Setup hypercore

Reduce your chunk size by up to 98% and speed up your queries by automatically converting data between the rowstore and columnstore

Hypercore is the hybrid row-columnar storage engine in TimescaleDB used by hypertables. Traditional databases force a trade-off between fast inserts (row-based storage) and efficient analytics (columnar storage). Hypercore eliminates this trade-off, allowing real-time analytics without sacrificing transactional capabilities.

Hypercore dynamically stores data in the most efficient format for its lifecycle:

• Row-based storage for recent data: the most recent chunk (and possibly more) is always stored in the rowstore, ensuring fast inserts, updates, and low-latency single record queries. Additionally, row-based storage is used as a writethrough for inserts and updates to columnar storage.
• Columnar storage for analytical performance: chunks are automatically compressed into the columnstore, optimizing storage efficiency and accelerating analytical queries.

Unlike traditional columnar databases, hypercore allows data to be inserted or modified at any stage, making it a flexible solution for both high-ingest transactional workloads and real-time analytics—within a single database.

When you convert chunks from the rowstore to the columnstore, multiple records are grouped into a single row. The columns of this row hold an array-like structure that stores all the data. For example, data in the following rowstore chunk:

Timestamp	Device ID	Device Type	CPU	Disk IO
12:00:01	A	SSD	70.11	13.4
12:00:01	B	HDD	69.70	20.5
12:00:02	A	SSD	70.12	13.2
12:00:02	B	HDD	69.69	23.4
12:00:03	A	SSD	70.14	13.0
12:00:03	B	HDD	69.70	25.2

Is converted and compressed into arrays in a row in the columnstore:

Timestamp	Device ID	Device Type	CPU	Disk IO
[12:00:01, 12:00:01, 12:00:02, 12:00:02, 12:00:03, 12:00:03]	[A, B, A, B, A, B]	[SSD, HDD, SSD, HDD, SSD, HDD]	[70.11, 69.70, 70.12, 69.69, 70.14, 69.70]	[13.4, 20.5, 13.2, 23.4, 13.0, 25.2]

Because a single row takes up less disk space, you can reduce your chunk size by up to 98%, and can also speed up your queries. This saves on storage costs, and keeps your queries operating at lightning speed.

For an in-depth explanation of how hypertables and hypercore work, see the Data model.

This page shows you how to get the best results when you set a policy to automatically convert chunks in a hypertable from the rowstore to the columnstore.

Prerequisites

To follow the steps on this page:

• Create a target Tiger Cloud service with Real-time analytics enabled.

  You need your connection details. This procedure also works for self-hosted TimescaleDB.

The code samples in this page use the crypto_sample.zip data from this key features tutorial.

Optimize your data with columnstore policies

The following figure shows the workflow to follow when you setup hypercore.

The compression ratio and query performance of data in the columnstore is dependent on the order and structure of your data. Rows that change over a dimension should be close to each other. With time-series data, you orderby the time dimension. For example, Timestamp:

Timestamp	Device ID	Device Type	CPU	Disk IO
12:00:01	A	SSD	70.11	13.4

This ensures that records are compressed and accessed in the same order. However, you would always have to access the data using the time dimension, then filter all the rows using other criteria. To make your queries more efficient, you segment your data based on the following:

• The way you want to access it. For example, to rapidly access data about a single device, you segmentby the Device ID column. This enables you to run much faster analytical queries on data in the columnstore.
• The compression rate you want to achieve. The lower the cardinality of the segmentby column, the better compression results you get.

When TimescaleDB converts a chunk to the columnstore, it automatically creates a different schema for your data. It also creates and uses custom indexes to incorporate the segmentby and orderby parameters when you write to and read from the columnstore.

To set up your hypercore automation:

1. Connect to your service

   In Tiger Console open an SQL editor. You can also connect to your service using psql.

2. Enable columnstore on a hypertable

   For efficient queries, remember to segmentby the column you will use most often to filter your data. For example:

   • Hypertables:

     Use CREATE TABLE for a hypertable

     CREATE TABLE crypto_ticks (
       "time" TIMESTAMPTZ,
       symbol TEXT,
       price DOUBLE PRECISION,
       day_volume NUMERIC
     ) WITH (
       timescaledb.hypertable,
       timescaledb.segmentby='symbol',
       timescaledb.orderby='time DESC'
     );
     

     When you create a hypertable using CREATE TABLE ... WITH ..., the default partitioning column is automatically the first column with a timestamp data type. Also, TimescaleDB creates a columnstore policy that automatically converts your data to the columnstore, after an interval equal to the value of the chunk_interval, defined through compress_after in the policy. This columnar format enables fast scanning and aggregation, optimizing performance for analytical workloads while also saving significant storage space. In the columnstore conversion, hypertable chunks are compressed by up to 98%, and organized for efficient, large-scale queries.

     You can customize this policy later using alter_job. However, to change after or created_before, the compression settings, or the hypertable the policy is acting on, you must remove the columnstore policy and add a new one.

     You can also manually convert chunks in a hypertable to the columnstore.

   • Continuous aggregates

     1. Use ALTER MATERIALIZED VIEW for a continuous aggregate:

        ALTER MATERIALIZED VIEW assets_candlestick_daily SET (
          timescaledb.enable_columnstore = true,
          timescaledb.segmentby = 'symbol'
        );
        

        Before you say “huh”, a continuous aggregate is a specialized hypertable.

     2. Add a policy to convert chunks to the columnstore at a specific time interval:

        Create a columnstore_policy that automatically converts chunks in a hypertable to the columnstore at a specific time interval. For example:

        CALL add_columnstore_policy('assets_candlestick_daily', after => INTERVAL '1d');
        

   TimescaleDB is optimized for fast updates on compressed data in the columnstore. To modify data in the columnstore, use standard SQL.

3. Check the columnstore policy

   1. View your data space saving:

      When you convert data to the columnstore, as well as being optimized for analytics, it is compressed by more than 90%. This helps you save on storage costs and keeps your queries operating at lightning speed. To see the amount of space saved:

      SELECT
        pg_size_pretty(before_compression_total_bytes) AS before,
        pg_size_pretty(after_compression_total_bytes) AS after
      FROM hypertable_columnstore_stats('crypto_ticks');
      

      You see something like:

      before	after
      194 MB	24 MB

   2. View the policies that you set or the policies that already exist:

      SELECT * FROM timescaledb_information.jobs
      WHERE proc_name='policy_compression';
      

      See timescaledb_information.jobs.

4. Pause a columnstore policy

   SELECT * FROM timescaledb_information.jobs WHERE
     proc_name = 'policy_compression' AND relname = 'crypto_ticks';
   
   -- Select the JOB_ID from the results, then use it in the next command
   
   SELECT alter_job(<JOB_ID>, scheduled => false);
   

   See alter_job.

5. Restart a columnstore policy

   SELECT alter_job(<JOB_ID>, scheduled => true);
   

   See alter_job.

6. Remove a columnstore policy

   CALL remove_columnstore_policy('crypto_ticks');
   

   See remove_columnstore_policy.

7. Disable columnstore

   If your table has chunks in the columnstore, you have to convert the chunks back to the rowstore before you disable the columnstore.

   ALTER TABLE crypto_ticks SET (timescaledb.enable_columnstore = false);
   

   See ALTER TABLE.

Reference

For integers, timestamps, and other integer-like types, data is compressed using delta encoding, delta-of-delta, simple-8b, and run-length encoding. For columns with few repeated values, XOR-based and dictionary compression is used. For all other types, dictionary compression is used.