Time-series delta encoding

By Pradyumna Chippigiri

February 10, 2026

Time-series databases compress data using a simple idea: delta encoding. Most TS data has low variance (consecutive values are usually close together.) That predictability is what makes compression so effective.

Delta encoding for values

Instead of storing the full value each time, store the difference from the previous point.

Example: CPU usage recorded every second:

Raw values:

[62.8]  [63.0]  [62.9]  [63.2]

Delta encoded:

[62.8]  [+0.2]  [-0.1]  [+0.3]

First value is stored as is; the rest are deltas.

Varints for smaller storage

Those deltas are usually tiny. Many time-series engines store them as varints (variable-length integers) : small deltas take fewer bytes than fixed 32/64-bit numbers. Storage becomes cheaper.

Trade-off: You use a bit of compute to reconstruct the values when reading.

Delta-of-delta for timestamps

Time-series databases do something extra for timestamps: delta-of-delta encoding.

When timestamps come at regular intervals (e.g., every 5 seconds):

Raw timestamps:

2000,  2005,  2010,  2015,  2020

First-order deltas:

5,  5,  5,  5,  5...

Delta-of-deltas:

5,  0,  0,  0,  0...

When intervals are consistent, the second-order deltas are mostly zeros and zeros compress extremely well.

Simple concept. Huge impact on storage.