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#
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from __future__ import annotations
import logging
from datetime import datetime, timedelta
import boto3
import tenacity
from tenacity import before_log, before_sleep_log
from airflow.decorators import task, task_group
from airflow.models.baseoperator import chain
from airflow.models.dag import DAG
from airflow.operators.empty import EmptyOperator
from airflow.providers.amazon.aws.operators.s3 import S3CreateBucketOperator, S3DeleteBucketOperator
from airflow.providers.amazon.aws.transfers.dynamodb_to_s3 import DynamoDBToS3Operator
from airflow.utils.edgemodifier import Label
from airflow.utils.trigger_rule import TriggerRule
from providers.tests.system.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}
)
@task_group
[docs]def incremental_export(table_name: str, start_time: datetime):
"""
Incremental export requires a minimum window of 15 minutes of data to export.
This task group allows us to have the sample code snippet for the docs while
skipping the task when we run the actual test.
"""
@task
def get_latest_export_time(table_name: str):
r = boto3.client("dynamodb").describe_continuous_backups(
TableName=table_name,
)
return r["ContinuousBackupsDescription"]["PointInTimeRecoveryDescription"]["LatestRestorableDateTime"]
end_time = get_latest_export_time(table_name)
# [START howto_transfer_dynamodb_to_s3_in_some_point_in_time_incremental_export]
backup_db_to_point_in_time_incremental_export = DynamoDBToS3Operator(
task_id="backup_db_to_point_in_time_incremental_export",
dynamodb_table_name=table_name,
s3_bucket_name=bucket_name,
point_in_time_export=True,
s3_key_prefix=f"{S3_KEY_PREFIX}-4-",
export_table_to_point_in_time_kwargs={
"ExportType": "INCREMENTAL_EXPORT",
"IncrementalExportSpecification": {
"ExportFromTime": start_time,
"ExportToTime": end_time,
"ExportViewType": "NEW_AND_OLD_IMAGES",
},
},
)
# [END howto_transfer_dynamodb_to_s3_in_some_point_in_time_incremental_export]
# This operation can take a long time to complete
backup_db_to_point_in_time_incremental_export.max_attempts = 90
@task.branch
def skip_incremental_export(start_time: datetime, end_time: datetime):
not_enough_time = end_time < (start_time + timedelta(minutes=15))
return (
end_workflow.task_id if not_enough_time else backup_db_to_point_in_time_incremental_export.task_id
)
skip_incremental = skip_incremental_export(start_time, end_time)
end_workflow = EmptyOperator(task_id="end_workflow", trigger_rule=TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS)
chain(end_time, skip_incremental, Label("Incremental backup skipped"), end_workflow)
chain(end_time, skip_incremental, backup_db_to_point_in_time_incremental_export, end_workflow)
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_full_export]
backup_db_to_point_in_time_full_export = DynamoDBToS3Operator(
task_id="backup_db_to_point_in_time_full_export",
dynamodb_table_name=table_name,
s3_bucket_name=bucket_name,
point_in_time_export=True,
export_time=export_time,
s3_key_prefix=f"{S3_KEY_PREFIX}-3-",
)
# [END howto_transfer_dynamodb_to_s3_in_some_point_in_time_full_export]
backup_db_to_point_in_time_full_export.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_full_export,
incremental_export(table_name=table_name, start_time=export_time),
# TEST TEARDOWN
delete_table,
delete_bucket,
)
from tests_common.test_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_common.test_utils.system_tests 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)