Grateful for our Head of Product, John Kutay, championing solutions like DuckDB—5-10x performance gains with zero extra cost is the perfect holiday win! ????
This holiday season, I’m especially thankful for the 5-10x performance boost we achieved by using DuckDB as a caching layer for read-heavy analytical queries—with no additional infrastructure, RAM, compute, or storage required?? With Striim Developer, a serverless, multi-tenant streaming platform, our operational control plane in AWS handles tenant management, monitors usage, and triggers external jobs and alerts. While powerful, our initial implementation relied heavily on hitting the operational database for every job, causing significant latency and throttling job frequency, ultimately impacting system redundancy. We explored common solutions: ? Materialized Views: Complex to maintain and refresh under high-frequency updates. ? Increasing Database Buffer Cache: More RAM = higher cost, and performance gains weren’t guaranteed. ? Script-Level Optimizations: Limited impact on our specific workload. Instead, we turned to DuckDB for its: ? Blazing-fast OLAP performance: Perfect for aggregated lookups. ? Lightweight design: Minimal memory footprint and no need for additional infrastructure. ? Seamless Python API: Made development quick, simple, and easy to test. The result? Massive performance improvements with zero additional cost, all while keeping our architecture clean and easy to maintain. DuckDB has quickly become my favorite caching solution for read-heavy analytical workloads—especially when avoiding the complexity of other solutions like materialized views. Kim D. and I will write a deep dive post on our implementation shortly ???