Which is the best strategy to create the initial snapshots of a transactional database with millions of entries when using Debezium and Kafka Connect?

Ensure data consistency and incremental loading with parallel processing.

When dealing with a transactional database with millions of entries and using Debezium with Kafka Connect for change data capture (CDC), creating the initial snapshots can be a resource-intensive process. The goal is to capture the existing data in the database and replicate it to Kafka topics so that you have a consistent starting point for further change events. Here are some strategies to consider:

Parallel Processing:

Break down the workload by dividing the database into smaller chunks or partitions based on some criteria (e.g., ranges of primary keys, tables, etc.).
Use multiple Kafka Connect workers to parallelize the snapshot process across these partitions.
Each worker can be responsible for capturing changes from a subset of tables or a specific range of primary keys.

Incremental Batching:

Instead of attempting to capture the entire database in a single snapshot, perform incremental snapshots in batches.
Start with a subset of tables or a range of primary keys and gradually extend the scope in subsequent runs until the entire database is covered.
This approach helps manage resource utilization and reduces the risk of overwhelming the system.

Off-Peak Hours Execution:

Schedule the initial snapshot during off-peak hours to minimize the impact on the production database and ensure that regular transactional processing is not affected.
Coordinate with the database administrators and other stakeholders to find a suitable time window for the snapshot.

Use Snapshot Mode Wisely:

Debezium supports two snapshot modes: "initial" and "when_needed."
Consider using the "initial" snapshot mode for the first run to capture the entire state of the database.
Once the initial snapshot is complete, switch to the "when_needed" mode to capture changes incrementally.

Optimize Database Configuration:

Tune the database configuration for read-intensive operations during the snapshot to ensure efficient data retrieval.
Adjust database parameters such as isolation levels, caching settings, and connection pool sizes to optimize performance.

Scale Resources:

Ensure that the Kafka Connect cluster and the underlying Kafka infrastructure are appropriately scaled to handle the increased load during the snapshot process.
Allocate sufficient CPU, memory, and network resources to the Kafka Connect workers involved in the snapshot.

Monitor and Tune:

Regularly monitor the performance of the Kafka Connect workers, the database, and the Kafka infrastructure during the snapshot.
Adjust configuration parameters as needed to optimize performance and address any bottlenecks.

Always thoroughly test your snapshot strategy in a non-production environment before applying it to a live system to ensure that it meets performance and reliability requirements without causing disruptions.