mirror of https://github.com/apache/druid.git
163 lines
4.0 KiB
Markdown
163 lines
4.0 KiB
Markdown
---
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layout: doc_page
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---
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# Aggregations
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Aggregations are specifications of processing over metrics available in Druid.
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Available aggregations are:
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### Count aggregator
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`count` computes the row count that match the filters
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```json
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{ "type" : "count", "name" : <output_name> }
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```
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### Sum aggregators
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#### `longSum` aggregator
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computes the sum of values as a 64-bit, signed integer
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```json
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{ "type" : "longSum", "name" : <output_name>, "fieldName" : <metric_name> }
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```
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`name` – output name for the summed value
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`fieldName` – name of the metric column to sum over
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#### `doubleSum` aggregator
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Computes the sum of values as 64-bit floating point value. Similar to `longSum`
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```json
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{ "type" : "doubleSum", "name" : <output_name>, "fieldName" : <metric_name> }
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```
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### Min / Max aggregators
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#### `min` aggregator
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`min` computes the minimum metric value
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```json
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{ "type" : "min", "name" : <output_name>, "fieldName" : <metric_name> }
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```
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#### `max` aggregator
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`max` computes the maximum metric value
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```json
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{ "type" : "max", "name" : <output_name>, "fieldName" : <metric_name> }
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```
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### JavaScript aggregator
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Computes an arbitrary JavaScript function over a set of columns (both metrics and dimensions).
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All JavaScript functions must return numerical values.
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```json
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{ "type": "javascript",
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"name": "<output_name>",
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"fieldNames" : [ <column1>, <column2>, ... ],
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"fnAggregate" : "function(current, column1, column2, ...) {
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<updates partial aggregate (current) based on the current row values>
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return <updated partial aggregate>
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}",
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"fnCombine" : "function(partialA, partialB) { return <combined partial results>; }",
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"fnReset" : "function() { return <initial value>; }"
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}
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```
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**Example**
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```json
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{
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"type": "javascript",
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"name": "sum(log(x)/y) + 10",
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"fieldNames": ["x", "y"],
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"fnAggregate" : "function(current, a, b) { return current + (Math.log(a) * b); }",
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"fnCombine" : "function(partialA, partialB) { return partialA + partialB; }",
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"fnReset" : "function() { return 10; }"
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}
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```
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### Cardinality aggregator
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Computes the cardinality of a set of Druid dimensions, using HyperLogLog to estimate the cardinality.
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```json
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{
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"type": "cardinality",
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"name": "<output_name>",
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"fieldNames": [ <dimension1>, <dimension2>, ... ],
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"byRow": <false | true> # (optional, defaults to false)
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}
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```
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#### Cardinality by value
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When setting `byRow` to `false` (the default) it computes the cardinality of the set composed of the union of all dimension values for all the given dimensions.
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* For a single dimension, this is equivalent to
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```sql
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SELECT COUNT(DISCTINCT(dimension)) FROM <datasource>
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```
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* For multiple dimensions, this is equivalent to something akin to
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```sql
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SELECT COUNT(DISTINCT(value)) FROM (
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SELECT dim_1 as value FROM <datasource>
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UNION
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SELECT dim_2 as value FROM <datasource>
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UNION
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SELECT dim_3 as value FROM <datasource>
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)
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```
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#### Cardinality by row
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When setting `byRow` to `true` it computes the cardinality by row, i.e. the cardinality of distinct dimension combinations
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This is equivalent to something akin to
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```sql
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SELECT COUNT(*) FROM ( SELECT DIM1, DIM2, DIM3 FROM <datasource> GROUP BY DIM1, DIM2, DIM3
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```
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**Example**
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Determine the number of distinct categories items are assigned to.
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```json
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{
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"type": "cardinality",
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"name": "distinct_values",
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"fieldNames": [ "main_category", "secondary_category" ]
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}
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```
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Determine the number of distinct are assigned to.
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```json
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{
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"type": "cardinality",
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"name": "distinct_values",
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"fieldNames": [ "", "secondary_category" ],
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"byRow" : true
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}
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```
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## Complex Aggregations
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### HyperUnique aggregator
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Uses [HyperLogLog](http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf) to compute the estimated cardinality of a dimension that has been aggregated as a hyperUnique metric at indexing time.
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```json
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{ "type" : "hyperUnique", "name" : <output_name>, "fieldName" : <metric_name> }
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```
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