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Price Cache with DragonflyDB

1 post · Aug 2026

What this trail covers

Agoda migrated its critical Price Cache from MSSQL to DragonflyDB to handle increasing data volumes and throughput. This involved a phased rollout, performance validation, and the implementation of a dual-cluster architecture for high availability and improved maintenance workflows. The new system offers better scalability, built-in expiry for volatile data, and reduced operational complexity compared to the previous sharded MSSQL setup. The migration included detailed parity checking and a gradual rollout strategy to ensure zero downtime. Post-migration, Agoda has implemented automated failover and optimized maintenance procedures using a dual-cluster setup.

All Agoda trails
  1. How Agoda Scaled Its Price Cache with DragonflyDBThis post details the migration of Agoda's Price Cache from a sharded MSSQL database to DragonflyDB. It outlines the challenges faced with scaling MSSQL, the selection criteria for DragonflyDB (multi-threading, Redis compatibility, built-in expiry, internal expertise), and performance validation through load testing. The migration strategy is described in four phases: planning/preparation (including initial hot data storage and later full dataset sharded deployment), parity checking (comparing supplier count and price-data length), gradual rollout via A/B experimentation, and post-migration optimizations like dual-cluster high availability and automated failover. The post quantifies performance improvements in write throughput and latency, and read latency under production load.