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AWS for Query.Farm

aws.glue.get_job_run

Example SQL Queries

SELECT * FROM
aws.glue.get_job_run
WHERE
"job_name" = 'VALUE'
AND "run_id" = 'VALUE';

Description

Retrieves the metadata for a given job run. Job run history is accessible for 90 days for your workflow and job run.

Table Definition

Column NameColumn Data Type
job_name Required Input Column

Name of the job definition being run.

VARCHAR
run_id Required Input Column

The ID of the job run.

VARCHAR
predecessors_included Input Column

True if a list of predecessor runs should be returned.

BOOLEAN
_aws_profile Input Column

The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role.

STRUCT(
    "type" VARCHAR,
    "name" VARCHAR,
    "account_id" VARCHAR,
    "via_profile_name" VARCHAR,
    "assumed_role_arn" VARCHAR,
    "organization" STRUCT(
        "account_name" VARCHAR,
        "id" VARCHAR,
        "tags" STRUCT(
            "key" VARCHAR,
            "value" VARCHAR
        )[],
        "master_account" STRUCT(
            "id" VARCHAR,
            "email" VARCHAR
        ),
        "parents" STRUCT(
            "type" VARCHAR,
            "id" VARCHAR,
            "name" VARCHAR,
            "tags" STRUCT(
                "key" VARCHAR,
                "value" VARCHAR
            )[]
        )[]
    )
)
Show child fields
_aws_profile.account_id

The AWS account id

_aws_profile.assumed_role_arn

The ARN of the assumed role

_aws_profile.name

The unique name of the profile.

_aws_profile.organization

Information about this profile's membership in the AWS organization.

Show child fields
_aws_profile.organization.account_name

The name of account speciifed by the organization

_aws_profile.organization.id

The organization id

_aws_profile.organization.master_account
Show child fields
_aws_profile.organization.master_account.email

The organization master account email address

_aws_profile.organization.master_account.id

The organization master account id

_aws_profile.organization.parents[]
Show child fields
_aws_profile.organization.parents[].id

The id of the parent

_aws_profile.organization.parents[].name

The name of the parent

_aws_profile.organization.parents[].tags[]
Show child fields
_aws_profile.organization.parents[].tags[].key
_aws_profile.organization.parents[].tags[].value
_aws_profile.organization.parents[].type

The type of parent can be an organization unit or a root

_aws_profile.organization.tags[]
Show child fields
_aws_profile.organization.tags[].key
_aws_profile.organization.tags[].value
_aws_profile.type

The type of profile, either 'credentials' or 'assumed_role'

_aws_profile.via_profile_name

This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.

_aws_region Input Column

The AWS region to use.

VARCHAR
job_run

The requested job-run metadata.

STRUCT(
    "id" VARCHAR,
    "attempt" BIGINT,
    "previous_run_id" VARCHAR,
    "trigger_name" VARCHAR,
    "job_name" VARCHAR,
    "job_mode" VARCHAR,
    "job_run_queuing_enabled" BOOLEAN,
    "started_on" TIMESTAMP_S,
    "last_modified_on" TIMESTAMP_S,
    "completed_on" TIMESTAMP_S,
    "job_run_state" VARCHAR,
    "arguments" MAP(VARCHAR, VARCHAR),
    "error_message" VARCHAR,
    "predecessor_runs" STRUCT(
        "job_name" VARCHAR,
        "run_id" VARCHAR
    )[],
    "allocated_capacity" BIGINT,
    "execution_time" BIGINT,
    "timeout" BIGINT,
    "max_capacity" DOUBLE,
    "worker_type" VARCHAR,
    "number_of_workers" BIGINT,
    "security_configuration" VARCHAR,
    "log_group_name" VARCHAR,
    "notification_property" STRUCT(
        "notify_delay_after" BIGINT
    ),
    "glue_version" VARCHAR,
    "dpu_seconds" DOUBLE,
    "execution_class" VARCHAR,
    "maintenance_window" VARCHAR,
    "profile_name" VARCHAR,
    "state_detail" VARCHAR
)
Show child fields
job_run.allocated_capacity

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.

job_run.arguments

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.

Job arguments may be logged. Do not pass plaintext secrets as arguments. Retrieve secrets from a Glue Connection, Secrets Manager or other secret management mechanism if you intend to keep them within the Job.

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 arguments you can provide to this field when configuring Spark jobs, see the Special Parameters Used by Glue topic in the developer guide.

For information about the arguments you can provide to this field when configuring Ray jobs, see Using job parameters in Ray jobs in the developer guide.

job_run.attempt

The number of the attempt to run this job.

job_run.completed_on

The date and time that this job run completed.

job_run.dpu_seconds

This field can be set for either job runs with execution class FLEX or when Auto Scaling is enabled, 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, 2 for G.2X, or 0.25 for G.025X 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.

job_run.error_message

An error message associated with this job run.

job_run.execution_class

Indicates whether the job is run with a standard or flexible execution class. The standard execution-class is ideal for time-sensitive workloads that require fast job startup and dedicated resources.

The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary.

Only jobs with Glue version 3.0 and above and command type glueetl will be allowed to set ExecutionClass to FLEX. The flexible execution class is available for Spark jobs.

job_run.execution_time

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

job_run.glue_version

In Spark jobs, GlueVersion determines the versions of Apache Spark and Python that Glue available in a job. The Python version indicates the version supported for jobs of type Spark.

Ray jobs should set GlueVersion to 4.0 or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the Runtime parameter of the Job command.

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.

job_run.id

The ID of this job run.

job_run.job_mode

A mode that describes how a job was created. Valid values are:

  • SCRIPT - The job was created using the Glue Studio script editor.

  • VISUAL - The job was created using the Glue Studio visual editor.

  • NOTEBOOK - The job was created using an interactive sessions notebook.

When the JobMode field is missing or null, SCRIPT is assigned as the default value.

job_run.job_name

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

job_run.job_run_queuing_enabled

Specifies whether job run queuing is enabled for the job run.

A value of true means job run queuing is enabled for the job run. If false or not populated, the job run will not be considered for queueing.

job_run.job_run_state

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

job_run.last_modified_on

The last time that this job run was modified.

job_run.log_group_name

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.

job_run.maintenance_window

This field specifies a day of the week and hour for a maintenance window for streaming jobs. Glue periodically performs maintenance activities. During these maintenance windows, Glue will need to restart your streaming jobs.

Glue will restart the job within 3 hours of the specified maintenance window. For instance, if you set up the maintenance window for Monday at 10:00AM GMT, your jobs will be restarted between 10:00AM GMT to 1:00PM GMT.

job_run.max_capacity

For Glue version 1.0 or earlier jobs, using the standard worker type, 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.

For Glue version 2.0+ jobs, you cannot specify a Maximum capacity. Instead, you should specify a Worker type and the Number of workers.

Do not set MaxCapacity if using WorkerType and NumberOfWorkers.

The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job, an Apache Spark ETL job, or an Apache Spark streaming 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") or Apache Spark streaming ETL job (JobCommand.Name="gluestreaming"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.

job_run.notification_property

Specifies configuration properties of a job run notification.

Show child fields
job_run.notification_property.notify_delay_after

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

job_run.number_of_workers

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

job_run.predecessor_runs[]
Show child fields
job_run.predecessor_runs[].job_name

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

job_run.predecessor_runs[].run_id

The job-run ID of the predecessor job run.

job_run.previous_run_id

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

job_run.profile_name

The name of an Glue usage profile associated with the job run.

job_run.security_configuration

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

job_run.started_on

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

job_run.state_detail

This field holds details that pertain to the state of a job run. The field is nullable.

For example, when a job run is in a WAITING state as a result of job run queuing, the field has the reason why the job run is in that state.

job_run.timeout

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. This value overrides the timeout value set in the parent job.

Streaming jobs must have timeout values less than 7 days or 10080 minutes. When the value is left blank, the job will be restarted after 7 days based if you have not setup a maintenance window. If you have setup maintenance window, it will be restarted during the maintenance window after 7 days.

job_run.trigger_name

The name of the trigger that started this job run.

job_run.worker_type

The type of predefined worker that is allocated when a job runs. Accepts a value of G.1X, G.2X, G.4X, G.8X or G.025X for Spark jobs. Accepts the value Z.2X for Ray jobs.

  • For the G.1X worker type, each worker maps to 1 DPU (4 vCPUs, 16 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

  • For the G.2X worker type, each worker maps to 2 DPU (8 vCPUs, 32 GB of memory) with 128GB disk (approximately 77GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

  • For the G.4X worker type, each worker maps to 4 DPU (16 vCPUs, 64 GB of memory) with 256GB disk (approximately 235GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs in the following Amazon Web Services Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).

  • For the G.8X worker type, each worker maps to 8 DPU (32 vCPUs, 128 GB of memory) with 512GB disk (approximately 487GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs, in the same Amazon Web Services Regions as supported for the G.4X worker type.

  • For the G.025X worker type, each worker maps to 0.25 DPU (2 vCPUs, 4 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for low volume streaming jobs. This worker type is only available for Glue version 3.0 streaming jobs.

  • For the Z.2X worker type, each worker maps to 2 M-DPU (8vCPUs, 64 GB of memory) with 128 GB disk (approximately 120GB free), and provides up to 8 Ray workers based on the autoscaler.