Null handling tutorial

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.

The tutorial assumes you are familiar with using the Query view 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:

{"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:

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:

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:

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:

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:

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:

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 not count null values in numeric comparisons.

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:

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:

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:

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
• "Generating and working with NULL values" notebook at Learn druid