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dcount_hll()

Applies to: ✅ Microsoft FabricAzure Data ExplorerAzure MonitorMicrosoft Sentinel

Calculates the distinct count from results generated by hll or hll_merge.

Read about the underlying algorithm (HyperLogLog) and estimation accuracy.

Syntax

dcount_hll(hll)

Learn more about syntax conventions.

Parameters

Name Type Required Description
hll string ✔️ An expression generated by hll or hll-merge to be used to find the distinct count.

Returns

Returns the distinct count of each value in hll.

Example

The following example shows the distinct count hll merged results.

StormEvents
| summarize hllRes = hll(DamageProperty) by bin(StartTime,10m)
| summarize hllMerged = hll_merge(hllRes)
| project dcount_hll(hllMerged)

Output

dcount_hll_hllMerged
315

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:

Graph showing the hll error distribution.