| Column Name | Column Data Type |
|---|
name Required Input Column
Name of the workflow that was run. | VARCHAR |
include_graph Input Column
Specifies whether to include the workflow graph in response or not. | 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 |
completed_on
The date and time when the workflow run completed. | TIMESTAMP_S |
error_message
This error message describes any error that may have occurred in starting the workflow run. Currently the only error message is "Concurrent runs exceeded for workflow: foo." | VARCHAR |
graph
The graph representing all the Glue components that belong to the workflow as nodes and directed connections between them as edges. | STRUCT(
"nodes" STRUCT(
"type" VARCHAR,
"name" VARCHAR,
"unique_id" VARCHAR,
"trigger_details" STRUCT(
"trigger" STRUCT(
"name" VARCHAR,
"workflow_name" VARCHAR,
"id" VARCHAR,
"type" VARCHAR,
"state" VARCHAR,
"description" VARCHAR,
"schedule" VARCHAR,
"actions" STRUCT(
"job_name" VARCHAR,
"arguments" MAP(VARCHAR, VARCHAR),
"timeout" BIGINT,
"security_configuration" VARCHAR,
"notification_property" STRUCT(
"notify_delay_after" BIGINT
),
"crawler_name" VARCHAR
)[],
"predicate" STRUCT(
"logical" VARCHAR,
"conditions" STRUCT(
"logical_operator" VARCHAR,
"job_name" VARCHAR,
"state" VARCHAR,
"crawler_name" VARCHAR,
"crawl_state" VARCHAR
)[]
),
"event_batching_condition" STRUCT(
"batch_size" BIGINT,
"batch_window" BIGINT
)
)
),
"job_details" STRUCT(
"job_runs" 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
)[]
),
"crawler_details" STRUCT(
"crawls" STRUCT(
"state" VARCHAR,
"started_on" TIMESTAMP_S,
"completed_on" TIMESTAMP_S,
"error_message" VARCHAR,
"log_group" VARCHAR,
"log_stream" VARCHAR
)[]
)
)[],
"edges" STRUCT(
"source_id" VARCHAR,
"destination_id" VARCHAR
)[]
) |
Show child fields- graph.edges[]
Show child fields- graph.edges[].destination_id
The unique of the node within the workflow where the edge ends. - graph.edges[].source_id
The unique of the node within the workflow where the edge starts.
- graph.nodes[]
Show child fields- graph.nodes[].crawler_details
Details of the crawler when the node represents a crawler. Show child fields- graph.nodes[].crawler_details.crawls[]
Show child fields- graph.nodes[].crawler_details.crawls[].completed_on
The date and time on which the crawl completed. - graph.nodes[].crawler_details.crawls[].error_message
The error message associated with the crawl. - graph.nodes[].crawler_details.crawls[].log_group
The log group associated with the crawl. - graph.nodes[].crawler_details.crawls[].log_stream
The log stream associated with the crawl. - graph.nodes[].crawler_details.crawls[].started_on
The date and time on which the crawl started. - graph.nodes[].crawler_details.crawls[].state
The state of the crawler.
- graph.nodes[].job_details
Details of the Job when the node represents a Job. Show child fields- graph.nodes[].job_details.job_runs[]
Show child fields- graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].attempt
The number of the attempt to run this job. - graph.nodes[].job_details.job_runs[].completed_on
The date and time that this job run completed. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].error_message
An error message associated with this job run. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].execution_time
The amount of time (in seconds) that the job run consumed resources. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].id
The ID of this job run. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].job_name
The name of the job definition being used in this run. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].last_modified_on
The last time that this job run was modified. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].notification_property
Specifies configuration properties of a job run notification. Show child fields- graph.nodes[].job_details.job_runs[].notification_property.notify_delay_after
After a job run starts, the number of minutes to wait before sending a job run delay notification.
- graph.nodes[].job_details.job_runs[].number_of_workers
The number of workers of a defined workerType that are allocated when a job runs. - graph.nodes[].job_details.job_runs[].predecessor_runs[]
Show child fields- graph.nodes[].job_details.job_runs[].predecessor_runs[].job_name
The name of the job definition used by the predecessor job run. - graph.nodes[].job_details.job_runs[].predecessor_runs[].run_id
The job-run ID of the predecessor job run.
- graph.nodes[].job_details.job_runs[].previous_run_id
The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action. - graph.nodes[].job_details.job_runs[].profile_name
The name of an Glue usage profile associated with the job run. - graph.nodes[].job_details.job_runs[].security_configuration
The name of the SecurityConfiguration structure to be used with this job run. - graph.nodes[].job_details.job_runs[].started_on
The date and time at which this job run was started. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].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. - graph.nodes[].job_details.job_runs[].trigger_name
The name of the trigger that started this job run. - graph.nodes[].job_details.job_runs[].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.
- graph.nodes[].name
The name of the Glue component represented by the node. - graph.nodes[].trigger_details
Details of the Trigger when the node represents a Trigger. Show child fields- graph.nodes[].trigger_details.trigger
The information of the trigger represented by the trigger node. Show child fields- graph.nodes[].trigger_details.trigger.actions[]
Show child fields- graph.nodes[].trigger_details.trigger.actions[].arguments
The job arguments used when this trigger fires. 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. - graph.nodes[].trigger_details.trigger.actions[].crawler_name
The name of the crawler to be used with this action. - graph.nodes[].trigger_details.trigger.actions[].job_name
The name of a job to be run. - graph.nodes[].trigger_details.trigger.actions[].notification_property
Specifies configuration properties of a job run notification. Show child fields- graph.nodes[].trigger_details.trigger.actions[].notification_property.notify_delay_after
After a job run starts, the number of minutes to wait before sending a job run delay notification.
- graph.nodes[].trigger_details.trigger.actions[].security_configuration
The name of the SecurityConfiguration structure to be used with this action. - graph.nodes[].trigger_details.trigger.actions[].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. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job.
- graph.nodes[].trigger_details.trigger.description
A description of this trigger. - graph.nodes[].trigger_details.trigger.event_batching_condition
Batch condition that must be met (specified number of events received or batch time window expired) before EventBridge event trigger fires. Show child fields- graph.nodes[].trigger_details.trigger.event_batching_condition.batch_size
Number of events that must be received from Amazon EventBridge before EventBridge event trigger fires. - graph.nodes[].trigger_details.trigger.event_batching_condition.batch_window
Window of time in seconds after which EventBridge event trigger fires. Window starts when first event is received.
- graph.nodes[].trigger_details.trigger.id
Reserved for future use. - graph.nodes[].trigger_details.trigger.name
The name of the trigger. - graph.nodes[].trigger_details.trigger.predicate
The predicate of this trigger, which defines when it will fire. Show child fields- graph.nodes[].trigger_details.trigger.predicate.conditions[]
Show child fields- graph.nodes[].trigger_details.trigger.predicate.conditions[].crawl_state
The state of the crawler to which this condition applies. - graph.nodes[].trigger_details.trigger.predicate.conditions[].crawler_name
The name of the crawler to which this condition applies. - graph.nodes[].trigger_details.trigger.predicate.conditions[].job_name
The name of the job whose JobRuns this condition applies to, and on which this trigger waits. - graph.nodes[].trigger_details.trigger.predicate.conditions[].logical_operator
A logical operator. - graph.nodes[].trigger_details.trigger.predicate.conditions[].state
The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED, STOPPED, FAILED, and TIMEOUT. The only crawler states that a trigger can listen for are SUCCEEDED, FAILED, and CANCELLED.
- graph.nodes[].trigger_details.trigger.predicate.logical
An optional field if only one condition is listed. If multiple conditions are listed, then this field is required.
- graph.nodes[].trigger_details.trigger.schedule
A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers. For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *). - graph.nodes[].trigger_details.trigger.state
The current state of the trigger. - graph.nodes[].trigger_details.trigger.type
The type of trigger that this is. - graph.nodes[].trigger_details.trigger.workflow_name
The name of the workflow associated with the trigger.
- graph.nodes[].type
The type of Glue component represented by the node. - graph.nodes[].unique_id
The unique Id assigned to the node within the workflow.
|
previous_run_id
The ID of the previous workflow run. | VARCHAR |
started_on
The date and time when the workflow run was started. | TIMESTAMP_S |
starting_event_batch_condition
The batch condition that started the workflow run. | STRUCT(
"batch_size" BIGINT,
"batch_window" BIGINT
) |
Show child fields- starting_event_batch_condition.batch_size
Number of events in the batch. - starting_event_batch_condition.batch_window
Duration of the batch window in seconds.
|
statistics
The statistics of the run. | STRUCT(
"total_actions" BIGINT,
"timeout_actions" BIGINT,
"failed_actions" BIGINT,
"stopped_actions" BIGINT,
"succeeded_actions" BIGINT,
"running_actions" BIGINT,
"errored_actions" BIGINT,
"waiting_actions" BIGINT
) |
Show child fields- statistics.errored_actions
Indicates the count of job runs in the ERROR state in the workflow run. - statistics.failed_actions
Total number of Actions that have failed. - statistics.running_actions
Total number Actions in running state. - statistics.stopped_actions
Total number of Actions that have stopped. - statistics.succeeded_actions
Total number of Actions that have succeeded. - statistics.timeout_actions
Total number of Actions that timed out. - statistics.total_actions
Total number of Actions in the workflow run. - statistics.waiting_actions
Indicates the count of job runs in WAITING state in the workflow run.
|
status
The status of the workflow run. | VARCHAR |
workflow_run_id
The ID of this workflow run. | VARCHAR |
workflow_run_properties
The workflow run properties which were set during the run. | MAP(VARCHAR, VARCHAR) |