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Core Architecture

Learn the three pillars of TimescaleDB

Tiger Data 101 → TimescaleDB | Section: What is TimescaleDB? | ⏱ Time: ~3 min

How it works: the core architecture

Section titled “How it works: the core architecture”

TimescaleDB introduces three core concepts that do the heavy lifting:

Hypertables

Section titled “Hypertables”

A hypertable is a regular PostgreSQL table from your application's perspective. Under the hood, TimescaleDB automatically partitions it into smaller chunks by time (and optionally by another dimension like device ID).

         Hypertable: sensor_readings
         ┌──────────────────────────┐
         │  time  │ device │ value  │
         └──────────────────────────┘
                      │
         ┌────────────┼────────────┐
         ▼            ▼            ▼
    [chunk: Jan]  [chunk: Feb]  [chunk: Mar]

Benefits:

  • Queries automatically target only the relevant chunks (no full-table scans)
  • Each chunk is a real PostgreSQL table with its own indexes
  • Old chunks can be compressed, moved to cheaper storage, or dropped automatically

Hypercore

Section titled “Hypercore”

hypercore is TimescaleDB's columnar storage engine for chunks. When you compress a chunk with hypercore, it stores data column-by-column instead of row-by-row, following the same principle as analytical databases like Parquet or Redshift.

Why it matters:

  • 10–40x storage compression on typical time-series data
  • Analytical queries that scan one column are dramatically faster
  • You can mix compressed (cold) and uncompressed (hot) chunks in the same hypertable

Continuous aggregates

Section titled “Continuous aggregates”

Continuous Aggregates (CAGGs) are materialized views that TimescaleDB keeps automatically up to date as new data arrives.

Instead of recomputing an expensive rollup on every query:

-- Without CAGGs: scans raw data every time (slow)
SELECT time_bucket('1 hour', time), avg(temperature)
FROM sensor_readings
GROUP BY 1;


-- With CAGGs: reads pre-computed results (fast)
SELECT bucket, avg_temperature FROM hourly_sensor_summary;

CAGGs can be layered (daily from hourly, monthly from daily), enabling fast dashboards at any granularity without redundant raw data scans.