Correct message ordering is critical in a Quantexa Kafka streaming solution to ensure functional accuracy and reliable data processing. This article explores how to design and configure Kafka to maintain message ordering across different topics, particularly internal topics used by Quantexa's Streaming tier to trigger functionalities that must execute in sequence.
Introduction
Kafka guarantees that messages within a partition are consumed in the exact order they were produced, adhering to a first-in, first-out (FIFO) principle. However, ordering is not preserved across multiple partitions or topics unless a deliberate strategy is applied.
Impact of incorrect message ordering
Out-of-sequence message ordering can significantly impact a Quantexa streaming system functionality, leading to:
- Incorrect updates in Elasticsearch – A record may be updated with an outdated message if messages to the Record Extraction service are out of order.
- Entity Store inconsistencies – Entities may be refreshed with outdated records.
- Inaccurate risk scoring and analytics – Graphs and scores may be built based on outdated data, affecting decision-making processes.
Given these risks, ensuring correct message sequencing is essential.
Kafka partitioning and its role in message ordering
What is a partition in Kafka?
A partition in Kafka is a unit of parallelism that distributes messages across brokers for scalability and high availability. Kafka ensures ordering within a partition but not across multiple partitions.
Kafka partitioning strategy
A Kafka partitioner determines which partition a message is assigned to. The default Kafka partitioning behavior is:
- When the event key is non-null: Events with the same key are consistently assigned to the same partition.
- When the key is null: Events are "sticky" to a partition until the batch is full or
linger.ms elapses, after which a new partition is chosen.
For more details, refer to the Kafka documentation.
Understanding Kafka event keys and IDs
Kafka messages typically include an event key and an event ID, which serve distinct purposes:
- Event key: Used for partitioning. Kafka assigns messages with the same key to the same partition, ensuring they are processed in order within that partition.
- Event ID: A unique identifier for tracking individual events. Unlike the event key, it does not influence partition assignment but aids in deduplication, auditing, and troubleshooting.
Understanding this distinction is crucial for designing a Quantexa streaming deployment that ensures ordering and data integrity by using the Event key.
Best practices for ensuring message ordering in Quantexa streaming solutions
To maintain ordering consistency in a Quantexa streaming solution, consider the following best practices:
- Use consistent Kafka event keys: We strongly recommend using Document ID as the Kafka event key to ensure messages related to the same entity are sent to the same partition.
- Control partition scaling: Avoid dynamically changing the number of partitions in a topic unless absolutely necessary, as this can disrupt message ordering.
- Monitor consumer lag: Track consumer lag metrics to detect processing delays that might impact message order.
- Implement idempotent processing: Upstream systems should ensure they produce Kafka messages with meaningful event keys (such as Document ID) to enable Quantexa’s Streaming tier services to follow idempotent processing, as supported by default configurations. Additionally, if deployment teams build bespoke services as part of their integration, they must ensure these custom services also adhere to idempotent processing principles when generating and producing events, to maintain consistency across the end-to-end streaming pipeline.
- Optimize consumer parallelism: Align the number of consumers with the number of partitions to prevent bottlenecks and ensure efficient processing.
Message ordering in Quantexa’s Streaming tier
Quantexa’s Streaming tier services can consume messages from multiple partitions, enabling:
- Performance optimization through parallelism.
- Load balancing across Streaming tier service instances.
For example, if the Streaming tier has one instance of the Record Extraction service consuming from a topic with six partitions, it will create six processing threads, ensuring that messages are consumed in the order they were produced within each partition.
Load balancing considerations
While the number of Quantexa Streaming tier service instances does not need to match the number of partitions, for optimal load balancing, the instance count should be a factor of the partition count. If there are more instances than partitions, some instances will remain idle.
Maintaining message ordering in Quantexa’s Streaming tier
With non-null event keys, Quantexa’s Streaming tier services remain agnostic of partitioning strategy when producing messages to Kafka output topics. The default Kafka partitioning strategy ensures that messages with the same key (e.g., Document ID) are hashed and sent to the same partition.
For example, if messages for adding and updating a document are produced to the same Kafka topic using Document ID as the event key, the Record Extraction service will:
- Consume these messages in order from the same partition.
- Process them in sequence.
- Publish outcome messages into the same partition.
This ensures that updates to the same document are processed in the correct order.
Conclusion
Maintaining message ordering across Kafka topics in Quantexa’s Streaming tier requires careful handling of partitioning strategies, event tracking, and fault tolerance mechanisms. By following best practices, such as using Document ID as the Kafka event key, partitioning based upon a non-null event key approach, implementing idempotent processing, and monitoring consumer lag, deployment teams can optimize performance and ordering guarantees, ensuring data integrity in event-driven workflows.