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explicit outputType for ExpressionPostAggregator, better documentatio…
…n for the differences between arrays and mvds (#15245) (#15307) * better documentation for the differences between arrays and mvds * add outputType to ExpressionPostAggregator to make docs true * add output coercion if outputType is defined on ExpressionPostAgg * updated post-aggregations.md to be consistent with aggregations.md and filters.md and use tables
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| --- | ||
| id: arrays | ||
| title: "Arrays" | ||
| --- | ||
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| <!-- | ||
| ~ Licensed to the Apache Software Foundation (ASF) under one | ||
| ~ or more contributor license agreements. See the NOTICE file | ||
| ~ distributed with this work for additional information | ||
| ~ regarding copyright ownership. The ASF licenses this file | ||
| ~ to you under the Apache License, Version 2.0 (the | ||
| ~ "License"); you may not use this file except in compliance | ||
| ~ with the License. You may obtain a copy of the License at | ||
| ~ | ||
| ~ http://www.apache.org/licenses/LICENSE-2.0 | ||
| ~ | ||
| ~ Unless required by applicable law or agreed to in writing, | ||
| ~ software distributed under the License is distributed on an | ||
| ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| ~ KIND, either express or implied. See the License for the | ||
| ~ specific language governing permissions and limitations | ||
| ~ under the License. | ||
| --> | ||
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| Apache Druid supports SQL standard `ARRAY` typed columns for `VARCHAR`, `BIGINT`, and `DOUBLE` types (native types `ARRAY<STRING>`, `ARRAY<LONG>`, and `ARRAY<DOUBLE>`). Other more complicated ARRAY types must be stored in [nested columns](nested-columns.md). Druid ARRAY types are distinct from [multi-value dimension](multi-value-dimensions.md), which have significantly different behavior than standard arrays. | ||
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| This document describes inserting, filtering, and grouping behavior for `ARRAY` typed columns. | ||
| Refer to the [Druid SQL data type documentation](sql-data-types.md#arrays) and [SQL array function reference](sql-array-functions.md) for additional details | ||
| about the functions available to use with ARRAY columns and types in SQL. | ||
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| The following sections describe inserting, filtering, and grouping behavior based on the following example data, which includes 3 array typed columns: | ||
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| ```json lines | ||
| {"timestamp": "2023-01-01T00:00:00", "label": "row1", "arrayString": ["a", "b"], "arrayLong":[1, null,3], "arrayDouble":[1.1, 2.2, null]} | ||
| {"timestamp": "2023-01-01T00:00:00", "label": "row2", "arrayString": [null, "b"], "arrayLong":null, "arrayDouble":[999, null, 5.5]} | ||
| {"timestamp": "2023-01-01T00:00:00", "label": "row3", "arrayString": [], "arrayLong":[1, 2, 3], "arrayDouble":[null, 2.2, 1.1]} | ||
| {"timestamp": "2023-01-01T00:00:00", "label": "row4", "arrayString": ["a", "b"], "arrayLong":[1, 2, 3], "arrayDouble":[]} | ||
| {"timestamp": "2023-01-01T00:00:00", "label": "row5", "arrayString": null, "arrayLong":[], "arrayDouble":null} | ||
| ``` | ||
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| ## Ingesting arrays | ||
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| ### Native batch and streaming ingestion | ||
| When using native [batch](../ingestion/native-batch.md) or streaming ingestion such as with [Apache Kafka](../development/extensions-core/kafka-ingestion.md), arrays can be ingested using the [`"auto"`](../ingestion/ingestion-spec.md#dimension-objects) type dimension schema which is shared with [type-aware schema discovery](../ingestion/schema-design.md#type-aware-schema-discovery). | ||
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| When ingesting from TSV or CSV data, you can specify the array delimiters using the `listDelimiter` field in the `inputFormat`. JSON data must be formatted as a JSON array to be ingested as an array type. JSON data does not require `inputFormat` configuration. | ||
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| The following shows an example `dimensionsSpec` for native ingestion of the data used in this document: | ||
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| ``` | ||
| "dimensions": [ | ||
| { | ||
| "type": "auto", | ||
| "name": "label" | ||
| }, | ||
| { | ||
| "type": "auto", | ||
| "name": "arrayString" | ||
| }, | ||
| { | ||
| "type": "auto", | ||
| "name": "arrayLong" | ||
| }, | ||
| { | ||
| "type": "auto", | ||
| "name": "arrayDouble" | ||
| } | ||
| ], | ||
| ``` | ||
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| ### SQL-based ingestion | ||
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| Arrays can also be inserted with [SQL-based ingestion](../multi-stage-query/index.md) when you include a query context parameter [`"arrayIngestMode":"array"`](../multi-stage-query/reference.md#context-parameters). | ||
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| For example, to insert the data used in this document: | ||
| ```sql | ||
| REPLACE INTO "array_example" OVERWRITE ALL | ||
| WITH "ext" AS ( | ||
| SELECT * | ||
| FROM TABLE( | ||
| EXTERN( | ||
| '{"type":"inline","data":"{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row1\", \"arrayString\": [\"a\", \"b\"], \"arrayLong\":[1, null,3], \"arrayDouble\":[1.1, 2.2, null]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row2\", \"arrayString\": [null, \"b\"], \"arrayLong\":null, \"arrayDouble\":[999, null, 5.5]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row3\", \"arrayString\": [], \"arrayLong\":[1, 2, 3], \"arrayDouble\":[null, 2.2, 1.1]} \n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row4\", \"arrayString\": [\"a\", \"b\"], \"arrayLong\":[1, 2, 3], \"arrayDouble\":[]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row5\", \"arrayString\": null, \"arrayLong\":[], \"arrayDouble\":null}"}', | ||
| '{"type":"json"}', | ||
| '[{"name":"timestamp", "type":"STRING"},{"name":"label", "type":"STRING"},{"name":"arrayString", "type":"ARRAY<STRING>"},{"name":"arrayLong", "type":"ARRAY<LONG>"},{"name":"arrayDouble", "type":"ARRAY<DOUBLE>"}]' | ||
| ) | ||
| ) | ||
| ) | ||
| SELECT | ||
| TIME_PARSE("timestamp") AS "__time", | ||
| "label", | ||
| "arrayString", | ||
| "arrayLong", | ||
| "arrayDouble" | ||
| FROM "ext" | ||
| PARTITIONED BY DAY | ||
| ``` | ||
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| ### SQL-based ingestion with rollup | ||
| These input arrays can also be grouped for rollup: | ||
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| ```sql | ||
| REPLACE INTO "array_example_rollup" OVERWRITE ALL | ||
| WITH "ext" AS ( | ||
| SELECT * | ||
| FROM TABLE( | ||
| EXTERN( | ||
| '{"type":"inline","data":"{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row1\", \"arrayString\": [\"a\", \"b\"], \"arrayLong\":[1, null,3], \"arrayDouble\":[1.1, 2.2, null]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row2\", \"arrayString\": [null, \"b\"], \"arrayLong\":null, \"arrayDouble\":[999, null, 5.5]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row3\", \"arrayString\": [], \"arrayLong\":[1, 2, 3], \"arrayDouble\":[null, 2.2, 1.1]} \n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row4\", \"arrayString\": [\"a\", \"b\"], \"arrayLong\":[1, 2, 3], \"arrayDouble\":[]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row5\", \"arrayString\": null, \"arrayLong\":[], \"arrayDouble\":null}"}', | ||
| '{"type":"json"}', | ||
| '[{"name":"timestamp", "type":"STRING"},{"name":"label", "type":"STRING"},{"name":"arrayString", "type":"ARRAY<STRING>"},{"name":"arrayLong", "type":"ARRAY<LONG>"},{"name":"arrayDouble", "type":"ARRAY<DOUBLE>"}]' | ||
| ) | ||
| ) | ||
| ) | ||
| SELECT | ||
| TIME_PARSE("timestamp") AS "__time", | ||
| "label", | ||
| "arrayString", | ||
| "arrayLong", | ||
| "arrayDouble", | ||
| COUNT(*) as "count" | ||
| FROM "ext" | ||
| GROUP BY 1,2,3,4,5 | ||
| PARTITIONED BY DAY | ||
| ``` | ||
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| ## Querying arrays | ||
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| ### Filtering | ||
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| All query types, as well as [filtered aggregators](aggregations.md#filtered-aggregator), can filter on array typed columns. Filters follow these rules for array types: | ||
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| - All filters match against the entire array value for the row | ||
| - Native value filters like [equality](filters.md#equality-filter) and [range](filters.md#range-filter) match on entire array values, as do SQL constructs that plan into these native filters | ||
| - The [`IS NULL`](filters.md#null-filter) filter will match rows where the entire array value is null | ||
| - [Array specific functions](sql-array-functions.md) like `ARRAY_CONTAINS` and `ARRAY_OVERLAP` follow the behavior specified by those functions | ||
| - All other filters do not directly support ARRAY types and will result in a query error | ||
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| #### Example: equality | ||
| ```sql | ||
| SELECT * | ||
| FROM "array_example" | ||
| WHERE arrayLong = ARRAY[1,2,3] | ||
| ``` | ||
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| ```json lines | ||
| {"__time":"2023-01-01T00:00:00.000Z","label":"row3","arrayString":"[]","arrayLong":"[1,2,3]","arrayDouble":"[null,2.2,1.1]"} | ||
| {"__time":"2023-01-01T00:00:00.000Z","label":"row4","arrayString":"[\"a\",\"b\"]","arrayLong":"[1,2,3]","arrayDouble":"[]"} | ||
| ``` | ||
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| #### Example: null | ||
| ```sql | ||
| SELECT * | ||
| FROM "array_example" | ||
| WHERE arrayLong IS NULL | ||
| ``` | ||
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| ```json lines | ||
| {"__time":"2023-01-01T00:00:00.000Z","label":"row2","arrayString":"[null,\"b\"]","arrayLong":null,"arrayDouble":"[999.0,null,5.5]"} | ||
| ``` | ||
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| #### Example: range | ||
| ```sql | ||
| SELECT * | ||
| FROM "array_example" | ||
| WHERE arrayString >= ARRAY['a','b'] | ||
| ``` | ||
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| ```json lines | ||
| {"__time":"2023-01-01T00:00:00.000Z","label":"row1","arrayString":"[\"a\",\"b\"]","arrayLong":"[1,null,3]","arrayDouble":"[1.1,2.2,null]"} | ||
| {"__time":"2023-01-01T00:00:00.000Z","label":"row4","arrayString":"[\"a\",\"b\"]","arrayLong":"[1,2,3]","arrayDouble":"[]"} | ||
| ``` | ||
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| #### Example: ARRAY_CONTAINS | ||
| ```sql | ||
| SELECT * | ||
| FROM "array_example" | ||
| WHERE ARRAY_CONTAINS(arrayString, 'a') | ||
| ``` | ||
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| ```json lines | ||
| {"__time":"2023-01-01T00:00:00.000Z","label":"row1","arrayString":"[\"a\",\"b\"]","arrayLong":"[1,null,3]","arrayDouble":"[1.1,2.2,null]"} | ||
| {"__time":"2023-01-01T00:00:00.000Z","label":"row4","arrayString":"[\"a\",\"b\"]","arrayLong":"[1,2,3]","arrayDouble":"[]"} | ||
| ``` | ||
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| ### Grouping | ||
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| When grouping on an array with SQL or a native [groupBy query](groupbyquery.md), grouping follows standard SQL behavior and groups on the entire array as a single value. The [`UNNEST`](sql.md#unnest) function allows grouping on the individual array elements. | ||
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| #### Example: SQL grouping query with no filtering | ||
| ```sql | ||
| SELECT label, arrayString | ||
| FROM "array_example" | ||
| GROUP BY 1,2 | ||
| ``` | ||
| results in: | ||
| ```json lines | ||
| {"label":"row1","arrayString":"[\"a\",\"b\"]"} | ||
| {"label":"row2","arrayString":"[null,\"b\"]"} | ||
| {"label":"row3","arrayString":"[]"} | ||
| {"label":"row4","arrayString":"[\"a\",\"b\"]"} | ||
| {"label":"row5","arrayString":null} | ||
| ``` | ||
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| #### Example: SQL grouping query with a filter | ||
| ```sql | ||
| SELECT label, arrayString | ||
| FROM "array_example" | ||
| WHERE arrayLong = ARRAY[1,2,3] | ||
| GROUP BY 1,2 | ||
| ``` | ||
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| results: | ||
| ```json lines | ||
| {"label":"row3","arrayString":"[]"} | ||
| {"label":"row4","arrayString":"[\"a\",\"b\"]"} | ||
| ``` | ||
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| #### Example: UNNEST | ||
| ```sql | ||
| SELECT label, strings | ||
| FROM "array_example" CROSS JOIN UNNEST(arrayString) as u(strings) | ||
| GROUP BY 1,2 | ||
| ``` | ||
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| results: | ||
| ```json lines | ||
| {"label":"row1","strings":"a"} | ||
| {"label":"row1","strings":"b"} | ||
| {"label":"row2","strings":null} | ||
| {"label":"row2","strings":"b"} | ||
| {"label":"row4","strings":"a"} | ||
| {"label":"row4","strings":"b"} | ||
| ``` | ||
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| ## Differences between arrays and multi-value dimensions | ||
| Avoid confusing string arrays with [multi-value dimensions](multi-value-dimensions.md). Arrays and multi-value dimensions are stored in different column types, and query behavior is different. You can use the functions `MV_TO_ARRAY` and `ARRAY_TO_MV` to convert between the two if needed. In general, we recommend using arrays whenever possible, since they are a newer and more powerful feature and have SQL compliant behavior. | ||
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| Use care during ingestion to ensure you get the type you want. | ||
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| To get arrays when performing an ingestion using JSON ingestion specs, such as [native batch](../ingestion/native-batch.md) or streaming ingestion such as with [Apache Kafka](../development/extensions-core/kafka-ingestion.md), use dimension type `auto` or enable `useSchemaDiscovery`. When performing a [SQL-based ingestion](../multi-stage-query/index.md), write a query that generates arrays and set the context parameter `"arrayIngestMode": "array"`. Arrays may contain strings or numbers. | ||
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| To get multi-value dimensions when performing an ingestion using JSON ingestion specs, use dimension type `string` and do not enable `useSchemaDiscovery`. When performing a [SQL-based ingestion](../multi-stage-query/index.md), wrap arrays in [`ARRAY_TO_MV`](multi-value-dimensions.md#sql-based-ingestion), which ensures you get multi-value dimensions in any `arrayIngestMode`. Multi-value dimensions can only contain strings. | ||
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| You can tell which type you have by checking the `INFORMATION_SCHEMA.COLUMNS` table, using a query like: | ||
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| ```sql | ||
| SELECT COLUMN_NAME, DATA_TYPE | ||
| FROM INFORMATION_SCHEMA.COLUMNS | ||
| WHERE TABLE_NAME = 'mytable' | ||
| ``` | ||
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| Arrays are type `ARRAY`, multi-value strings are type `VARCHAR`. |
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