Dictionary compression

One of the earliest lossless compression algorithms, dictionary compression is the basis of many popular compression methods. Dictionary compression can also be found in areas outside of computer science, such as medical coding.

Instead of storing values directly, dictionary compression works by making a list of the possible values that can appear, and then storing an index into a dictionary containing the unique values. This technique is quite versatile, can be used regardless of data type, and works especially well when you have a limited set of values that repeat frequently.

For example, if you had the list of temperatures shown earlier, but you wanted an additional column storing a city location for each measurement, you might have a set of values like this:

City
New York
San Francisco
San Francisco
Los Angeles

Instead of storing all the city names directly, you can instead store a dictionary, like this:

<CodeBlock canCopy={false} showLineNumbers={false} children={\{0: "New York", 1: "San Francisco", 2: "Los Angeles",\}} />

You can then store just the indices in your column, like this:

City
0
1
1
2

For a dataset with a lot of repetition, this can offer significant compression. In the example, each city name is on average 11 bytes in length, while the indices are never going to be more than 4 bytes long, reducing space usage nearly 3 times. In TimescaleDB, the list of indices is compressed even further with the Simple-8b+RLE method, making the storage cost even smaller.

Dictionary compression doesn't always result in savings. If your dataset doesn't have a lot of repeated values, then the dictionary is the same size as the original data. TimescaleDB automatically detects this case, and falls back to not using a dictionary in that scenario.

===== PAGE: https://docs.tigerdata.com/use-timescale/compression/modify-a-schema/ =====

---

Changelog

URL: llms-txt#changelog

Contents:

• TimescaleDB 2.22.1 – configurable indexing, enhanced partitioning, and faster queries
  • Highlighted features
  • Deprecations
• Kafka Source Connector (beta)
• Phased update rollouts, pg_cron, larger compute options, and backup reports
  • 🛡️ Phased rollouts for TimescaleDB minor releases
  • ⏰ pg_cron extension
  • ⚡️ Larger compute options: 48 and 64 CPU
  • 📋 Backup report for compliance
  • 🗺️ New router for Tiger Cloud Console

All the latest features and updates to Tiger Cloud.

TimescaleDB 2.22.1 – configurable indexing, enhanced partitioning, and faster queries

TimescaleDB 2.22.1 introduces major performance and flexibility improvements across indexing, compression, and query execution. TimescaleDB 2.22.1 was released on September 30th and is now available to all users of Tiger.