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Optimizing Time Series Data Storage and Querying: Migrating `candle_data` from PostgreSQL to QuestDB for Enhanced Performance - DEV Community

https://dev.to/denlava/optimizing-time-series-data-storage-and-querying-migrating-candledata-from-postgresql-to-4l8e

April 2, 2026 at 07:38 PM JST•The archive page, viewer, and downloads use this saved version.

April 2, 2026 at 07:38 PM JST·dev.to

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Original URLhttps://dev.to/denlava/optimizing-time-series-data-storage-and-querying-migrating-candledata-from-postgresql-to-4l8e

StartedApril 2, 2026 at 07:38 PM JST

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This page discusses challenges of handling large-scale time series data in PostgreSQL and proposes migration to QuestDB for optimization. PostgreSQL's row-oriented storage architecture causes performance degradation through index bloat, disk contention, and inefficient handling of sequential data. QuestDB offers columnar storage and vectorized execution, specifically designed for time series workloads with native optimizations like columnar compression, reducing storage overhead and accelerating analytical queries.

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