druid/docs/content/Querying.md

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Querying

Queries are made using an HTTP REST style request to a Broker, Historical, or Realtime node. The query is expressed in JSON and each of these node types expose the same REST query interface.

We start by describing an example query with additional comments that mention possible variations. Query operators are also summarized in a table below.

Example Query "rand"

Here is the query in the examples/rand subproject (file is query.body), followed by a commented version of the same.

{
  "queryType": "groupBy",
  "dataSource": "randSeq",
  "granularity": "all",
  "dimensions": [],
  "aggregations": [
    { "type": "count", "name": "rows" },
    { "type": "doubleSum", "fieldName": "events", "name": "e" },
    { "type": "doubleSum", "fieldName": "outColumn", "name": "randomNumberSum" }
  ],
  "postAggregations": [{
     "type": "arithmetic",
     "name": "avg_random",
     "fn": "/",
     "fields": [
       { "type": "fieldAccess", "fieldName": "randomNumberSum" },
       { "type": "fieldAccess", "fieldName": "rows" }
     ]
  }],
  "intervals": ["2012-10-01T00:00/2020-01-01T00"]
}

This query could be submitted via curl like so (assuming the query object is in a file "query.json").

curl -X POST "http://host:port/druid/v2/?pretty" -H 'content-type: application/json' -d @query.json

The "pretty" query parameter gets the results formatted a bit nicer.

Details of Example Query "rand"

The queryType JSON field identifies which kind of query operator is to be used, in this case it is groupBy, the most frequently used kind (which corresponds to an internal implementation class GroupByQuery registered as "groupBy"), and it has a set of required fields that are also part of this query. The queryType can also be "search" or "timeBoundary" which have similar or different required fields summarized below:

{
  "queryType": "groupBy",

The dataSource JSON field shown next identifies where to apply the query. In this case, randSeq corresponds to the examples/rand/rand_realtime.spec file schema:

  "dataSource": "randSeq",

The granularity JSON field specifies the bucket size for values. It could be a built-in time interval like "second", "minute", "fifteen_minute", "thirty_minute", "hour" or "day". It can also be an expression like {"type": "period", "period":"PT6m"} meaning "6 minute buckets". See Granularities for more information on the different options for this field. In this example, it is set to the special value "all" which means bucket all data points together into the same time bucket.

  "granularity": "all",

The dimensions JSON field value is an array of zero or more fields as defined in the dataSource spec file or defined in the input records and carried forward. These are used to constrain the grouping. If empty, then one value per time granularity bucket is requested in the groupBy:

  "dimensions": [],

A groupBy also requires the JSON field "aggregations" (See Aggregations), which are applied to the column specified by fieldName and the output of the aggregation will be named according to the value in the "name" field:

  "aggregations": [
    { "type": "count", "name": "rows" },
    { "type": "doubleSum", "fieldName": "events", "name": "e" },
    { "type": "doubleSum", "fieldName": "outColumn", "name": "randomNumberSum" }
  ],

You can also specify postAggregations, which are applied after data has been aggregated for the current granularity and dimensions bucket. See Post Aggregations for a detailed description. In the rand example, an arithmetic type operation (division, as specified by "fn") is performed with the result "name" of "avg_random". The "fields" field specifies the inputs from the aggregation stage to this expression. Note that identifiers corresponding to "name" JSON field inside the type "fieldAccess" are required but not used outside this expression, so they are prefixed with "dummy" for clarity:

  "postAggregations": [{
     "type": "arithmetic",
     "name": "avg_random",
     "fn": "/",
     "fields": [
       { "type": "fieldAccess", "fieldName": "randomNumberSum" },
       { "type": "fieldAccess", "fieldName": "rows" }
     ]
  }],

The time range(s) of the query; data outside the specified intervals will not be used; this example specifies from October 1, 2012 until January 1, 2020:

  "intervals": ["2012-10-01T00:00/2020-01-01T00"]
}

Query Operators

The following table summarizes query properties.

Properties shared by all query types

property	description	required?
dataSource	query is applied to this data source	yes
intervals	range of time series to include in query	yes
context	This is a key-value map used to alter some of the behavior of a query. See Query Context below	no

query type	property	description	required?
timeseries, topN, groupBy, search	filter	Specifies the filter (the "WHERE" clause in SQL) for the query. See Filters	no
timeseries, topN, groupBy, search	granularity	the timestamp granularity to bucket results into (i.e. "hour"). See Granularities for more information.	no
timeseries, topN, groupBy	aggregations	aggregations that combine values in a bucket. See Aggregations.	yes
timeseries, topN, groupBy	postAggregations	aggregations of aggregations. See Post Aggregations.	yes
groupBy	dimensions	constrains the groupings; if empty, then one value per time granularity bucket	yes
search	limit	maximum number of results (default is 1000), a system-level maximum can also be set via com.metamx.query.search.maxSearchLimit	no
search	searchDimensions	Dimensions to apply the search query to. If not specified, it will search through all dimensions.	no
search	query	The query portion of the search query. This is essentially a predicate that specifies if something matches.	yes

Query Context

property	default	description
timeout	0 (no timeout)	Query timeout in milliseconds, beyond which unfinished queries will be cancelled
priority	0	Query Priority. Queries with higher priority get precedence for computational resources.
queryId	auto-generated	Unique identifier given to this query. If a query ID is set or known, this can be used to cancel the query
useCache	true	Flag indicating whether to leverage the query cache for this query. This may be overriden in the broker or historical node configuration
populateCache	true	Flag indicating whether to save the results of the query to the query cache. Primarily used for debugging. This may be overriden in the broker or historical node configuration
bySegment	false	Return "by segment" results. Pimarily used for debugging, setting it to true returns results associated with the data segment they came from
finalize	true	Flag indicating whether to "finalize" aggregation results. Primarily used for debugging. For instance, the hyperUnique aggregator will return the full HyperLogLog sketch instead of the estimated cardinality when this flag is set to false
chunkPeriod	0 (off)	At broker, Long-interval queries (of any type) may be broken into shorter interval queries, reducing the impact on resources. Use ISO 8601 periods. For example, if this property is set to P1M (one month), then a query covering a year would be broken into 12 smaller queries. All the query chunks will be processed asynchronously inside query processing executor service. Make sure "druid.processing.numThreads" is configured appropriately.

Query Cancellation

Queries can be cancelled explicitely using their unique identifier. If the query identifier is set at the time of query, or is otherwise known, the following endpoint can be used on the broker or router to cancel the query.

DELETE /druid/v2/{queryId}

For example, if the query ID is abc123, the query can be cancelled as follows:

curl -X DELETE "http://host:port/druid/v2/abc123"