docs: Migration guide for MVDs to arrays (#16516)

Co-authored-by: Clint Wylie <cjwylie@gmail.com>
Co-authored-by: Katya Macedo  <38017980+ektravel@users.noreply.github.com>
Co-authored-by: Benedict Jin <asdf2014@apache.org>
Co-authored-by: Katya Macedo <38017980+ektravel@users.noreply.github.com>

docs/querying/sql-functions.md

@@ -1143,6 +1143,14 @@ Adds the expression to the beginning of the array.
 
 Returns a slice of the array from the zero-based start and end indexes.
 
+## MV_TO_ARRAY
+
+`MV_TO_ARRAY(str)`
+
+**Function type:** [Multi-value string](sql-multivalue-string-functions.md)
+
+Converts a multi-value string from a `VARCHAR` to a `VARCHAR ARRAY`.
+
 ## MV_TO_STRING
 
 `MV_TO_STRING(arr, str)`

docs/querying/sql.md

@@ -209,7 +209,10 @@ The UNNEST clause unnests ARRAY typed values. The source for UNNEST can be an ar
 The following is the general syntax for UNNEST, specifically a query that returns the column that gets unnested:
 
 ```sql
-SELECT column_alias_name FROM datasource CROSS JOIN UNNEST(source_expression1) AS table_alias_name1(column_alias_name1) CROSS JOIN UNNEST(source_expression2) AS table_alias_name2(column_alias_name2) ...
+SELECT column_alias_name
+FROM datasource
+CROSS JOIN UNNEST(source_expression1) AS table_alias_name1(column_alias_name1)
+CROSS JOIN UNNEST(source_expression2) AS table_alias_name2(column_alias_name2) ...
 ```
 
 * The `datasource` for UNNEST can be any Druid datasource, such as the following:
@@ -405,4 +408,4 @@ To solve this issue, explicitly provide the type of the dynamic parameter using
 
 ```
 SELECT * FROM druid.foo WHERE dim1 like CONCAT('%', CAST (? AS VARCHAR), '%')
-```
+```

docs/release-info/migr-mvd-array.md

@@ -0,0 +1,246 @@
+---
+id: migr-mvd-array
+title: "Migration guide: MVDs to arrays"
+sidebar_label: MVDs to arrays
+---
+
+<!--
+  ~ 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.
+-->
+
+
+Druid now supports SQL-compliant [arrays](../querying/arrays.md). We recommend using arrays over [multi-value dimensions](../querying/multi-value-dimensions.md) (MVDs) whenever possible.
+For new projects and complex use cases involving multiple data types, use arrays. Use MVDs for specific use cases, such as operating directly on individual elements like regular strings. If your operations involve entire arrays of values, including the ordering of values within a row, use arrays over MVDs.
+
+## Comparison between arrays and MVDs
+
+The following table compares the general behavior between arrays and MVDs.
+For specific query differences between arrays and MVDs, see [Querying arrays and MVDs](#querying-arrays-and-mvds).
+
+|  | Array| MVD |
+|---|---|---|
+| Data types | Supports VARCHAR, BIGINT, and DOUBLE types (ARRAY<STRING\>, ARRAY<LONG\>, ARRAY<DOUBLE\>) | Only supports arrays of strings (VARCHAR) |
+| SQL compliance | Behaves like standard SQL arrays with SQL-compliant behavior | Behaves like SQL VARCHAR rather than standard SQL arrays and requires special SQL functions to achieve array-like behavior. See the [examples](#examples). |
+| Ingestion | <ul><li>JSON arrays are ingested as Druid arrays</li><li>Managed through the query context parameter `arrayIngestMode` in SQL-based ingestion. Supported options are `array`, `mvd`, and `none`. Note that if you set this mode to `none`, Druid raises an exception if you try to store any type of array.</li></ul> | <ul><li>JSON arrays are ingested as MVDs</li><li>Managed using functions like [ARRAY_TO_MV](../querying/sql-functions.md#array_to_mv) in SQL-based ingestion</li></ul> |
+| Filtering and grouping | <ul><li>Filters and groupings match the entire array value</li><li>Can be used as GROUP BY keys, grouping based on the entire array value</li><li>Use the [UNNEST operator](#group-by-array-elements) to group based on individual array elements</li></ul> | <ul><li>Filters match any value within the array</li><li>Grouping generates a group for each individual value, similar to an implicit UNNEST</li></ul> |
+| Conversion | Convert an MVD to an array using [MV_TO_ARRAY](../querying/sql-functions.md#mv_to_array) | Convert an array to an MVD using [ARRAY_TO_MV](../querying/sql-functions.md#array_to_mv) |
+
+## Querying arrays and MVDs
+
+In SQL queries, Druid operates on arrays differently than MVDs.
+A value in an array column is treated as a single array entity (SQL ARRAY), whereas a value in an MVD column is treated as individual strings (SQL VARCHAR).
+This behavior applies even though multiple string values within the same MVD are still stored as a single field in the MVD column.
+
+For example, consider the same value, `['a', 'b', 'c']` ingested into an array column and an MVD column.
+In your query, you want to filter results by comparing some value with `['a', 'b', 'c']`.
+
+* For array columns, Druid only returns the row when an equality filter matches the entire array.  
+For example: `WHERE "array_column" = ARRAY['a', 'b', 'c']`.
+
+* For MVD columns, Druid returns the row when an equality filter matches any value of the MVD.  
+For example, any of the following filters return the row for the query:  
+`WHERE "mvd_column" = 'a'`  
+`WHERE "mvd_column" = 'b'`  
+`WHERE "mvd_column" = 'c'`
+
+Note this difference between arrays and MVDs when you write queries that involve filtering or grouping.
+
+When your query applies both filters and grouping, MVDs may return rows that don't seem to match the filter,
+since the grouping occurs after Druid applies the filter. For an example, see [Filter and group by array elements](#filter-and-group-by-array-elements).
+
+## Examples
+
+The following examples highlight a few analogous queries between arrays and MVDs.
+For more information and examples, see [Querying arrays](../querying/arrays.md#querying-arrays) and [Querying multi-value dimensions](../querying/multi-value-dimensions.md#querying-multi-value-dimensions).
+
+### Filter by an array element
+
+Filter rows that have a certain value in the array or MVD.
+
+#### Array
+
+```sql
+SELECT label, tags
+FROM "array_example"
+WHERE ARRAY_CONTAINS(tags, 't3')
+```
+
+#### MVD
+
+```sql
+SELECT label, tags
+FROM "mvd_example"
+WHERE tags = 't3'
+```
+
+### Filter by one or more elements
+
+Filter rows for which the array or MVD contains one or more elements.
+Notice that [ARRAY_OVERLAP](../querying/sql-functions.md#array_overlap) checks for any overlapping elements, whereas [ARRAY_CONTAINS](../querying/sql-functions.md#array_contains) in the previous example checks that all elements are included.
+
+#### Array
+
+```sql
+SELECT *
+FROM "array_example"
+WHERE ARRAY_OVERLAP(tags, ARRAY['t1', 't7'])
+```
+
+#### MVD
+
+```sql
+SELECT *
+FROM "mvd_example"
+WHERE tags = 't1' OR tags = 't7'
+```
+
+### Filter using array equality
+
+Filter rows for which the array or MVD is equivalent to a reference array.
+
+#### Array
+
+```sql
+SELECT *
+FROM "array_example"
+WHERE tags = ARRAY['t1', 't2', 't3']
+```
+
+#### MVD
+
+```sql
+SELECT *
+FROM "mvd_example"
+WHERE MV_TO_ARRAY(tags) = ARRAY['t1', 't2', 't3']
+```
+
+### Group results by array
+
+Group results by the array or MVD.
+
+#### Array
+
+```sql
+SELECT label, tags
+FROM "array_example"
+GROUP BY 1, 2
+```
+
+#### MVD
+
+```sql
+SELECT label, MV_TO_ARRAY(tags)
+FROM "mvd_example"
+GROUP BY 1, 2
+```
+
+### Group by array elements
+
+Group results by individual array or MVD elements.
+
+#### Array
+
+```sql
+SELECT label, strings
+FROM "array_example" CROSS JOIN UNNEST(tags) as u(strings)
+GROUP BY 1, 2
+```
+
+#### MVD
+
+```sql
+SELECT label, tags
+FROM "mvd_example"
+GROUP BY 1, 2
+```
+
+### Filter and group by array elements
+
+Filter rows that have a certain value, then group by elements in the array or MVD.
+This example illustrates that while the results of filtering may match between arrays and MVDs,
+be aware that MVDs implicitly unnest their values so that results differ when you also apply a GROUP BY.
+
+For example, consider the queries from [Filter by an array element](#filter-by-an-array-element).
+Both queries return the following rows:
+
+```json
+{"label":"row1","tags":["t1","t2","t3"]}
+{"label":"row2","tags":["t3","t4","t5"]}
+```
+
+However, adding `GROUP BY 1, 2` to both queries changes the output.
+The two queries are now:
+
+```sql
+-- Array
+SELECT label, tags
+FROM "array_example"
+WHERE ARRAY_CONTAINS(tags, 't3')
+GROUP BY 1, 2
+
+-- MVD
+SELECT label, tags
+FROM "mvd_example"
+WHERE tags = 't3'
+GROUP BY 1, 2
+```
+
+The array query returns the following:
+
+```json
+{"label":"row1","tags":["t1","t2","t3"]}
+{"label":"row2","tags":["t3","t4","t5"]}
+```
+
+The MVD query returns the following:
+
+```json
+{"label":"row1","tags":"t1"}
+{"label":"row1","tags":"t2"}
+{"label":"row1","tags":"t3"}
+{"label":"row2","tags":"t3"}
+{"label":"row2","tags":"t4"}
+{"label":"row2","tags":"t5"}
+```
+
+The MVD results appear to show four extra rows for which `tags` does not equal `t3`.
+However, the rows match the filter based on how Druid evaluates equalities for MVDs.
+
+For the equivalent query on MVDs, use the [MV_FILTER_ONLY](../querying/sql-functions.md#mv_filter_only) function:
+
+```sql
+SELECT label, MV_FILTER_ONLY(tags, ARRAY['t3'])
+FROM "mvd_example"
+WHERE tags = 't3'
+GROUP BY 1, 2
+```
+
+
+## How to ingest data as arrays
+
+You can ingest arrays in Druid as follows:
+
+* For native batch and streaming ingestion, configure the dimensions in [`dimensionsSpec`](../ingestion/ingestion-spec.md#dimensionsspec).
+Within `dimensionsSpec`, set `"useSchemaDiscovery": true`, and use `dimensions` to list the array inputs with type `auto`.  
+For an example, see [Ingesting arrays: Native batch and streaming ingestion](../querying/arrays.md#native-batch-and-streaming-ingestion).
+
+* For SQL-based batch ingestion, include the [query context parameter](../multi-stage-query/reference.md#context-parameters) `"arrayIngestMode": "array"` and reference the relevant array type (`VARCHAR ARRAY`, `BIGINT ARRAY`, or `DOUBLE ARRAY`) in the [EXTEND clause](../multi-stage-query/reference.md#extern-function) that lists the column names and data types.
+For examples, see [Ingesting arrays: SQL-based ingestion](../querying/arrays.md#sql-based-ingestion).
+
+   As a best practice, always use the ARRAY data type in your input schema. If you want to ingest MVDs, explicitly wrap the string array in [ARRAY_TO_MV](../querying/sql-functions.md#array_to_mv). For an example, see [Multi-value dimensions: SQL-based ingestion](/querying/multi-value-dimensions.md#sql-based-ingestion).
+

docs/release-info/migration-guide.md

@@ -25,20 +25,17 @@ description: How to migrate from legacy features to get the most from Druid upda
 
 In general, when we introduce new features and behaviors into Apache Druid, we make every effort to avoid breaking existing features when introducing new behaviors. However, sometimes there are either bugs or performance limitations with the old behaviors that are not possible to fix in a backward-compatible way. In these cases, we must introduce breaking changes for the future maintainability of Druid. 
 
-The guides in this section outline breaking changes introduced in Druid 25 and later. Each guide provides instructions to migrate to new features.
+The guides in this section outline breaking changes introduced in Druid 25.0.0 and later. Each guide provides instructions to migrate to new features.
 
-<!--
 
 ## Migrate to arrays from multi-value dimensions
 
-Druid now supports SQL-compliant array types. Whenever possible, you should use the array type over multi-value dimensions. See []()>.
-
--->
+Druid now supports SQL-compliant array types. Whenever possible, you should use the array type over multi-value dimensions. See [Migration guide: MVDs to arrays](migr-mvd-array.md).
 
 ## Migrate to front-coded dictionary encoding
 
 Druid encodes string columns into dictionaries for better compression. Front-coded dictionary encoding reduces storage and improves performance by optimizing for strings that share similar beginning substrings. See [Migration guide: front-coded dictionaries](migr-front-coded-dict.md) for more information.
 
 ## Migrate to `maxSubqueryBytes` from `maxSubqueryRows`
 
-Druid allows you to set a byte-based limit on subquery size to prevent Brokers from running out of memory when handling large subqueries. The byte-based subquery limit overrides Druid's row-based subquery limit. We recommend that you move towards using byte-based limits starting in Druid 30.0. See [Migration guide: subquery limit](migr-subquery-limit.md) for more information.
+Druid allows you to set a byte-based limit on subquery size to prevent Brokers from running out of memory when handling large subqueries. The byte-based subquery limit overrides Druid's row-based subquery limit. We recommend that you move towards using byte-based limits starting in Druid 30.0.0. See [Migration guide: subquery limit](migr-subquery-limit.md) for more information.

website/sidebars.json

@@ -375,7 +375,8 @@
       },
       "items": [
         "release-info/migr-front-coded-dict",
-        "release-info/migr-subquery-limit"
+        "release-info/migr-subquery-limit",
+        "release-info/migr-mvd-array"
       ]
     }
   ]},