druid/docs/content/querying/aggregations.md

---
layout: doc_page
title: "Aggregations"
---

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# Aggregations

Aggregations can be provided at ingestion time as part of the ingestion spec as a way of summarizing data before it enters Druid. 
Aggregations can also be specified as part of many queries at query time.

Available aggregations are:

### Count aggregator

`count` computes the count of Druid rows that match the filters.

```json
{ "type" : "count", "name" : <output_name> }
```

Please note the count aggregator counts the number of Druid rows, which does not always reflect the number of raw events ingested. 
This is because Druid can be configured to roll up data at ingestion time. To 
count the number of ingested rows of data, include a count aggregator at ingestion time, and a longSum aggregator at 
query time.

### Sum aggregators

#### `longSum` aggregator

computes the sum of values as a 64-bit, signed integer

```json
{ "type" : "longSum", "name" : <output_name>, "fieldName" : <metric_name> }
```

`name` – output name for the summed value
`fieldName` – name of the metric column to sum over

#### `doubleSum` aggregator

Computes and stores the sum of values as 64-bit floating point value. Similar to `longSum`

```json
{ "type" : "doubleSum", "name" : <output_name>, "fieldName" : <metric_name> }
```

#### `floatSum` aggregator

Computes and stores the sum of values as 32-bit floating point value. Similar to `longSum` and `doubleSum`

```json
{ "type" : "floatSum", "name" : <output_name>, "fieldName" : <metric_name> }
```

### Min / Max aggregators

#### `doubleMin` aggregator

`doubleMin` computes the minimum of all metric values and Double.POSITIVE_INFINITY

```json
{ "type" : "doubleMin", "name" : <output_name>, "fieldName" : <metric_name> }
```

#### `doubleMax` aggregator

`doubleMax` computes the maximum of all metric values and Double.NEGATIVE_INFINITY

```json
{ "type" : "doubleMax", "name" : <output_name>, "fieldName" : <metric_name> }
```

#### `floatMin` aggregator

`floatMin` computes the minimum of all metric values and Float.POSITIVE_INFINITY

```json
{ "type" : "floatMin", "name" : <output_name>, "fieldName" : <metric_name> }
```

#### `floatMax` aggregator

`floatMax` computes the maximum of all metric values and Float.NEGATIVE_INFINITY

```json
{ "type" : "floatMax", "name" : <output_name>, "fieldName" : <metric_name> }
```

#### `longMin` aggregator

`longMin` computes the minimum of all metric values and Long.MAX_VALUE

```json
{ "type" : "longMin", "name" : <output_name>, "fieldName" : <metric_name> }
```

#### `longMax` aggregator

`longMax` computes the maximum of all metric values and Long.MIN_VALUE

```json
{ "type" : "longMax", "name" : <output_name>, "fieldName" : <metric_name> }
```

### First / Last aggregator

(Double/Float/Long) First and Last aggregator cannot be used in ingestion spec, and should only be specified as part of queries.

Note that queries with first/last aggregators on a segment created with rollup enabled will return the rolled up value, and not the last value within the raw ingested data.

#### `doubleFirst` aggregator

`doubleFirst` computes the metric value with the minimum timestamp or 0 if no row exist

```json
{
  "type" : "doubleFirst",
  "name" : <output_name>,
  "fieldName" : <metric_name>
}
```

#### `doubleLast` aggregator

`doubleLast` computes the metric value with the maximum timestamp or 0 if no row exist

```json
{
  "type" : "doubleLast",
  "name" : <output_name>,
  "fieldName" : <metric_name>
}
```

#### `floatFirst` aggregator

`floatFirst` computes the metric value with the minimum timestamp or 0 if no row exist

```json
{
  "type" : "floatFirst",
  "name" : <output_name>,
  "fieldName" : <metric_name>
}
```

#### `floatLast` aggregator

`floatLast` computes the metric value with the maximum timestamp or 0 if no row exist

```json
{
  "type" : "floatLast",
  "name" : <output_name>,
  "fieldName" : <metric_name>
}
```

#### `longFirst` aggregator

`longFirst` computes the metric value with the minimum timestamp or 0 if no row exist

```json
{
  "type" : "longFirst",
  "name" : <output_name>,
  "fieldName" : <metric_name>
}
```

#### `longLast` aggregator

`longLast` computes the metric value with the maximum timestamp or 0 if no row exist

```json
{ 
  "type" : "longLast",
  "name" : <output_name>, 
  "fieldName" : <metric_name>,
}
```

#### `stringFirst` aggregator

`stringFirst` computes the metric value with the minimum timestamp or `null` if no row exist

```json
{
  "type" : "stringFirst",
  "name" : <output_name>,
  "fieldName" : <metric_name>,
  "maxStringBytes" : <integer> # (optional, defaults to 1024),
  "filterNullValues" : <boolean> # (optional, defaults to false)
}
```



#### `stringLast` aggregator

`stringLast` computes the metric value with the maximum timestamp or `null` if no row exist

```json
{
  "type" : "stringLast",
  "name" : <output_name>,
  "fieldName" : <metric_name>,
  "maxStringBytes" : <integer> # (optional, defaults to 1024),
  "filterNullValues" : <boolean> # (optional, defaults to false)
}
```

### JavaScript aggregator

Computes an arbitrary JavaScript function over a set of columns (both metrics and dimensions are allowed). Your
JavaScript functions are expected to return floating-point values.

```json
{ "type": "javascript",
  "name": "<output_name>",
  "fieldNames"  : [ <column1>, <column2>, ... ],
  "fnAggregate" : "function(current, column1, column2, ...) {
                     <updates partial aggregate (current) based on the current row values>
                     return <updated partial aggregate>
                   }",
  "fnCombine"   : "function(partialA, partialB) { return <combined partial results>; }",
  "fnReset"     : "function()                   { return <initial value>; }"
}
```

**Example**

```json
{
  "type": "javascript",
  "name": "sum(log(x)*y) + 10",
  "fieldNames": ["x", "y"],
  "fnAggregate" : "function(current, a, b)      { return current + (Math.log(a) * b); }",
  "fnCombine"   : "function(partialA, partialB) { return partialA + partialB; }",
  "fnReset"     : "function()                   { return 10; }"
}
```

<div class="note info">
JavaScript-based functionality is disabled by default. Please refer to the Druid <a href="../development/javascript.html">JavaScript programming guide</a> for guidelines about using Druid's JavaScript functionality, including instructions on how to enable it.
</div>

<a name="approx" />
## Approximate Aggregations

### Count distinct

#### DataSketches Theta Sketch

The [DataSketches Theta Sketch](../development/extensions-core/datasketches-theta.html) extension-provided aggregator gives distinct count estimates with support for set union, intersection, and difference post-aggregators, using Theta sketches from the [datasketches](http://datasketches.github.io/) library.

#### DataSketches HLL Sketch

The [DataSketches HLL Sketch](../development/extensions-core/datasketches-hll.html) extension-provided aggregator gives distinct count estimates using the HyperLogLog algorithm. The HLL Sketch is faster and requires less storage than the Theta Sketch, but does not support intersection or difference operations.

#### Cardinality/HyperUnique

The [Cardinality and HyperUnique](../hll-old.html) aggregators are older aggregator implementations available by default in Druid that also provide distinct count estimates using the HyperLogLog algorithm. The newer [DataSketches HLL Sketch](../development/extensions-core/datasketches-hll.html) extension-provided aggregator has superior accuracy and performance and is recommended instead. 

Please note that DataSketches HLL aggregators and `hyperUnique` aggregators are not mutually compatible.

### Histograms and quantiles

#### DataSketches Quantiles Sketch

The [DataSketches Quantiles Sketch](../development/extensions-core/datasketches-quantiles.html) extension-provided aggregator provides quantile estimates and histogram approximations using the numeric quantiles DoublesSketch from the [datasketches](http://datasketches.github.io/) library.

#### Approximate Histogram

The [Approximate Histogram](../development/extensions-core/approxiate-histograms.html) extension-provided aggregator also provides quantile estimates and histogram approximations, based on [http://jmlr.org/papers/volume11/ben-haim10a/ben-haim10a.pdf](http://jmlr.org/papers/volume11/ben-haim10a/ben-haim10a.pdf).

## Miscellaneous Aggregations

### Filtered Aggregator

A filtered aggregator wraps any given aggregator, but only aggregates the values for which the given dimension filter matches.

This makes it possible to compute the results of a filtered and an unfiltered aggregation simultaneously, without having to issue multiple queries, and use both results as part of post-aggregations.

*Note:* If only the filtered results are required, consider putting the filter on the query itself, which will be much faster since it does not require scanning all the data.

```json
{
  "type" : "filtered",
  "filter" : {
    "type" : "selector",
    "dimension" : <dimension>,
    "value" : <dimension value>
  }
  "aggregator" : <aggregation>
}
```