Query granularities

Apache Druid supports two query languages: Druid SQL and native queries. This document describes the native language. For information about time functions available in SQL, refer to the SQL documentation.

Granularity determines how to bucket data across the time dimension, or how to aggregate data by hour, day, minute, etc.

For example, use time granularities in native queries to bucket results by time, and in the dataSchema \ granularitySpec section of ingestion specifications to segment incoming data.

You can specify a time period as a simple string, as a duration in milliseconds, or as an arbitrary ISO8601 period.

Simple Granularities

Simple granularities are specified as a string and bucket timestamps by their UTC time (e.g., days start at 00:00 UTC).

Supported granularity strings are: all, none, second, minute, fifteen_minute, thirty_minute, hour, day, week, month, quarter and year.

  • all buckets everything into a single bucket
  • none does not bucket data (it actually uses the granularity of the index - minimum here is none which means millisecond granularity). Using none in a TimeseriesQuery is currently not recommended (the system will try to generate 0 values for all milliseconds that didn’t exist, which is often a lot).

Example:

Suppose you have data below stored in Apache Druid with millisecond ingestion granularity,

  1. {"timestamp": "2013-08-31T01:02:33Z", "page": "AAA", "language" : "en"}
  2. {"timestamp": "2013-09-01T01:02:33Z", "page": "BBB", "language" : "en"}
  3. {"timestamp": "2013-09-02T23:32:45Z", "page": "CCC", "language" : "en"}
  4. {"timestamp": "2013-09-03T03:32:45Z", "page": "DDD", "language" : "en"}

After submitting a groupBy query with hour granularity,

  1. {
  2.    "queryType":"groupBy",
  3.    "dataSource":"my_dataSource",
  4.    "granularity":"hour",
  5.    "dimensions":[
  6.       "language"
  7.    ],
  8.    "aggregations":[
  9.       {
  10.          "type":"count",
  11.          "name":"count"
  12.       }
  13.    ],
  14.    "intervals":[
  15.       "2000-01-01T00:00Z/3000-01-01T00:00Z"
  16.    ]
  17. }

you will get

  1. [ {
  2.   "version" : "v1",
  3.   "timestamp" : "2013-08-31T01:00:00.000Z",
  4.   "event" : {
  5.     "count" : 1,
  6.     "language" : "en"
  7.   }
  8. }, {
  9.   "version" : "v1",
  10.   "timestamp" : "2013-09-01T01:00:00.000Z",
  11.   "event" : {
  12.     "count" : 1,
  13.     "language" : "en"
  14.   }
  15. }, {
  16.   "version" : "v1",
  17.   "timestamp" : "2013-09-02T23:00:00.000Z",
  18.   "event" : {
  19.     "count" : 1,
  20.     "language" : "en"
  21.   }
  22. }, {
  23.   "version" : "v1",
  24.   "timestamp" : "2013-09-03T03:00:00.000Z",
  25.   "event" : {
  26.     "count" : 1,
  27.     "language" : "en"
  28.   }
  29. } ]

Note that all the empty buckets are discarded.

If you change the granularity to day, you will get

  1. [ {
  2.   "version" : "v1",
  3.   "timestamp" : "2013-08-31T00:00:00.000Z",
  4.   "event" : {
  5.     "count" : 1,
  6.     "language" : "en"
  7.   }
  8. }, {
  9.   "version" : "v1",
  10.   "timestamp" : "2013-09-01T00:00:00.000Z",
  11.   "event" : {
  12.     "count" : 1,
  13.     "language" : "en"
  14.   }
  15. }, {
  16.   "version" : "v1",
  17.   "timestamp" : "2013-09-02T00:00:00.000Z",
  18.   "event" : {
  19.     "count" : 1,
  20.     "language" : "en"
  21.   }
  22. }, {
  23.   "version" : "v1",
  24.   "timestamp" : "2013-09-03T00:00:00.000Z",
  25.   "event" : {
  26.     "count" : 1,
  27.     "language" : "en"
  28.   }
  29. } ]

If you change the granularity to none, you will get the same results as setting it to the ingestion granularity.

  1. [ {
  2.   "version" : "v1",
  3.   "timestamp" : "2013-08-31T01:02:33.000Z",
  4.   "event" : {
  5.     "count" : 1,
  6.     "language" : "en"
  7.   }
  8. }, {
  9.   "version" : "v1",
  10.   "timestamp" : "2013-09-01T01:02:33.000Z",
  11.   "event" : {
  12.     "count" : 1,
  13.     "language" : "en"
  14.   }
  15. }, {
  16.   "version" : "v1",
  17.   "timestamp" : "2013-09-02T23:32:45.000Z",
  18.   "event" : {
  19.     "count" : 1,
  20.     "language" : "en"
  21.   }
  22. }, {
  23.   "version" : "v1",
  24.   "timestamp" : "2013-09-03T03:32:45.000Z",
  25.   "event" : {
  26.     "count" : 1,
  27.     "language" : "en"
  28.   }
  29. } ]

Having a query time granularity that is smaller than the queryGranularity parameter set at ingestion time is unreasonable because information about that smaller granularity is not present in the indexed data. So, if the query time granularity is smaller than the ingestion time query granularity, Druid produces results that are equivalent to having set granularity to queryGranularity.

If you change the granularity to all, you will get everything aggregated in 1 bucket,

  1. [ {
  2.   "version" : "v1",
  3.   "timestamp" : "2000-01-01T00:00:00.000Z",
  4.   "event" : {
  5.     "count" : 4,
  6.     "language" : "en"
  7.   }
  8. } ]

Duration Granularities

Duration granularities are specified as an exact duration in milliseconds and timestamps are returned as UTC. Duration granularity values are in millis.

They also support specifying an optional origin, which defines where to start counting time buckets from (defaults to 1970-01-01T00:00:00Z).

  1. {"type": "duration", "duration": 7200000}

This chunks up every 2 hours.

  1. {"type": "duration", "duration": 3600000, "origin": "2012-01-01T00:30:00Z"}

This chunks up every hour on the half-hour.

Example:

Reusing the data in the previous example, after submitting a groupBy query with 24 hours duration,

  1. {
  2.    "queryType":"groupBy",
  3.    "dataSource":"my_dataSource",
  4.    "granularity":{"type": "duration", "duration": "86400000"},
  5.    "dimensions":[
  6.       "language"
  7.    ],
  8.    "aggregations":[
  9.       {
  10.          "type":"count",
  11.          "name":"count"
  12.       }
  13.    ],
  14.    "intervals":[
  15.       "2000-01-01T00:00Z/3000-01-01T00:00Z"
  16.    ]
  17. }

you will get

  1. [ {
  2.   "version" : "v1",
  3.   "timestamp" : "2013-08-31T00:00:00.000Z",
  4.   "event" : {
  5.     "count" : 1,
  6.     "language" : "en"
  7.   }
  8. }, {
  9.   "version" : "v1",
  10.   "timestamp" : "2013-09-01T00:00:00.000Z",
  11.   "event" : {
  12.     "count" : 1,
  13.     "language" : "en"
  14.   }
  15. }, {
  16.   "version" : "v1",
  17.   "timestamp" : "2013-09-02T00:00:00.000Z",
  18.   "event" : {
  19.     "count" : 1,
  20.     "language" : "en"
  21.   }
  22. }, {
  23.   "version" : "v1",
  24.   "timestamp" : "2013-09-03T00:00:00.000Z",
  25.   "event" : {
  26.     "count" : 1,
  27.     "language" : "en"
  28.   }
  29. } ]

if you set the origin for the granularity to 2012-01-01T00:30:00Z,

  1.    "granularity":{"type": "duration", "duration": "86400000", "origin":"2012-01-01T00:30:00Z"}

you will get

  1. [ {
  2.   "version" : "v1",
  3.   "timestamp" : "2013-08-31T00:30:00.000Z",
  4.   "event" : {
  5.     "count" : 1,
  6.     "language" : "en"
  7.   }
  8. }, {
  9.   "version" : "v1",
  10.   "timestamp" : "2013-09-01T00:30:00.000Z",
  11.   "event" : {
  12.     "count" : 1,
  13.     "language" : "en"
  14.   }
  15. }, {
  16.   "version" : "v1",
  17.   "timestamp" : "2013-09-02T00:30:00.000Z",
  18.   "event" : {
  19.     "count" : 1,
  20.     "language" : "en"
  21.   }
  22. }, {
  23.   "version" : "v1",
  24.   "timestamp" : "2013-09-03T00:30:00.000Z",
  25.   "event" : {
  26.     "count" : 1,
  27.     "language" : "en"
  28.   }
  29. } ]

Note that the timestamp for each bucket starts at the 30th minute.

Period Granularities

Period granularities are specified as arbitrary period combinations of years, months, weeks, hours, minutes and seconds (e.g. P2W, P3M, PT1H30M, PT0.750S) in ISO8601 format. They support specifying a time zone which determines where period boundaries start as well as the timezone of the returned timestamps. By default, years start on the first of January, months start on the first of the month and weeks start on Mondays unless an origin is specified.

Time zone is optional (defaults to UTC). Origin is optional (defaults to 1970-01-01T00:00:00 in the given time zone).

  1. {"type": "period", "period": "P2D", "timeZone": "America/Los_Angeles"}

This will bucket by two-day chunks in the Pacific timezone.

  1. {"type": "period", "period": "P3M", "timeZone": "America/Los_Angeles", "origin": "2012-02-01T00:00:00-08:00"}

This will bucket by 3-month chunks in the Pacific timezone where the three-month quarters are defined as starting from February.

Example

Reusing the data in the previous example, if you submit a groupBy query with 1 day period in Pacific timezone,

  1. {
  2.    "queryType":"groupBy",
  3.    "dataSource":"my_dataSource",
  4.    "granularity":{"type": "period", "period": "P1D", "timeZone": "America/Los_Angeles"},
  5.    "dimensions":[
  6.       "language"
  7.    ],
  8.    "aggregations":[
  9.       {
  10.          "type":"count",
  11.          "name":"count"
  12.       }
  13.    ],
  14.    "intervals":[
  15.       "1999-12-31T16:00:00.000-08:00/2999-12-31T16:00:00.000-08:00"
  16.    ]
  17. }

you will get

  1. [ {
  2.   "version" : "v1",
  3.   "timestamp" : "2013-08-30T00:00:00.000-07:00",
  4.   "event" : {
  5.     "count" : 1,
  6.     "language" : "en"
  7.   }
  8. }, {
  9.   "version" : "v1",
  10.   "timestamp" : "2013-08-31T00:00:00.000-07:00",
  11.   "event" : {
  12.     "count" : 1,
  13.     "language" : "en"
  14.   }
  15. }, {
  16.   "version" : "v1",
  17.   "timestamp" : "2013-09-02T00:00:00.000-07:00",
  18.   "event" : {
  19.     "count" : 2,
  20.     "language" : "en"
  21.   }
  22. } ]

Note that the timestamp for each bucket has been converted to Pacific time. Row {"timestamp": "2013-09-02T23:32:45Z", "page": "CCC", "language" : "en"} and {"timestamp": "2013-09-03T03:32:45Z", "page": "DDD", "language" : "en"} are put in the same bucket because they are in the same day in Pacific time.

Also note that the intervals in groupBy query will not be converted to the timezone specified, the timezone specified in granularity is only applied on the query results.

If you set the origin for the granularity to 1970-01-01T20:30:00-08:00,

  1.    "granularity":{"type": "period", "period": "P1D", "timeZone": "America/Los_Angeles", "origin": "1970-01-01T20:30:00-08:00"}

you will get

  1. [ {
  2.   "version" : "v1",
  3.   "timestamp" : "2013-08-29T20:30:00.000-07:00",
  4.   "event" : {
  5.     "count" : 1,
  6.     "language" : "en"
  7.   }
  8. }, {
  9.   "version" : "v1",
  10.   "timestamp" : "2013-08-30T20:30:00.000-07:00",
  11.   "event" : {
  12.     "count" : 1,
  13.     "language" : "en"
  14.   }
  15. }, {
  16.   "version" : "v1",
  17.   "timestamp" : "2013-09-01T20:30:00.000-07:00",
  18.   "event" : {
  19.     "count" : 1,
  20.     "language" : "en"
  21.   }
  22. }, {
  23.   "version" : "v1",
  24.   "timestamp" : "2013-09-02T20:30:00.000-07:00",
  25.   "event" : {
  26.     "count" : 1,
  27.     "language" : "en"
  28.   }
  29. } ]

Note that the origin you specified has nothing to do with the timezone, it only serves as a starting point for locating the very first granularity bucket. In this case, Row {"timestamp": "2013-09-02T23:32:45Z", "page": "CCC", "language" : "en"} and {"timestamp": "2013-09-03T03:32:45Z", "page": "DDD", "language" : "en"} are not in the same bucket.

Supported Time Zones

Timezone support is provided by the Joda Time library, which uses the standard IANA time zones. See the Joda Time supported timezones.