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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.
An example groupBy query object is shown below:
{
[queryType]() “groupBy”,
[dataSource]() “sample\_datasource”,
[granularity]() “day”,
[dimensions]() [“dim1”, “dim2”],
[limitSpec]() {
[type]() “doc_page”,
[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” : ,
“dim2” : ,
“sample\_name1” : ,
“sample\_name2” :,
“sample\_divide” :
}
}, {
“version” : “v1”,
“timestamp” : “2012-01-01T00:00:00.000Z”,
“event” : {
“dim1” : ,
“dim2” : ,
“sample\_name1” : ,
“sample\_name2” :,
“sample\_divide” :
}
},
…
]