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#
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from __future__ import annotations
import logging
from datetime import datetime
import boto3
import tenacity
from tenacity import before_log, before_sleep_log
from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.models.dag import DAG
from airflow.providers.amazon.aws.operators.s3 import S3CreateBucketOperator, S3DeleteBucketOperator
from airflow.providers.amazon.aws.transfers.dynamodb_to_s3 import DynamoDBToS3Operator
from airflow.utils.trigger_rule import TriggerRule
from tests.system.providers.amazon.aws.utils import ENV_ID_KEY, SystemTestContextBuilder
[docs]log = logging.getLogger(__name__)
[docs]DAG_ID = "example_dynamodb_to_s3"
[docs]sys_test_context_task = SystemTestContextBuilder().build()
[docs]TABLE_ATTRIBUTES = [
{"AttributeName": "ID", "AttributeType": "S"},
{"AttributeName": "Value", "AttributeType": "S"},
]
[docs]TABLE_KEY_SCHEMA = [
{"AttributeName": "ID", "KeyType": "HASH"},
{"AttributeName": "Value", "KeyType": "RANGE"},
]
[docs]TABLE_THROUGHPUT = {"ReadCapacityUnits": 1, "WriteCapacityUnits": 1}
[docs]S3_KEY_PREFIX = "dynamodb-segmented-file"
# UpdateContinuousBackups API might need multiple attempts to succeed
# Sometimes the API returns the error "Backups are being enabled for the table: <...>. Please retry later"
# Using a retry strategy with exponential backoff to remediate that
@tenacity.retry(
stop=tenacity.stop_after_attempt(20),
wait=tenacity.wait_exponential(min=5),
before=before_log(log, logging.INFO),
before_sleep=before_sleep_log(log, logging.WARNING),
)
[docs]def enable_point_in_time_recovery(table_name: str):
boto3.client("dynamodb").update_continuous_backups(
TableName=table_name,
PointInTimeRecoverySpecification={
"PointInTimeRecoveryEnabled": True,
},
)
@task
[docs]def set_up_table(table_name: str):
dynamo_resource = boto3.resource("dynamodb")
table = dynamo_resource.create_table(
AttributeDefinitions=TABLE_ATTRIBUTES,
TableName=table_name,
KeySchema=TABLE_KEY_SCHEMA,
ProvisionedThroughput=TABLE_THROUGHPUT,
)
boto3.client("dynamodb").get_waiter("table_exists").wait(
TableName=table_name, WaiterConfig={"Delay": 10, "MaxAttempts": 10}
)
enable_point_in_time_recovery(table_name)
table.put_item(Item={"ID": "123", "Value": "Testing"})
@task
[docs]def get_export_time(table_name: str):
r = boto3.client("dynamodb").describe_continuous_backups(
TableName=table_name,
)
return r["ContinuousBackupsDescription"]["PointInTimeRecoveryDescription"]["EarliestRestorableDateTime"]
@task
[docs]def wait_for_bucket(s3_bucket_name):
waiter = boto3.client("s3").get_waiter("bucket_exists")
waiter.wait(Bucket=s3_bucket_name)
@task(trigger_rule=TriggerRule.ALL_DONE)
[docs]def delete_dynamodb_table(table_name: str):
boto3.resource("dynamodb").Table(table_name).delete()
boto3.client("dynamodb").get_waiter("table_not_exists").wait(
TableName=table_name, WaiterConfig={"Delay": 10, "MaxAttempts": 10}
)
with DAG(
dag_id=DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
tags=["example"],
) as dag:
[docs] test_context = sys_test_context_task()
env_id = test_context[ENV_ID_KEY]
table_name = f"{env_id}-dynamodb-table"
bucket_name = f"{env_id}-dynamodb-bucket"
create_table = set_up_table(table_name=table_name)
create_bucket = S3CreateBucketOperator(task_id="create_bucket", bucket_name=bucket_name)
# [START howto_transfer_dynamodb_to_s3]
backup_db = DynamoDBToS3Operator(
task_id="backup_db",
dynamodb_table_name=table_name,
s3_bucket_name=bucket_name,
# Max output file size in bytes. If the Table is too large, multiple files will be created.
file_size=20,
)
# [END howto_transfer_dynamodb_to_s3]
# [START howto_transfer_dynamodb_to_s3_segmented]
# Segmenting allows the transfer to be parallelized into {segment} number of parallel tasks.
backup_db_segment_1 = DynamoDBToS3Operator(
task_id="backup_db_segment_1",
dynamodb_table_name=table_name,
s3_bucket_name=bucket_name,
# Max output file size in bytes. If the Table is too large, multiple files will be created.
file_size=1000,
s3_key_prefix=f"{S3_KEY_PREFIX}-1-",
dynamodb_scan_kwargs={
"TotalSegments": 2,
"Segment": 0,
},
)
backup_db_segment_2 = DynamoDBToS3Operator(
task_id="backup_db_segment_2",
dynamodb_table_name=table_name,
s3_bucket_name=bucket_name,
# Max output file size in bytes. If the Table is too large, multiple files will be created.
file_size=1000,
s3_key_prefix=f"{S3_KEY_PREFIX}-2-",
dynamodb_scan_kwargs={
"TotalSegments": 2,
"Segment": 1,
},
)
# [END howto_transfer_dynamodb_to_s3_segmented]
export_time = get_export_time(table_name)
# [START howto_transfer_dynamodb_to_s3_in_some_point_in_time]
backup_db_to_point_in_time = DynamoDBToS3Operator(
task_id="backup_db_to_point_in_time",
dynamodb_table_name=table_name,
file_size=1000,
s3_bucket_name=bucket_name,
export_time=export_time,
s3_key_prefix=f"{S3_KEY_PREFIX}-3-",
)
# [END howto_transfer_dynamodb_to_s3_in_some_point_in_time]
# This operation can take a long time to complete
backup_db_to_point_in_time.max_attempts = 90
delete_table = delete_dynamodb_table(table_name=table_name)
delete_bucket = S3DeleteBucketOperator(
task_id="delete_bucket",
bucket_name=bucket_name,
trigger_rule=TriggerRule.ALL_DONE,
force_delete=True,
)
chain(
# TEST SETUP
test_context,
create_table,
create_bucket,
wait_for_bucket(s3_bucket_name=bucket_name),
# TEST BODY
backup_db,
backup_db_segment_1,
backup_db_segment_2,
export_time,
backup_db_to_point_in_time,
# TEST TEARDOWN
delete_table,
delete_bucket,
)
from tests.system.utils.watcher import watcher
# This test needs watcher in order to properly mark success/failure
# when "tearDown" task with trigger rule is part of the DAG
list(dag.tasks) >> watcher()
from tests.system.utils import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)