# dcount() (aggregation function)

Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer ✅ Azure Monitor ✅ Microsoft Sentinel

Calculates an estimate of the number of distinct values that are taken by a scalar expression in the summary group.

Null values are ignored and don't factor into the calculation.

Note

The `dcount()`

aggregation function is primarily useful for estimating the cardinality of huge sets. It trades accuracy for performance, and may return a result that varies between executions. The order of inputs may have an effect on its output.

Note

This function is used in conjunction with the summarize operator.

## Syntax

`dcount`

`(`

*expr*[`,`

*accuracy*]`)`

Learn more about syntax conventions.

## Parameters

Name | Type | Required | Description |
---|---|---|---|

expr |
`string` |
✔️ | The input whose distinct values are to be counted. |

accuracy |
`int` |
The value that defines the requested estimation accuracy. The default value is `1` . See Estimation accuracy for supported values. |

## Returns

Returns an estimate of the number of distinct values of *expr* in the group.

## Example

This example shows how many types of storm events happened in each state.

```
StormEvents
| summarize DifferentEvents=dcount(EventType) by State
| order by DifferentEvents
```

The results table shown includes only the first 10 rows.

State | DifferentEvents |
---|---|

TEXAS | 27 |

CALIFORNIA | 26 |

PENNSYLVANIA | 25 |

GEORGIA | 24 |

ILLINOIS | 23 |

MARYLAND | 23 |

NORTH CAROLINA | 23 |

MICHIGAN | 22 |

FLORIDA | 22 |

OREGON | 21 |

KANSAS | 21 |

... | ... |

## Estimation accuracy

This function uses a variant of the HyperLogLog (HLL) algorithm, which does a stochastic estimation of set cardinality. The algorithm provides a "knob" that can be used to balance accuracy and execution time per memory size:

Accuracy | Error (%) | Entry count |
---|---|---|

0 | 1.6 | 2^{12} |

1 | 0.8 | 2^{14} |

2 | 0.4 | 2^{16} |

3 | 0.28 | 2^{17} |

4 | 0.2 | 2^{18} |

Note

The "entry count" column is the number of 1-byte counters in the HLL implementation.

The algorithm includes some provisions for doing a perfect count (zero error), if the set cardinality is small enough:

- When the accuracy level is
`1`

, 1000 values are returned - When the accuracy level is
`2`

, 8000 values are returned

The error bound is probabilistic, not a theoretical bound. The value is the standard deviation of error distribution (the sigma), and 99.7% of the estimations will have a relative error of under 3 x sigma.

The following image shows the probability distribution function of the relative estimation error, in percentages, for all supported accuracy settings: