druid/docs/content/GroupByQuery.md

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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 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	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>
    }
  },
...
]