mirror of https://github.com/apache/druid.git
207 lines
11 KiB
Markdown
207 lines
11 KiB
Markdown
---
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layout: doc_page
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---
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# groupBy Queries
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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
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grouping asked for by the query.
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<div class="note info">
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Note: If you are doing aggregations with time as your only grouping, or an ordered groupBy over a single dimension,
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consider <a href="timeseriesquery.html">Timeseries</a> and <a href="topnquery.html">TopN</a> queries as well as
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groupBy. Their performance may be better in some cases. See <a href="#alternatives">Alternatives</a> below for more details.
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</div>
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An example groupBy query object is shown below:
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``` json
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{
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"queryType": "groupBy",
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"dataSource": "sample_datasource",
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"granularity": "day",
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"dimensions": ["country", "device"],
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"limitSpec": { "type": "default", "limit": 5000, "columns": ["country", "data_transfer"] },
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"filter": {
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"type": "and",
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"fields": [
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{ "type": "selector", "dimension": "carrier", "value": "AT&T" },
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{ "type": "or",
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"fields": [
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{ "type": "selector", "dimension": "make", "value": "Apple" },
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{ "type": "selector", "dimension": "make", "value": "Samsung" }
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]
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}
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]
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},
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"aggregations": [
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{ "type": "longSum", "name": "total_usage", "fieldName": "user_count" },
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{ "type": "doubleSum", "name": "data_transfer", "fieldName": "data_transfer" }
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],
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"postAggregations": [
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{ "type": "arithmetic",
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"name": "avg_usage",
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"fn": "/",
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"fields": [
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{ "type": "fieldAccess", "fieldName": "data_transfer" },
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{ "type": "fieldAccess", "fieldName": "total_usage" }
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]
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}
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],
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"intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ],
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"having": {
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"type": "greaterThan",
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"aggregation": "total_usage",
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"value": 100
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}
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}
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```
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There are 11 main parts to a groupBy query:
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|property|description|required?|
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|--------|-----------|---------|
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|queryType|This String should always be "groupBy"; this is the first thing Druid looks at to figure out how to interpret the query|yes|
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|dataSource|A String or Object defining the data source to query, very similar to a table in a relational database. See [DataSource](../querying/datasource.html) for more information.|yes|
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|dimensions|A JSON list of dimensions to do the groupBy over; or see [DimensionSpec](../querying/dimensionspecs.html) for ways to extract dimensions. |yes|
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|limitSpec|See [LimitSpec](../querying/limitspec.html).|no|
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|having|See [Having](../querying/having.html).|no|
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|granularity|Defines the granularity of the query. See [Granularities](../querying/granularities.html)|yes|
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|filter|See [Filters](../querying/filters.html)|no|
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|aggregations|See [Aggregations](../querying/aggregations.html)|no|
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|postAggregations|See [Post Aggregations](../querying/post-aggregations.html)|no|
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|intervals|A JSON Object representing ISO-8601 Intervals. This defines the time ranges to run the query over.|yes|
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|context|An additional JSON Object which can be used to specify certain flags.|no|
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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 `country` dimension, m is the cardinality of the `device` dimension, each day between 2012-01-01 and 2012-01-03, from the `sample_datasource` table. Each data point contains the (long) sum of `total_usage` if the value of the data point is greater than 100, the (double) sum of `data_transfer` and the (double) result of `total_usage` divided by `data_transfer` for the filter set for a particular grouping of `country` and `device`. The output looks like this:
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```json
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[
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{
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"version" : "v1",
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"timestamp" : "2012-01-01T00:00:00.000Z",
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"event" : {
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"country" : <some_dim_value_one>,
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"device" : <some_dim_value_two>,
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"total_usage" : <some_value_one>,
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"data_transfer" :<some_value_two>,
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"avg_usage" : <some_avg_usage_value>
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}
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},
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{
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"version" : "v1",
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"timestamp" : "2012-01-01T00:00:12.000Z",
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"event" : {
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"dim1" : <some_other_dim_value_one>,
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"dim2" : <some_other_dim_value_two>,
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"sample_name1" : <some_other_value_one>,
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"sample_name2" :<some_other_value_two>,
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"avg_usage" : <some_other_avg_usage_value>
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}
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},
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...
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]
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```
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### Behavior on multi-value dimensions
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groupBy queries can group on multi-value dimensions. When grouping on a multi-value dimension, _all_ values
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from matching rows will be used to generate one group per value. It's possible for a query to return more groups than
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there are rows. For example, a groupBy on the dimension `tags` with filter `"t1" AND "t3"` would match only row1, and
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generate a result with three groups: `t1`, `t2`, and `t3`. If you only need to include values that match
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your filter, you can use a [filtered dimensionSpec](dimensionspecs.html#filtered-dimensionspecs). This can also
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improve performance.
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See [Multi-value dimensions](multi-value-dimensions.html) for more details.
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### Implementation details
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#### Strategies
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GroupBy queries can be executed using two different strategies. The default strategy for a cluster is determined by the
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"druid.query.groupBy.defaultStrategy" runtime property on the broker. This can be overridden using "groupByStrategy" in
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the query context. If neither the context field nor the property is set, the "v2" strategy will be used.
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- "v1", the default, generates per-segment results on data nodes (historical, realtime, middleManager) using a map which
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is partially on-heap (dimension keys and the map itself) and partially off-heap (the aggregated values). Data nodes then
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merge the per-segment results using Druid's indexing mechanism. This merging is multi-threaded by default, but can
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optionally be single-threaded. The broker merges the final result set using Druid's indexing mechanism again. The broker
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merging is always single-threaded. Because the broker merges results using the indexing mechanism, it must materialize
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the full result set before returning any results. On both the data nodes and the broker, the merging index is fully
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on-heap by default, but it can optionally store aggregated values off-heap.
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- "v2" (experimental) is designed to offer better performance and memory management. This strategy generates
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per-segment results using a fully off-heap map. Data nodes merge the per-segment results using a fully off-heap
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concurrent facts map combined with an on-heap string dictionary. This may optionally involve spilling to disk. Data
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nodes return sorted results to the broker, which merges result streams using an N-way merge. The broker materializes
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the results if necessary (e.g. if the query sorts on columns other than its dimensions). Otherwise, it streams results
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back as they are merged.
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#### Alternatives
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There are some situations where other query types may be a better choice than groupBy.
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- For queries with no "dimensions" (i.e. grouping by time only) the [Timeseries query](timeseriesquery.html) will
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generally be faster than groupBy. The major differences are that it is implemented in a fully streaming manner (taking
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advantage of the fact that segments are already sorted on time) and does not need to use a hash table for merging.
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- For queries with a single "dimensions" element (i.e. grouping by one string dimension), the [TopN query](topnquery.html)
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will sometimes be faster than groupBy. This is especially true if you are ordering by a metric and find approximate
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results acceptable.
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#### Nested groupBys
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Nested groupBys (dataSource of type "query") are performed differently for "v1" and "v2". The broker first runs the
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inner groupBy query in the usual way. "v1" strategy then materializes the inner query's results on-heap with Druid's
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indexing mechanism, and runs the outer query on these materialized results. "v2" strategy runs the outer query on the
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inner query's results stream with off-heap fact map and on-heap string dictionary that can spill to disk. Both
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strategy perform the outer query on the broker in a single-threaded fashion.
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#### Server configuration
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When using the "v1" strategy, the following runtime properties apply:
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|Property|Description|Default|
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|--------|-----------|-------|
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|`druid.query.groupBy.defaultStrategy`|Default groupBy query strategy.|v2|
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|`druid.query.groupBy.maxIntermediateRows`|Maximum number of intermediate rows for the per-segment grouping engine. This is a tuning parameter that does not impose a hard limit; rather, it potentially shifts merging work from the per-segment engine to the overall merging index. Queries that exceed this limit will not fail.|50000|
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|`druid.query.groupBy.maxResults`|Maximum number of results. Queries that exceed this limit will fail.|500000|
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|`druid.query.groupBy.singleThreaded`|Merge results using a single thread.|false|
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When using the "v2" strategy, the following runtime properties apply:
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|Property|Description|Default|
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|--------|-----------|-------|
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|`druid.query.groupBy.defaultStrategy`|Default groupBy query strategy.|v2|
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|`druid.query.groupBy.bufferGrouperInitialBuckets`|Initial number of buckets in the off-heap hash table used for grouping results. Set to 0 to use a reasonable default.|0|
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|`druid.query.groupBy.bufferGrouperMaxLoadFactor`|Maximum load factor of the off-heap hash table used for grouping results. When the load factor exceeds this size, the table will be grown or spilled to disk. Set to 0 to use a reasonable default.|0|
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|`druid.query.groupBy.maxMergingDictionarySize`|Maximum amount of heap space (approximately) to use for the string dictionary during merging. When the dictionary exceeds this size, a spill to disk will be triggered.|100000000|
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|`druid.query.groupBy.maxOnDiskStorage`|Maximum amount of disk space to use, per-query, for spilling result sets to disk when either the merging buffer or the dictionary fills up. Queries that exceed this limit will fail. Set to zero to disable disk spilling.|0 (disabled)|
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This may require allocating more direct memory. The amount of direct memory needed by Druid is at least
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`druid.processing.buffer.sizeBytes * (druid.processing.numMergeBuffers + druid.processing.numThreads + 1)`. You can
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ensure at least this amount of direct memory is available by providing `-XX:MaxDirectMemorySize=<VALUE>` at the command
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line.
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#### Query context
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When using the "v1" strategy, the following query context parameters apply:
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|Property|Description|
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|--------|-----------|
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|`groupByStrategy`|Overrides the value of `druid.query.groupBy.defaultStrategy` for this query.|
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|`groupByIsSingleThreaded`|Overrides the value of `druid.query.groupBy.singleThreaded` for this query.|
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|`maxIntermediateRows`|Can be used to lower the value of `druid.query.groupBy.maxIntermediateRows` for this query.|
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|`maxResults`|Can be used to lower the value of `druid.query.groupBy.maxResults` for this query.|
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|`useOffheap`|Set to true to store aggregations off-heap when merging results.|
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When using the "v2" strategy, the following query context parameters apply:
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|Property|Description|
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|--------|-----------|
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|`groupByStrategy`|Overrides the value of `druid.query.groupBy.defaultStrategy` for this query.|
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|`bufferGrouperInitialBuckets`|Overrides the value of `druid.query.groupBy.bufferGrouperInitialBuckets` for this query.|
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|`bufferGrouperMaxLoadFactor`|Overrides the value of `druid.query.groupBy.bufferGrouperMaxLoadFactor` for this query.|
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|`maxMergingDictionarySize`|Can be used to lower the value of `druid.query.groupBy.maxMergingDictionarySize` for this query.|
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|`maxOnDiskStorage`|Can be used to lower the value of `druid.query.groupBy.maxOnDiskStorage` for this query.|
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|`sortByDimsFirst`|Sort the results first by dimension values and then by timestamp.|
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