How to Process Azure Cosmos DB Change Streams in Parallel with Java (and Stop Leaving Throughput on the Table)

The given text describes a method for efficiently migrating data from MongoDB to another system using the change stream feature. Here are the key points and concepts covered:

1. Change Stream Basics:

   • Change streams allow you to monitor all document changes in a collection or database.
   • They provide real-time updates similar to event sourcing.

2. Threading Model:

   • The migration process uses multiple threads, each handling one partition of the data.
   • This parallel processing helps achieve higher throughput by leveraging multi-core CPUs and distributed systems.

3. Partitioning Strategy:

   • Partitions are created based on a sharding key (e.g., user_id).
   • Each thread processes changes for a specific range of shard keys, ensuring that the workload is evenly distributed.

4. Efficient Cursor Management:

   • Instead of reading one document at a time from each partition, cursors are used to read batches of documents.
   • This reduces overhead and improves performance by minimizing I/O operations.

5. Batch Processing:

   • Changes are accumulated in memory until a batch size threshold is reached.
   • Once the batch size is met, all changes are processed together (e.g., written to

Read the full article at DEV Community

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.