mirror of https://github.com/apache/druid.git
98 lines
4.2 KiB
Markdown
98 lines
4.2 KiB
Markdown
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---
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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
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grouping asked for by the query. Note: If you only want to do straight aggregates for some time range, we highly recommend
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using [TimeseriesQueries](../querying/timeseriesquery.html) instead. The performance will be substantially better. If you want to
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do an ordered groupBy over a single dimension, please look at [TopN](../querying/topnquery.html) queries. The performance for that use case is also 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": ["country", "device"],
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"limitSpec": { "type": "default", "limit": 5000, "columns": ["country", "data_transfer"] },
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"filter": {
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"type": "and",
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"fields": [
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{ "type": "selector", "dimension": "carrier", "value": "AT&T" },
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{ "type": "or",
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"fields": [
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{ "type": "selector", "dimension": "make", "value": "Apple" },
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{ "type": "selector", "dimension": "make", "value": "Samsung" }
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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": "total_usage", "fieldName": "user_count" },
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{ "type": "doubleSum", "name": "data_transfer", "fieldName": "data_transfer" }
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],
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"postAggregations": [
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{ "type": "arithmetic",
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"name": "avg_usage",
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"fn": "/",
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"fields": [
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{ "type": "fieldAccess", "fieldName": "data_transfer" },
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{ "type": "fieldAccess", "fieldName": "total_usage" }
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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": {
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"type": "greaterThan",
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"aggregation": "total_usage",
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"value": 100
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}
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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 or Object defining the data source to query, very similar to a table in a relational database. See [DataSource](../querying/datasource.html) for more information.|yes|
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|dimensions|A JSON list of dimensions to do the groupBy over; or see [DimensionSpec](../querying/dimensionspecs.html) for ways to extract dimensions. |yes|
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|limitSpec|See [LimitSpec](../querying/limitspec.html).|no|
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|having|See [Having](../querying/having.html).|no|
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|granularity|Defines the granularity of the query. See [Granularities](../querying/granularities.html)|yes|
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|filter|See [Filters](../querying/filters.html)|no|
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|aggregations|See [Aggregations](../querying/aggregations.html)|yes|
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|postAggregations|See [Post Aggregations](../querying/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 `country` dimension, m is the cardinality of the `device` dimension, each day between 2012-01-01 and 2012-01-03, from the `sample_datasource` table. Each data point contains the (long) sum of `total_usage` if the value of the data point is greater than 100, the (double) sum of `data_transfer` and the (double) result of `total_usage` divided by `data_transfer` for the filter set for a particular grouping of `country` and `device`. 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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"country" : <some_dim_value_one>,
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"device" : <some_dim_value_two>,
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"total_usage" : <some_value_one>,
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"data_transfer" :<some_value_two>,
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"avg_usage" : <some_avg_usage_value>
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}
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},
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{
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"version" : "v1",
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"timestamp" : "2012-01-01T00:00:12.000Z",
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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_value_one>,
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"sample_name2" :<some_other_value_two>,
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"avg_usage" : <some_other_avg_usage_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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