get-job-run¶

Description¶

Retrieves the metadata for a given job run.

Synopsis¶

  get-job-run
--job-name <value>
--run-id <value>
[--predecessors-included | --no-predecessors-included]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options¶

--job-name (string)

Name of the job definition being run.

--run-id (string)

The ID of the job run.

--predecessors-included | --no-predecessors-included (boolean)

True if a list of predecessor runs should be returned.

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See ‘aws help’ for descriptions of global parameters.

Examples¶

To get information about a job run

The following get-job-run example retrieves information about a job run.

aws glue get-job-run \
    --job-name "Combine legistators data" \
    --run-id jr_012e176506505074d94d761755e5c62538ee1aad6f17d39f527e9140cf0c9a5e

Output:

{
    "JobRun": {
        "Id": "jr_012e176506505074d94d761755e5c62538ee1aad6f17d39f527e9140cf0c9a5e",
        "Attempt": 0,
        "JobName": "Combine legistators data",
        "StartedOn": 1602873931.255,
        "LastModifiedOn": 1602874075.985,
        "CompletedOn": 1602874075.985,
        "JobRunState": "SUCCEEDED",
        "Arguments": {
            "--enable-continuous-cloudwatch-log": "true",
            "--enable-metrics": "",
            "--enable-spark-ui": "true",
            "--job-bookmark-option": "job-bookmark-enable",
            "--spark-event-logs-path": "s3://aws-glue-assets-111122223333-us-east-1/sparkHistoryLogs/"
        },
        "PredecessorRuns": [],
        "AllocatedCapacity": 10,
        "ExecutionTime": 117,
        "Timeout": 2880,
        "MaxCapacity": 10.0,
        "WorkerType": "G.1X",
        "NumberOfWorkers": 10,
        "LogGroupName": "/aws-glue/jobs",
        "GlueVersion": "2.0"
    }
}

For more information, see Job Runs in the AWS Glue Developer Guide.

Output¶

JobRun -> (structure)

The requested job-run metadata.

Id -> (string)

The ID of this job run.

Attempt -> (integer)

The number of the attempt to run this job.

PreviousRunId -> (string)

The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action.

TriggerName -> (string)

The name of the trigger that started this job run.

JobName -> (string)

The name of the job definition being used in this run.

StartedOn -> (timestamp)

The date and time at which this job run was started.

LastModifiedOn -> (timestamp)

The last time that this job run was modified.

CompletedOn -> (timestamp)

The date and time that this job run completed.

JobRunState -> (string)

The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see Glue Job Run Statuses .

Arguments -> (map)

The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself.

You can specify arguments here that your own job-execution script consumes, as well as arguments that Glue itself consumes.

For information about how to specify and consume your own job arguments, see the Calling Glue APIs in Python topic in the developer guide.

For information about the key-value pairs that Glue consumes to set up your job, see the Special Parameters Used by Glue topic in the developer guide.

key -> (string)

value -> (string)

ErrorMessage -> (string)

An error message associated with this job run.

PredecessorRuns -> (list)

A list of predecessors to this job run.

(structure)

A job run that was used in the predicate of a conditional trigger that triggered this job run.

JobName -> (string)

The name of the job definition used by the predecessor job run.

RunId -> (string)

The job-run ID of the predecessor job run.

AllocatedCapacity -> (integer)

This field is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page .

ExecutionTime -> (integer)

The amount of time (in seconds) that the job run consumed resources.

Timeout -> (integer)

The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job.

MaxCapacity -> (double)

The number of Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page .

Do not set Max Capacity if using WorkerType and NumberOfWorkers .

The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job:

When you specify a Python shell job (JobCommand.Name =”pythonshell”), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.

When you specify an Apache Spark ETL job (JobCommand.Name =”glueetl”), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.

WorkerType -> (string)

The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X.

For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker.

For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker.

For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker.

NumberOfWorkers -> (integer)

The number of workers of a defined workerType that are allocated when a job runs.

The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X .

SecurityConfiguration -> (string)

The name of the SecurityConfiguration structure to be used with this job run.

LogGroupName -> (string)

The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using KMS. This name can be /aws-glue/jobs/ , in which case the default encryption is NONE . If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/ ), then that security configuration is used to encrypt the log group.

NotificationProperty -> (structure)

Specifies configuration properties of a job run notification.

NotifyDelayAfter -> (integer)

After a job run starts, the number of minutes to wait before sending a job run delay notification.

GlueVersion -> (string)

Glue version determines the versions of Apache Spark and Python that Glue supports. The Python version indicates the version supported for jobs of type Spark.

For more information about the available Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide.

Jobs that are created without specifying a Glue version default to Glue 0.9.

DPUSeconds -> (double)

This field populates only when an Auto Scaling job run completes, and represents the total time each executor ran during the lifecycle of a job run in seconds, multiplied by a DPU factor (1 for G.1X and 2 for G.2X workers). This value may be different than the executionEngineRuntime * MaxCapacity as in the case of Auto Scaling jobs, as the number of executors running at a given time may be less than the MaxCapacity . Therefore, it is possible that the value of DPUSeconds is less than executionEngineRuntime * MaxCapacity .

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