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airflow.providers.amazon.aws.hooks.batch_client

A client for AWS Batch services.

See also

• https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html

• https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/batch.html

• https://docs.aws.amazon.com/batch/latest/APIReference/Welcome.html

Module Contents

Classes

BatchProtocol	A structured Protocol for boto3.client('batch') -> botocore.client.Batch.
BatchClientHook	Interact with AWS Batch.

class airflow.providers.amazon.aws.hooks.batch_client.BatchProtocol

Bases: airflow.typing_compat.Protocol

A structured Protocol for boto3.client('batch') -> botocore.client.Batch.

This is used for type hints on BatchClient.client(); it covers only the subset of client methods required.

See also

• https://mypy.readthedocs.io/en/latest/protocols.html

• https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/batch.html

describe_jobs(jobs)

Get job descriptions from AWS Batch.

Parameters

jobs (list[str]) – a list of JobId to describe

Returns

an API response to describe jobs

Return type

dict

get_waiter(waiterName)

Get an AWS Batch service waiter.

Parameters

waiterName (str) – The name of the waiter. The name should match the name (including the casing) of the key name in the waiter model file (typically this is CamelCasing).

Returns

a waiter object for the named AWS Batch service

Return type

botocore.waiter.Waiter

Note

AWS Batch might not have any waiters (until botocore PR-1307 is released).

import boto3

boto3.client("batch").waiter_names == []

See also

• https://boto3.amazonaws.com/v1/documentation/api/latest/guide/clients.html#waiters

• https://github.com/boto/botocore/pull/1307

submit_job(jobName, jobQueue, jobDefinition, arrayProperties, parameters, containerOverrides, ecsPropertiesOverride, eksPropertiesOverride, tags)

Submit a Batch job.

Parameters

• jobName (str) – the name for the AWS Batch job

• jobQueue (str) – the queue name on AWS Batch

• jobDefinition (str) – the job definition name on AWS Batch

• arrayProperties (dict) – the same parameter that boto3 will receive

• parameters (dict) – the same parameter that boto3 will receive

• containerOverrides (dict) – the same parameter that boto3 will receive

• ecsPropertiesOverride (dict) – the same parameter that boto3 will receive

• eksPropertiesOverride (dict) – the same parameter that boto3 will receive

• tags (dict) – the same parameter that boto3 will receive

Returns

an API response

Return type

dict

terminate_job(jobId, reason)

Terminate a Batch job.

Parameters

• jobId (str) – a job ID to terminate

• reason (str) – a reason to terminate job ID

Returns

an API response

Return type

dict

class airflow.providers.amazon.aws.hooks.batch_client.BatchClientHook(*args, max_retries=None, status_retries=None, **kwargs)

Bases: airflow.providers.amazon.aws.hooks.base_aws.AwsBaseHook

Interact with AWS Batch.

Provide thick wrapper around boto3.client("batch").

Parameters

• max_retries (int | None) – exponential back-off retries, 4200 = 48 hours; polling is only used when waiters is None

• status_retries (int | None) – number of HTTP retries to get job status, 10; polling is only used when waiters is None

Note

Several methods use a default random delay to check or poll for job status, i.e. random.uniform(DEFAULT_DELAY_MIN, DEFAULT_DELAY_MAX) Using a random interval helps to avoid AWS API throttle limits when many concurrent tasks request job-descriptions.

To modify the global defaults for the range of jitter allowed when a random delay is used to check Batch job status, modify these defaults, e.g.: .. code-block:

BatchClient.DEFAULT_DELAY_MIN = 0
BatchClient.DEFAULT_DELAY_MAX = 5

When explicit delay values are used, a 1 second random jitter is applied to the delay (e.g. a delay of 0 sec will be a random.uniform(0, 1) delay. It is generally recommended that random jitter is added to API requests. A convenience method is provided for this, e.g. to get a random delay of 10 sec +/- 5 sec: delay = BatchClient.add_jitter(10, width=5, minima=0)

Additional arguments (such as aws_conn_id) may be specified and are passed down to the underlying AwsBaseHook.

See also

• airflow.providers.amazon.aws.hooks.base_aws.AwsBaseHook

• https://docs.aws.amazon.com/general/latest/gr/api-retries.html

• https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/

property client: BatchProtocol | botocore.client.BaseClient

An AWS API client for Batch services.

Returns

a boto3 ‘batch’ client for the .region_name

Return type

BatchProtocol | botocore.client.BaseClient

MAX_RETRIES = 4200

STATUS_RETRIES = 10

DEFAULT_DELAY_MIN = 1

DEFAULT_DELAY_MAX = 10

FAILURE_STATE = 'FAILED'

SUCCESS_STATE = 'SUCCEEDED'

RUNNING_STATE = 'RUNNING'

INTERMEDIATE_STATES = ('SUBMITTED', 'PENDING', 'RUNNABLE', 'STARTING')

COMPUTE_ENVIRONMENT_TERMINAL_STATUS = ('VALID', 'DELETED')

COMPUTE_ENVIRONMENT_INTERMEDIATE_STATUS = ('CREATING', 'UPDATING', 'DELETING')

JOB_QUEUE_TERMINAL_STATUS = ('VALID', 'DELETED')

JOB_QUEUE_INTERMEDIATE_STATUS = ('CREATING', 'UPDATING', 'DELETING')

terminate_job(job_id, reason)

Terminate a Batch job.

Parameters

• job_id (str) – a job ID to terminate

• reason (str) – a reason to terminate job ID

Returns

an API response

Return type

dict

check_job_success(job_id)

Check the final status of the Batch job.

Return True if the job ‘SUCCEEDED’, else raise an AirflowException.

Parameters

job_id (str) – a Batch job ID

Raises

AirflowException

wait_for_job(job_id, delay=None, get_batch_log_fetcher=None)

Wait for Batch job to complete.

Parameters

• job_id (str) – a Batch job ID

• delay (int | float | None) – a delay before polling for job status

:param get_batch_log_fetcher : a method that returns batch_log_fetcher

Raises

AirflowException

poll_for_job_running(job_id, delay=None)

Poll for job running.

The status that indicates a job is running or already complete are: ‘RUNNING’|’SUCCEEDED’|’FAILED’.

So the status options that this will wait for are the transitions from: ‘SUBMITTED’>’PENDING’>’RUNNABLE’>’STARTING’>’RUNNING’|’SUCCEEDED’|’FAILED’

The completed status options are included for cases where the status changes too quickly for polling to detect a RUNNING status that moves quickly from STARTING to RUNNING to completed (often a failure).

Parameters

• job_id (str) – a Batch job ID

• delay (int | float | None) – a delay before polling for job status

Raises

AirflowException

poll_for_job_complete(job_id, delay=None)

Poll for job completion.

The status that indicates job completion are: ‘SUCCEEDED’|’FAILED’.

So the status options that this will wait for are the transitions from: ‘SUBMITTED’>’PENDING’>’RUNNABLE’>’STARTING’>’RUNNING’>’SUCCEEDED’|’FAILED’

Parameters

• job_id (str) – a Batch job ID

• delay (int | float | None) – a delay before polling for job status

Raises

AirflowException

poll_job_status(job_id, match_status)

Poll for job status using an exponential back-off strategy (with max_retries).

Parameters

• job_id (str) – a Batch job ID

• match_status (list[str]) – a list of job status to match; the Batch job status are: ‘SUBMITTED’|’PENDING’|’RUNNABLE’|’STARTING’|’RUNNING’|’SUCCEEDED’|’FAILED’

Raises

AirflowException

get_job_description(job_id)

Get job description (using status_retries).

Parameters

job_id (str) – a Batch job ID

Returns

an API response for describe jobs

Raises

AirflowException

Return type

dict

static parse_job_description(job_id, response)

Parse job description to extract description for job_id.

Parameters

• job_id (str) – a Batch job ID

• response (dict) – an API response for describe jobs

Returns

an API response to describe job_id

Raises

AirflowException

Return type

dict

get_job_awslogs_info(job_id)

get_job_all_awslogs_info(job_id)

Parse job description to extract AWS CloudWatch information.

Parameters

job_id (str) – AWS Batch Job ID

static add_jitter(delay, width=1, minima=0)

Use delay +/- width for random jitter.

Adding jitter to status polling can help to avoid AWS Batch API limits for monitoring Batch jobs with a high concurrency in Airflow tasks.

Parameters

• delay (int | float) – number of seconds to pause; delay is assumed to be a positive number

• width (int | float) – delay +/- width for random jitter; width is assumed to be a positive number

• minima (int | float) – minimum delay allowed; minima is assumed to be a non-negative number

Returns

uniform(delay - width, delay + width) jitter and it is a non-negative number

Return type

float

static delay(delay=None)

Pause execution for delay seconds.

Parameters

delay (int | float | None) – a delay to pause execution using time.sleep(delay); a small 1 second jitter is applied to the delay.

Note

This method uses a default random delay, i.e. random.uniform(DEFAULT_DELAY_MIN, DEFAULT_DELAY_MAX); using a random interval helps to avoid AWS API throttle limits when many concurrent tasks request job-descriptions.

static exponential_delay(tries)

Apply an exponential back-off delay, with random jitter.

There is a maximum interval of 10 minutes (with random jitter between 3 and 10 minutes). This is used in the poll_for_job_status() method.

Examples of behavior:

def exp(tries):
    max_interval = 600.0  # 10 minutes in seconds
    delay = 1 + pow(tries * 0.6, 2)
    delay = min(max_interval, delay)
    print(delay / 3, delay)


for tries in range(10):
    exp(tries)

#  0.33  1.0
#  0.45  1.35
#  0.81  2.44
#  1.41  4.23
#  2.25  6.76
#  3.33 10.00
#  4.65 13.95
#  6.21 18.64
#  8.01 24.04
# 10.05 30.15

See also

• https://docs.aws.amazon.com/general/latest/gr/api-retries.html

• https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/

Parameters

tries (int) – Number of tries

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