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docs: add tutorial with examples of sql null handling (#16185) 

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docs/querying/sql-data-types.md
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@ -140,23 +140,41 @@ as regular JSON arrays instead of in stringified form.
## NULL values ## NULL values
By default, Druid treats NULL values similarly to the ANSI SQL standard.
In the default mode:
- numeric NULL is permitted.
- NULL values and empty strings are not equal.
This manner of null handling applies to both storage and queries.
The [`druid.generic.useDefaultValueForNull`](../configuration/index.md#sql-compatible-null-handling) The [`druid.generic.useDefaultValueForNull`](../configuration/index.md#sql-compatible-null-handling)
runtime property controls Druid's NULL handling mode. For the most SQL compliant behavior, set this to `false` (the default). runtime property controls Druid's NULL handling mode. For the most SQL compliant behavior, maintain the default value of `false`.
When `druid.generic.useDefaultValueForNull = false` (the default), NULLs are treated more closely to the SQL standard. In this mode, There is some performance impact for null handling. see [segment internals](../design/segments.md#handling-null-values) for more information.
numeric NULL is permitted, and NULLs and empty strings are no longer treated as interchangeable. This property For examples of null handling, see the [null handling tutorial](../tutorials/tutorial-sql-null.md).
affects both storage and querying, and must be set on all Druid service types to be available at both ingestion time
and query time. There is some overhead associated with the ability to handle NULLs; see
the [segment internals](../design/segments.md#handling-null-values) documentation for more details.
When `druid.generic.useDefaultValueForNull = true` (deprecated legacy mode), Druid treats NULLs and empty strings ### Legacy null handling mode
interchangeably, rather than according to the SQL standard. In this mode Druid SQL only has partial support for NULLs.
For example, the expressions `col IS NULL` and `col = ''` are equivalent, and both evaluate to true if `col` contains :::info
an empty string. Similarly, the expression `COALESCE(col1, col2)` returns `col2` if `col1` is an empty string. While To ensure Druid always behaves in an ANSI SQL compatible manner, this mode will be removed in a future release.
the `COUNT(*)` aggregator counts all rows, the `COUNT(expr)` aggregator counts the number of rows where `expr` is :::
neither null nor the empty string. Numeric columns in this mode are not nullable; any null or missing values are
treated as zeroes. This was the default prior to Druid 28.0.0, but will be removed in a future release so that Druid 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.
always behaves in an SQL compatible manner.
When running in the deprecated legacy mode, Druid treats NULL values and empty strings interchangeably.
In this mode:
- Druid does not distinguish between empty strings and nulls.
- Druid SQL only has partial support for NULLs.
- Numeric columns are not nullable; null or missing values are treated as 0.
For example, the following expressions are equivalent:
- col IS NULL
- col = ''
Both evaluate to true if col contains an empty string.
Similarly, the expression COALESCE(`col1`, `col2`) returns `col2` if `col1` is an empty string.
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.
## Boolean logic ## Boolean logic

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---
id: tutorial-sql-null
title: Null handling tutorial
sidebar_label: Handling null values
description: Introduction to null handling in Druid
---
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This tutorial introduces the basic concepts of null handling for string and numeric columns in Apache Druid.
The tutorial focuses on filters using the logical NOT operation on columns with NULL values.
## Prerequisites
Before starting this tutorial, download and run Apache Druid on your local machine as described in
the [Local quickstart](index.md).
The tutorial assumes you are familiar with using the [Query view](./tutorial-sql-query-view.md) to ingest and query data.
The tutorial also assumes you have not changed any of the default settings for null handling.
## Load data with null values
The sample data for the tutorial contains null values for string and numeric columns as follows:
```json
{"date": "1/1/2024 1:02:00","title": "example_1","string_value": "some_value","numeric_value": 1}
{"date": "1/1/2024 1:03:00","title": "example_2","string_value": "another_value","numeric_value": 2}
{"date": "1/1/2024 1:04:00","title": "example_3","string_value": "", "numeric_value": null}
{"date": "1/1/2024 1:05:00","title": "example_4","string_value": null, "numeric_value": null}
```
Run the following query in the Druid Console to load the data:
```sql
REPLACE INTO "null_example" OVERWRITE ALL
WITH "ext" AS (
SELECT *
FROM TABLE(
EXTERN(
'{"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}"}',
'{"type":"json"}'
)
) EXTEND ("date" VARCHAR, "title" VARCHAR, "string_value" VARCHAR, "numeric_value" BIGINT)
)
SELECT
TIME_PARSE("date", 'd/M/yyyy H:mm:ss') AS "__time",
"title",
"string_value",
"numeric_value"
FROM "ext"
PARTITIONED BY DAY
```
After Druid finishes loading the data, run the following query to see the table:
```sql
SELECT * FROM "null_example"
```
Druid returns the following:
|`__time`|`title`|`string_value`|`numeric_value`|
|---|---|---|---|
|`2024-01-01T01:02:00.000Z`|`example_1`|`some_value`|1|
|`2024-01-01T01:03:00.000Z`|`example_2`|`another_value`|2|
|`2024-01-01T01:04:00.000Z`|`example_3`|`empty`|`null`|
|`2024-01-01T01:05:00.000Z`|`example_4`|`null`|`null`|
Note the difference in the empty string value for example 3 and the null string value for example 4.
## String query example
The queries in this section illustrate null handling with strings.
The following query filters rows where the string value is not equal to `some_value`:
```sql
SELECT COUNT(*)
FROM "null_example"
WHERE "string_value" != 'some_value'
```
Druid returns 2 for `another_value` and the empty string `""`. The null value is not counted.
Note that the null value is included in `COUNT(*)` but not as a count of the values in the column as follows:
```sql
SELECT "string_value",
COUNT(*) AS count_all_rows,
COUNT("string_value") AS count_values
FROM "inline_data"
GROUP BY 1
```
Druid returns the following:
|`string_value`|`count_all_rows`|`count_values`|
|---|---|---|
|`null`|1|0|
|`empty`|1|1|
|`another_value`|1|1|
|`some_value`|1|1|
Also note that GROUP BY expressions yields distinct entries for `null` and the empty string.
### Filter for empty strings in addition to null
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:
```sql
SELECT *
FROM "null_example"
WHERE "string_value" IS NULL OR "string_value" = ''
```
Druid returns the following:
|`__time`|`title`|`string_value`|`numeric_value`|
|---|---|---|---|---|---|
|`2024-01-01T01:04:00.000Z`|`example_3`|`empty`|`null`|
|`2024-01-01T01:05:00.000Z`|`example_4`|`null`|`null`|
For another example, if you do not want to count empty strings, use a FILTER to exclude them. For example:
```sql
SELECT COUNT("string_value") FILTER(WHERE "string_value" <> '')
FROM "null_example"
```
Druid returns 2. Both the empty string and null values are excluded.
## Numeric query examples
Druid does does not count null values in numeric comparisons.
```sql
SELECT COUNT(*)
FROM "null_example"
WHERE "numeric_value" < 2
```
Druid returns 1. The `null` values for examples 3 and 4 are excluded.
Additionally, be aware that null values do not behave as 0. For examples:
```sql
SELECT numeric_value + 1
FROM "null_example"
WHERE "__time" > '2024-01-01 01:04:00.000Z'
```
Druid returns `null` and not 1. One option is to use the COALESCE function for null handling. For example:
```sql
SELECT COALESCE(numeric_value, 0) + 1
FROM "null_example"
WHERE "__time" > '2024-01-01 01:04:00.000Z'
```
In this case, Druid returns 1.
## Ingestion time filtering
The same null handling rules apply at ingestion time.
The following query replaces the example data with data filtered with a WHERE clause:
```sql
REPLACE INTO "null_example" OVERWRITE ALL
WITH "ext" AS (
SELECT *
FROM TABLE(
EXTERN(
'{"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}"}',
'{"type":"json"}'
)
) EXTEND ("date" VARCHAR, "title" VARCHAR, "string_value" VARCHAR, "numeric_value" BIGINT)
)
SELECT
TIME_PARSE("date", 'd/M/yyyy H:mm:ss') AS "__time",
"title",
"string_value",
"numeric_value"
FROM "ext"
WHERE "string_value" != 'some_value'
PARTITIONED BY DAY
```
The resulting data set only includes two rows. Druid has filtered out example 1 (`some_value`) and example 4 (`null`):
|`__time`|`title`|`string_value`|`numeric_value`|
|---|---|---|---|
|`2024-01-01T01:03:00.000Z`|`example_2`|`another_value`|2|
|`2024-01-01T01:04:00.000Z`|`example_3`|`empty`|`null`|
## Learn more
See the following for more information:
- [Null values](../querying/sql-data-types.md#null-values)
- "Generating and working with NULL values" notebook at [Learn druid](https://github.com/implydata/learn-druid/)

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"tutorials/tutorial-sql-query-view", "tutorials/tutorial-sql-query-view",
"tutorials/tutorial-sql-null",
"tutorials/tutorial-unnest-arrays", "tutorials/tutorial-unnest-arrays",
"tutorials/tutorial-query-deep-storage", "tutorials/tutorial-query-deep-storage",
"tutorials/tutorial-jdbc" "tutorials/tutorial-jdbc"