The timestamp associated with the current state.
  "environment": { # Describes the environment in which a Dataflow Job runs. # Optional. The environment for the job.
    "clusterManagerApiService": "A String", # The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".
    "dataset": "A String", # Optional. The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}
    "debugOptions": { # Describes any options that have an effect on the debugging of pipelines. # Optional. Any debugging options to be supplied to the job.
      "dataSampling": { # Configuration options for sampling elements. # Configuration options for sampling elements from a running pipeline.
        "behaviors": [ # List of given sampling behaviors to enable. For example, specifying behaviors = [ALWAYS_ON] samples in-flight elements but does not sample exceptions. Can be used to specify multiple behaviors like, behaviors = [ALWAYS_ON, EXCEPTIONS] for specifying periodic sampling and exception sampling. If DISABLED is in the list, then sampling will be disabled and ignore the other given behaviors. Ordering does not matter.
          "A String",
        ],
      },
      "enableHotKeyLogging": True or False, # Optional. When true, enables the logging of the literal hot key to the user's Cloud Logging.
    },
    "experiments": [ # The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.
      "A String",
    ],
    "flexResourceSchedulingGoal": "A String", # Optional. Which Flexible Resource Scheduling mode to run in.
    "internalExperiments": { # Experimental settings.
      "a_key": "", # Properties of the object. Contains field @type with type URL.
    },
    "sdkPipelineOptions": { # The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.
      "a_key": "", # Properties of the object.
    },
    "serviceAccountEmail": "A String", # Optional. Identity to run virtual machines as. Defaults to the default account.
    "serviceKmsKeyName": "A String", # Optional. If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY
    "serviceOptions": [ # Optional. The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).
      "A String",
    ],
    "shuffleMode": "A String", # Output only. The shuffle mode used for the job.
    "streamingMode": "A String", # Optional. Specifies the Streaming Engine message processing guarantees. Reduces cost and latency but might result in duplicate messages committed to storage. Designed to run simple mapping streaming ETL jobs at the lowest cost. For example, Change Data Capture (CDC) to BigQuery is a canonical use case. For more information, see [Set the pipeline streaming mode](https://cloud.google.com/dataflow/docs/guides/streaming-modes).
    "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
    "useStreamingEngineResourceBasedBilling": True or False, # Output only. Whether the job uses the Streaming Engine resource-based billing model.
    "userAgent": { # A description of the process that generated the request.
      "a_key": "", # Properties of the object.
    },
    "version": { # A structure describing which components and their versions of the service are required in order to run the job.
      "a_key": "", # Properties of the object.
    },
    "workerPools": [ # The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.
      { # Describes one particular pool of Cloud Dataflow workers to be instantiated by the Cloud Dataflow service in order to perform the computations required by a job. Note that a workflow job may use multiple pools, in order to match the various computational requirements of the various stages of the job.
        "autoscalingSettings": { # Settings for WorkerPool autoscaling. # Settings for autoscaling of this WorkerPool.
          "algorithm": "A String", # The algorithm to use for autoscaling.
          "maxNumWorkers": 42, # The maximum number of workers to cap scaling at.
        },
        "dataDisks": [ # Data disks that are used by a VM in this workflow.
          { # Describes the data disk used by a workflow job.
            "diskType": "A String", # Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard
            "mountPoint": "A String", # Directory in a VM where disk is mounted.
            "sizeGb": 42, # Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
          },
        ],
        "defaultPackageSet": "A String", # The default package set to install. This allows the service to select a default set of packages which are useful to worker harnesses written in a particular language.
        "diskSizeGb": 42, # Size of root disk for VMs, in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
        "diskSourceImage": "A String", # Fully qualified source image for disks.
        "diskType": "A String", # Type of root disk for VMs. If empty or unspecified, the service will attempt to choose a reasonable default.
        "ipConfiguration": "A String", # Configuration for VM IPs.
        "kind": "A String", # The kind of the worker pool; currently only `harness` and `shuffle` are supported.
        "machineType": "A String", # Machine type (e.g. "n1-standard-1"). If empty or unspecified, the service will attempt to choose a reasonable default.
        "metadata": { # Metadata to set on the Google Compute Engine VMs.
          "a_key": "A String",
        },
        "network": "A String", # Network to which VMs will be assigned. If empty or unspecified, the service will use the network "default".
        "numThreadsPerWorker": 42, # The number of threads per worker harness. If empty or unspecified, the service will choose a number of threads (according to the number of cores on the selected machine type for batch, or 1 by convention for streaming).
        "numWorkers": 42, # Number of Google Compute Engine workers in this pool needed to execute the job. If zero or unspecified, the service will attempt to choose a reasonable default.
        "onHostMaintenance": "A String", # The action to take on host maintenance, as defined by the Google Compute Engine API.
        "packages": [ # Packages to be installed on workers.
          { # The packages that must be installed in order for a worker to run the steps of the Cloud Dataflow job that will be assigned to its worker pool. This is the mechanism by which the Cloud Dataflow SDK causes code to be loaded onto the workers. For example, the Cloud Dataflow Java SDK might use this to install jars containing the user's code and all of the various dependencies (libraries, data files, etc.) required in order for that code to run.
            "location": "A String", # The resource to read the package from. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket} bucket.storage.googleapis.com/
            "name": "A String", # The name of the package.
          },
        ],
        "poolArgs": { # Extra arguments for this worker pool.
          "a_key": "", # Properties of the object. Contains field @type with type URL.
        },
        "sdkHarnessContainerImages": [ # Set of SDK harness containers needed to execute this pipeline. This will only be set in the Fn API path. For non-cross-language pipelines this should have only one entry. Cross-language pipelines will have two or more entries.
          { # Defines an SDK harness container for executing Dataflow pipelines.
            "capabilities": [ # The set of capabilities enumerated in the above Environment proto. See also [beam_runner_api.proto](https://github.com/apache/beam/blob/master/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/beam_runner_api.proto)
              "A String",
            ],
            "containerImage": "A String", # A docker container image that resides in Google Container Registry.
            "environmentId": "A String", # Environment ID for the Beam runner API proto Environment that corresponds to the current SDK Harness.
            "useSingleCorePerContainer": True or False, # If true, recommends the Dataflow service to use only one core per SDK container instance with this image. If false (or unset) recommends using more than one core per SDK container instance with this image for efficiency. Note that Dataflow service may choose to override this property if needed.
          },
        ],
        "subnetwork": "A String", # Subnetwork to which VMs will be assigned, if desired. Expected to be of the form "regions/REGION/subnetworks/SUBNETWORK".
        "taskrunnerSettings": { # Taskrunner configuration settings. # Settings passed through to Google Compute Engine workers when using the standard Dataflow task runner. Users should ignore this field.
          "alsologtostderr": True or False, # Whether to also send taskrunner log info to stderr.
          "baseTaskDir": "A String", # The location on the worker for task-specific subdirectories.
          "baseUrl": "A String", # The base URL for the taskrunner to use when accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
          "commandlinesFileName": "A String", # The file to store preprocessing commands in.
          "continueOnException": True or False, # Whether to continue taskrunner if an exception is hit.
          "dataflowApiVersion": "A String", # The API version of endpoint, e.g. "v1b3"
          "harnessCommand": "A String", # The command to launch the worker harness.
          "languageHint": "A String", # The suggested backend language.
          "logDir": "A String", # The directory on the VM to store logs.
          "logToSerialconsole": True or False, # Whether to send taskrunner log info to Google Compute Engine VM serial console.
          "logUploadLocation": "A String", # Indicates where to put logs. If this is not specified, the logs will not be uploaded. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
          "oauthScopes": [ # The OAuth2 scopes to be requested by the taskrunner in order to access the Cloud Dataflow API.
            "A String",
          ],
          "parallelWorkerSettings": { # Provides data to pass through to the worker harness. # The settings to pass to the parallel worker harness.
            "baseUrl": "A String", # The base URL for accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
            "reportingEnabled": True or False, # Whether to send work progress updates to the service.
            "servicePath": "A String", # The Cloud Dataflow service path relative to the root URL, for example, "dataflow/v1b3/projects".
            "shuffleServicePath": "A String", # The Shuffle service path relative to the root URL, for example, "shuffle/v1beta1".
            "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
            "workerId": "A String", # The ID of the worker running this pipeline.
          },
          "streamingWorkerMainClass": "A String", # The streaming worker main class name.
          "taskGroup": "A String", # The UNIX group ID on the worker VM to use for tasks launched by taskrunner; e.g. "wheel".
          "taskUser": "A String", # The UNIX user ID on the worker VM to use for tasks launched by taskrunner; e.g. "root".
          "tempStoragePrefix": "A String", # The prefix of the resources the taskrunner should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
          "vmId": "A String", # The ID string of the VM.
          "workflowFileName": "A String", # The file to store the workflow in.
        },
        "teardownPolicy": "A String", # Sets the policy for determining when to turndown worker pool. Allowed values are: `TEARDOWN_ALWAYS`, `TEARDOWN_ON_SUCCESS`, and `TEARDOWN_NEVER`. `TEARDOWN_ALWAYS` means workers are always torn down regardless of whether the job succeeds. `TEARDOWN_ON_SUCCESS` means workers are torn down if the job succeeds. `TEARDOWN_NEVER` means the workers are never torn down. If the workers are not torn down by the service, they will continue to run and use Google Compute Engine VM resources in the user's project until they are explicitly terminated by the user. Because of this, Google recommends using the `TEARDOWN_ALWAYS` policy except for small, manually supervised test jobs. If unknown or unspecified, the service will attempt to choose a reasonable default.
        "workerHarnessContainerImage": "A String", # Required. Docker container image that executes the Cloud Dataflow worker harness, residing in Google Container Registry. Deprecated for the Fn API path. Use sdk_harness_container_images instead.
        "zone": "A String", # Zone to run the worker pools in. If empty or unspecified, the service will attempt to choose a reasonable default.
      },
    ],
    "workerRegion": "A String", # Optional. The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.
    "workerZone": "A String", # Optional. The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.
  },
  "executionInfo": { # Additional information about how a Cloud Dataflow job will be executed that isn't contained in the submitted job. # Deprecated.
    "stages": { # A mapping from each stage to the information about that stage.
      "a_key": { # Contains information about how a particular google.dataflow.v1beta3.Step will be executed.
        "stepName": [ # The steps associated with the execution stage. Note that stages may have several steps, and that a given step might be run by more than one stage.
          "A String",
        ],
      },
    },
  },
  "id": "A String", # The unique ID of this job. This field is set by the Dataflow service when the job is created, and is immutable for the life of the job.
  "jobMetadata": { # Metadata available primarily for filtering jobs. Will be included in the ListJob response and Job SUMMARY view. # This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.
    "bigTableDetails": [ # Identification of a Cloud Bigtable source used in the Dataflow job.
      { # Metadata for a Cloud Bigtable connector used by the job.
        "instanceId": "A String", # InstanceId accessed in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
        "tableId": "A String", # TableId accessed in the connection.
      },
    ],
    "bigqueryDetails": [ # Identification of a BigQuery source used in the Dataflow job.
      { # Metadata for a BigQuery connector used by the job.
        "dataset": "A String", # Dataset accessed in the connection.
        "projectId": "A String", # Project accessed in the connection.
        "query": "A String", # Query used to access data in the connection.
        "table": "A String", # Table accessed in the connection.
      },
    ],
    "datastoreDetails": [ # Identification of a Datastore source used in the Dataflow job.
      { # Metadata for a Datastore connector used by the job.
        "namespace": "A String", # Namespace used in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
      },
    ],
    "fileDetails": [ # Identification of a File source used in the Dataflow job.
      { # Metadata for a File connector used by the job.
        "filePattern": "A String", # File Pattern used to access files by the connector.
      },
    ],
    "pubsubDetails": [ # Identification of a Pub/Sub source used in the Dataflow job.
      { # Metadata for a Pub/Sub connector used by the job.
        "subscription": "A String", # Subscription used in the connection.
        "topic": "A String", # Topic accessed in the connection.
      },
    ],
    "sdkVersion": { # The version of the SDK used to run the job. # The SDK version used to run the job.
      "bugs": [ # Output only. Known bugs found in this SDK version.
        { # A bug found in the Dataflow SDK.
          "severity": "A String", # Output only. How severe the SDK bug is.
          "type": "A String", # Output only. Describes the impact of this SDK bug.
          "uri": "A String", # Output only. Link to more information on the bug.
        },
      ],
      "sdkSupportStatus": "A String", # The support status for this SDK version.
      "version": "A String", # The version of the SDK used to run the job.
      "versionDisplayName": "A String", # A readable string describing the version of the SDK.
    },
    "spannerDetails": [ # Identification of a Spanner source used in the Dataflow job.
      { # Metadata for a Spanner connector used by the job.
        "databaseId": "A String", # DatabaseId accessed in the connection.
        "instanceId": "A String", # InstanceId accessed in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
      },
    ],
    "userDisplayProperties": { # List of display properties to help UI filter jobs.
      "a_key": "A String",
    },
  },
  "labels": { # User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.
    "a_key": "A String",
  },
  "location": "A String", # Optional. The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.
  "name": "A String", # Optional. The user-specified Dataflow job name. Only one active job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a job with the same name as an active job that already exists, the attempt returns the existing job. The name must match the regular expression `[a-z]([-a-z0-9]{0,1022}[a-z0-9])?`
  "pipelineDescription": { # A descriptive representation of submitted pipeline as well as the executed form. This data is provided by the Dataflow service for ease of visualizing the pipeline and interpreting Dataflow provided metrics. # Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.
    "displayData": [ # Pipeline level display data.
      { # Data provided with a pipeline or transform to provide descriptive info.
        "boolValue": True or False, # Contains value if the data is of a boolean type.
        "durationValue": "A String", # Contains value if the data is of duration type.
        "floatValue": 3.14, # Contains value if the data is of float type.
        "int64Value": "A String", # Contains value if the data is of int64 type.
        "javaClassValue": "A String", # Contains value if the data is of java class type.
        "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
        "label": "A String", # An optional label to display in a dax UI for the element.
        "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
        "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
        "strValue": "A String", # Contains value if the data is of string type.
        "timestampValue": "A String", # Contains value if the data is of timestamp type.
        "url": "A String", # An optional full URL.
      },
    ],
    "executionPipelineStage": [ # Description of each stage of execution of the pipeline.
      { # Description of the composing transforms, names/ids, and input/outputs of a stage of execution. Some composing transforms and sources may have been generated by the Dataflow service during execution planning.
        "componentSource": [ # Collections produced and consumed by component transforms of this stage.
          { # Description of an interstitial value between transforms in an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "userName": "A String", # Human-readable name for this transform; may be user or system generated.
          },
        ],
        "componentTransform": [ # Transforms that comprise this execution stage.
          { # Description of a transform executed as part of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransform": "A String", # User name for the original user transform with which this transform is most closely associated.
            "userName": "A String", # Human-readable name for this transform; may be user or system generated.
          },
        ],
        "id": "A String", # Dataflow service generated id for this stage.
        "inputSource": [ # Input sources for this stage.
          { # Description of an input or output of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "sizeBytes": "A String", # Size of the source, if measurable.
            "userName": "A String", # Human-readable name for this source; may be user or system generated.
          },
        ],
        "kind": "A String", # Type of transform this stage is executing.
        "name": "A String", # Dataflow service generated name for this stage.
        "outputSource": [ # Output sources for this stage.
          { # Description of an input or output of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "sizeBytes": "A String", # Size of the source, if measurable.
            "userName": "A String", # Human-readable name for this source; may be user or system generated.
          },
        ],
        "prerequisiteStage": [ # Other stages that must complete before this stage can run.
          "A String",
        ],
      },
    ],
    "originalPipelineTransform": [ # Description of each transform in the pipeline and collections between them.
      { # Description of the type, names/ids, and input/outputs for a transform.
        "displayData": [ # Transform-specific display data.
          { # Data provided with a pipeline or transform to provide descriptive info.
            "boolValue": True or False, # Contains value if the data is of a boolean type.
            "durationValue": "A String", # Contains value if the data is of duration type.
            "floatValue": 3.14, # Contains value if the data is of float type.
            "int64Value": "A String", # Contains value if the data is of int64 type.
            "javaClassValue": "A String", # Contains value if the data is of java class type.
            "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
            "label": "A String", # An optional label to display in a dax UI for the element.
            "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
            "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
            "strValue": "A String", # Contains value if the data is of string type.
            "timestampValue": "A String", # Contains value if the data is of timestamp type.
            "url": "A String", # An optional full URL.
          },
        ],
        "id": "A String", # SDK generated id of this transform instance.
        "inputCollectionName": [ # User names for all collection inputs to this transform.
          "A String",
        ],
        "kind": "A String", # Type of transform.
        "name": "A String", # User provided name for this transform instance.
        "outputCollectionName": [ # User names for all collection outputs to this transform.
          "A String",
        ],
      },
    ],
    "stepNamesHash": "A String", # A hash value of the submitted pipeline portable graph step names if exists.
  },
  "projectId": "A String", # The ID of the Google Cloud project that the job belongs to.
  "replaceJobId": "A String", # If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a `CreateJobRequest`, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.
  "replacedByJobId": "A String", # If another job is an update of this job (and thus, this job is in `JOB_STATE_UPDATED`), this field contains the ID of that job.
  "requestedState": "A String", # The job's requested state. Applies to `UpdateJob` requests. Set `requested_state` with `UpdateJob` requests to switch between the states `JOB_STATE_STOPPED` and `JOB_STATE_RUNNING`. You can also use `UpdateJob` requests to change a job's state from `JOB_STATE_RUNNING` to `JOB_STATE_CANCELLED`, `JOB_STATE_DONE`, or `JOB_STATE_DRAINED`. These states irrevocably terminate the job if it hasn't already reached a terminal state. This field has no effect on `CreateJob` requests.
  "runtimeUpdatableParams": { # Additional job parameters that can only be updated during runtime using the projects.jobs.update method. These fields have no effect when specified during job creation. # This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.
    "maxNumWorkers": 42, # The maximum number of workers to cap autoscaling at. This field is currently only supported for Streaming Engine jobs.
    "minNumWorkers": 42, # The minimum number of workers to scale down to. This field is currently only supported for Streaming Engine jobs.
    "workerUtilizationHint": 3.14, # Target worker utilization, compared against the aggregate utilization of the worker pool by autoscaler, to determine upscaling and downscaling when absent other constraints such as backlog. For more information, see [Update an existing pipeline](https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline).
  },
  "satisfiesPzi": True or False, # Output only. Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
  "satisfiesPzs": True or False, # Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
  "serviceResources": { # Resources used by the Dataflow Service to run the job. # Output only. Resources used by the Dataflow Service to run the job.
    "zones": [ # Output only. List of Cloud Zones being used by the Dataflow Service for this job. Example: us-central1-c
      "A String",
    ],
  },
  "stageStates": [ # This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.
    { # A message describing the state of a particular execution stage.
      "currentStateTime": "A String", # The time at which the stage transitioned to this state.
      "executionStageName": "A String", # The name of the execution stage.
      "executionStageState": "A String", # Executions stage states allow the same set of values as JobState.
    },
  ],
  "startTime": "A String", # The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.
  "steps": [ # Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.
    { # Defines a particular step within a Cloud Dataflow job. A job consists of multiple steps, each of which performs some specific operation as part of the overall job. Data is typically passed from one step to another as part of the job. **Note:** The properties of this object are not stable and might change. Here's an example of a sequence of steps which together implement a Map-Reduce job: * Read a collection of data from some source, parsing the collection's elements. * Validate the elements. * Apply a user-defined function to map each element to some value and extract an element-specific key value. * Group elements with the same key into a single element with that key, transforming a multiply-keyed collection into a uniquely-keyed collection. * Write the elements out to some data sink. Note that the Cloud Dataflow service may be used to run many different types of jobs, not just Map-Reduce.
      "kind": "A String", # The kind of step in the Cloud Dataflow job.
      "name": "A String", # The name that identifies the step. This must be unique for each step with respect to all other steps in the Cloud Dataflow job.
      "properties": { # Named properties associated with the step. Each kind of predefined step has its own required set of properties. Must be provided on Create. Only retrieved with JOB_VIEW_ALL.
        "a_key": "", # Properties of the object.
      },
    },
  ],
  "stepsLocation": "A String", # The Cloud Storage location where the steps are stored.
  "tempFiles": [ # A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
    "A String",
  ],
  "transformNameMapping": { # Optional. The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.
    "a_key": "A String",
  },
  "type": "A String", # Optional. The type of Dataflow job.
}

getExecutionDetails(projectId, location, jobId, pageSize=None, pageToken=None, x__xgafv=None)

Request detailed information about the execution status of the job. EXPERIMENTAL. This API is subject to change or removal without notice.

Args:
  projectId: string, A project id. (required)
  location: string, The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains the job specified by job_id. (required)
  jobId: string, The job to get execution details for. (required)
  pageSize: integer, If specified, determines the maximum number of stages to return. If unspecified, the service may choose an appropriate default, or may return an arbitrarily large number of results.
  pageToken: string, If supplied, this should be the value of next_page_token returned by an earlier call. This will cause the next page of results to be returned.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Information about the execution of a job.
  "nextPageToken": "A String", # If present, this response does not contain all requested tasks. To obtain the next page of results, repeat the request with page_token set to this value.
  "stages": [ # The stages of the job execution.
    { # Information about a particular execution stage of a job.
      "endTime": "A String", # End time of this stage. If the work item is completed, this is the actual end time of the stage. Otherwise, it is the predicted end time.
      "metrics": [ # Metrics for this stage.
        { # Describes the state of a metric.
          "cumulative": True or False, # True if this metric is reported as the total cumulative aggregate value accumulated since the worker started working on this WorkItem. By default this is false, indicating that this metric is reported as a delta that is not associated with any WorkItem.
          "distribution": "", # A struct value describing properties of a distribution of numeric values.
          "gauge": "", # A struct value describing properties of a Gauge. Metrics of gauge type show the value of a metric across time, and is aggregated based on the newest value.
          "internal": "", # Worker-computed aggregate value for internal use by the Dataflow service.
          "kind": "A String", # Metric aggregation kind. The possible metric aggregation kinds are "Sum", "Max", "Min", "Mean", "Set", "And", "Or", and "Distribution". The specified aggregation kind is case-insensitive. If omitted, this is not an aggregated value but instead a single metric sample value.
          "meanCount": "", # Worker-computed aggregate value for the "Mean" aggregation kind. This holds the count of the aggregated values and is used in combination with mean_sum above to obtain the actual mean aggregate value. The only possible value type is Long.
          "meanSum": "", # Worker-computed aggregate value for the "Mean" aggregation kind. This holds the sum of the aggregated values and is used in combination with mean_count below to obtain the actual mean aggregate value. The only possible value types are Long and Double.
          "name": { # Identifies a metric, by describing the source which generated the metric. # Name of the metric.
            "context": { # Zero or more labeled fields which identify the part of the job this metric is associated with, such as the name of a step or collection. For example, built-in counters associated with steps will have context['step'] = . Counters associated with PCollections in the SDK will have context['pcollection'] = .
              "a_key": "A String",
            },
            "name": "A String", # Worker-defined metric name.
            "origin": "A String", # Origin (namespace) of metric name. May be blank for user-define metrics; will be "dataflow" for metrics defined by the Dataflow service or SDK.
          },
          "scalar": "", # Worker-computed aggregate value for aggregation kinds "Sum", "Max", "Min", "And", and "Or". The possible value types are Long, Double, and Boolean.
          "set": "", # Worker-computed aggregate value for the "Set" aggregation kind. The only possible value type is a list of Values whose type can be Long, Double, or String, according to the metric's type. All Values in the list must be of the same type.
          "updateTime": "A String", # Timestamp associated with the metric value. Optional when workers are reporting work progress; it will be filled in responses from the metrics API.
        },
      ],
      "progress": { # Information about the progress of some component of job execution. # Progress for this stage. Only applicable to Batch jobs.
        "currentProgress": 3.14, # The current progress of the component, in the range [0,1].
        "dataPoints": [ # History of progress for the component. Points are sorted by time.
          { # A point in the timeseries.
            "time": "A String", # The timestamp of the point.
            "value": 3.14, # The value of the point.
          },
        ],
      },
      "stageId": "A String", # ID of this stage
      "startTime": "A String", # Start time of this stage.
      "state": "A String", # State of this stage.
      "stragglerSummary": { # Summarized straggler identification details. # Straggler summary for this stage.
        "recentStragglers": [ # The most recent stragglers.
          { # Information for a straggler.
            "batchStraggler": { # Information useful for straggler identification and debugging. # Batch straggler identification and debugging information.
              "causes": { # The straggler causes, keyed by the string representation of the StragglerCause enum and contains specialized debugging information for each straggler cause.
                "a_key": { # Information useful for debugging a straggler. Each type will provide specialized debugging information relevant for a particular cause. The StragglerDebuggingInfo will be 1:1 mapping to the StragglerCause enum.
                  "hotKey": { # Information useful for debugging a hot key detection. # Hot key debugging details.
                    "detectedHotKeys": { # Debugging information for each detected hot key. Keyed by a hash of the key.
                      "a_key": { # Information about a hot key.
                        "hotKeyAge": "A String", # The age of the hot key measured from when it was first detected.
                        "key": "A String", # A detected hot key that is causing limited parallelism. This field will be populated only if the following flag is set to true: "--enable_hot_key_logging".
                        "keyTruncated": True or False, # If true, then the above key is truncated and cannot be deserialized. This occurs if the key above is populated and the key size is >5MB.
                      },
                    },
                  },
                },
              },
              "startTime": "A String", # The time when the work item attempt became a straggler.
            },
            "streamingStraggler": { # Information useful for streaming straggler identification and debugging. # Streaming straggler identification and debugging information.
              "dataWatermarkLag": "A String", # The event-time watermark lag at the time of the straggler detection.
              "endTime": "A String", # End time of this straggler.
              "startTime": "A String", # Start time of this straggler.
              "systemWatermarkLag": "A String", # The system watermark lag at the time of the straggler detection.
              "workerName": "A String", # Name of the worker where the straggler was detected.
            },
          },
        ],
        "stragglerCauseCount": { # Aggregated counts of straggler causes, keyed by the string representation of the StragglerCause enum.
          "a_key": "A String",
        },
        "totalStragglerCount": "A String", # The total count of stragglers.
      },
    },
  ],
}

getExecutionDetails_next()

Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call 'execute()' on to request the next
          page. Returns None if there are no more items in the collection.
        

getMetrics(projectId, location, jobId, startTime=None, x__xgafv=None)

Request the job status. To request the status of a job, we recommend using `projects.locations.jobs.getMetrics` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.getMetrics` is not recommended, as you can only request the status of jobs that are running in `us-central1`.

Args:
  projectId: string, A project id. (required)
  location: string, The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains the job specified by job_id. (required)
  jobId: string, The job to get metrics for. (required)
  startTime: string, Return only metric data that has changed since this time. Default is to return all information about all metrics for the job.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # JobMetrics contains a collection of metrics describing the detailed progress of a Dataflow job. Metrics correspond to user-defined and system-defined metrics in the job. For more information, see [Dataflow job metrics] (https://cloud.google.com/dataflow/docs/guides/using-monitoring-intf). This resource captures only the most recent values of each metric; time-series data can be queried for them (under the same metric names) from Cloud Monitoring.
  "metricTime": "A String", # Timestamp as of which metric values are current.
  "metrics": [ # All metrics for this job.
    { # Describes the state of a metric.
      "cumulative": True or False, # True if this metric is reported as the total cumulative aggregate value accumulated since the worker started working on this WorkItem. By default this is false, indicating that this metric is reported as a delta that is not associated with any WorkItem.
      "distribution": "", # A struct value describing properties of a distribution of numeric values.
      "gauge": "", # A struct value describing properties of a Gauge. Metrics of gauge type show the value of a metric across time, and is aggregated based on the newest value.
      "internal": "", # Worker-computed aggregate value for internal use by the Dataflow service.
      "kind": "A String", # Metric aggregation kind. The possible metric aggregation kinds are "Sum", "Max", "Min", "Mean", "Set", "And", "Or", and "Distribution". The specified aggregation kind is case-insensitive. If omitted, this is not an aggregated value but instead a single metric sample value.
      "meanCount": "", # Worker-computed aggregate value for the "Mean" aggregation kind. This holds the count of the aggregated values and is used in combination with mean_sum above to obtain the actual mean aggregate value. The only possible value type is Long.
      "meanSum": "", # Worker-computed aggregate value for the "Mean" aggregation kind. This holds the sum of the aggregated values and is used in combination with mean_count below to obtain the actual mean aggregate value. The only possible value types are Long and Double.
      "name": { # Identifies a metric, by describing the source which generated the metric. # Name of the metric.
        "context": { # Zero or more labeled fields which identify the part of the job this metric is associated with, such as the name of a step or collection. For example, built-in counters associated with steps will have context['step'] = . Counters associated with PCollections in the SDK will have context['pcollection'] = .
          "a_key": "A String",
        },
        "name": "A String", # Worker-defined metric name.
        "origin": "A String", # Origin (namespace) of metric name. May be blank for user-define metrics; will be "dataflow" for metrics defined by the Dataflow service or SDK.
      },
      "scalar": "", # Worker-computed aggregate value for aggregation kinds "Sum", "Max", "Min", "And", and "Or". The possible value types are Long, Double, and Boolean.
      "set": "", # Worker-computed aggregate value for the "Set" aggregation kind. The only possible value type is a list of Values whose type can be Long, Double, or String, according to the metric's type. All Values in the list must be of the same type.
      "updateTime": "A String", # Timestamp associated with the metric value. Optional when workers are reporting work progress; it will be filled in responses from the metrics API.
    },
  ],
}

list(projectId, location, filter=None, name=None, pageSize=None, pageToken=None, view=None, x__xgafv=None)

List the jobs of a project. To list the jobs of a project in a region, we recommend using `projects.locations.jobs.list` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). To list the all jobs across all regions, use `projects.jobs.aggregated`. Using `projects.jobs.list` is not recommended, because you can only get the list of jobs that are running in `us-central1`. `projects.locations.jobs.list` and `projects.jobs.list` support filtering the list of jobs by name. Filtering by name isn't supported by `projects.jobs.aggregated`.

Args:
  projectId: string, The project which owns the jobs. (required)
  location: string, The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job. (required)
  filter: string, The kind of filter to use.
    Allowed values
      UNKNOWN - The filter isn't specified, or is unknown. This returns all jobs ordered on descending `JobUuid`.
      ALL - Returns all running jobs first ordered on creation timestamp, then returns all terminated jobs ordered on the termination timestamp.
      TERMINATED - Filters the jobs that have a terminated state, ordered on the termination timestamp. Example terminated states: `JOB_STATE_STOPPED`, `JOB_STATE_UPDATED`, `JOB_STATE_DRAINED`, etc.
      ACTIVE - Filters the jobs that are running ordered on the creation timestamp.
  name: string, Optional. The job name.
  pageSize: integer, If there are many jobs, limit response to at most this many. The actual number of jobs returned will be the lesser of max_responses and an unspecified server-defined limit.
  pageToken: string, Set this to the 'next_page_token' field of a previous response to request additional results in a long list.
  view: string, Deprecated. ListJobs always returns summaries now. Use GetJob for other JobViews.
    Allowed values
      JOB_VIEW_UNKNOWN - The job view to return isn't specified, or is unknown. Responses will contain at least the `JOB_VIEW_SUMMARY` information, and may contain additional information.
      JOB_VIEW_SUMMARY - Request summary information only: Project ID, Job ID, job name, job type, job status, start/end time, and Cloud SDK version details.
      JOB_VIEW_ALL - Request all information available for this job. When the job is in `JOB_STATE_PENDING`, the job has been created but is not yet running, and not all job information is available. For complete job information, wait until the job in is `JOB_STATE_RUNNING`. For more information, see [JobState](https://cloud.google.com/dataflow/docs/reference/rest/v1b3/projects.jobs#jobstate).
      JOB_VIEW_DESCRIPTION - Request summary info and limited job description data for steps, labels and environment.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response to a request to list Cloud Dataflow jobs in a project. This might be a partial response, depending on the page size in the ListJobsRequest. However, if the project does not have any jobs, an instance of ListJobsResponse is not returned and the requests's response body is empty {}.
  "failedLocation": [ # Zero or more messages describing the [regional endpoints] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that failed to respond.
    { # Indicates which [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) failed to respond to a request for data.
      "name": "A String", # The name of the [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that failed to respond.
    },
  ],
  "jobs": [ # A subset of the requested job information.
    { # Defines a job to be run by the Cloud Dataflow service. Do not enter confidential information when you supply string values using the API.
      "clientRequestId": "A String", # The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.
      "createTime": "A String", # The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.
      "createdFromSnapshotId": "A String", # If this is specified, the job's initial state is populated from the given snapshot.
      "currentState": "A String", # The current state of the job. Jobs are created in the `JOB_STATE_STOPPED` state unless otherwise specified. A job in the `JOB_STATE_RUNNING` state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field might be mutated by the Dataflow service; callers cannot mutate it.
      "currentStateTime": "A String", # The timestamp associated with the current state.
      "environment": { # Describes the environment in which a Dataflow Job runs. # Optional. The environment for the job.
        "clusterManagerApiService": "A String", # The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".
        "dataset": "A String", # Optional. The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}
        "debugOptions": { # Describes any options that have an effect on the debugging of pipelines. # Optional. Any debugging options to be supplied to the job.
          "dataSampling": { # Configuration options for sampling elements. # Configuration options for sampling elements from a running pipeline.
            "behaviors": [ # List of given sampling behaviors to enable. For example, specifying behaviors = [ALWAYS_ON] samples in-flight elements but does not sample exceptions. Can be used to specify multiple behaviors like, behaviors = [ALWAYS_ON, EXCEPTIONS] for specifying periodic sampling and exception sampling. If DISABLED is in the list, then sampling will be disabled and ignore the other given behaviors. Ordering does not matter.
              "A String",
            ],
          },
          "enableHotKeyLogging": True or False, # Optional. When true, enables the logging of the literal hot key to the user's Cloud Logging.
        },
        "experiments": [ # The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.
          "A String",
        ],
        "flexResourceSchedulingGoal": "A String", # Optional. Which Flexible Resource Scheduling mode to run in.
        "internalExperiments": { # Experimental settings.
          "a_key": "", # Properties of the object. Contains field @type with type URL.
        },
        "sdkPipelineOptions": { # The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.
          "a_key": "", # Properties of the object.
        },
        "serviceAccountEmail": "A String", # Optional. Identity to run virtual machines as. Defaults to the default account.
        "serviceKmsKeyName": "A String", # Optional. If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY
        "serviceOptions": [ # Optional. The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).
          "A String",
        ],
        "shuffleMode": "A String", # Output only. The shuffle mode used for the job.
        "streamingMode": "A String", # Optional. Specifies the Streaming Engine message processing guarantees. Reduces cost and latency but might result in duplicate messages committed to storage. Designed to run simple mapping streaming ETL jobs at the lowest cost. For example, Change Data Capture (CDC) to BigQuery is a canonical use case. For more information, see [Set the pipeline streaming mode](https://cloud.google.com/dataflow/docs/guides/streaming-modes).
        "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
        "useStreamingEngineResourceBasedBilling": True or False, # Output only. Whether the job uses the Streaming Engine resource-based billing model.
        "userAgent": { # A description of the process that generated the request.
          "a_key": "", # Properties of the object.
        },
        "version": { # A structure describing which components and their versions of the service are required in order to run the job.
          "a_key": "", # Properties of the object.
        },
        "workerPools": [ # The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.
          { # Describes one particular pool of Cloud Dataflow workers to be instantiated by the Cloud Dataflow service in order to perform the computations required by a job. Note that a workflow job may use multiple pools, in order to match the various computational requirements of the various stages of the job.
            "autoscalingSettings": { # Settings for WorkerPool autoscaling. # Settings for autoscaling of this WorkerPool.
              "algorithm": "A String", # The algorithm to use for autoscaling.
              "maxNumWorkers": 42, # The maximum number of workers to cap scaling at.
            },
            "dataDisks": [ # Data disks that are used by a VM in this workflow.
              { # Describes the data disk used by a workflow job.
                "diskType": "A String", # Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard
                "mountPoint": "A String", # Directory in a VM where disk is mounted.
                "sizeGb": 42, # Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
              },
            ],
            "defaultPackageSet": "A String", # The default package set to install. This allows the service to select a default set of packages which are useful to worker harnesses written in a particular language.
            "diskSizeGb": 42, # Size of root disk for VMs, in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
            "diskSourceImage": "A String", # Fully qualified source image for disks.
            "diskType": "A String", # Type of root disk for VMs. If empty or unspecified, the service will attempt to choose a reasonable default.
            "ipConfiguration": "A String", # Configuration for VM IPs.
            "kind": "A String", # The kind of the worker pool; currently only `harness` and `shuffle` are supported.
            "machineType": "A String", # Machine type (e.g. "n1-standard-1"). If empty or unspecified, the service will attempt to choose a reasonable default.
            "metadata": { # Metadata to set on the Google Compute Engine VMs.
              "a_key": "A String",
            },
            "network": "A String", # Network to which VMs will be assigned. If empty or unspecified, the service will use the network "default".
            "numThreadsPerWorker": 42, # The number of threads per worker harness. If empty or unspecified, the service will choose a number of threads (according to the number of cores on the selected machine type for batch, or 1 by convention for streaming).
            "numWorkers": 42, # Number of Google Compute Engine workers in this pool needed to execute the job. If zero or unspecified, the service will attempt to choose a reasonable default.
            "onHostMaintenance": "A String", # The action to take on host maintenance, as defined by the Google Compute Engine API.
            "packages": [ # Packages to be installed on workers.
              { # The packages that must be installed in order for a worker to run the steps of the Cloud Dataflow job that will be assigned to its worker pool. This is the mechanism by which the Cloud Dataflow SDK causes code to be loaded onto the workers. For example, the Cloud Dataflow Java SDK might use this to install jars containing the user's code and all of the various dependencies (libraries, data files, etc.) required in order for that code to run.
                "location": "A String", # The resource to read the package from. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket} bucket.storage.googleapis.com/
                "name": "A String", # The name of the package.
              },
            ],
            "poolArgs": { # Extra arguments for this worker pool.
              "a_key": "", # Properties of the object. Contains field @type with type URL.
            },
            "sdkHarnessContainerImages": [ # Set of SDK harness containers needed to execute this pipeline. This will only be set in the Fn API path. For non-cross-language pipelines this should have only one entry. Cross-language pipelines will have two or more entries.
              { # Defines an SDK harness container for executing Dataflow pipelines.
                "capabilities": [ # The set of capabilities enumerated in the above Environment proto. See also [beam_runner_api.proto](https://github.com/apache/beam/blob/master/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/beam_runner_api.proto)
                  "A String",
                ],
                "containerImage": "A String", # A docker container image that resides in Google Container Registry.
                "environmentId": "A String", # Environment ID for the Beam runner API proto Environment that corresponds to the current SDK Harness.
                "useSingleCorePerContainer": True or False, # If true, recommends the Dataflow service to use only one core per SDK container instance with this image. If false (or unset) recommends using more than one core per SDK container instance with this image for efficiency. Note that Dataflow service may choose to override this property if needed.
              },
            ],
            "subnetwork": "A String", # Subnetwork to which VMs will be assigned, if desired. Expected to be of the form "regions/REGION/subnetworks/SUBNETWORK".
            "taskrunnerSettings": { # Taskrunner configuration settings. # Settings passed through to Google Compute Engine workers when using the standard Dataflow task runner. Users should ignore this field.
              "alsologtostderr": True or False, # Whether to also send taskrunner log info to stderr.
              "baseTaskDir": "A String", # The location on the worker for task-specific subdirectories.
              "baseUrl": "A String", # The base URL for the taskrunner to use when accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
              "commandlinesFileName": "A String", # The file to store preprocessing commands in.
              "continueOnException": True or False, # Whether to continue taskrunner if an exception is hit.
              "dataflowApiVersion": "A String", # The API version of endpoint, e.g. "v1b3"
              "harnessCommand": "A String", # The command to launch the worker harness.
              "languageHint": "A String", # The suggested backend language.
              "logDir": "A String", # The directory on the VM to store logs.
              "logToSerialconsole": True or False, # Whether to send taskrunner log info to Google Compute Engine VM serial console.
              "logUploadLocation": "A String", # Indicates where to put logs. If this is not specified, the logs will not be uploaded. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
              "oauthScopes": [ # The OAuth2 scopes to be requested by the taskrunner in order to access the Cloud Dataflow API.
                "A String",
              ],
              "parallelWorkerSettings": { # Provides data to pass through to the worker harness. # The settings to pass to the parallel worker harness.
                "baseUrl": "A String", # The base URL for accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
                "reportingEnabled": True or False, # Whether to send work progress updates to the service.
                "servicePath": "A String", # The Cloud Dataflow service path relative to the root URL, for example, "dataflow/v1b3/projects".
                "shuffleServicePath": "A String", # The Shuffle service path relative to the root URL, for example, "shuffle/v1beta1".
                "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
                "workerId": "A String", # The ID of the worker running this pipeline.
              },
              "streamingWorkerMainClass": "A String", # The streaming worker main class name.
              "taskGroup": "A String", # The UNIX group ID on the worker VM to use for tasks launched by taskrunner; e.g. "wheel".
              "taskUser": "A String", # The UNIX user ID on the worker VM to use for tasks launched by taskrunner; e.g. "root".
              "tempStoragePrefix": "A String", # The prefix of the resources the taskrunner should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
              "vmId": "A String", # The ID string of the VM.
              "workflowFileName": "A String", # The file to store the workflow in.
            },
            "teardownPolicy": "A String", # Sets the policy for determining when to turndown worker pool. Allowed values are: `TEARDOWN_ALWAYS`, `TEARDOWN_ON_SUCCESS`, and `TEARDOWN_NEVER`. `TEARDOWN_ALWAYS` means workers are always torn down regardless of whether the job succeeds. `TEARDOWN_ON_SUCCESS` means workers are torn down if the job succeeds. `TEARDOWN_NEVER` means the workers are never torn down. If the workers are not torn down by the service, they will continue to run and use Google Compute Engine VM resources in the user's project until they are explicitly terminated by the user. Because of this, Google recommends using the `TEARDOWN_ALWAYS` policy except for small, manually supervised test jobs. If unknown or unspecified, the service will attempt to choose a reasonable default.
            "workerHarnessContainerImage": "A String", # Required. Docker container image that executes the Cloud Dataflow worker harness, residing in Google Container Registry. Deprecated for the Fn API path. Use sdk_harness_container_images instead.
            "zone": "A String", # Zone to run the worker pools in. If empty or unspecified, the service will attempt to choose a reasonable default.
          },
        ],
        "workerRegion": "A String", # Optional. The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.
        "workerZone": "A String", # Optional. The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.
      },
      "executionInfo": { # Additional information about how a Cloud Dataflow job will be executed that isn't contained in the submitted job. # Deprecated.
        "stages": { # A mapping from each stage to the information about that stage.
          "a_key": { # Contains information about how a particular google.dataflow.v1beta3.Step will be executed.
            "stepName": [ # The steps associated with the execution stage. Note that stages may have several steps, and that a given step might be run by more than one stage.
              "A String",
            ],
          },
        },
      },
      "id": "A String", # The unique ID of this job. This field is set by the Dataflow service when the job is created, and is immutable for the life of the job.
      "jobMetadata": { # Metadata available primarily for filtering jobs. Will be included in the ListJob response and Job SUMMARY view. # This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.
        "bigTableDetails": [ # Identification of a Cloud Bigtable source used in the Dataflow job.
          { # Metadata for a Cloud Bigtable connector used by the job.
            "instanceId": "A String", # InstanceId accessed in the connection.
            "projectId": "A String", # ProjectId accessed in the connection.
            "tableId": "A String", # TableId accessed in the connection.
          },
        ],
        "bigqueryDetails": [ # Identification of a BigQuery source used in the Dataflow job.
          { # Metadata for a BigQuery connector used by the job.
            "dataset": "A String", # Dataset accessed in the connection.
            "projectId": "A String", # Project accessed in the connection.
            "query": "A String", # Query used to access data in the connection.
            "table": "A String", # Table accessed in the connection.
          },
        ],
        "datastoreDetails": [ # Identification of a Datastore source used in the Dataflow job.
          { # Metadata for a Datastore connector used by the job.
            "namespace": "A String", # Namespace used in the connection.
            "projectId": "A String", # ProjectId accessed in the connection.
          },
        ],
        "fileDetails": [ # Identification of a File source used in the Dataflow job.
          { # Metadata for a File connector used by the job.
            "filePattern": "A String", # File Pattern used to access files by the connector.
          },
        ],
        "pubsubDetails": [ # Identification of a Pub/Sub source used in the Dataflow job.
          { # Metadata for a Pub/Sub connector used by the job.
            "subscription": "A String", # Subscription used in the connection.
            "topic": "A String", # Topic accessed in the connection.
          },
        ],
        "sdkVersion": { # The version of the SDK used to run the job. # The SDK version used to run the job.
          "bugs": [ # Output only. Known bugs found in this SDK version.
            { # A bug found in the Dataflow SDK.
              "severity": "A String", # Output only. How severe the SDK bug is.
              "type": "A String", # Output only. Describes the impact of this SDK bug.
              "uri": "A String", # Output only. Link to more information on the bug.
            },
          ],
          "sdkSupportStatus": "A String", # The support status for this SDK version.
          "version": "A String", # The version of the SDK used to run the job.
          "versionDisplayName": "A String", # A readable string describing the version of the SDK.
        },
        "spannerDetails": [ # Identification of a Spanner source used in the Dataflow job.
          { # Metadata for a Spanner connector used by the job.
            "databaseId": "A String", # DatabaseId accessed in the connection.
            "instanceId": "A String", # InstanceId accessed in the connection.
            "projectId": "A String", # ProjectId accessed in the connection.
          },
        ],
        "userDisplayProperties": { # List of display properties to help UI filter jobs.
          "a_key": "A String",
        },
      },
      "labels": { # User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.
        "a_key": "A String",
      },
      "location": "A String", # Optional. The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.
      "name": "A String", # Optional. The user-specified Dataflow job name. Only one active job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a job with the same name as an active job that already exists, the attempt returns the existing job. The name must match the regular expression `[a-z]([-a-z0-9]{0,1022}[a-z0-9])?`
      "pipelineDescription": { # A descriptive representation of submitted pipeline as well as the executed form. This data is provided by the Dataflow service for ease of visualizing the pipeline and interpreting Dataflow provided metrics. # Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.
        "displayData": [ # Pipeline level display data.
          { # Data provided with a pipeline or transform to provide descriptive info.
            "boolValue": True or False, # Contains value if the data is of a boolean type.
            "durationValue": "A String", # Contains value if the data is of duration type.
            "floatValue": 3.14, # Contains value if the data is of float type.
            "int64Value": "A String", # Contains value if the data is of int64 type.
            "javaClassValue": "A String", # Contains value if the data is of java class type.
            "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
            "label": "A String", # An optional label to display in a dax UI for the element.
            "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
            "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
            "strValue": "A String", # Contains value if the data is of string type.
            "timestampValue": "A String", # Contains value if the data is of timestamp type.
            "url": "A String", # An optional full URL.
          },
        ],
        "executionPipelineStage": [ # Description of each stage of execution of the pipeline.
          { # Description of the composing transforms, names/ids, and input/outputs of a stage of execution. Some composing transforms and sources may have been generated by the Dataflow service during execution planning.
            "componentSource": [ # Collections produced and consumed by component transforms of this stage.
              { # Description of an interstitial value between transforms in an execution stage.
                "name": "A String", # Dataflow service generated name for this source.
                "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
                "userName": "A String", # Human-readable name for this transform; may be user or system generated.
              },
            ],
            "componentTransform": [ # Transforms that comprise this execution stage.
              { # Description of a transform executed as part of an execution stage.
                "name": "A String", # Dataflow service generated name for this source.
                "originalTransform": "A String", # User name for the original user transform with which this transform is most closely associated.
                "userName": "A String", # Human-readable name for this transform; may be user or system generated.
              },
            ],
            "id": "A String", # Dataflow service generated id for this stage.
            "inputSource": [ # Input sources for this stage.
              { # Description of an input or output of an execution stage.
                "name": "A String", # Dataflow service generated name for this source.
                "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
                "sizeBytes": "A String", # Size of the source, if measurable.
                "userName": "A String", # Human-readable name for this source; may be user or system generated.
              },
            ],
            "kind": "A String", # Type of transform this stage is executing.
            "name": "A String", # Dataflow service generated name for this stage.
            "outputSource": [ # Output sources for this stage.
              { # Description of an input or output of an execution stage.
                "name": "A String", # Dataflow service generated name for this source.
                "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
                "sizeBytes": "A String", # Size of the source, if measurable.
                "userName": "A String", # Human-readable name for this source; may be user or system generated.
              },
            ],
            "prerequisiteStage": [ # Other stages that must complete before this stage can run.
              "A String",
            ],
          },
        ],
        "originalPipelineTransform": [ # Description of each transform in the pipeline and collections between them.
          { # Description of the type, names/ids, and input/outputs for a transform.
            "displayData": [ # Transform-specific display data.
              { # Data provided with a pipeline or transform to provide descriptive info.
                "boolValue": True or False, # Contains value if the data is of a boolean type.
                "durationValue": "A String", # Contains value if the data is of duration type.
                "floatValue": 3.14, # Contains value if the data is of float type.
                "int64Value": "A String", # Contains value if the data is of int64 type.
                "javaClassValue": "A String", # Contains value if the data is of java class type.
                "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
                "label": "A String", # An optional label to display in a dax UI for the element.
                "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
                "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
                "strValue": "A String", # Contains value if the data is of string type.
                "timestampValue": "A String", # Contains value if the data is of timestamp type.
                "url": "A String", # An optional full URL.
              },
            ],
            "id": "A String", # SDK generated id of this transform instance.
            "inputCollectionName": [ # User names for all collection inputs to this transform.
              "A String",
            ],
            "kind": "A String", # Type of transform.
            "name": "A String", # User provided name for this transform instance.
            "outputCollectionName": [ # User names for all collection outputs to this transform.
              "A String",
            ],
          },
        ],
        "stepNamesHash": "A String", # A hash value of the submitted pipeline portable graph step names if exists.
      },
      "projectId": "A String", # The ID of the Google Cloud project that the job belongs to.
      "replaceJobId": "A String", # If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a `CreateJobRequest`, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.
      "replacedByJobId": "A String", # If another job is an update of this job (and thus, this job is in `JOB_STATE_UPDATED`), this field contains the ID of that job.
      "requestedState": "A String", # The job's requested state. Applies to `UpdateJob` requests. Set `requested_state` with `UpdateJob` requests to switch between the states `JOB_STATE_STOPPED` and `JOB_STATE_RUNNING`. You can also use `UpdateJob` requests to change a job's state from `JOB_STATE_RUNNING` to `JOB_STATE_CANCELLED`, `JOB_STATE_DONE`, or `JOB_STATE_DRAINED`. These states irrevocably terminate the job if it hasn't already reached a terminal state. This field has no effect on `CreateJob` requests.
      "runtimeUpdatableParams": { # Additional job parameters that can only be updated during runtime using the projects.jobs.update method. These fields have no effect when specified during job creation. # This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.
        "maxNumWorkers": 42, # The maximum number of workers to cap autoscaling at. This field is currently only supported for Streaming Engine jobs.
        "minNumWorkers": 42, # The minimum number of workers to scale down to. This field is currently only supported for Streaming Engine jobs.
        "workerUtilizationHint": 3.14, # Target worker utilization, compared against the aggregate utilization of the worker pool by autoscaler, to determine upscaling and downscaling when absent other constraints such as backlog. For more information, see [Update an existing pipeline](https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline).
      },
      "satisfiesPzi": True or False, # Output only. Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
      "satisfiesPzs": True or False, # Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
      "serviceResources": { # Resources used by the Dataflow Service to run the job. # Output only. Resources used by the Dataflow Service to run the job.
        "zones": [ # Output only. List of Cloud Zones being used by the Dataflow Service for this job. Example: us-central1-c
          "A String",
        ],
      },
      "stageStates": [ # This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.
        { # A message describing the state of a particular execution stage.
          "currentStateTime": "A String", # The time at which the stage transitioned to this state.
          "executionStageName": "A String", # The name of the execution stage.
          "executionStageState": "A String", # Executions stage states allow the same set of values as JobState.
        },
      ],
      "startTime": "A String", # The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.
      "steps": [ # Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.
        { # Defines a particular step within a Cloud Dataflow job. A job consists of multiple steps, each of which performs some specific operation as part of the overall job. Data is typically passed from one step to another as part of the job. **Note:** The properties of this object are not stable and might change. Here's an example of a sequence of steps which together implement a Map-Reduce job: * Read a collection of data from some source, parsing the collection's elements. * Validate the elements. * Apply a user-defined function to map each element to some value and extract an element-specific key value. * Group elements with the same key into a single element with that key, transforming a multiply-keyed collection into a uniquely-keyed collection. * Write the elements out to some data sink. Note that the Cloud Dataflow service may be used to run many different types of jobs, not just Map-Reduce.
          "kind": "A String", # The kind of step in the Cloud Dataflow job.
          "name": "A String", # The name that identifies the step. This must be unique for each step with respect to all other steps in the Cloud Dataflow job.
          "properties": { # Named properties associated with the step. Each kind of predefined step has its own required set of properties. Must be provided on Create. Only retrieved with JOB_VIEW_ALL.
            "a_key": "", # Properties of the object.
          },
        },
      ],
      "stepsLocation": "A String", # The Cloud Storage location where the steps are stored.
      "tempFiles": [ # A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
        "A String",
      ],
      "transformNameMapping": { # Optional. The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.
        "a_key": "A String",
      },
      "type": "A String", # Optional. The type of Dataflow job.
    },
  ],
  "nextPageToken": "A String", # Set if there may be more results than fit in this response.
}

list_next()

Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call 'execute()' on to request the next
          page. Returns None if there are no more items in the collection.
        

snapshot(projectId, location, jobId, body=None, x__xgafv=None)

Snapshot the state of a streaming job.

Args:
  projectId: string, The project which owns the job to be snapshotted. (required)
  location: string, The location that contains this job. (required)
  jobId: string, The job to be snapshotted. (required)
  body: object, The request body.
    The object takes the form of:

{ # Request to create a snapshot of a job.
  "description": "A String", # User specified description of the snapshot. Maybe empty.
  "location": "A String", # The location that contains this job.
  "snapshotSources": True or False, # If true, perform snapshots for sources which support this.
  "ttl": "A String", # TTL for the snapshot.
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Represents a snapshot of a job.
  "creationTime": "A String", # The time this snapshot was created.
  "description": "A String", # User specified description of the snapshot. Maybe empty.
  "diskSizeBytes": "A String", # The disk byte size of the snapshot. Only available for snapshots in READY state.
  "id": "A String", # The unique ID of this snapshot.
  "projectId": "A String", # The project this snapshot belongs to.
  "pubsubMetadata": [ # Pub/Sub snapshot metadata.
    { # Represents a Pubsub snapshot.
      "expireTime": "A String", # The expire time of the Pubsub snapshot.
      "snapshotName": "A String", # The name of the Pubsub snapshot.
      "topicName": "A String", # The name of the Pubsub topic.
    },
  ],
  "region": "A String", # Cloud region where this snapshot lives in, e.g., "us-central1".
  "sourceJobId": "A String", # The job this snapshot was created from.
  "state": "A String", # State of the snapshot.
  "ttl": "A String", # The time after which this snapshot will be automatically deleted.
}

update(projectId, location, jobId, body=None, updateMask=None, x__xgafv=None)

Updates the state of an existing Cloud Dataflow job. To update the state of an existing job, we recommend using `projects.locations.jobs.update` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.update` is not recommended, as you can only update the state of jobs that are running in `us-central1`.

Args:
  projectId: string, The ID of the Cloud Platform project that the job belongs to. (required)
  location: string, The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job. (required)
  jobId: string, The job ID. (required)
  body: object, The request body.
    The object takes the form of:

{ # Defines a job to be run by the Cloud Dataflow service. Do not enter confidential information when you supply string values using the API.
  "clientRequestId": "A String", # The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.
  "createTime": "A String", # The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.
  "createdFromSnapshotId": "A String", # If this is specified, the job's initial state is populated from the given snapshot.
  "currentState": "A String", # The current state of the job. Jobs are created in the `JOB_STATE_STOPPED` state unless otherwise specified. A job in the `JOB_STATE_RUNNING` state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field might be mutated by the Dataflow service; callers cannot mutate it.
  "currentStateTime": "A String", # The timestamp associated with the current state.
  "environment": { # Describes the environment in which a Dataflow Job runs. # Optional. The environment for the job.
    "clusterManagerApiService": "A String", # The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".
    "dataset": "A String", # Optional. The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}
    "debugOptions": { # Describes any options that have an effect on the debugging of pipelines. # Optional. Any debugging options to be supplied to the job.
      "dataSampling": { # Configuration options for sampling elements. # Configuration options for sampling elements from a running pipeline.
        "behaviors": [ # List of given sampling behaviors to enable. For example, specifying behaviors = [ALWAYS_ON] samples in-flight elements but does not sample exceptions. Can be used to specify multiple behaviors like, behaviors = [ALWAYS_ON, EXCEPTIONS] for specifying periodic sampling and exception sampling. If DISABLED is in the list, then sampling will be disabled and ignore the other given behaviors. Ordering does not matter.
          "A String",
        ],
      },
      "enableHotKeyLogging": True or False, # Optional. When true, enables the logging of the literal hot key to the user's Cloud Logging.
    },
    "experiments": [ # The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.
      "A String",
    ],
    "flexResourceSchedulingGoal": "A String", # Optional. Which Flexible Resource Scheduling mode to run in.
    "internalExperiments": { # Experimental settings.
      "a_key": "", # Properties of the object. Contains field @type with type URL.
    },
    "sdkPipelineOptions": { # The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.
      "a_key": "", # Properties of the object.
    },
    "serviceAccountEmail": "A String", # Optional. Identity to run virtual machines as. Defaults to the default account.
    "serviceKmsKeyName": "A String", # Optional. If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY
    "serviceOptions": [ # Optional. The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).
      "A String",
    ],
    "shuffleMode": "A String", # Output only. The shuffle mode used for the job.
    "streamingMode": "A String", # Optional. Specifies the Streaming Engine message processing guarantees. Reduces cost and latency but might result in duplicate messages committed to storage. Designed to run simple mapping streaming ETL jobs at the lowest cost. For example, Change Data Capture (CDC) to BigQuery is a canonical use case. For more information, see [Set the pipeline streaming mode](https://cloud.google.com/dataflow/docs/guides/streaming-modes).
    "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
    "useStreamingEngineResourceBasedBilling": True or False, # Output only. Whether the job uses the Streaming Engine resource-based billing model.
    "userAgent": { # A description of the process that generated the request.
      "a_key": "", # Properties of the object.
    },
    "version": { # A structure describing which components and their versions of the service are required in order to run the job.
      "a_key": "", # Properties of the object.
    },
    "workerPools": [ # The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.
      { # Describes one particular pool of Cloud Dataflow workers to be instantiated by the Cloud Dataflow service in order to perform the computations required by a job. Note that a workflow job may use multiple pools, in order to match the various computational requirements of the various stages of the job.
        "autoscalingSettings": { # Settings for WorkerPool autoscaling. # Settings for autoscaling of this WorkerPool.
          "algorithm": "A String", # The algorithm to use for autoscaling.
          "maxNumWorkers": 42, # The maximum number of workers to cap scaling at.
        },
        "dataDisks": [ # Data disks that are used by a VM in this workflow.
          { # Describes the data disk used by a workflow job.
            "diskType": "A String", # Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard
            "mountPoint": "A String", # Directory in a VM where disk is mounted.
            "sizeGb": 42, # Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
          },
        ],
        "defaultPackageSet": "A String", # The default package set to install. This allows the service to select a default set of packages which are useful to worker harnesses written in a particular language.
        "diskSizeGb": 42, # Size of root disk for VMs, in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
        "diskSourceImage": "A String", # Fully qualified source image for disks.
        "diskType": "A String", # Type of root disk for VMs. If empty or unspecified, the service will attempt to choose a reasonable default.
        "ipConfiguration": "A String", # Configuration for VM IPs.
        "kind": "A String", # The kind of the worker pool; currently only `harness` and `shuffle` are supported.
        "machineType": "A String", # Machine type (e.g. "n1-standard-1"). If empty or unspecified, the service will attempt to choose a reasonable default.
        "metadata": { # Metadata to set on the Google Compute Engine VMs.
          "a_key": "A String",
        },
        "network": "A String", # Network to which VMs will be assigned. If empty or unspecified, the service will use the network "default".
        "numThreadsPerWorker": 42, # The number of threads per worker harness. If empty or unspecified, the service will choose a number of threads (according to the number of cores on the selected machine type for batch, or 1 by convention for streaming).
        "numWorkers": 42, # Number of Google Compute Engine workers in this pool needed to execute the job. If zero or unspecified, the service will attempt to choose a reasonable default.
        "onHostMaintenance": "A String", # The action to take on host maintenance, as defined by the Google Compute Engine API.
        "packages": [ # Packages to be installed on workers.
          { # The packages that must be installed in order for a worker to run the steps of the Cloud Dataflow job that will be assigned to its worker pool. This is the mechanism by which the Cloud Dataflow SDK causes code to be loaded onto the workers. For example, the Cloud Dataflow Java SDK might use this to install jars containing the user's code and all of the various dependencies (libraries, data files, etc.) required in order for that code to run.
            "location": "A String", # The resource to read the package from. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket} bucket.storage.googleapis.com/
            "name": "A String", # The name of the package.
          },
        ],
        "poolArgs": { # Extra arguments for this worker pool.
          "a_key": "", # Properties of the object. Contains field @type with type URL.
        },
        "sdkHarnessContainerImages": [ # Set of SDK harness containers needed to execute this pipeline. This will only be set in the Fn API path. For non-cross-language pipelines this should have only one entry. Cross-language pipelines will have two or more entries.
          { # Defines an SDK harness container for executing Dataflow pipelines.
            "capabilities": [ # The set of capabilities enumerated in the above Environment proto. See also [beam_runner_api.proto](https://github.com/apache/beam/blob/master/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/beam_runner_api.proto)
              "A String",
            ],
            "containerImage": "A String", # A docker container image that resides in Google Container Registry.
            "environmentId": "A String", # Environment ID for the Beam runner API proto Environment that corresponds to the current SDK Harness.
            "useSingleCorePerContainer": True or False, # If true, recommends the Dataflow service to use only one core per SDK container instance with this image. If false (or unset) recommends using more than one core per SDK container instance with this image for efficiency. Note that Dataflow service may choose to override this property if needed.
          },
        ],
        "subnetwork": "A String", # Subnetwork to which VMs will be assigned, if desired. Expected to be of the form "regions/REGION/subnetworks/SUBNETWORK".
        "taskrunnerSettings": { # Taskrunner configuration settings. # Settings passed through to Google Compute Engine workers when using the standard Dataflow task runner. Users should ignore this field.
          "alsologtostderr": True or False, # Whether to also send taskrunner log info to stderr.
          "baseTaskDir": "A String", # The location on the worker for task-specific subdirectories.
          "baseUrl": "A String", # The base URL for the taskrunner to use when accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
          "commandlinesFileName": "A String", # The file to store preprocessing commands in.
          "continueOnException": True or False, # Whether to continue taskrunner if an exception is hit.
          "dataflowApiVersion": "A String", # The API version of endpoint, e.g. "v1b3"
          "harnessCommand": "A String", # The command to launch the worker harness.
          "languageHint": "A String", # The suggested backend language.
          "logDir": "A String", # The directory on the VM to store logs.
          "logToSerialconsole": True or False, # Whether to send taskrunner log info to Google Compute Engine VM serial console.
          "logUploadLocation": "A String", # Indicates where to put logs. If this is not specified, the logs will not be uploaded. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
          "oauthScopes": [ # The OAuth2 scopes to be requested by the taskrunner in order to access the Cloud Dataflow API.
            "A String",
          ],
          "parallelWorkerSettings": { # Provides data to pass through to the worker harness. # The settings to pass to the parallel worker harness.
            "baseUrl": "A String", # The base URL for accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
            "reportingEnabled": True or False, # Whether to send work progress updates to the service.
            "servicePath": "A String", # The Cloud Dataflow service path relative to the root URL, for example, "dataflow/v1b3/projects".
            "shuffleServicePath": "A String", # The Shuffle service path relative to the root URL, for example, "shuffle/v1beta1".
            "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
            "workerId": "A String", # The ID of the worker running this pipeline.
          },
          "streamingWorkerMainClass": "A String", # The streaming worker main class name.
          "taskGroup": "A String", # The UNIX group ID on the worker VM to use for tasks launched by taskrunner; e.g. "wheel".
          "taskUser": "A String", # The UNIX user ID on the worker VM to use for tasks launched by taskrunner; e.g. "root".
          "tempStoragePrefix": "A String", # The prefix of the resources the taskrunner should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
          "vmId": "A String", # The ID string of the VM.
          "workflowFileName": "A String", # The file to store the workflow in.
        },
        "teardownPolicy": "A String", # Sets the policy for determining when to turndown worker pool. Allowed values are: `TEARDOWN_ALWAYS`, `TEARDOWN_ON_SUCCESS`, and `TEARDOWN_NEVER`. `TEARDOWN_ALWAYS` means workers are always torn down regardless of whether the job succeeds. `TEARDOWN_ON_SUCCESS` means workers are torn down if the job succeeds. `TEARDOWN_NEVER` means the workers are never torn down. If the workers are not torn down by the service, they will continue to run and use Google Compute Engine VM resources in the user's project until they are explicitly terminated by the user. Because of this, Google recommends using the `TEARDOWN_ALWAYS` policy except for small, manually supervised test jobs. If unknown or unspecified, the service will attempt to choose a reasonable default.
        "workerHarnessContainerImage": "A String", # Required. Docker container image that executes the Cloud Dataflow worker harness, residing in Google Container Registry. Deprecated for the Fn API path. Use sdk_harness_container_images instead.
        "zone": "A String", # Zone to run the worker pools in. If empty or unspecified, the service will attempt to choose a reasonable default.
      },
    ],
    "workerRegion": "A String", # Optional. The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.
    "workerZone": "A String", # Optional. The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.
  },
  "executionInfo": { # Additional information about how a Cloud Dataflow job will be executed that isn't contained in the submitted job. # Deprecated.
    "stages": { # A mapping from each stage to the information about that stage.
      "a_key": { # Contains information about how a particular google.dataflow.v1beta3.Step will be executed.
        "stepName": [ # The steps associated with the execution stage. Note that stages may have several steps, and that a given step might be run by more than one stage.
          "A String",
        ],
      },
    },
  },
  "id": "A String", # The unique ID of this job. This field is set by the Dataflow service when the job is created, and is immutable for the life of the job.
  "jobMetadata": { # Metadata available primarily for filtering jobs. Will be included in the ListJob response and Job SUMMARY view. # This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.
    "bigTableDetails": [ # Identification of a Cloud Bigtable source used in the Dataflow job.
      { # Metadata for a Cloud Bigtable connector used by the job.
        "instanceId": "A String", # InstanceId accessed in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
        "tableId": "A String", # TableId accessed in the connection.
      },
    ],
    "bigqueryDetails": [ # Identification of a BigQuery source used in the Dataflow job.
      { # Metadata for a BigQuery connector used by the job.
        "dataset": "A String", # Dataset accessed in the connection.
        "projectId": "A String", # Project accessed in the connection.
        "query": "A String", # Query used to access data in the connection.
        "table": "A String", # Table accessed in the connection.
      },
    ],
    "datastoreDetails": [ # Identification of a Datastore source used in the Dataflow job.
      { # Metadata for a Datastore connector used by the job.
        "namespace": "A String", # Namespace used in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
      },
    ],
    "fileDetails": [ # Identification of a File source used in the Dataflow job.
      { # Metadata for a File connector used by the job.
        "filePattern": "A String", # File Pattern used to access files by the connector.
      },
    ],
    "pubsubDetails": [ # Identification of a Pub/Sub source used in the Dataflow job.
      { # Metadata for a Pub/Sub connector used by the job.
        "subscription": "A String", # Subscription used in the connection.
        "topic": "A String", # Topic accessed in the connection.
      },
    ],
    "sdkVersion": { # The version of the SDK used to run the job. # The SDK version used to run the job.
      "bugs": [ # Output only. Known bugs found in this SDK version.
        { # A bug found in the Dataflow SDK.
          "severity": "A String", # Output only. How severe the SDK bug is.
          "type": "A String", # Output only. Describes the impact of this SDK bug.
          "uri": "A String", # Output only. Link to more information on the bug.
        },
      ],
      "sdkSupportStatus": "A String", # The support status for this SDK version.
      "version": "A String", # The version of the SDK used to run the job.
      "versionDisplayName": "A String", # A readable string describing the version of the SDK.
    },
    "spannerDetails": [ # Identification of a Spanner source used in the Dataflow job.
      { # Metadata for a Spanner connector used by the job.
        "databaseId": "A String", # DatabaseId accessed in the connection.
        "instanceId": "A String", # InstanceId accessed in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
      },
    ],
    "userDisplayProperties": { # List of display properties to help UI filter jobs.
      "a_key": "A String",
    },
  },
  "labels": { # User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.
    "a_key": "A String",
  },
  "location": "A String", # Optional. The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.
  "name": "A String", # Optional. The user-specified Dataflow job name. Only one active job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a job with the same name as an active job that already exists, the attempt returns the existing job. The name must match the regular expression `[a-z]([-a-z0-9]{0,1022}[a-z0-9])?`
  "pipelineDescription": { # A descriptive representation of submitted pipeline as well as the executed form. This data is provided by the Dataflow service for ease of visualizing the pipeline and interpreting Dataflow provided metrics. # Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.
    "displayData": [ # Pipeline level display data.
      { # Data provided with a pipeline or transform to provide descriptive info.
        "boolValue": True or False, # Contains value if the data is of a boolean type.
        "durationValue": "A String", # Contains value if the data is of duration type.
        "floatValue": 3.14, # Contains value if the data is of float type.
        "int64Value": "A String", # Contains value if the data is of int64 type.
        "javaClassValue": "A String", # Contains value if the data is of java class type.
        "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
        "label": "A String", # An optional label to display in a dax UI for the element.
        "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
        "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
        "strValue": "A String", # Contains value if the data is of string type.
        "timestampValue": "A String", # Contains value if the data is of timestamp type.
        "url": "A String", # An optional full URL.
      },
    ],
    "executionPipelineStage": [ # Description of each stage of execution of the pipeline.
      { # Description of the composing transforms, names/ids, and input/outputs of a stage of execution. Some composing transforms and sources may have been generated by the Dataflow service during execution planning.
        "componentSource": [ # Collections produced and consumed by component transforms of this stage.
          { # Description of an interstitial value between transforms in an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "userName": "A String", # Human-readable name for this transform; may be user or system generated.
          },
        ],
        "componentTransform": [ # Transforms that comprise this execution stage.
          { # Description of a transform executed as part of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransform": "A String", # User name for the original user transform with which this transform is most closely associated.
            "userName": "A String", # Human-readable name for this transform; may be user or system generated.
          },
        ],
        "id": "A String", # Dataflow service generated id for this stage.
        "inputSource": [ # Input sources for this stage.
          { # Description of an input or output of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "sizeBytes": "A String", # Size of the source, if measurable.
            "userName": "A String", # Human-readable name for this source; may be user or system generated.
          },
        ],
        "kind": "A String", # Type of transform this stage is executing.
        "name": "A String", # Dataflow service generated name for this stage.
        "outputSource": [ # Output sources for this stage.
          { # Description of an input or output of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "sizeBytes": "A String", # Size of the source, if measurable.
            "userName": "A String", # Human-readable name for this source; may be user or system generated.
          },
        ],
        "prerequisiteStage": [ # Other stages that must complete before this stage can run.
          "A String",
        ],
      },
    ],
    "originalPipelineTransform": [ # Description of each transform in the pipeline and collections between them.
      { # Description of the type, names/ids, and input/outputs for a transform.
        "displayData": [ # Transform-specific display data.
          { # Data provided with a pipeline or transform to provide descriptive info.
            "boolValue": True or False, # Contains value if the data is of a boolean type.
            "durationValue": "A String", # Contains value if the data is of duration type.
            "floatValue": 3.14, # Contains value if the data is of float type.
            "int64Value": "A String", # Contains value if the data is of int64 type.
            "javaClassValue": "A String", # Contains value if the data is of java class type.
            "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
            "label": "A String", # An optional label to display in a dax UI for the element.
            "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
            "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
            "strValue": "A String", # Contains value if the data is of string type.
            "timestampValue": "A String", # Contains value if the data is of timestamp type.
            "url": "A String", # An optional full URL.
          },
        ],
        "id": "A String", # SDK generated id of this transform instance.
        "inputCollectionName": [ # User names for all collection inputs to this transform.
          "A String",
        ],
        "kind": "A String", # Type of transform.
        "name": "A String", # User provided name for this transform instance.
        "outputCollectionName": [ # User names for all collection outputs to this transform.
          "A String",
        ],
      },
    ],
    "stepNamesHash": "A String", # A hash value of the submitted pipeline portable graph step names if exists.
  },
  "projectId": "A String", # The ID of the Google Cloud project that the job belongs to.
  "replaceJobId": "A String", # If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a `CreateJobRequest`, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.
  "replacedByJobId": "A String", # If another job is an update of this job (and thus, this job is in `JOB_STATE_UPDATED`), this field contains the ID of that job.
  "requestedState": "A String", # The job's requested state. Applies to `UpdateJob` requests. Set `requested_state` with `UpdateJob` requests to switch between the states `JOB_STATE_STOPPED` and `JOB_STATE_RUNNING`. You can also use `UpdateJob` requests to change a job's state from `JOB_STATE_RUNNING` to `JOB_STATE_CANCELLED`, `JOB_STATE_DONE`, or `JOB_STATE_DRAINED`. These states irrevocably terminate the job if it hasn't already reached a terminal state. This field has no effect on `CreateJob` requests.
  "runtimeUpdatableParams": { # Additional job parameters that can only be updated during runtime using the projects.jobs.update method. These fields have no effect when specified during job creation. # This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.
    "maxNumWorkers": 42, # The maximum number of workers to cap autoscaling at. This field is currently only supported for Streaming Engine jobs.
    "minNumWorkers": 42, # The minimum number of workers to scale down to. This field is currently only supported for Streaming Engine jobs.
    "workerUtilizationHint": 3.14, # Target worker utilization, compared against the aggregate utilization of the worker pool by autoscaler, to determine upscaling and downscaling when absent other constraints such as backlog. For more information, see [Update an existing pipeline](https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline).
  },
  "satisfiesPzi": True or False, # Output only. Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
  "satisfiesPzs": True or False, # Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
  "serviceResources": { # Resources used by the Dataflow Service to run the job. # Output only. Resources used by the Dataflow Service to run the job.
    "zones": [ # Output only. List of Cloud Zones being used by the Dataflow Service for this job. Example: us-central1-c
      "A String",
    ],
  },
  "stageStates": [ # This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.
    { # A message describing the state of a particular execution stage.
      "currentStateTime": "A String", # The time at which the stage transitioned to this state.
      "executionStageName": "A String", # The name of the execution stage.
      "executionStageState": "A String", # Executions stage states allow the same set of values as JobState.
    },
  ],
  "startTime": "A String", # The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.
  "steps": [ # Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.
    { # Defines a particular step within a Cloud Dataflow job. A job consists of multiple steps, each of which performs some specific operation as part of the overall job. Data is typically passed from one step to another as part of the job. **Note:** The properties of this object are not stable and might change. Here's an example of a sequence of steps which together implement a Map-Reduce job: * Read a collection of data from some source, parsing the collection's elements. * Validate the elements. * Apply a user-defined function to map each element to some value and extract an element-specific key value. * Group elements with the same key into a single element with that key, transforming a multiply-keyed collection into a uniquely-keyed collection. * Write the elements out to some data sink. Note that the Cloud Dataflow service may be used to run many different types of jobs, not just Map-Reduce.
      "kind": "A String", # The kind of step in the Cloud Dataflow job.
      "name": "A String", # The name that identifies the step. This must be unique for each step with respect to all other steps in the Cloud Dataflow job.
      "properties": { # Named properties associated with the step. Each kind of predefined step has its own required set of properties. Must be provided on Create. Only retrieved with JOB_VIEW_ALL.
        "a_key": "", # Properties of the object.
      },
    },
  ],
  "stepsLocation": "A String", # The Cloud Storage location where the steps are stored.
  "tempFiles": [ # A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
    "A String",
  ],
  "transformNameMapping": { # Optional. The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.
    "a_key": "A String",
  },
  "type": "A String", # Optional. The type of Dataflow job.
}

  updateMask: string, The list of fields to update relative to Job. If empty, only RequestedJobState will be considered for update. If the FieldMask is not empty and RequestedJobState is none/empty, The fields specified in the update mask will be the only ones considered for update. If both RequestedJobState and update_mask are specified, an error will be returned as we cannot update both state and mask.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Defines a job to be run by the Cloud Dataflow service. Do not enter confidential information when you supply string values using the API.
  "clientRequestId": "A String", # The client's unique identifier of the job, re-used across retried attempts. If this field is set, the service will ensure its uniqueness. The request to create a job will fail if the service has knowledge of a previously submitted job with the same client's ID and job name. The caller may use this field to ensure idempotence of job creation across retried attempts to create a job. By default, the field is empty and, in that case, the service ignores it.
  "createTime": "A String", # The timestamp when the job was initially created. Immutable and set by the Cloud Dataflow service.
  "createdFromSnapshotId": "A String", # If this is specified, the job's initial state is populated from the given snapshot.
  "currentState": "A String", # The current state of the job. Jobs are created in the `JOB_STATE_STOPPED` state unless otherwise specified. A job in the `JOB_STATE_RUNNING` state may asynchronously enter a terminal state. After a job has reached a terminal state, no further state updates may be made. This field might be mutated by the Dataflow service; callers cannot mutate it.
  "currentStateTime": "A String", # The timestamp associated with the current state.
  "environment": { # Describes the environment in which a Dataflow Job runs. # Optional. The environment for the job.
    "clusterManagerApiService": "A String", # The type of cluster manager API to use. If unknown or unspecified, the service will attempt to choose a reasonable default. This should be in the form of the API service name, e.g. "compute.googleapis.com".
    "dataset": "A String", # Optional. The dataset for the current project where various workflow related tables are stored. The supported resource type is: Google BigQuery: bigquery.googleapis.com/{dataset}
    "debugOptions": { # Describes any options that have an effect on the debugging of pipelines. # Optional. Any debugging options to be supplied to the job.
      "dataSampling": { # Configuration options for sampling elements. # Configuration options for sampling elements from a running pipeline.
        "behaviors": [ # List of given sampling behaviors to enable. For example, specifying behaviors = [ALWAYS_ON] samples in-flight elements but does not sample exceptions. Can be used to specify multiple behaviors like, behaviors = [ALWAYS_ON, EXCEPTIONS] for specifying periodic sampling and exception sampling. If DISABLED is in the list, then sampling will be disabled and ignore the other given behaviors. Ordering does not matter.
          "A String",
        ],
      },
      "enableHotKeyLogging": True or False, # Optional. When true, enables the logging of the literal hot key to the user's Cloud Logging.
    },
    "experiments": [ # The list of experiments to enable. This field should be used for SDK related experiments and not for service related experiments. The proper field for service related experiments is service_options.
      "A String",
    ],
    "flexResourceSchedulingGoal": "A String", # Optional. Which Flexible Resource Scheduling mode to run in.
    "internalExperiments": { # Experimental settings.
      "a_key": "", # Properties of the object. Contains field @type with type URL.
    },
    "sdkPipelineOptions": { # The Cloud Dataflow SDK pipeline options specified by the user. These options are passed through the service and are used to recreate the SDK pipeline options on the worker in a language agnostic and platform independent way.
      "a_key": "", # Properties of the object.
    },
    "serviceAccountEmail": "A String", # Optional. Identity to run virtual machines as. Defaults to the default account.
    "serviceKmsKeyName": "A String", # Optional. If set, contains the Cloud KMS key identifier used to encrypt data at rest, AKA a Customer Managed Encryption Key (CMEK). Format: projects/PROJECT_ID/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY
    "serviceOptions": [ # Optional. The list of service options to enable. This field should be used for service related experiments only. These experiments, when graduating to GA, should be replaced by dedicated fields or become default (i.e. always on).
      "A String",
    ],
    "shuffleMode": "A String", # Output only. The shuffle mode used for the job.
    "streamingMode": "A String", # Optional. Specifies the Streaming Engine message processing guarantees. Reduces cost and latency but might result in duplicate messages committed to storage. Designed to run simple mapping streaming ETL jobs at the lowest cost. For example, Change Data Capture (CDC) to BigQuery is a canonical use case. For more information, see [Set the pipeline streaming mode](https://cloud.google.com/dataflow/docs/guides/streaming-modes).
    "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The system will append the suffix "/temp-{JOBNAME} to this resource prefix, where {JOBNAME} is the value of the job_name field. The resulting bucket and object prefix is used as the prefix of the resources used to store temporary data needed during the job execution. NOTE: This will override the value in taskrunner_settings. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
    "useStreamingEngineResourceBasedBilling": True or False, # Output only. Whether the job uses the Streaming Engine resource-based billing model.
    "userAgent": { # A description of the process that generated the request.
      "a_key": "", # Properties of the object.
    },
    "version": { # A structure describing which components and their versions of the service are required in order to run the job.
      "a_key": "", # Properties of the object.
    },
    "workerPools": [ # The worker pools. At least one "harness" worker pool must be specified in order for the job to have workers.
      { # Describes one particular pool of Cloud Dataflow workers to be instantiated by the Cloud Dataflow service in order to perform the computations required by a job. Note that a workflow job may use multiple pools, in order to match the various computational requirements of the various stages of the job.
        "autoscalingSettings": { # Settings for WorkerPool autoscaling. # Settings for autoscaling of this WorkerPool.
          "algorithm": "A String", # The algorithm to use for autoscaling.
          "maxNumWorkers": 42, # The maximum number of workers to cap scaling at.
        },
        "dataDisks": [ # Data disks that are used by a VM in this workflow.
          { # Describes the data disk used by a workflow job.
            "diskType": "A String", # Disk storage type, as defined by Google Compute Engine. This must be a disk type appropriate to the project and zone in which the workers will run. If unknown or unspecified, the service will attempt to choose a reasonable default. For example, the standard persistent disk type is a resource name typically ending in "pd-standard". If SSD persistent disks are available, the resource name typically ends with "pd-ssd". The actual valid values are defined the Google Compute Engine API, not by the Cloud Dataflow API; consult the Google Compute Engine documentation for more information about determining the set of available disk types for a particular project and zone. Google Compute Engine Disk types are local to a particular project in a particular zone, and so the resource name will typically look something like this: compute.googleapis.com/projects/project-id/zones/zone/diskTypes/pd-standard
            "mountPoint": "A String", # Directory in a VM where disk is mounted.
            "sizeGb": 42, # Size of disk in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
          },
        ],
        "defaultPackageSet": "A String", # The default package set to install. This allows the service to select a default set of packages which are useful to worker harnesses written in a particular language.
        "diskSizeGb": 42, # Size of root disk for VMs, in GB. If zero or unspecified, the service will attempt to choose a reasonable default.
        "diskSourceImage": "A String", # Fully qualified source image for disks.
        "diskType": "A String", # Type of root disk for VMs. If empty or unspecified, the service will attempt to choose a reasonable default.
        "ipConfiguration": "A String", # Configuration for VM IPs.
        "kind": "A String", # The kind of the worker pool; currently only `harness` and `shuffle` are supported.
        "machineType": "A String", # Machine type (e.g. "n1-standard-1"). If empty or unspecified, the service will attempt to choose a reasonable default.
        "metadata": { # Metadata to set on the Google Compute Engine VMs.
          "a_key": "A String",
        },
        "network": "A String", # Network to which VMs will be assigned. If empty or unspecified, the service will use the network "default".
        "numThreadsPerWorker": 42, # The number of threads per worker harness. If empty or unspecified, the service will choose a number of threads (according to the number of cores on the selected machine type for batch, or 1 by convention for streaming).
        "numWorkers": 42, # Number of Google Compute Engine workers in this pool needed to execute the job. If zero or unspecified, the service will attempt to choose a reasonable default.
        "onHostMaintenance": "A String", # The action to take on host maintenance, as defined by the Google Compute Engine API.
        "packages": [ # Packages to be installed on workers.
          { # The packages that must be installed in order for a worker to run the steps of the Cloud Dataflow job that will be assigned to its worker pool. This is the mechanism by which the Cloud Dataflow SDK causes code to be loaded onto the workers. For example, the Cloud Dataflow Java SDK might use this to install jars containing the user's code and all of the various dependencies (libraries, data files, etc.) required in order for that code to run.
            "location": "A String", # The resource to read the package from. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket} bucket.storage.googleapis.com/
            "name": "A String", # The name of the package.
          },
        ],
        "poolArgs": { # Extra arguments for this worker pool.
          "a_key": "", # Properties of the object. Contains field @type with type URL.
        },
        "sdkHarnessContainerImages": [ # Set of SDK harness containers needed to execute this pipeline. This will only be set in the Fn API path. For non-cross-language pipelines this should have only one entry. Cross-language pipelines will have two or more entries.
          { # Defines an SDK harness container for executing Dataflow pipelines.
            "capabilities": [ # The set of capabilities enumerated in the above Environment proto. See also [beam_runner_api.proto](https://github.com/apache/beam/blob/master/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/beam_runner_api.proto)
              "A String",
            ],
            "containerImage": "A String", # A docker container image that resides in Google Container Registry.
            "environmentId": "A String", # Environment ID for the Beam runner API proto Environment that corresponds to the current SDK Harness.
            "useSingleCorePerContainer": True or False, # If true, recommends the Dataflow service to use only one core per SDK container instance with this image. If false (or unset) recommends using more than one core per SDK container instance with this image for efficiency. Note that Dataflow service may choose to override this property if needed.
          },
        ],
        "subnetwork": "A String", # Subnetwork to which VMs will be assigned, if desired. Expected to be of the form "regions/REGION/subnetworks/SUBNETWORK".
        "taskrunnerSettings": { # Taskrunner configuration settings. # Settings passed through to Google Compute Engine workers when using the standard Dataflow task runner. Users should ignore this field.
          "alsologtostderr": True or False, # Whether to also send taskrunner log info to stderr.
          "baseTaskDir": "A String", # The location on the worker for task-specific subdirectories.
          "baseUrl": "A String", # The base URL for the taskrunner to use when accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
          "commandlinesFileName": "A String", # The file to store preprocessing commands in.
          "continueOnException": True or False, # Whether to continue taskrunner if an exception is hit.
          "dataflowApiVersion": "A String", # The API version of endpoint, e.g. "v1b3"
          "harnessCommand": "A String", # The command to launch the worker harness.
          "languageHint": "A String", # The suggested backend language.
          "logDir": "A String", # The directory on the VM to store logs.
          "logToSerialconsole": True or False, # Whether to send taskrunner log info to Google Compute Engine VM serial console.
          "logUploadLocation": "A String", # Indicates where to put logs. If this is not specified, the logs will not be uploaded. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
          "oauthScopes": [ # The OAuth2 scopes to be requested by the taskrunner in order to access the Cloud Dataflow API.
            "A String",
          ],
          "parallelWorkerSettings": { # Provides data to pass through to the worker harness. # The settings to pass to the parallel worker harness.
            "baseUrl": "A String", # The base URL for accessing Google Cloud APIs. When workers access Google Cloud APIs, they logically do so via relative URLs. If this field is specified, it supplies the base URL to use for resolving these relative URLs. The normative algorithm used is defined by RFC 1808, "Relative Uniform Resource Locators". If not specified, the default value is "http://www.googleapis.com/"
            "reportingEnabled": True or False, # Whether to send work progress updates to the service.
            "servicePath": "A String", # The Cloud Dataflow service path relative to the root URL, for example, "dataflow/v1b3/projects".
            "shuffleServicePath": "A String", # The Shuffle service path relative to the root URL, for example, "shuffle/v1beta1".
            "tempStoragePrefix": "A String", # The prefix of the resources the system should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
            "workerId": "A String", # The ID of the worker running this pipeline.
          },
          "streamingWorkerMainClass": "A String", # The streaming worker main class name.
          "taskGroup": "A String", # The UNIX group ID on the worker VM to use for tasks launched by taskrunner; e.g. "wheel".
          "taskUser": "A String", # The UNIX user ID on the worker VM to use for tasks launched by taskrunner; e.g. "root".
          "tempStoragePrefix": "A String", # The prefix of the resources the taskrunner should use for temporary storage. The supported resource type is: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
          "vmId": "A String", # The ID string of the VM.
          "workflowFileName": "A String", # The file to store the workflow in.
        },
        "teardownPolicy": "A String", # Sets the policy for determining when to turndown worker pool. Allowed values are: `TEARDOWN_ALWAYS`, `TEARDOWN_ON_SUCCESS`, and `TEARDOWN_NEVER`. `TEARDOWN_ALWAYS` means workers are always torn down regardless of whether the job succeeds. `TEARDOWN_ON_SUCCESS` means workers are torn down if the job succeeds. `TEARDOWN_NEVER` means the workers are never torn down. If the workers are not torn down by the service, they will continue to run and use Google Compute Engine VM resources in the user's project until they are explicitly terminated by the user. Because of this, Google recommends using the `TEARDOWN_ALWAYS` policy except for small, manually supervised test jobs. If unknown or unspecified, the service will attempt to choose a reasonable default.
        "workerHarnessContainerImage": "A String", # Required. Docker container image that executes the Cloud Dataflow worker harness, residing in Google Container Registry. Deprecated for the Fn API path. Use sdk_harness_container_images instead.
        "zone": "A String", # Zone to run the worker pools in. If empty or unspecified, the service will attempt to choose a reasonable default.
      },
    ],
    "workerRegion": "A String", # Optional. The Compute Engine region (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1". Mutually exclusive with worker_zone. If neither worker_region nor worker_zone is specified, default to the control plane's region.
    "workerZone": "A String", # Optional. The Compute Engine zone (https://cloud.google.com/compute/docs/regions-zones/regions-zones) in which worker processing should occur, e.g. "us-west1-a". Mutually exclusive with worker_region. If neither worker_region nor worker_zone is specified, a zone in the control plane's region is chosen based on available capacity.
  },
  "executionInfo": { # Additional information about how a Cloud Dataflow job will be executed that isn't contained in the submitted job. # Deprecated.
    "stages": { # A mapping from each stage to the information about that stage.
      "a_key": { # Contains information about how a particular google.dataflow.v1beta3.Step will be executed.
        "stepName": [ # The steps associated with the execution stage. Note that stages may have several steps, and that a given step might be run by more than one stage.
          "A String",
        ],
      },
    },
  },
  "id": "A String", # The unique ID of this job. This field is set by the Dataflow service when the job is created, and is immutable for the life of the job.
  "jobMetadata": { # Metadata available primarily for filtering jobs. Will be included in the ListJob response and Job SUMMARY view. # This field is populated by the Dataflow service to support filtering jobs by the metadata values provided here. Populated for ListJobs and all GetJob views SUMMARY and higher.
    "bigTableDetails": [ # Identification of a Cloud Bigtable source used in the Dataflow job.
      { # Metadata for a Cloud Bigtable connector used by the job.
        "instanceId": "A String", # InstanceId accessed in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
        "tableId": "A String", # TableId accessed in the connection.
      },
    ],
    "bigqueryDetails": [ # Identification of a BigQuery source used in the Dataflow job.
      { # Metadata for a BigQuery connector used by the job.
        "dataset": "A String", # Dataset accessed in the connection.
        "projectId": "A String", # Project accessed in the connection.
        "query": "A String", # Query used to access data in the connection.
        "table": "A String", # Table accessed in the connection.
      },
    ],
    "datastoreDetails": [ # Identification of a Datastore source used in the Dataflow job.
      { # Metadata for a Datastore connector used by the job.
        "namespace": "A String", # Namespace used in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
      },
    ],
    "fileDetails": [ # Identification of a File source used in the Dataflow job.
      { # Metadata for a File connector used by the job.
        "filePattern": "A String", # File Pattern used to access files by the connector.
      },
    ],
    "pubsubDetails": [ # Identification of a Pub/Sub source used in the Dataflow job.
      { # Metadata for a Pub/Sub connector used by the job.
        "subscription": "A String", # Subscription used in the connection.
        "topic": "A String", # Topic accessed in the connection.
      },
    ],
    "sdkVersion": { # The version of the SDK used to run the job. # The SDK version used to run the job.
      "bugs": [ # Output only. Known bugs found in this SDK version.
        { # A bug found in the Dataflow SDK.
          "severity": "A String", # Output only. How severe the SDK bug is.
          "type": "A String", # Output only. Describes the impact of this SDK bug.
          "uri": "A String", # Output only. Link to more information on the bug.
        },
      ],
      "sdkSupportStatus": "A String", # The support status for this SDK version.
      "version": "A String", # The version of the SDK used to run the job.
      "versionDisplayName": "A String", # A readable string describing the version of the SDK.
    },
    "spannerDetails": [ # Identification of a Spanner source used in the Dataflow job.
      { # Metadata for a Spanner connector used by the job.
        "databaseId": "A String", # DatabaseId accessed in the connection.
        "instanceId": "A String", # InstanceId accessed in the connection.
        "projectId": "A String", # ProjectId accessed in the connection.
      },
    ],
    "userDisplayProperties": { # List of display properties to help UI filter jobs.
      "a_key": "A String",
    },
  },
  "labels": { # User-defined labels for this job. The labels map can contain no more than 64 entries. Entries of the labels map are UTF8 strings that comply with the following restrictions: * Keys must conform to regexp: \p{Ll}\p{Lo}{0,62} * Values must conform to regexp: [\p{Ll}\p{Lo}\p{N}_-]{0,63} * Both keys and values are additionally constrained to be <= 128 bytes in size.
    "a_key": "A String",
  },
  "location": "A String", # Optional. The [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) that contains this job.
  "name": "A String", # Optional. The user-specified Dataflow job name. Only one active job with a given name can exist in a project within one region at any given time. Jobs in different regions can have the same name. If a caller attempts to create a job with the same name as an active job that already exists, the attempt returns the existing job. The name must match the regular expression `[a-z]([-a-z0-9]{0,1022}[a-z0-9])?`
  "pipelineDescription": { # A descriptive representation of submitted pipeline as well as the executed form. This data is provided by the Dataflow service for ease of visualizing the pipeline and interpreting Dataflow provided metrics. # Preliminary field: The format of this data may change at any time. A description of the user pipeline and stages through which it is executed. Created by Cloud Dataflow service. Only retrieved with JOB_VIEW_DESCRIPTION or JOB_VIEW_ALL.
    "displayData": [ # Pipeline level display data.
      { # Data provided with a pipeline or transform to provide descriptive info.
        "boolValue": True or False, # Contains value if the data is of a boolean type.
        "durationValue": "A String", # Contains value if the data is of duration type.
        "floatValue": 3.14, # Contains value if the data is of float type.
        "int64Value": "A String", # Contains value if the data is of int64 type.
        "javaClassValue": "A String", # Contains value if the data is of java class type.
        "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
        "label": "A String", # An optional label to display in a dax UI for the element.
        "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
        "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
        "strValue": "A String", # Contains value if the data is of string type.
        "timestampValue": "A String", # Contains value if the data is of timestamp type.
        "url": "A String", # An optional full URL.
      },
    ],
    "executionPipelineStage": [ # Description of each stage of execution of the pipeline.
      { # Description of the composing transforms, names/ids, and input/outputs of a stage of execution. Some composing transforms and sources may have been generated by the Dataflow service during execution planning.
        "componentSource": [ # Collections produced and consumed by component transforms of this stage.
          { # Description of an interstitial value between transforms in an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "userName": "A String", # Human-readable name for this transform; may be user or system generated.
          },
        ],
        "componentTransform": [ # Transforms that comprise this execution stage.
          { # Description of a transform executed as part of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransform": "A String", # User name for the original user transform with which this transform is most closely associated.
            "userName": "A String", # Human-readable name for this transform; may be user or system generated.
          },
        ],
        "id": "A String", # Dataflow service generated id for this stage.
        "inputSource": [ # Input sources for this stage.
          { # Description of an input or output of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "sizeBytes": "A String", # Size of the source, if measurable.
            "userName": "A String", # Human-readable name for this source; may be user or system generated.
          },
        ],
        "kind": "A String", # Type of transform this stage is executing.
        "name": "A String", # Dataflow service generated name for this stage.
        "outputSource": [ # Output sources for this stage.
          { # Description of an input or output of an execution stage.
            "name": "A String", # Dataflow service generated name for this source.
            "originalTransformOrCollection": "A String", # User name for the original user transform or collection with which this source is most closely associated.
            "sizeBytes": "A String", # Size of the source, if measurable.
            "userName": "A String", # Human-readable name for this source; may be user or system generated.
          },
        ],
        "prerequisiteStage": [ # Other stages that must complete before this stage can run.
          "A String",
        ],
      },
    ],
    "originalPipelineTransform": [ # Description of each transform in the pipeline and collections between them.
      { # Description of the type, names/ids, and input/outputs for a transform.
        "displayData": [ # Transform-specific display data.
          { # Data provided with a pipeline or transform to provide descriptive info.
            "boolValue": True or False, # Contains value if the data is of a boolean type.
            "durationValue": "A String", # Contains value if the data is of duration type.
            "floatValue": 3.14, # Contains value if the data is of float type.
            "int64Value": "A String", # Contains value if the data is of int64 type.
            "javaClassValue": "A String", # Contains value if the data is of java class type.
            "key": "A String", # The key identifying the display data. This is intended to be used as a label for the display data when viewed in a dax monitoring system.
            "label": "A String", # An optional label to display in a dax UI for the element.
            "namespace": "A String", # The namespace for the key. This is usually a class name or programming language namespace (i.e. python module) which defines the display data. This allows a dax monitoring system to specially handle the data and perform custom rendering.
            "shortStrValue": "A String", # A possible additional shorter value to display. For example a java_class_name_value of com.mypackage.MyDoFn will be stored with MyDoFn as the short_str_value and com.mypackage.MyDoFn as the java_class_name value. short_str_value can be displayed and java_class_name_value will be displayed as a tooltip.
            "strValue": "A String", # Contains value if the data is of string type.
            "timestampValue": "A String", # Contains value if the data is of timestamp type.
            "url": "A String", # An optional full URL.
          },
        ],
        "id": "A String", # SDK generated id of this transform instance.
        "inputCollectionName": [ # User names for all collection inputs to this transform.
          "A String",
        ],
        "kind": "A String", # Type of transform.
        "name": "A String", # User provided name for this transform instance.
        "outputCollectionName": [ # User names for all collection outputs to this transform.
          "A String",
        ],
      },
    ],
    "stepNamesHash": "A String", # A hash value of the submitted pipeline portable graph step names if exists.
  },
  "projectId": "A String", # The ID of the Google Cloud project that the job belongs to.
  "replaceJobId": "A String", # If this job is an update of an existing job, this field is the job ID of the job it replaced. When sending a `CreateJobRequest`, you can update a job by specifying it here. The job named here is stopped, and its intermediate state is transferred to this job.
  "replacedByJobId": "A String", # If another job is an update of this job (and thus, this job is in `JOB_STATE_UPDATED`), this field contains the ID of that job.
  "requestedState": "A String", # The job's requested state. Applies to `UpdateJob` requests. Set `requested_state` with `UpdateJob` requests to switch between the states `JOB_STATE_STOPPED` and `JOB_STATE_RUNNING`. You can also use `UpdateJob` requests to change a job's state from `JOB_STATE_RUNNING` to `JOB_STATE_CANCELLED`, `JOB_STATE_DONE`, or `JOB_STATE_DRAINED`. These states irrevocably terminate the job if it hasn't already reached a terminal state. This field has no effect on `CreateJob` requests.
  "runtimeUpdatableParams": { # Additional job parameters that can only be updated during runtime using the projects.jobs.update method. These fields have no effect when specified during job creation. # This field may ONLY be modified at runtime using the projects.jobs.update method to adjust job behavior. This field has no effect when specified at job creation.
    "maxNumWorkers": 42, # The maximum number of workers to cap autoscaling at. This field is currently only supported for Streaming Engine jobs.
    "minNumWorkers": 42, # The minimum number of workers to scale down to. This field is currently only supported for Streaming Engine jobs.
    "workerUtilizationHint": 3.14, # Target worker utilization, compared against the aggregate utilization of the worker pool by autoscaler, to determine upscaling and downscaling when absent other constraints such as backlog. For more information, see [Update an existing pipeline](https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline).
  },
  "satisfiesPzi": True or False, # Output only. Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
  "satisfiesPzs": True or False, # Reserved for future use. This field is set only in responses from the server; it is ignored if it is set in any requests.
  "serviceResources": { # Resources used by the Dataflow Service to run the job. # Output only. Resources used by the Dataflow Service to run the job.
    "zones": [ # Output only. List of Cloud Zones being used by the Dataflow Service for this job. Example: us-central1-c
      "A String",
    ],
  },
  "stageStates": [ # This field may be mutated by the Cloud Dataflow service; callers cannot mutate it.
    { # A message describing the state of a particular execution stage.
      "currentStateTime": "A String", # The time at which the stage transitioned to this state.
      "executionStageName": "A String", # The name of the execution stage.
      "executionStageState": "A String", # Executions stage states allow the same set of values as JobState.
    },
  ],
  "startTime": "A String", # The timestamp when the job was started (transitioned to JOB_STATE_PENDING). Flexible resource scheduling jobs are started with some delay after job creation, so start_time is unset before start and is updated when the job is started by the Cloud Dataflow service. For other jobs, start_time always equals to create_time and is immutable and set by the Cloud Dataflow service.
  "steps": [ # Exactly one of step or steps_location should be specified. The top-level steps that constitute the entire job. Only retrieved with JOB_VIEW_ALL.
    { # Defines a particular step within a Cloud Dataflow job. A job consists of multiple steps, each of which performs some specific operation as part of the overall job. Data is typically passed from one step to another as part of the job. **Note:** The properties of this object are not stable and might change. Here's an example of a sequence of steps which together implement a Map-Reduce job: * Read a collection of data from some source, parsing the collection's elements. * Validate the elements. * Apply a user-defined function to map each element to some value and extract an element-specific key value. * Group elements with the same key into a single element with that key, transforming a multiply-keyed collection into a uniquely-keyed collection. * Write the elements out to some data sink. Note that the Cloud Dataflow service may be used to run many different types of jobs, not just Map-Reduce.
      "kind": "A String", # The kind of step in the Cloud Dataflow job.
      "name": "A String", # The name that identifies the step. This must be unique for each step with respect to all other steps in the Cloud Dataflow job.
      "properties": { # Named properties associated with the step. Each kind of predefined step has its own required set of properties. Must be provided on Create. Only retrieved with JOB_VIEW_ALL.
        "a_key": "", # Properties of the object.
      },
    },
  ],
  "stepsLocation": "A String", # The Cloud Storage location where the steps are stored.
  "tempFiles": [ # A set of files the system should be aware of that are used for temporary storage. These temporary files will be removed on job completion. No duplicates are allowed. No file patterns are supported. The supported files are: Google Cloud Storage: storage.googleapis.com/{bucket}/{object} bucket.storage.googleapis.com/{object}
    "A String",
  ],
  "transformNameMapping": { # Optional. The map of transform name prefixes of the job to be replaced to the corresponding name prefixes of the new job.
    "a_key": "A String",
  },
  "type": "A String", # Optional. The type of Dataflow job.
}