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
Hide content
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:
- More efficient because multiple accessors can reuse the same aggregate
- Easier to reason about performance, because aggregation is separate from final computation
- Easier to understand when calculations can be rolled up into larger intervals, especially in window functions and continuous aggregates
- 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
Aggregate data into a hyperloglog for approximate counting
Alternate aggregate
ExperimentalAggregate data into a hyperloglog for approximate counting without specifying the number of buckets
Accessor
Estimate the number of distinct values from a hyperloglog
Estimate the relative standard error of a hyperloglog
Rollup
Roll up multiple hyperloglogs
Function details
hyperloglog()
Stabilized in Toolkit v1.3.0
Hide content
`
hyperloglog(
`
buckets INTEGER,
value AnyElement
`
) 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
Name | Type | Description |
---|---|---|
buckets | INTEGER | Number of buckets in the hyperloglog. Increasing the number of buckets improves accuracy but increases memory use. Value is rounded up to the next power of 2, and must be between 2^4 (16) and 2^18. Setting a value less than 2^10 (1,024) may result in poor accuracy if the true cardinality is high and is not recommended. If unsure, start experimenting with 8,192 (2^13) which has an approximate error rate of 1.15%. |
value | AnyElement | The column containing the elements to count. The type must have an extended, 64-bit, hash function. |
Returns
Column | Type | Description |
---|---|---|
hyperloglog | Hyperloglog | A hyperloglog object which can be passed to other hyperloglog APIs for rollups and final calculation |
Examples
Given a table called samples
, with a column called weights
, return a hyperloglog
over the weights
column:
SELECT hyperloglog(32768, weights) FROM samples;
Using the same data, build a view from the aggregate that you can pass to other hyperloglog
functions:
CREATE VIEW hll AS SELECT hyperloglog(32768, data) FROM samples;
approx_count_distinct()
Introduced in Toolkit v1.8.0
Hide content
`
approx_count_distinct(
`
value AnyElement
`
) 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
Name | Type | Description |
---|---|---|
value | AnyElement | The column containing the elements to count. The type must have an extended, 64-bit, hash function. |
Returns
Column | Type | Description |
---|---|---|
hyperloglog | Hyperloglog | A hyperloglog object which can be passed to other hyperloglog APIs for rollups and final calculation |
Examples
Given a table called samples
, with a column called weights
, return a hyperloglog
over the weights
column::
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:
CREATE VIEW hll AS SELECT toolkit_experimental.approx_count_distinct(data) FROM samples;
distinct_count()
Stabilized in Toolkit v1.3.0
Hide content
`
distinct_count(
`
hyperloglog Hyperloglog
`
) RETURNS BIGINT
`
Estimate the number of distinct values from a hyperloglog
Required arguments
Name | Type | Description |
---|---|---|
hyperloglog | Hyperloglog | The hyperloglog to extract the count from. |
Returns
Column | Type | Description |
---|---|---|
distinct_count | BIGINT | The number of distinct elements counted by the hyperloglog. |
Examples
Estimate the number of distinct values from a hyperloglog named hyperloglog
. The expected output is 98,814:
SELECT distinct_count(hyperloglog(8192, data))
FROM generate_series(1, 100000) data
distinct_count
----------------
98814
stderror()
Stabilized in Toolkit v1.3.0
Hide content
`
stderror(
`
hyperloglog Hyperloglog
`
) 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
Name | Type | Description |
---|---|---|
hyperloglog | Hyperloglog | The hyperloglog to estimate the error of. |
Returns
Column | Type | Description |
---|---|---|
stderror | DOUBLE PRECISION | The approximate relative standard error of the hyperloglog. |
Examples
Estimate the relative standard error of a hyperloglog named hyperloglog
. The expected output is 0.011490485194281396:
SELECT stderror(hyperloglog(8192, data))
FROM generate_series(1, 100000) data
stderror
----------------------
0.011490485194281396
rollup()
Stabilized in Toolkit v1.3.0
Hide content
`
rollup(
`
hyperloglog Hyperloglog
`
) 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
Name | Type | Description |
---|---|---|
hyperloglog | Hyperloglog | The hyperloglog aggregates to roll up. |
Returns
Column | Type | Description |
---|---|---|
rollup | Hyperloglog | A new hyperloglog aggregate created by combining the input hyperloglog aggregates. |
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%:
SELECT distinct_count(rollup(logs))
FROM (
(SELECT hyperloglog(4096, v::text) logs FROM generate_series(1, 100000) v)
UNION ALL
(SELECT hyperloglog(4096, v::text) FROM generate_series(50000, 150000) v)
) hll;
Output:
distinct_count
----------------
150552
Approximate relative errors
These are the approximate errors for each bucket size:
precision | registers (bucket size) | error | column size (in bytes) |
---|---|---|---|
4 | 16 | 0.2600 | 12 |
5 | 32 | 0.1838 | 24 |
6 | 64 | 0.1300 | 48 |
7 | 128 | 0.0919 | 96 |
8 | 256 | 0.0650 | 192 |
9 | 512 | 0.0460 | 384 |
10 | 1024 | 0.0325 | 768 |
11 | 2048 | 0.0230 | 1536 |
12 | 4096 | 0.0163 | 3072 |
13 | 8192 | 0.0115 | 6144 |
14 | 16384 | 0.0081 | 12288 |
15 | 32768 | 0.0057 | 24576 |
16 | 65536 | 0.0041 | 49152 |
17 | 131072 | 0.0029 | 98304 |
18 | 262144 | 0.0020 | 196608 |