Table of Contents

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",
  "descending": "true",
  "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": "postAgg__sample_name1", "fieldName": "sample_name1" },
        { "type": "fieldAccess", "name": "postAgg__sample_name2", "fieldName": "sample_name2" }
      ]
    }
  ],
  "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 or Object defining the data source to query, very similar to a table in a relational database. See DataSource for more information. yes
descending Whether to make descending ordered result. Default is false(ascending). no
intervals A JSON Object representing ISO-8601 Intervals. This defines the time ranges to run the query over. yes
granularity Defines the granularity to bucket query results. See Granularities yes
filter See Filters no
aggregations See Aggregations no
postAggregations See Post Aggregations no
context See Context 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) 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> }
  }
]

Zero-filling

Timeseries queries normally fill empty interior time buckets with zeroes. For example, if you issue a "day" granularity timeseries query for the interval 2012-01-01/2012-01-04, and no data exists for 2012-01-02, you will receive:

[
  {
    "timestamp": "2012-01-01T00:00:00.000Z",
    "result": { "sample_name1": <some_value> }
  },
  {
   "timestamp": "2012-01-02T00:00:00.000Z",
   "result": { "sample_name1": 0 }
  },
  {
    "timestamp": "2012-01-03T00:00:00.000Z",
    "result": { "sample_name1": <some_value> }
  }
]

Time buckets that lie completely outside the data interval are not zero-filled.

You can disable all zero-filling with the context flag "skipEmptyBuckets". In this mode, the data point for 2012-01-02 would be omitted from the results.

A query with this context flag set would look like:

{
  "queryType": "timeseries",
  "dataSource": "sample_datasource",
  "granularity": "day",
  "aggregations": [
    { "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" }
  ],
  "intervals": [ "2012-01-01T00:00:00.000/2012-01-04T00:00:00.000" ],
  "context" : {
    "skipEmptyBuckets": "true"
  }
}