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documenting querying behavior on multi-valued dimensions
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Himanshu Gupta
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---
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layout: doc_page
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---
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Druid supports "multi-valued" dimensions. See the section on multi-valued columns in [segments](../design/segments.html) for internal representation details. This document describes the behavior of groupBy(topN has similar behavior) queries on multi-valued dimensions when they are used as a dimension being grouped by.
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Suppose, you have a dataSource with a segment that contains following rows with a multi-valued dimension called tags.
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```
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2772011-01-12T00:00:00.000Z,["t1","t2","t3"], #row1
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2782011-01-13T00:00:00.000Z,["t3","t4","t5"], #row2
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2792011-01-14T00:00:00.000Z,["t5","t6","t7"] #row3
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```
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### Group-By query with no filtering
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See [GroupBy querying](groupbyquery.html) for details.
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```json
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{
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"queryType": "groupBy",
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"dataSource": "test",
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"intervals": [
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"1970-01-01T00:00:00.000Z/3000-01-01T00:00:00.000Z"
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],
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"granularity": {
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"type": "all"
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},
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"dimensions": [
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{
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"type": "default",
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"dimension": "tags",
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"outputName": "tags"
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}
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],
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"aggregations": [
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{
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"type": "count",
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"name": "count"
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}
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]
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}
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```
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returns following result.
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```json
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[
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 1,
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"tags": "t1"
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}
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},
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 1,
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"tags": "t2"
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}
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},
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 2,
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"tags": "t3"
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}
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},
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 1,
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"tags": "t4"
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}
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},
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 2,
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"tags": "t5"
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}
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},
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 1,
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"tags": "t6"
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}
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},
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 1,
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"tags": "t7"
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}
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}
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]
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```
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notice how original rows are "exploded" into multiple rows and merged.
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### Group-By query with a selector query filter
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See [query filters](filters.html) for details of selector query filter.
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```json
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{
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"queryType": "groupBy",
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"dataSource": "test",
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"intervals": [
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"1970-01-01T00:00:00.000Z/3000-01-01T00:00:00.000Z"
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],
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"filter": {
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"type": "selector",
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"dimension": "tags",
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"value": "t3"
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},
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"granularity": {
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"type": "all"
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},
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"dimensions": [
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{
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"type": "default",
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"dimension": "tags",
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"outputName": "tags"
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}
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],
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"aggregations": [
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{
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"type": "count",
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"name": "count"
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}
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]
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}
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```
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returns following result.
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```json
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[
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 1,
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"tags": "t1"
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}
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},
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 1,
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"tags": "t2"
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}
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},
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 2,
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"tags": "t3"
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}
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},
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 1,
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"tags": "t4"
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}
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},
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 1,
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"tags": "t5"
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}
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}
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]
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```
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You might be surprised to see inclusion of "t1", "t2", "t4" and "t5" in the results. It happens because query filter is applied on the row before explosion. For multi-valued dimensions, selector filter for "t3" would match row1 and row2, after which exploding is done. For multi-valued dimensions, query filter matches a row if any individual value inside the multiple values matches the query filter.
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### Group-By query with a selector query filter and additional filter in "dimensions" attributes
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To solve the problem above and to get only rows for "t3" returned, you would have to use a "filtered dimension spec" as in the query below.
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See section on filtered dimensionSpecs in [dimensionSpecs](dimensionspecs.html) for details.
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```json
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{
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"queryType": "groupBy",
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"dataSource": "test",
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"intervals": [
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"1970-01-01T00:00:00.000Z/3000-01-01T00:00:00.000Z"
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],
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"filter": {
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"type": "selector",
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"dimension": "tags",
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"value": "t3"
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},
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"granularity": {
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"type": "all"
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},
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"dimensions": [
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{
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"type": "listFiltered",
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"delegate": {
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"type": "default",
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"dimension": "tags",
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"outputName": "tags"
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},
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"values": ["t3"]
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}
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],
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"aggregations": [
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{
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"type": "count",
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"name": "count"
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}
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]
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}
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```
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returns following result.
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```json
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[
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{
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"timestamp": "1970-01-01T00:00:00.000Z",
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"event": {
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"count": 2,
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"tags": "t3"
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}
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}
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]
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```
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Note that, for groupBy queries, you could get similar result with a [having spec](having.html) but using a filtered dimensionSpec would be much more efficient because that gets applied at the lowest level in the query processing pipeline while having spec is applied at the highest level of groupBy query processing.
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@ -38,6 +38,7 @@ h2. Querying
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** "Context":../querying/query-context.html
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* "SQL":../querying/sql.html
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* "Joins":../querying/joins.html
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* "Multi-Valued Dimensions":../querying/multi-valued-dimensions.html
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h2. Design
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* "Overview":../design/design.html
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