#apache-kafka

#apache-kafka

This engine takes topic data schemas, metadata, and test rules as inputs to create a set of FlinkSQL-based test definitions. A Flink job then executes these tests, consuming messages from production Kafka topics and forwarding any errors to Grab's observability platform. FlinkSQL was selected because its ability to represent stream data as dynamic tables allowed the team to automatically generate data filters for rules that could be efficiently implemented.

The main feature of the 3.0 release is a new decoupled architecture. This change fixes a key limitation found in earlier versions. In earlier versions of Mimir, the ingester component handled both reading and writing. This setup meant that heavy query loads could hurt ingestion performance. The new design adds Apache Kafka as an asynchronous buffer between ingestion and query tasks. This allows each path to scale on its own and removes the cross-path dependencies that affected system stability before.