> For the complete documentation index, see [llms.txt](https://docs.pinot.apache.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pinot.apache.org/release-0.4.0/users/user-guide-query/pinot-query-language/how-to-handle-unique-counting.md).

# Unique Counting

Unique counting is a classic problem. Pinot solves it with multiple ways to trade-off between accuracy and latency.

## Accurate Results

Functions:

* ***DistinctCount**(x) -> LONG*

Returns accurate count for all unique values in a column.

The underlying implementation is using a IntOpenHashSet in library: `it.unimi.dsi:fastutil:8.2.3` to hold all the unique values.

## Approximation Results

Usually it takes a lot of resources and time to compute accurate results for unique counting. In some circumstance, users could tolerate with certain error rate, then we could use approximation functions to tackle this problem.

### HyperLogLog

[HyperLogLog](https://en.wikipedia.org/wiki/HyperLogLog) is one approximation algorithm for unique counting. It uses fixed number of bits to estimate the cardinality of given data set.

Pinot leverages [HyperLogLog Class](https://github.com/addthis/stream-lib/blob/master/src/main/java/com/clearspring/analytics/stream/cardinality/HyperLogLog.java) in library `com.clearspring.analytics:stream:2.7.0`as the data structure to hold intermediate results.

Functions:

* ***DistinctCountHLL**(x) -> LONG*

For column type **INT**/**LONG**/**FLOAT**/**DOUBLE**/**STRING** , Pinot treats each value as an individual entry to add into HyperLogLog Object, then compute the approximation by calling method ***cardinality()***.

For column type **BYTES**, Pinot treats each value as a serialized HyperLogLog Object with pre-aggregated values inside. The bytes value is generated by `org.apache.pinot.core.common.ObjectSerDeUtils.HYPER_LOG_LOG_SER_DE.serialize(hyperLogLog)`.

All deserialized HyperLogLog object will be merged into one then calling method ***cardinality()** to get the approximated unique count.*


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.pinot.apache.org/release-0.4.0/users/user-guide-query/pinot-query-language/how-to-handle-unique-counting.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.