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 instead. The performance will be substantially better. An example groupBy query object is shown below:
{ "queryType": "groupBy", "dataSource": "sample_datasource", "granularity": "day", "dimensions": ["dim1", "dim2"], "limitSpec": { "type": "default", "limit": 5000, "columns": ["dim1", "metric1"] }, "filter": { "type": "and", "fields": [ { "type": "selector", "dimension": "sample_dimension1", "value": "sample_value1" }, { "type": "or", "fields": [ { "type": "selector", "dimension": "sample_dimension2", "value": "sample_value2" }, { "type": "selector", "dimension": "sample_dimension3", "value": "sample_value3" } ] } ] }, "aggregations": [ { "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" }, { "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" } ], "postAggregations": [ { "type": "arithmetic", "name": "sample_divide", "fn": "/", "fields": [ { "type": "fieldAccess", "name": "sample_name1", "fieldName": "sample_fieldName1" }, { "type": "fieldAccess", "name": "sample_name2", "fieldName": "sample_fieldName2" } ] } ], "intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ], "having": { "type": "greaterThan", "aggregation": "sample_name1", "value": 0 } }
There are 9 main parts to a groupBy query:
| property | description | required? |
|---|---|---|
| queryType | This String should always be “groupBy”; this is the first thing Druid looks at to figure out how to interpret the query | yes |
| dataSource | A String defining the data source to query, very similar to a table in a relational database, or a DataSource structure. | yes |
| dimensions | A JSON list of dimensions to do the groupBy over | yes |
| orderBy | See OrderBy. | no |
| having | See Having. | no |
| granularity | Defines the granularity of the query. See Granularities | yes |
| filter | See Filters | no |
| aggregations | See Aggregations | yes |
| postAggregations | See Post Aggregations | no |
| intervals | A JSON Object representing ISO-8601 Intervals. This defines the time ranges to run the query over. | yes |
| context | An additional JSON Object which can be used to specify certain flags. | no |
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:
[ { "version" : "v1", "timestamp" : "2012-01-01T00:00:00.000Z", "event" : { "dim1" : <some_dim_value_one>, "dim2" : <some_dim_value_two>, "sample_name1" : <some_sample_name_value_one>, "sample_name2" :<some_sample_name_value_two>, "sample_divide" : <some_sample_divide_value> } }, { "version" : "v1", "timestamp" : "2012-01-01T00:00:00.000Z", "event" : { "dim1" : <some_other_dim_value_one>, "dim2" : <some_other_dim_value_two>, "sample_name1" : <some_other_sample_name_value_one>, "sample_name2" :<some_other_sample_name_value_two>, "sample_divide" : <some_other_sample_divide_value> } }, ... ]