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dcountif() (aggregation function)
Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer ✅ Azure Monitor ✅ Microsoft Sentinel
Estimates the number of distinct values of expr for rows in which predicate evaluates to true.
Null values are ignored and don't factor into the calculation.
Note
This function is used in conjunction with the summarize operator.
Syntax
dcountif (expr, predicate, [, accuracy])
Learn more about syntax conventions.
Parameters
| Name | Type | Required | Description |
|---|---|---|---|
| expr | string |
✔️ | The expression used for the aggregation calculation. |
| predicate | string |
✔️ | The expression used to filter rows. |
| accuracy | int |
The control between speed and accuracy. If unspecified, the default value is 1. See Estimation accuracy for supported values. |
Returns
Returns an estimate of the number of distinct values of expr for rows in which predicate evaluates to true.
Tip
dcountif() may return an error in cases where all, or none of the rows pass the Predicate expression.
Example
This example shows how many types of fatal storm events happened in each state.
StormEvents
| summarize DifferentFatalEvents=dcountif(EventType,(DeathsDirect + DeathsIndirect)>0) by State
| where DifferentFatalEvents > 0
| order by DifferentFatalEvents
The results table shown includes only the first 10 rows.
| State | DifferentFatalEvents |
|---|---|
| CALIFORNIA | 12 |
| TEXAS | 12 |
| OKLAHOMA | 10 |
| ILLINOIS | 9 |
| KANSAS | 9 |
| NEW YORK | 9 |
| NEW JERSEY | 7 |
| WASHINGTON | 7 |
| MICHIGAN | 7 |
| MISSOURI | 7 |
| ... | ... |
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 | 212 |
| 1 | 0.8 | 214 |
| 2 | 0.4 | 216 |
| 3 | 0.28 | 217 |
| 4 | 0.2 | 218 |
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:
