Implementing retry logic in Glue jobs

AWS Glue is a fully managed ETL (Extract, Transform, Load) service that makes it easy to prepare and transform data for analytics. However, like any data processing system, Glue jobs can occasionally fail due to network issues, temporary data inconsistencies, or transient AWS service errors. To make your data pipelines more resilient and robust, it’s important to implement retry logic in your AWS Glue jobs.

In this blog post, we’ll explore why retry logic matters, the different ways to implement it in Glue, and best practices for handling job failures gracefully.

Why Retry Logic is Important

Retry logic helps handle transient failures that are not related to bugs in your code but rather external or temporary issues. Without retries, your job might fail unnecessarily, interrupting the entire data pipeline.

Common scenarios where retries are helpful include:

• Temporary loss of database connection
• Throttling from APIs or AWS services
• Timeouts in reading from S3 or writing to Redshift
• Unavailable resources due to high load

Built-In Retry Mechanism in AWS Glue

AWS Glue provides basic retry capabilities out-of-the-box. When a job fails, it can automatically retry based on configuration.

Configuring Retries

You can configure retries when you create or edit a job in the AWS Glue console:

Maximum number of retries: Set this to a value between 0 and 10. If a job fails, AWS Glue will automatically retry it the specified number of times.

Example:

bash

Maximum retries: 2

This means the job will run up to 3 times (1 initial run + 2 retries).

Limitations:

• Retries are immediate, without delay between attempts.
• No control over retry conditions or custom logic.
• No exponential backoff mechanism.

Implementing Custom Retry Logic in Code

For more control, especially in Glue Python shell jobs or Spark jobs, you can implement retry logic using standard Python constructs.

Example: Retrying a Database Connection

python

import time

import psycopg2

from psycopg2 import OperationalError

def connect_with_retry(retries=3, delay=5):

    attempt = 0

    while attempt < retries:

        try:

            connection = psycopg2.connect(

                dbname='mydb',

                user='myuser',

                password='mypassword',

                host='myhost',

                port='5432'

            )

            print("Connection successful")

            return connection

        except OperationalError as e:

            print(f"Connection failed: {e}")

            attempt += 1

            if attempt == retries:

                raise

            print(f"Retrying in {delay} seconds...")

            time.sleep(delay)

This pattern can be used for any operation that may fail intermittently, such as S3 reads, REST API calls, or database operations.

Best Practices

1. Use exponential backoff: Delay increases with each retry to reduce stress on the system.
2. Log each failure: Include error messages and timestamps for debugging.
3. Retry only safe operations: Ensure the operation is idempotent (safe to repeat).
4. Set realistic retry limits: Avoid infinite loops or retries that prolong job execution unnecessarily.
5. Use CloudWatch and Glue job bookmarks: Monitor job performance and avoid duplicate processing in retries.

Conclusion

Implementing retry logic in AWS Glue jobs is crucial for building resilient data pipelines. While AWS Glue provides basic retry settings, adding custom retry logic in your job script allows for finer control and better fault tolerance. By combining built-in features with smart scripting practices, you can minimize disruptions and ensure your ETL workflows run smoothly, even in the face of transient issues.

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Read More: Leveraging AWS Step Functions for data orchestration

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