Next Generation DB Ingestion at Pinterest

Summary

The article outlines Pinterest's transition from a legacy batch-oriented database ingestion system to a modern, real-time ingestion framework utilizing Change Data Capture (CDC) technologies. The new architecture addresses critical issues such as high data latency, inefficient resource usage, and operational complexity. By implementing a unified CDC-based framework that integrates Kafka, Flink, Spark, and Iceberg, Pinterest has achieved significant improvements in data processing speed and reliability. The article details the architecture, key components, and optimizations made to enhance performance, including partitioning strategies and handling small files during upsert operations. It concludes with a preview of future developments, particularly in automated schema evolution.

Key Learnings

• 1Implementing a unified CDC framework can significantly reduce data latency and improve real-time analytics capabilities.
• 2Partitioning strategies, such as bucketing by primary key hash, can enhance the efficiency of upsert operations in large datasets.
• 3Utilizing a temporary table for bucket joins can bypass costly shuffles during merge operations, leading to reduced compute costs.
• 4The choice between Merge-on-Read and Copy-on-Write strategies in Iceberg has substantial implications for storage and compute costs.
• 5Addressing the small files problem through distributed writes can lead to improved performance in data ingestion workflows.

Who Should Read This

Senior Data Engineers designing scalable and efficient data ingestion pipelines using real-time processing technologies.

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