In this lesson, you will learn how to scale your Change Data Capture (CDC) pipelines using batching and parallel processing to handle massive transaction volumes without inducing replication lag.
The Challenge of High-Velocity Logs
When a database processes thousands of transactions per second, your log miner can quickly fall behind. If you process these changes serially—reading one log entry, writing to Kafka, then moving to the next—the I/O overhead and network latency will cause a "lag" that grows indefinitely. To keep the source database and your downstream streams synchronized, we must move from single-threaded, record-at-a-time processing to high-throughput batching and parallel architectures.
Implementing Batching
Instead of committing every individual log change to your messaging system immediately, group changes into small, temporal, or size-based buffers. By flushing a List<ChangeEvent> to your downstream sink in one go, you reduce the number of network round-trips and transaction commits.
// Conceptual batching strategy
List<ChangeEvent> buffer = new ArrayList<>();
while (logReader.hasNext()) {
buffer.add(logReader.next());
if (buffer.size() >= BATCH_SIZE || timer.expired()) {
kafkaProducer.send(buffer);
buffer.clear();
}
}
Leveraging Parallel Processing
Batching helps, but it is often limited by a single CPU thread. To truly maximize throughput, you should employ parallel task execution. This involves partitioning your transaction logs based on primary keys or table IDs. By assigning different partitions to separate processing threads or consumer instances, you ensure that the load is distributed across your infrastructure.
When implementing this, remember that ordering matters. Ensure that changes for the same record or row are always routed to the same partition or thread. This preserves the sequential integrity of your data (e.g., an UPDATE must always be processed after an INSERT) while allowing concurrent processing of different rows. By decoupling the reading of the transaction log from the writing to the sink through parallel workers, you effectively mitigate backpressure and keep your replication latency near zero, even during peak database activity.
Key Takeaway: By grouping log entries into batches to minimize I/O overhead and distributing workload across parallel threads while maintaining record-level ordering, you can maintain low-latency CDC performance under extreme database loads.