AI BriefingPinterest's Next-Generation DB Ingestion Technology
Key point
Pinterest built a unified CDC-based DB ingestion framework to reduce data latency and cut costs.
Details
Pinterest introduced a new framework to solve the limitations of its existing batch-centric ingestion approach, namely high data latency (24+ hours), unnecessary resource waste, and difficulties with data compliance.
The new system is centered on CDC (Change Data Capture) and was built using Debezium/TiCDC, Kafka, Flink, Spark, and Iceberg. This shortened the data update cycle from hours to minutes, and by processing only changed records, significantly reduced infrastructure costs.
The architecture consists of three main layers.
- CDC Layer: Writes database changes to Kafka with sub-second latency.
- Streaming Layer: Uses Flink to process CDC events in real time and stores them in CDC Iceberg tables on S3.
- Batch Layer: Uses Spark to periodically Upsert changes from the CDC tables into Base Iceberg tables.
This framework supports various databases such as MySQL, TiDB, and KVStore, prevents data loss through at-least-once guarantees, and has the scalability to handle petabyte-scale data.
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