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Timeseries queries

These types of queries take a timeseries query object and return an array of JSON objects where each object represents a value asked for by the timeseries query.

An example timeseries query object is shown below:

{
  "queryType": "timeseries",
  "dataSource": "sample_datasource",
  "granularity": "day",
  "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" ]
}

There are 7 main parts to a timeseries query:

property	description	required?
queryType	This String should always be “timeseries”; 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
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 2 data points, one for each day between 2012-01-01 and 2012-01-03, from the “sample_datasource” table. Each data point would be the (long) sum of sample_fieldName1, the (double) sum of sample_fieldName2 and the (double) the result of sample_fieldName1 divided by sample_fieldName2 for the filter set. The output looks like this:

[
  {
    "timestamp": "2012-01-01T00:00:00.000Z",
    "result": { "sample_name1": <some_value>, "sample_name2": <some_value>, "sample_divide": <some_value> } 
  },
  {
    "timestamp": "2012-01-02T00:00:00.000Z",
    "result": { "sample_name1": <some_value>, "sample_name2": <some_value>, "sample_divide": <some_value> }
  }
]