Resequencer

The Resequencer pattern addresses the problem of ensuring events are processed in the correct order when dealing with distributed systems or asynchronous processing where event delivery order is not guaranteed. It acts as an intermediary, buffering and reordering events based on a defined sequence identifier before passing them on to the final processor. This is crucial for maintaining data consistency and avoiding unexpected behavior in systems relying on sequential operations.

This pattern is particularly useful in scenarios involving Command Query Responsibility Segregation (CQRS), Event Sourcing, and microservices architectures. It’s also valuable when integrating with external systems that might not adhere to strict ordering guarantees, or when dealing with events that can arrive ‘out of order’ due to network latency or processing delays, and where the order of processing is critical for correctness.

Usage

The Resequencer pattern is commonly used in:

• Financial Transaction Processing: Ensuring transactions are applied to accounts in the correct order, even if network delays cause them to arrive scrambled.
• Log Aggregation and Analysis: Ordering log entries from multiple sources by timestamp to reconstruct a proper sequence of events.
• Game Development: Maintaining the correct order of game actions, especially in multiplayer games, to ensure a consistent game state across clients.
• Distributed Databases: Handling writes to different shards in a distributed database to achieve total order for specific logical units of operation.

Examples

• Apache Kafka with Kafka Streams: Kafka’s partitioning allows for parallel processing, but may not guarantee order within a partition. Kafka Streams provides features like windowing and aggregation, which, combined with careful partition key selection, can effectively act as a resequencer, ensuring related events are processed together and in order. Specifically, the KStream’s sort() operation or using a single partition for related events achieves resequencing.

• AWS Kinesis Data Streams: Kinesis streams ingest data in a sequence, but parallel processing by multiple consumers may lead to out-of-order results. A Kinesis Data Analytics application, or a custom downstream process, can utilize windowing or sessionization techniques along with ordering attributes within the ingested records to re-sequence the data before writing to a final data store like Amazon S3 or DynamoDB. DynamoDB streams alongside Lambda functions can achieve resequencing through a buffer utility using state.