mirror of https://github.com/apache/druid.git
docs: add tutorial with examples of sql null handling (#16185)
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>
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Charles Smith
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@ -140,23 +140,41 @@ as regular JSON arrays instead of in stringified form.
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## NULL values
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By default, Druid treats NULL values similarly to the ANSI SQL standard.
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In the default mode:
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- numeric NULL is permitted.
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- NULL values and empty strings are not equal.
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This manner of null handling applies to both storage and queries.
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The [`druid.generic.useDefaultValueForNull`](../configuration/index.md#sql-compatible-null-handling)
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runtime property controls Druid's NULL handling mode. For the most SQL compliant behavior, set this to `false` (the default).
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runtime property controls Druid's NULL handling mode. For the most SQL compliant behavior, maintain the default value of `false`.
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When `druid.generic.useDefaultValueForNull = false` (the default), NULLs are treated more closely to the SQL standard. In this mode,
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numeric NULL is permitted, and NULLs and empty strings are no longer treated as interchangeable. This property
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affects both storage and querying, and must be set on all Druid service types to be available at both ingestion time
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and query time. There is some overhead associated with the ability to handle NULLs; see
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the [segment internals](../design/segments.md#handling-null-values) documentation for more details.
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There is some performance impact for null handling. see [segment internals](../design/segments.md#handling-null-values) for more information.
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For examples of null handling, see the [null handling tutorial](../tutorials/tutorial-sql-null.md).
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When `druid.generic.useDefaultValueForNull = true` (deprecated legacy mode), Druid treats NULLs and empty strings
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interchangeably, rather than according to the SQL standard. In this mode Druid SQL only has partial support for NULLs.
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For example, the expressions `col IS NULL` and `col = ''` are equivalent, and both evaluate to true if `col` contains
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an empty string. Similarly, the expression `COALESCE(col1, col2)` returns `col2` if `col1` is an empty string. While
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the `COUNT(*)` aggregator counts all rows, the `COUNT(expr)` aggregator counts the number of rows where `expr` is
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neither null nor the empty string. Numeric columns in this mode are not nullable; any null or missing values are
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treated as zeroes. This was the default prior to Druid 28.0.0, but will be removed in a future release so that Druid
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always behaves in an SQL compatible manner.
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### Legacy null handling mode
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:::info
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To ensure Druid always behaves in an ANSI SQL compatible manner, this mode will be removed in a future release.
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:::
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You can set `druid.generic.useDefaultValueForNull = true` to revert to Druid's deprecated legacy null handling mode, the default for Druid 27.0.0 and prior releases. This mode is not recommended.
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When running in the deprecated legacy mode, Druid treats NULL values and empty strings interchangeably.
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In this mode:
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- Druid does not distinguish between empty strings and nulls.
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- Druid SQL only has partial support for NULLs.
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- Numeric columns are not nullable; null or missing values are treated as 0.
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For example, the following expressions are equivalent:
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- col IS NULL
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- col = ''
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Both evaluate to true if col contains an empty string.
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Similarly, the expression COALESCE(`col1`, `col2`) returns `col2` if `col1` is an empty string.
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The COUNT(*) aggregator counts all rows but the COUNT(expr) aggregator counts the number of rows where expr is neither null nor the empty string.
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## Boolean logic
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@ -0,0 +1,216 @@
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---
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id: tutorial-sql-null
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title: Null handling tutorial
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sidebar_label: Handling null values
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description: Introduction to null handling in Druid
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---
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<!--
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~ Licensed to the Apache Software Foundation (ASF) under one
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~ or more contributor license agreements. See the NOTICE file
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~ distributed with this work for additional information
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~ regarding copyright ownership. The ASF licenses this file
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~ to you under the Apache License, Version 2.0 (the
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~ "License"); you may not use this file except in compliance
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~ with the License. You may obtain a copy of the License at
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~
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~ http://www.apache.org/licenses/LICENSE-2.0
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~
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~ Unless required by applicable law or agreed to in writing,
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~ software distributed under the License is distributed on an
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~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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~ KIND, either express or implied. See the License for the
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~ specific language governing permissions and limitations
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~ under the License.
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-->
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This tutorial introduces the basic concepts of null handling for string and numeric columns in Apache Druid.
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The tutorial focuses on filters using the logical NOT operation on columns with NULL values.
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## Prerequisites
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Before starting this tutorial, download and run Apache Druid on your local machine as described in
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the [Local quickstart](index.md).
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The tutorial assumes you are familiar with using the [Query view](./tutorial-sql-query-view.md) to ingest and query data.
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The tutorial also assumes you have not changed any of the default settings for null handling.
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## Load data with null values
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The sample data for the tutorial contains null values for string and numeric columns as follows:
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```json
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{"date": "1/1/2024 1:02:00","title": "example_1","string_value": "some_value","numeric_value": 1}
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{"date": "1/1/2024 1:03:00","title": "example_2","string_value": "another_value","numeric_value": 2}
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{"date": "1/1/2024 1:04:00","title": "example_3","string_value": "", "numeric_value": null}
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{"date": "1/1/2024 1:05:00","title": "example_4","string_value": null, "numeric_value": null}
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```
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Run the following query in the Druid Console to load the data:
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```sql
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REPLACE INTO "null_example" OVERWRITE ALL
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WITH "ext" AS (
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SELECT *
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FROM TABLE(
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EXTERN(
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'{"type":"inline","data":"{\"date\": \"1/1/2024 1:02:00\",\"title\": \"example_1\",\"string_value\": \"some_value\",\"numeric_value\": 1}\n{\"date\": \"1/1/2024 1:03:00\",\"title\": \"example_2\",\"string_value\": \"another_value\",\"numeric_value\": 2}\n{\"date\": \"1/1/2024 1:04:00\",\"title\": \"example_3\",\"string_value\": \"\", \"numeric_value\": null}\n{\"date\": \"1/1/2024 1:05:00\",\"title\": \"example_4\",\"string_value\": null, \"numeric_value\": null}"}',
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'{"type":"json"}'
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)
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) EXTEND ("date" VARCHAR, "title" VARCHAR, "string_value" VARCHAR, "numeric_value" BIGINT)
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)
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SELECT
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TIME_PARSE("date", 'd/M/yyyy H:mm:ss') AS "__time",
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"title",
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"string_value",
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"numeric_value"
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FROM "ext"
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PARTITIONED BY DAY
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```
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After Druid finishes loading the data, run the following query to see the table:
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```sql
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SELECT * FROM "null_example"
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```
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Druid returns the following:
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|`__time`|`title`|`string_value`|`numeric_value`|
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|---|---|---|---|
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|`2024-01-01T01:02:00.000Z`|`example_1`|`some_value`|1|
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|`2024-01-01T01:03:00.000Z`|`example_2`|`another_value`|2|
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|`2024-01-01T01:04:00.000Z`|`example_3`|`empty`|`null`|
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|`2024-01-01T01:05:00.000Z`|`example_4`|`null`|`null`|
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Note the difference in the empty string value for example 3 and the null string value for example 4.
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## String query example
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The queries in this section illustrate null handling with strings.
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The following query filters rows where the string value is not equal to `some_value`:
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```sql
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SELECT COUNT(*)
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FROM "null_example"
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WHERE "string_value" != 'some_value'
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```
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Druid returns 2 for `another_value` and the empty string `""`. The null value is not counted.
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Note that the null value is included in `COUNT(*)` but not as a count of the values in the column as follows:
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```sql
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SELECT "string_value",
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COUNT(*) AS count_all_rows,
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COUNT("string_value") AS count_values
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FROM "inline_data"
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GROUP BY 1
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```
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Druid returns the following:
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|`string_value`|`count_all_rows`|`count_values`|
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|---|---|---|
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|`null`|1|0|
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|`empty`|1|1|
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|`another_value`|1|1|
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|`some_value`|1|1|
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Also note that GROUP BY expressions yields distinct entries for `null` and the empty string.
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### Filter for empty strings in addition to null
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If your queries rely on treating empty strings and null values the same, you can use an OR operator in the filter. For example to select all rows with null values or empty strings:
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```sql
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SELECT *
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FROM "null_example"
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WHERE "string_value" IS NULL OR "string_value" = ''
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```
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Druid returns the following:
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|`__time`|`title`|`string_value`|`numeric_value`|
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|---|---|---|---|---|---|
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|`2024-01-01T01:04:00.000Z`|`example_3`|`empty`|`null`|
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|`2024-01-01T01:05:00.000Z`|`example_4`|`null`|`null`|
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For another example, if you do not want to count empty strings, use a FILTER to exclude them. For example:
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```sql
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SELECT COUNT("string_value") FILTER(WHERE "string_value" <> '')
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FROM "null_example"
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```
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Druid returns 2. Both the empty string and null values are excluded.
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## Numeric query examples
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Druid does does not count null values in numeric comparisons.
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```sql
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SELECT COUNT(*)
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FROM "null_example"
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WHERE "numeric_value" < 2
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```
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Druid returns 1. The `null` values for examples 3 and 4 are excluded.
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Additionally, be aware that null values do not behave as 0. For examples:
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```sql
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SELECT numeric_value + 1
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FROM "null_example"
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WHERE "__time" > '2024-01-01 01:04:00.000Z'
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```
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Druid returns `null` and not 1. One option is to use the COALESCE function for null handling. For example:
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```sql
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SELECT COALESCE(numeric_value, 0) + 1
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FROM "null_example"
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WHERE "__time" > '2024-01-01 01:04:00.000Z'
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```
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In this case, Druid returns 1.
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## Ingestion time filtering
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The same null handling rules apply at ingestion time.
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The following query replaces the example data with data filtered with a WHERE clause:
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```sql
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REPLACE INTO "null_example" OVERWRITE ALL
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WITH "ext" AS (
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SELECT *
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FROM TABLE(
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EXTERN(
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'{"type":"inline","data":"{\"date\": \"1/1/2024 1:02:00\",\"title\": \"example_1\",\"string_value\": \"some_value\",\"numeric_value\": 1}\n{\"date\": \"1/1/2024 1:03:00\",\"title\": \"example_2\",\"string_value\": \"another_value\",\"numeric_value\": 2}\n{\"date\": \"1/1/2024 1:04:00\",\"title\": \"example_3\",\"string_value\": \"\", \"numeric_value\": null}\n{\"date\": \"1/1/2024 1:05:00\",\"title\": \"example_4\",\"string_value\": null, \"numeric_value\": null}"}',
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'{"type":"json"}'
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)
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) EXTEND ("date" VARCHAR, "title" VARCHAR, "string_value" VARCHAR, "numeric_value" BIGINT)
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)
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SELECT
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TIME_PARSE("date", 'd/M/yyyy H:mm:ss') AS "__time",
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"title",
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"string_value",
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"numeric_value"
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FROM "ext"
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WHERE "string_value" != 'some_value'
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PARTITIONED BY DAY
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```
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The resulting data set only includes two rows. Druid has filtered out example 1 (`some_value`) and example 4 (`null`):
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|`__time`|`title`|`string_value`|`numeric_value`|
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|---|---|---|---|
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|`2024-01-01T01:03:00.000Z`|`example_2`|`another_value`|2|
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|`2024-01-01T01:04:00.000Z`|`example_3`|`empty`|`null`|
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## Learn more
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See the following for more information:
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- [Null values](../querying/sql-data-types.md#null-values)
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- "Generating and working with NULL values" notebook at [Learn druid](https://github.com/implydata/learn-druid/)
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@ -24,6 +24,7 @@
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"tutorials/docker",
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"tutorials/tutorial-kerberos-hadoop",
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"tutorials/tutorial-sql-query-view",
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"tutorials/tutorial-sql-null",
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"tutorials/tutorial-unnest-arrays",
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"tutorials/tutorial-query-deep-storage",
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"tutorials/tutorial-jdbc"
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