Syntax Reference

The ksqlDB SQL language enables queries, transforms, aggregations, joins, and other operations on streaming data. ksqlDB SQL has a familiar syntax that's similar to ANSI SQL.

SQL quick reference

For a summary of supported SQL statements and keywords, see the ksqlDB SQL quick reference.

SQL statements

• Terminate SQL statements with a semicolon character (;).
• Statements can span multiple lines.
• The hyphen character (-) isn't supported in names for streams, tables, topics, and columns.
• Don't use quotes around stream names or table names when you CREATE them.
• Escape single-quote characters (') inside string literals by using two successive single quotes (''). For example, to escape 'T', write ''T''.
• Use backticks around column and source names with characters that are unparseable by ksqlDB or when you want to control case. For more information, see How to control the case of identifiers.

Statement parser and grammar

The ksqlDB statement parser is based on ANTLR and is implemented in the io.confluent.ksql.parser package. The grammar is defined in SqlBase.g4. For more information, see ANTLR Grammar Structure.

Terminology

ksqlDB SQL uses standard relational database terminology and extends it for stream processing.

Stream

A ksqlDB stream is an unbounded sequence of structured data. Each individual unit of data represents a fact and may be referred to as a "record", "message", or "event". For example, a stream could be a sequence of financial transactions, like "Alice sent $100 to Bob, then Charlie sent $50 to Bob".

Facts in a stream are immutable, which means that new facts can be inserted into a stream, but existing facts can never be updated or deleted.

You can create a stream from an Apache Kafka® topic or derive one from an existing stream. A stream's underlying data is durably stored, or persisted, in a topic on the Kafka brokers.

Create a stream by using the CREATE STREAM or CREATE STREAM AS SELECT statements.

Table

A ksqlDB table is a view of a stream or another table. A table represents a collection of evolving facts. For example, a table might contain the latest financial information for an account, like "Bob's current balance is $150". A ksqlDB table is the equivalent of a traditional database table, enriched with streaming semantics, like windowing.

Facts in a table are mutable, which means that new facts can be inserted to the table, and existing facts can be updated and deleted.

You can create a table from a Kafka topic or derive one from an existing stream or table. In both cases, a table's underlying data is durably persisted in a topic on the Kafka brokers.

Create a table by using the CREATE TABLE or CREATE TABLE AS SELECT statements.

Join

You can use ksqlDB to merge streams of events in real time by using the JOIN statement, which has a SQL join syntax. A ksqlDB join and a relational database join are similar in that they both combine data from two or more sources based on common values. The result of a ksqlDB join is a new stream or table that's populated with the column values that you specify in a SELECT statement.

For more information, see Joins.

Aggregation

ksqlDB supports several aggregate functions, like COUNT and SUM. You can use these to build stateful aggregates on streaming data. For the full list, see Aggregate functions.

You can create your own aggregation logic by implementing a User Defined Aggregation Function (UDAF). For more information, see UDAFs.

Aggregations return results per partition. To get results across all partitions, you can use PARTITION BY with a fixed key, for example:

CREATE STREAM allplaycounts AS
  SELECT 'fixed_key', COUNT(*) FROM
    playcountsstream PARTITION BY 'fixed_key';

Also, you can get results across all partitions as part of the aggregate query, instead of requiring a separate step to partition by a fixed key. In the following example, GROUP BY causes COUNT to aggregate over all records.

SELECT 'totalCount', COUNT(*) FROM
  ksql_processing_log GROUP BY 'totalCount';

Grouping by a single constant indicates that ksqlDB uses one partition containing all of the records for the aggregation. If you're creating a persistent query, you can set the partition count of the sink topic to 1 by using WITH(PARTITIONS=1). We recommend this if you're grouping by a fixed key because otherwise all but one of the sink topic's partitions will be unused and empty.

Window

The WINDOW clause controls how to group input records that have the same key into a window, for operations like aggregations or joins. Windows are tracked per record key.

Windowing adds two additional system columns to the data, which provide the window bounds: WINDOWSTART and WINDOWEND.

For more information, see Time and Windows.

Time units

The following list shows valid time units for the SIZE, ADVANCE BY, SESSION, and WITHIN clauses.

• DAY, DAYS
• HOUR, HOURS
• MINUTE, MINUTES
• SECOND, SECONDS
• MILLISECOND, MILLISECONDS

For more information, see Windows in SQL Queries.

Timestamp formats

Time-based operations, like windowing, process records according to the timestamp in ROWTIME. By default, the implicit ROWTIME pseudo column is the timestamp of a message in a Kafka topic. Timestamps have an accuracy of one millisecond.

Use the TIMESTAMP property to override ROWTIME with the contents of the specified column. Define the format of a record's timestamp by using the TIMESTAMP_FORMAT property.

If you use the TIMESTAMP property but don't set TIMESTAMP_FORMAT, ksqlDB assumes that the timestamp field is a bigint. If you set TIMESTAMP_FORMAT, the TIMESTAMP field must be of type varchar and have a format that the DateTimeFormatter Java class can parse.

If your timestamp format has embedded single quotes, you can escape them by using two successive single quotes, ''. For example, to escape 'T', write ''T''. The following examples show how to escape the ' character in SQL statements.

-- Example timestamp format: yyyy-MM-dd'T'HH:mm:ssX
CREATE STREAM TEST (id BIGINT KEY, event_timestamp VARCHAR)
  WITH (
    kafka_topic='test_topic',
    value_format='JSON',
    timestamp='event_timestamp',
    timestamp_format='yyyy-MM-dd''T''HH:mm:ssX'
  );

-- Example timestamp format: yyyy.MM.dd G 'at' HH:mm:ss z
CREATE STREAM TEST (id BIGINT KEY, event_timestamp VARCHAR)
  WITH (
    kafka_topic='test_topic',
    value_format='JSON',
    timestamp='event_timestamp',
    timestamp_format='yyyy.MM.dd G ''at'' HH:mm:ss z'
  );

-- Example timestamp format: hh 'o'clock' a, zzzz
CREATE STREAM TEST (id BIGINT KEY, event_timestamp VARCHAR)
  WITH (
    kafka_topic='test_topic',
    value_format='JSON',
    timestamp='event_timestamp',
    timestamp_format='hh ''o''clock'' a, zzzz'
  );

For more information on timestamp formats, see DateTimeFormatter.

Data types

ksqlDB supports the following data types. For information on how ksqlDB serializes different data types, see ksqlDB Serialization.

Primitive types

ksqlDB supports the following primitive data types:

• BOOLEAN
• INTEGER or [INT]
• BIGINT
• DOUBLE
• VARCHAR (or STRING)

Array

ARRAY<ElementType>

Note

The DELIMITED format doesn't support arrays.

ksqlDB supports fields that are arrays of another type. All the elements in the array must be of the same type. The element type can be any valid SQL type.

The elements of an array are one-indexed and can be accessed by using the [] operator passing in the index. For example, SOME_ARRAY[1] retrieves the first element from the array. For more information, see Operators.

You can define arrays within a CREATE TABLE or CREATE STREAM statement by using the syntax ARRAY<ElementType>. For example, ARRAY<INT> defines an array of integers.

Also, you can output an array from a query by using a SELECT statement. The following example creates an array from a stream named s1.

SELECT ARRAY[1, 2] FROM s1 EMIT CHANGES;

Starting in version 0.7.1, the built-in AS_ARRAY function syntax for creating arrays doesn't work. Replace AS_ARRAY with the ARRAY constructor syntax. For example, replace this legacy query:

CREATE STREAM OUTPUT AS SELECT cube_explode(as_array(col1, col2)) VAL1, ABS(col3) VAL2 FROM TEST;

With this query:

CREATE STREAM OUTPUT AS SELECT cube_explode(array[col1, col2]) VAL1, ABS(col3) VAL2 FROM TEST;

Map

MAP<KeyType, ValueType>

Note

The DELIMITED format doesn't support maps.

ksqlDB supports fields that are maps. A map has a key and value type. All of the keys must be of the same type, and all of the values must be also be of the same type. Currently only STRING keys are supported. The value type can be any valid SQL type.

Access the values of a map by using the [] operator and passing in the key. For example, SOME_MAP['cost'] retrieves the value for the entry with key cost, or null For more information, see Operators.

You can define maps within a CREATE TABLE or CREATE STREAM statement by using the syntax MAP<KeyType, ValueType>. For example, MAP<STRING, INT> defines a map with string keys and integer values.

Also, you can output a map from a query by using a SELECT statement. The following example creates a map from a stream named s1.

SELECT MAP(k1:=v1, k2:=v1*2) FROM s1 EMIT CHANGES;

Struct

STRUCT<FieldName FieldType, ...>

ksqlDB supports fields that are structs. A struct represents strongly typed structured, or nested, data. A struct is an ordered collection of named fields that have a specific type. The field types can be any valid SQL type.

Access the fields of a struct by using the -> operator. For example, SOME_STRUCT->ID retrieves the value of the struct's ID field.

You can define a struct within a CREATE TABLE or CREATE STREAM statement by using the syntax STRUCT<FieldName FieldType, ...>. For example, the following statement defines a struct with three fields, with the supplied names and types.

STRUCT<ID BIGINT, NAME STRING, AGE INT>

You can read structured data in Avro, Protobuf, JSON, and JSON_SR formats by using the STRUCT type in CREATE STREAM and CREATE TABLE statements.

Note

The DELIMITED format doesn't support structs.

You can use the STRUCT type in these SQL statements:

• CREATE STREAM/TABLE (from a topic)
• CREATE STREAM/TABLE AS SELECT (from existing streams/tables)
• SELECT (non-persistent query)

The STRUCT type requires a list of fields. For each field, you specify the field name and field type. The field type can be any of the supported ksqlDB types, including the complex types MAP, ARRAY, and STRUCT.

Note

Properties is not a valid field name.

The following example CREATE STREAM statement uses a STRUCT to encapsulate a street address and a postal code.

CREATE STREAM orders (
  ID BIGINT KEY,
  address STRUCT<street VARCHAR, zip INTEGER>) WITH (...);

Access the fields in address by using the dereference operator (->):

SELECT address->city, address->zip FROM orders;

You can create a STRUCT in a query by specifying the names of the columns and expressions that construct the values, separated by commas. The following example SELECT statement creates a schema that has a STRUCT.

SELECT STRUCT(name := col0, ageInDogYears := col1*7) AS dogs FROM animals

If col0 is a string and col1 is an integer, the resulting schema is:

col0 STRUCT<name VARCHAR, ageInDogYears INTEGER>

Decimal

DECIMAL(Precision, Scale)

ksqlDB supports fields that are numeric data types with fixed precision and scale:

• Precision is the maximum total number of decimal digits to be stored, including values to the left and right of the decimal point. The precision must be greater than 1. There is no default precision.
• Scale is the number of decimal digits to the right of the decimal points. This number must be greater than 0 and less than or equal to the value for Precision.

Mathematical operations between DOUBLE and DECIMAL cause the decimal to be converted to a double value automatically. Converting from the decimal data type to any floating point type (DOUBLE) may cause loss of precision.

Constants

• String constants are enclosed in single quotation marks and may include any unicode character (e.g. 'hello', '1.2').
• Integer constants are represented by numbers that are not enclosed in quotation marks and do not contain decimal points (e.g. 1, 2).
• Decimal constants are represented by a string of numbers that are no enclosed in quotation marks and contain a decimal point (e.g. 1.2, 87., .94). The type of the decimal constant will be DECIMAL(p, s) where p is the total number of numeric characters in the string and s is the total number of numeric characters that appear to the right of the decimal point.
• Double constants are numeric strings represented in scientific notation (e.g. 1E0, .42E-3).
• Boolean constants are the unquoted strings that are exactly (case-insensitive) TRUE or FALSE.

Quoted identifiers for source and column names

Quoted identifiers in column names and source names are supported. If you have names that ksqlDB can't parse, or if you need to control the case of your column names, enclose them in backtick characters, like this: `identifier`.

For example, a record with the following unparseable column names is still usable.

{"@id": 42, "col.val": value}

Use backtick characters to reference the columns:

-- Enclose unparseable column names with backticks:
CREATE STREAM s1 (K STRING KEY, `@id` integer, `col.val` string) …

Also, you can use backtick characters for the names of sources, like streams and tables. For example, you can create a stream name that has an embedded hyphen:

CREATE STREAM `foo-bar` (id VARCHAR) WITH (kafka_topic='foo', value_format='JSON', partitions=1);

You can use the hyphenated stream name in SQL statements by enclosing it with backticks:

INSERT INTO `foo-bar` (id) VALUES ('123');
CREATE STREAM `foo-too` AS SELECT * FROM `foo-bar`;

 Message
------------------------------------------------------------------------------------
 Stream foo-too created and running. Created by query with query ID: CSAS_foo-too_5
------------------------------------------------------------------------------------

Note

By default, ksqlDB converts source and column names automatically to all capital letters. Use quoted identifiers to override this behavior and fully control your source and column names. For more information, see How to control the case of identifiers.

Key Requirements

Message keys

The CREATE STREAM and CREATE TABLE statements define streams and tables over data in Kafka topics. They allow you to specify which columns should be read from the Kafka message key, as opposed to the value, by using the KEY and PRIMARY KEY keywords, for streams and tables, respectively.

Example:

CREATE TABLE users (
    userId INT PRIMARY KEY, -- userId will be read from the Kafka message key 
    registertime BIGINT,    -- all other columns from the value
    gender VARCHAR, 
    regionid VARCHAR
  ) WITH (
    KAFKA_TOPIC='users', 
    VALUE_FORMAT='JSON'
  );

While tables require a PRIMARY KEY, the KEY column of a stream is optional.

Joins involving tables can be joined to the table on the PRIMARY KEY column. Joins involving streams have no such limitation. Stream joins on any expression other than the stream's KEY column require an internal repartition, but joins on the stream's KEY column do not.

Important

Kafka guarantees the relative order of any two messages from one source partition only if they are also both in the same partition after the repartition. Otherwise, Kafka is likely to interleave messages. The use case will determine if these ordering guarantees are acceptable.

What to do if your key is not set or is in a different format

Streams

For streams, just leave out the KEY column from the column list. ksqlDB takes care of repartitioning the stream for you, using the value(s) from the GROUP BY columns for aggregates and the join criteria for joins.

Tables

For tables, you can still use ksqlDB if the message key isn't set or if it isn't in the required format, as long as the key can be rebuilt from the value data, and one of the following statements is true:

• The message key is a unary function of the value in the desired key column.
• It's acceptable for the messages in the topic to be re-ordered before being inserted into the table.

Create a stream that writes the message key, and declare the table on the output topic of this stream.

For example, imagine that you need to create a table from a topic that's keyed by a userid of type INT. But the required key is in the message value as a column named userid, which is a string containing the integer, and the actual message key in Kafka contains the userId in a format that ksqlDB doesn't recognize.

-- Create a stream on the original topic without a KEY columns:
CREATE STREAM users_with_wrong_key (
     userid STRING,
     username VARCHAR, 
     email VARCHAR
  ) WITH (
     KAFKA_TOPIC='users', 
     VALUE_FORMAT='JSON'
  );

-- Derive a new stream with the required key changes.
-- 1) The CAST statement converts userId to the required SQL type.
-- 2) The PARTITION BY clause re-partitions the stream based on the new, converted key.
-- 3) The SELECT clause selects the required value columns, all in this case.
-- The resulting schema is: USERID INT KEY, USERNAME STRING, EMAIL STRING.
CREATE STREAM users_with_proper_key
  WITH (KAFKA_TOPIC='users-with-proper-key') AS
  SELECT 
    CAST(userid AS BIGINT) AS userId,
    userName,
    email
  FROM users_with_wrong_key
  PARTITION BY CAST(userid AS BIGINT)
  EMIT CHANGES;

-- Now you can create the table on the properly keyed stream.
CREATE TABLE users_table (
    userId INT PRIMARY KEY, 
    username VARCHAR, 
    email VARCHAR
  ) WITH (
    KAFKA_TOPIC='users-with-proper-key',
    VALUE_FORMAT='JSON'
  );

-- Or, if you prefer, you can keep userId in the value of the repartitioned data
-- by using the AS_VALUE() function.
-- The resulting schema is: USERID INT KEY, USERNAME STRING, EMAIL STRING, VUSERID INT
CREATE STREAM users_with_proper_key_and_user_id
  WITH(KAFKA_TOPIC='users_with_proper_key_and_user_id') AS
  SELECT 
    CAST(userid AS BIGINT) as USERID,
    username, 
    email, 
    AS_VALUE(CAST(userid AS BIGINT)) as vUserId,
  FROM users_with_wrong_key
  PARTITION BY CAST(userid AS BIGINT)
  EMIT CHANGES;

-- Now you can create the table on the properly keyed stream.
CREATE TABLE users_table_2 (
     userId INT PRIMARY KEY, 
     username VARCHAR, 
     email VARCHAR, 
     vUserId INT
  ) WITH (
     KAFKA_TOPIC='users_with_proper_key_and_user_id',
     VALUE_FORMAT='JSON'
  );

For more information, see Partition Data to Enable Joins.

ksqlDB CLI commands

The ksqlDB CLI commands can be run after starting the ksqlDB CLI. You can view the ksqlDB CLI help by running <path-to-confluent>/bin/ksql --help.

Tip

You can search and browse your command history in the ksqlDB CLI with Ctrl-R. After pressing Ctrl-R, start typing the command or any part of the command to show an auto-complete of past commands.

NAME
        ksql - KSQL CLI

SYNOPSIS
        ksql [ --config-file <configFile> ] [ {-h | --help} ]
                [ --output <outputFormat> ]
                [ --query-row-limit <streamedQueryRowLimit> ]
                [ --query-timeout <streamedQueryTimeoutMs> ] [--] <server>

OPTIONS
        --config-file <configFile>
            A file specifying configs for Ksql and its underlying Kafka Streams
            instance(s). Refer to KSQL documentation for a list of available
            configs.

        -h, --help
            Display help information

        --output <outputFormat>
            The output format to use (either 'JSON' or 'TABULAR'; can be changed
            during REPL as well; defaults to TABULAR)

        --query-row-limit <streamedQueryRowLimit>
            An optional maximum number of rows to read from streamed queries

            This options value must fall in the following range: value >= 1


        --query-timeout <streamedQueryTimeoutMs>
            An optional time limit (in milliseconds) for streamed queries

            This options value must fall in the following range: value >= 1


        --
            This option can be used to separate command-line options from the
            list of arguments (useful when arguments might be mistaken for
            command-line options)

        <server>
            The address of the Ksql server to connect to (ex:
            http://confluent.io:9098)

            This option may occur a maximum of 1 times

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Last update: 2020-10-19