mirror of https://github.com/apache/druid.git
91 lines
4.0 KiB
Markdown
91 lines
4.0 KiB
Markdown
---
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layout: doc_page
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---
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# groupBy Queries
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These types of queries take a groupBy query object and return an array of JSON objects where each object represents a grouping asked for by the query. Note: If you only want to do straight aggregates for some time range, we highly recommend using [TimeseriesQueries](TimeseriesQuery.html) instead. The performance will be substantially better.
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An example groupBy query object is shown below:
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``` json
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{
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"queryType": "groupBy",
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"dataSource": "sample_datasource",
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"granularity": "day",
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"dimensions": ["dim1", "dim2"],
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"limitSpec": { "type": "default", "limit": 5000, "columns": ["dim1", "metric1"] },
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"filter": {
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"type": "and",
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"fields": [
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{ "type": "selector", "dimension": "sample_dimension1", "value": "sample_value1" },
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{ "type": "or",
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"fields": [
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{ "type": "selector", "dimension": "sample_dimension2", "value": "sample_value2" },
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{ "type": "selector", "dimension": "sample_dimension3", "value": "sample_value3" }
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]
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}
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]
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},
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"aggregations": [
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{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" },
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{ "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" }
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],
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"postAggregations": [
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{ "type": "arithmetic",
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"name": "sample_divide",
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"fn": "/",
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"fields": [
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{ "type": "fieldAccess", "name": "sample_name1", "fieldName": "sample_fieldName1" },
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{ "type": "fieldAccess", "name": "sample_name2", "fieldName": "sample_fieldName2" }
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]
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}
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],
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"intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ],
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"having": { "type": "greaterThan", "aggregation": "sample_name1", "value": 0 }
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}
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```
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There are 11 main parts to a groupBy query:
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|property|description|required?|
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|--------|-----------|---------|
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|queryType|This String should always be "groupBy"; this is the first thing Druid looks at to figure out how to interpret the query|yes|
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|dataSource|A String defining the data source to query, very similar to a table in a relational database, or a [DataSource](DataSource.html) structure.|yes|
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|dimensions|A JSON list of dimensions to do the groupBy over|yes|
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|limitSpec|See [LimitSpec](LimitSpec.html).|no|
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|having|See [Having](Having.html).|no|
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|granularity|Defines the granularity of the query. See [Granularities](Granularities.html)|yes|
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|filter|See [Filters](Filters.html)|no|
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|aggregations|See [Aggregations](Aggregations.html)|yes|
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|postAggregations|See [Post Aggregations](Post-aggregations.html)|no|
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|intervals|A JSON Object representing ISO-8601 Intervals. This defines the time ranges to run the query over.|yes|
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|context|An additional JSON Object which can be used to specify certain flags.|no|
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To pull it all together, the above query would return *n\*m* data points, up to a maximum of 5000 points, where n is the cardinality of the "dim1" dimension, m is the cardinality of the "dim2" dimension, each day between 2012-01-01 and 2012-01-03, from the "sample_datasource" table. Each data point contains the (long) sum of sample_fieldName1 if the value of the data point is greater than 0, the (double) sum of sample_fieldName2 and the (double) the result of sample_fieldName1 divided by sample_fieldName2 for the filter set for a particular grouping of "dim1" and "dim2". The output looks like this:
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```json
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[
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{
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"version" : "v1",
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"timestamp" : "2012-01-01T00:00:00.000Z",
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"event" : {
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"dim1" : <some_dim_value_one>,
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"dim2" : <some_dim_value_two>,
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"sample_name1" : <some_sample_name_value_one>,
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"sample_name2" :<some_sample_name_value_two>,
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"sample_divide" : <some_sample_divide_value>
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}
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},
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{
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"version" : "v1",
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"timestamp" : "2012-01-01T00:00:00.000Z",
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"event" : {
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"dim1" : <some_other_dim_value_one>,
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"dim2" : <some_other_dim_value_two>,
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"sample_name1" : <some_other_sample_name_value_one>,
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"sample_name2" :<some_other_sample_name_value_two>,
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"sample_divide" : <some_other_sample_divide_value>
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}
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},
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...
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]
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```
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