AWS Glue with JDBC connections: Best practices

As organizations increasingly rely on data warehouses and relational databases for business intelligence, AWS Glue has emerged as a popular managed ETL (Extract, Transform, Load) service to streamline data processing. One of Glue’s most powerful features is its seamless integration with JDBC data sources—enabling you to connect to MySQL, PostgreSQL, Oracle, Microsoft SQL Server, Amazon Redshift, and other relational systems. However, to get the most out of AWS Glue’s JDBC capabilities—both in terms of performance and security—it’s important to follow some best practices. Below are key recommendations to ensure a robust, scalable, and maintainable Glue architecture.

1. Securely Store and Manage Connection Credentials

1. Use AWS Secrets Manager: Instead of embedding usernames and passwords directly in Glue scripts or connection definitions, store credentials in AWS Secrets Manager. Glue can retrieve secrets at runtime, reducing the risk of credential leakage.
2. Encrypt JDBC Connections: Ensure that your JDBC connections are configured to use TLS/SSL. When defining a Glue Connection, specify the JDBC URL with parameters like sslmode=require (PostgreSQL) or encrypt=true;trustServerCertificate=false (SQL Server).
3. Least-Privilege IAM Roles: Assign Glue jobs an IAM role that only has permissions to read the specific Secrets Manager secret and access to the VPC/subnet configurations needed for your database. Avoid granting overly broad permissions.

2. Leverage Glue Connections and VPC Configuration

Glue Connection Objects: Define reusable Glue Connection objects in the AWS Glue console or via CloudFormation. Under “Connections,” specify your JDBC URL, Secrets Manager ARN, and VPC/Subnet/ Security Group settings. This centralized approach reduces duplication and eases maintenance.

VPC Endpoints and Security Groups: Place your Glue workers in the same VPC and subnet(s) as your database (or a peered VPC). Configure Security Groups to allow inbound traffic on the database port (e.g., 5432 for PostgreSQL). Use VPC endpoints for S3 and Secrets Manager to minimize public internet exposure.

3. Optimize Performance with Pushdown Predicates and Partitioning

Predicate Pushdown: When reading large tables, use Spark’s pushdown_predicate parameter in your Glue DynamicFrame. For example:

python

dyf = glueContext.create_dynamic_frame.from_options(

    connection_type="jdbc",

    connection_options={

        "url": jdbc_url,

        "dbtable": "public.orders",

        "user": username,

        "password": password,

        "push_down_predicate": "order_date >= '2023-01-01'"

    }

)

This ensures that SQL filters run on the database side, reducing data transfer and improving performance.

Partitioned Reads: If your table has a uniformly distributed integer or date column (e.g., id or created_at), define parameters like partitionColumn, lowerBound, upperBound, and numPartitions in connection_options. Glue will parallelize reads across multiple executors:

python

"partitionColumn": "id",

"lowerBound": "1",

"upperBound": "1000000",

"numPartitions": "8"

Fetch Size Tuning: Adjust the fetchsize parameter to balance memory consumption with retrieval throughput. A larger fetch size can reduce the number of round trips but may increase memory usage.

4. Efficient Writes and Transaction Handling

Batch Inserts/Upserts: When writing back to the JDBC target, use Spark SQL’s jdbc() write mode with batchsize to group inserts into batches (e.g., batchsize=1000). For upserts, consider a two-step approach: write to a staging table and then run a stored procedure or SQL MERGE.

Avoid Full Table Overwrites: If possible, avoid using mode("overwrite") on large tables. Instead, write incremental updates or leverage staging tables and database-side merge logic to minimize locking and performance hits.

5. Monitor and Troubleshoot Glue JDBC Jobs

CloudWatch Metrics and Logs: Monitor Glue job logs in CloudWatch to identify slow SQL queries, connection timeouts, or memory errors. Enable Spark UI on Glue for deeper insights into executor performance, shuffle operations, and job stages.

Database Monitoring: Keep an eye on your source/target database’s monitoring dashboards (CloudWatch for RDS, Redshift console, or native database tools) to ensure that Glue’s parallel queries aren’t overloading the system. Throttle numPartitions or adjust fetchsize if you observe high CPU or I/O.

Conclusion

AWS Glue’s JDBC integration makes it simple to build scalable ETL pipelines that interact with relational data stores. By following best practices—storing credentials securely, configuring VPC and connections properly, optimizing read/write performance with pushdown predicates and partitioning, and monitoring both Glue jobs and your databases—you’ll ensure reliable, performant, and secure data workflows. With these guidelines in place, you can confidently leverage AWS Glue to transform and move data between your relational systems and the AWS analytics ecosystem.

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