How TimescaleDB compresses time-series data
TimescaleDB utilizes a hybrid row-columnar engine called 'hypercore' to achieve up to 98% compression for time-series data. By using specialized algorithms like Gorilla XOR and delta encoding, it optimizes storage for IoT workloads in ways that standard PostgreSQL TOAST mechanisms cannot.
Why it matters
Efficient data compression is critical for reducing storage costs and improving query performance in large-scale data analytics and IoT infrastructure.
TimescaleDB can achieve compression of up to 98% for typical time-series data. Compressing time-series data requires a fundamentally different approach than the general-purpose algorithms used in OLTP databases. In TimescaleDB this is handled by the hypercore engine — a hybrid row-columnar engine that uses specialized algorithms: delta encoding, delta-of-delta, Gorilla XOR and run-length encoding. This article explains how it works and how to configure compression so that you actually achieve that ratio.
The article is a technical explanation of database architecture.
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