hyperloglog() and approx_count_distinct() functions

Introduction

Estimate the number of distinct values in a dataset. This is also known as cardinality estimation. For large datasets and datasets with high cardinality (many distinct values), this can be much more efficient in both CPU and memory than an exact count using count(DISTINCT).

The estimation uses the hyperloglog++ algorithm. If you aren’t sure what parameters to set for the hyperloglog, try using the approx_count_distinct aggregate, which sets some reasonable default values.

Two-step aggregation

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This group of functions uses the two-step aggregation pattern.

Rather than calculating the final result in one step, you first create an intermediate aggregate by using the aggregate function.

Then, use any of the accessors on the intermediate aggregate to calculate a final result. You can also roll up multiple intermediate aggregates with the rollup functions.

The two-step aggregation pattern has several advantages:

1. More efficient because multiple accessors can reuse the same aggregate
2. Easier to reason about performance, because aggregation is separate from final computation
3. Easier to understand when calculations can be rolled up into larger intervals, especially in window functions and continuous aggregates
4. Can perform retrospective analysis even when underlying data is dropped, because the intermediate aggregate stores extra information not available in the final result

To learn more, see the blog post on two-step aggregates.

Functions in this group

warning

This function group includes some experimental functions. Experimental functions might change or be removed in future releases. We do not recommend using them in production. Experimental functions are marked with an Experimental tag.

Aggregate

hyperloglog

Aggregate data into a hyperloglog for approximate counting

Alternate aggregate

approx_count_distinct

ExperimentalAggregate data into a hyperloglog for approximate counting without specifying the number of buckets

Accessor

distinct_count

Estimate the number of distinct values from a hyperloglog

stderror

Estimate the relative standard error of a hyperloglog

Rollup

rollup

Roll up multiple hyperloglogs

Function details

hyperloglog()

Stabilized in Toolkit v1.3.0

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`

1. hyperloglog(

`

1. buckets INTEGER,
2. value AnyElement

`

1. ) RETURNS Hyperloglog

`

This is the first step for estimating the approximate number of distinct values using the hyperloglog algorithm. Use hyperloglog to create an intermediate aggregate from your raw data. This intermediate form can then be used by one or more accessors in this group to compute final results.

Optionally, multiple such intermediate aggregate objects can be combined using rollup() before an accessor is applied.

If you’re not sure what value to set for buckets, try using the alternate aggregate function, approx_count_distinct(). approx_count_distinct also creates a hyperloglog, but it sets a default bucket value that should work for many use cases.

Required arguments

Returns

Examples

Given a table called samples, with a column called weights, return a hyperloglog over the weights column:

2. SELECT hyperloglog(32768, weights) FROM samples;

Using the same data, build a view from the aggregate that you can pass to other hyperloglog functions:

2. CREATE VIEW hll AS SELECT hyperloglog(32768, data) FROM samples;

approx_count_distinct()

ExperimentalExperimental TimescaleDB Toolkit functions are not suitable for production environments. They may have bugs and may cause data loss. Click to learn more.

Introduced in Toolkit v1.8.0

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`

1. approx_count_distinct(

`

1. value AnyElement

`

1. ) RETURNS Hyperloglog

`

This is an alternate first step for approximating the number of distinct values. It provides some added convenience by using some sensible default parameters to create a hyperloglog.

Use approx_count_distinct to create an intermediate aggregate from your raw data. This intermediate form can then be used by one or more accessors in this group to compute final results.

Optionally, multiple such intermediate aggregate objects can be combined using rollup() before an accessor is applied.

Required arguments

Returns

Examples

Given a table called samples, with a column called weights, return a hyperloglog over the weights column::

2. SELECT toolkit_experimental.approx_count_distinct(weights) FROM samples;

Using the same data, build a view from the aggregate that you can pass to other hyperloglog functions:

2. CREATE VIEW hll AS SELECT toolkit_experimental.approx_count_distinct(data) FROM samples;

distinct_count()

Stabilized in Toolkit v1.3.0

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`

1. distinct_count(

`

1. hyperloglog Hyperloglog

`

1. ) RETURNS BIGINT

`

Estimate the number of distinct values from a hyperloglog

Required arguments

Returns

Examples

Estimate the number of distinct values from a hyperloglog named hyperloglog. The expected output is 98,814:

2. SELECT distinct_count(hyperloglog(8192, data))

4. FROM generate_series(1, 100000) data

6. distinct_count

8. ----------------

10. 98814

stderror()

Stabilized in Toolkit v1.3.0

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`

1. stderror(

`

1. hyperloglog Hyperloglog

`

1. ) RETURNS DOUBLE PRECISION

`

Estimate the relative standard error of a Hyperloglog. For approximate relative errors by number of buckets, see the relative errors section.

Required arguments

Returns

Examples

Estimate the relative standard error of a hyperloglog named hyperloglog. The expected output is 0.011490485194281396:

2. SELECT stderror(hyperloglog(8192, data))

4. FROM generate_series(1, 100000) data

6. stderror

9. ----------------------

11. 0.011490485194281396

rollup()

Stabilized in Toolkit v1.3.0

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`

1. rollup(

`

1. hyperloglog Hyperloglog

`

1. ) RETURNS Hyperloglog

`

Combine multiple intermediate hyperloglog aggregates, produced by hyperloglog, into a single intermediate hyperloglog aggregate. For example, you can use rollup to combine hyperloglog from 15-minute buckets into daily buckets.

Required arguments

Returns

Extended examples

Roll up two hyperloglogs

Roll up two hyperloglogs. The first hyperloglog buckets the integers from 1 to 100,000, and the second hyperloglog buckets the integers from 50,000 to 150,000. Accounting for overlap, the exact number of distinct values in the combined set is 150,000.

Calling distinct_count on the rolled-up hyperloglog yields a final value of 150,552, so the approximation is off by only 0.368%:

2. SELECT distinct_count(rollup(logs))

4. FROM (

6. (SELECT hyperloglog(4096, v::text) logs FROM generate_series(1, 100000) v)

8. UNION ALL

10. (SELECT hyperloglog(4096, v::text) FROM generate_series(50000, 150000) v)

12. ) hll;

Output:

2. distinct_count

5. ----------------

7. 150552

Approximate relative errors

These are the approximate errors for each bucket size: