Improve hypertable and query performance

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Hypertables are Postgres tables that help you improve insert and query performance by automatically partitioning your data by time. Each hypertable is made up of child tables called chunks. Each chunk is assigned a range of time, and only contains data from that range. When you run a query, TimescaleDB identifies the correct chunk and runs the query on it, instead of going through the entire table. This page shows you how to tune hypertables to increase performance even more.

Optimize hypertable chunk intervals: choose the optimum chunk size for your data - manually or automatically
Enable chunk skipping: skip chunks on non-partitioning columns in hypertables when you query your data
Analyze your hypertables: use Postgres ANALYZE to create the best query plan

Optimize hypertable chunk intervals

Tuning your hypertable chunk interval can improve performance in your database, especially as your workload evolves over time. With TimescaleDB, you can do this manually or automatically.

Manual tuning

To manually adjust the chunk size in your database, take the following steps:

Choose an optimum chunk interval
Postgres builds the index on the fly during ingestion. That means that to build a new entry on the index, a significant portion of the index needs to be traversed during every row insertion. When the index does not fit into memory, it is constantly flushed to disk and read back. This wastes IO resources which would otherwise be used for writing the heap/WAL data to disk.
The default chunk interval is 7 days. However, best practice is to set chunk_interval so that prior to processing, the indexes for chunks currently being ingested into fit within 25% of main memory. For example, on a system with 64 GB of memory, if index growth is approximately 2 GB per day, a 1-week chunk interval is appropriate. If index growth is around 10 GB per day, use a 1-day interval.
You set chunk_interval when you create a hypertable, or by calling set_chunk_time_interval on an existing hypertable.
In the following example you create a hypertable called conditions that stores time values in the time column and has chunks that store data for a chunk_interval of one day:
```
CREATE TABLE conditions (
   time        TIMESTAMPTZ       NOT NULL,
   location    TEXT              NOT NULL,
   device      TEXT              NOT NULL,
   temperature DOUBLE PRECISION  NULL,
   humidity    DOUBLE PRECISION  NULL
) WITH (
   tsdb.hypertable,
   tsdb.chunk_interval='1 day'
);
```

Check the current setting for chunk intervals

Query the TimescaleDB catalog for a hypertable. For example:


SELECT * 
  FROM timescaledb_information.dimensions 
  WHERE hypertable_name = 'conditions';

The result looks like:


hypertable_schema | hypertable_name | dimension_number | column_name |       column_type        | dimension_type | time_interval | integer_interval | integer_now_func | num_partitions
-------------------+-----------------+------------------+-------------+--------------------------+----------------+---------------+------------------+------------------+----------------
 public           | metrics          |                1 | recorded    | timestamp with time zone | Time           | 1 day         |                  |                  |

Time-based interval lengths are reported in microseconds.

Change the chunk interval length on an existing hypertable
To change the chunk interval on an already existing hypertable, call set_chunk_time_interval.
```
SELECT set_chunk_time_interval('conditions', INTERVAL '24 hours');
```
The updated chunk interval only applies to new chunks. This means setting an overly long interval might take a long time to correct. For example, if you set chunk_interval to 1 year and start inserting data, you can no longer shorten the chunk for that year. If you need to correct this situation, create a new hypertable and migrate your data.
While chunk turnover does not degrade performance, chunk creation does take longer lock time than a normal INSERT operation into a chunk that has already been created. This means that if multiple chunks are being created at the same time, the transactions block each other until the first transaction is completed.

If you use expensive index types, such as some PostGIS geospatial indexes, take care to check the total size of the chunk and its index using chunks_detailed_size.

Automated tuning

Automated chunk tuning manages chunk intervals for you. Tiger Cloud continuously evaluates your hypertable and adjusts the interval when it detects the current setting is no longer optimal. Updated chunk intervals apply to new chunks only; existing historical chunks are not affected.

Note

Automated chunk tuning is currently in beta.

When you enable automated chunk tuning on a hypertable, the interval is evaluated and updated within 5 minutes if necessary. The interval is changed incrementally rather than jumping directly to the target interval:

Large increase: if the recommended interval is more than double the current one, the interval is only doubled each evaluation cycle rather than jumping directly to the target. For example, if the current interval is 1 day and the recommended interval is 8 days, the interval increases to 2 days first, then 4 days, then 8 days over successive evaluations.
Large decrease: if the recommended interval is less than half the current one, the interval is only halved each evaluation cycle rather than jumping directly to the target. For example, if the current interval is 8 days and the recommended interval is 1 day, the interval decreases to 4 days first, then 2 days, then 1 day over successive evaluations.
Small change: if the recommended interval is within 2x of the current one, bigger or smaller, the new interval is applied directly. For example, if the current interval is 4 days and the recommended interval is 6 days, the interval changes to 6 days immediately.

If a columnstore policy is configured on the hypertable, automated chunk tuning respects it. The chunk interval is never increased beyond the after value. For example, if your compression policy compresses data older than 1 day, the maximum chunk interval is 1 day.

You can track all interval changes in the Activity tab.

Automated tuning is off by default and is configured at the individual hypertable level. Even when disabled, Tiger Console shows the recommended chunk interval for every hypertable so you can evaluate whether a change is warranted before opting in.

Manage chunk tuning

To enable automated chunk tuning:

In Tiger Console, select your service
Click Explorer and select your hypertable
Toggle on Automated chunk tuning
For a #prod-tagged service, click Request beta access. Our team will manually review your hypertable and the recommended intervals before enabling

The recommended chunk interval is visible regardless of whether automated tuning is enabled, so you can preview the suggested value before turning it on.

To disable automated chunk tuning:

In Tiger Console, select your service
Click Explorer and select your hypertable
Toggle of Automated chunk tuning
Optionally set your preferred interval by clicking Change under the interval size. The next new chunk will use the interval you specify.

Limitations

Automated chunk tuning is not supported for:

Hypertables not owned by tsdbadmin.
Hypertables that do not use a time-based partition column.

Enable chunk skipping

Early access: TimescaleDB v2.17.1

One of the key purposes of hypertables is to make your analytical queries run with the lowest latency possible. When you execute a query on a hypertable, you do not parse the whole table; you only access the chunks necessary to satisfy the query. This works well when the WHERE clause of a query uses the column by which a hypertable is partitioned. For example, in a hypertable where every day of the year is a separate chunk, a query for September 1 accesses only the chunk for that day.

However, many queries use columns other than the partitioning one. For example, a satellite company might have a table with two columns: one for when data was gathered by a satellite and one for when it was added to the database. If you partition by the date of gathering, a query by the date of adding accesses all chunks in the hypertable and slows the performance.

To improve query performance, TimescaleDB enables you to skip chunks on non-partitioning columns in hypertables.

Important

Chunk skipping only works on chunks converted to the columnstore after you enable_chunk_skipping.

How chunk skipping works

You enable chunk skipping on a column in a hypertable. TimescaleDB tracks the minimum and maximum values for that column in each chunk. These ranges are stored in the start (inclusive) and end (exclusive) format in the chunk_column_stats catalog table. TimescaleDB uses these ranges for dynamic chunk exclusion when the WHERE clause of an SQL query specifies ranges on the column.

You can enable chunk skipping on hypertables compressed into the columnstore for smallint, int, bigint, serial, bigserial, date, timestamp, or timestamptz type columns.

When to enable chunk skipping

You can enable chunk skipping on as many columns as you need. However, best practice is to enable it on columns that are both:

Correlated, that is, related to the partitioning column in some way.
Referenced in the WHERE clauses of the queries.

In the satellite example, the time of adding data to a database inevitably follows the time of gathering. Sequential IDs and the creation timestamp for both entities also increase synchronously. This means those two columns are correlated.

For a more in-depth look on chunk skipping, see our blog post.

Enable chunk skipping

To enable chunk skipping on a column, call enable_chunk_skipping on a hypertable for a column_name. For example, the following query enables chunk skipping on the order_id column in the orders table:


SELECT enable_chunk_skipping('orders', 'order_id');

For more details on how to implement chunk skipping, see the API Reference.

Analyze your hypertables

You can use the Postgres ANALYZE command to query all chunks in your hypertable. The statistics collected by the ANALYZE command are used by the Postgres planner to create the best query plan. For more information about the ANALYZE command, see the Postgres documentation.

Note

Important

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