Write streaming queries against Apache Kafka® using ksqlDB (Local)

This tutorial demonstrates a simple workflow using ksqlDB to write streaming queries against messages in Kafka.

To get started, you must start a Kafka cluster, including ZooKeeper and a Kafka broker. ksqlDB will then query messages from this Kafka cluster. ksqlDB is installed in Confluent Platform by default.

Prerequisites:

  • Confluent Platform is installed and running. This installation includes a Kafka broker, ksqlDB, Control Center, ZooKeeper, Schema Registry, REST Proxy, and Connect.
  • If you installed Confluent Platform using TAR or ZIP, navigate into the installation directory. The paths and commands used throughout this tutorial assume that you are in this installation directory.
  • Consider installing the Confluent CLI to start a local installation of Confluent Platform.
  • Java: Minimum version 1.8. Install Oracle Java JRE or JDK >= 1.8 on your local machine

Create Topics and Produce Data

Create and produce data to the Kafka topics pageviews and users. These steps use the ksqlDB datagen tool that’s included with Confluent Platform.

  1. Open a new terminal window and run the folowing command to create the pageviews topic and produce data using the data generator. The following example continuously generates data in DELIMITED format.

    $CONFLUENT_HOME/bin/ksql-datagen quickstart=pageviews format=json topic=pageviews msgRate=5
    
  2. Open another terminal window and run the following command to produce Kafka data to the users topic using the data generator. The following example continuously generates data with in DELIMITED format.

    $CONFLUENT_HOME/bin/ksql-datagen quickstart=users format=avro topic=users msgRate=1
    

Tip

You can also produce Kafka data using the kafka-console-producer CLI provided with Confluent Platform.

Launch the ksqlDB CLI

Open a new terminal window and run the following command to set the LOG_DIR environment variable and launch the ksqlDB CLI.

LOG_DIR=./ksql_logs $CONFLUENT_HOME/bin/ksql

This command routes the CLI logs to the ./ksql_logs directory, relative to your current directory. By default, the CLI looks for a ksqlDB Server running at http://localhost:8088.

Important

By default ksqlDB attempts to store its logs in a directory called logs that is relative to the location of the ksql executable. For example, if ksql is installed at /usr/local/bin/ksql, then it would attempt to store its logs in /usr/local/logs. If you are running ksql from the default Confluent Platform location, $CONFLUENT_HOME/bin, you must override this default behavior by using the LOG_DIR variable.

After ksqlDB is started, your terminal should resemble this.

                  ===========================================
                  =       _              _ ____  ____       =
                  =      | | _____  __ _| |  _ \| __ )      =
                  =      | |/ / __|/ _` | | | | |  _ \      =
                  =      |   <\__ \ (_| | | |_| | |_) |     =
                  =      |_|\_\___/\__, |_|____/|____/      =
                  =                   |_|                   =
                  =  Event Streaming Database purpose-built =
                  =        for stream processing apps       =
                  ===========================================

Copyright 2017-2021 Confluent Inc.

CLI v6.2.0, Server v6.2.0 located at http://localhost:8088
Server Status: RUNNING

Having trouble? Type 'help' (case-insensitive) for a rundown of how things work!

ksql>

Inspect Kafka Topics By Using SHOW and PRINT Statements

ksqlDB enables inspecting Kafka topics and messages in real time.

  • Use the SHOW TOPICS statement to list the available topics in the Kafka cluster.
  • Use the PRINT statement to see a topic’s messages as they arrive.

In the ksqlDB CLI, run the following statement:

SHOW TOPICS;

Your output should resemble:

 Kafka Topic                 | Partitions | Partition Replicas
--------------------------------------------------------------
 default_ksql_processing_log | 1          | 1
 pageviews                   | 1          | 1
 users                       | 1          | 1
--------------------------------------------------------------

By default, ksqlDB hides internal and system topics. Use the SHOW ALL TOPICS statement to see the full list of topics in the Kafka cluster:

SHOW ALL TOPICS;

Your output should resemble:

 Kafka Topic                                                                                   | Partitions | Partition Replicas
---------------------------------------------------------------------------------------------------------------------------------
 _confluent-command                                                                            | 1          | 1
 _confluent-controlcenter-...                                                                  | 2          | 1
 ...
 _confluent-ksql-default__command_topic                                                        | 1          | 1
 _confluent-license                                                                            | 1          | 1
 _confluent-metrics                                                                            | 12         | 1
 _confluent-monitoring                                                                         | 2          | 1
 _confluent-telemetry-metrics                                                                  | 12         | 1
 _confluent_balancer_api_state                                                                 | 1          | 1
 _confluent_balancer_broker_samples                                                            | 32         | 1
 _confluent_balancer_partition_samples                                                         | 32         | 1
 _schemas                                                                                      | 1          | 1
 connect-configs                                                                               | 1          | 1
 connect-offsets                                                                               | 25         | 1
 connect-statuses                                                                              | 5          | 1
 default_ksql_processing_log                                                                   | 1          | 1
 pageviews                                                                                     | 1          | 1
 users                                                                                         | 1          | 1
---------------------------------------------------------------------------------------------------------------------------------

Note

Your output should show numerous _confluent-controlcenter topics. These have been removed for clarity.

Inspect the users topic by using the PRINT statement:

PRINT users;

Note

The PRINT statement is one of the few case-sensitive commands in ksqlDB, even when the topic name is not quoted.

Your output should resemble:

Key format: KAFKA_STRING
Value format: AVRO
rowtime: 2021/05/06 21:39:36.663 Z, key: User_5, value: {"registertime":1512340535197,"userid":"User_5","regionid":"Region_2","gender":"FEMALE"}, partition: 0
rowtime: 2021/05/06 21:39:37.662 Z, key: User_7, value: {"registertime":1506274681444,"userid":"User_7","regionid":"Region_7","gender":"OTHER"}, partition: 0
rowtime: 2021/05/06 21:39:38.662 Z, key: User_1, value: {"registertime":1497598939522,"userid":"User_1","regionid":"Region_5","gender":"OTHER"}, partition: 0
^CTopic printing ceased

Press CTRL+C to stop printing messages.

Inspect the pageviews topic by using the PRINT statement:

PRINT pageviews;

Your output should resemble:

Key format: KAFKA_BIGINT or KAFKA_DOUBLE
Value format: JSON or KAFKA_STRING
rowtime: 2021/05/06 21:40:25.843 Z, key: 1620337225843, value: {"viewtime":1620337225843,"userid":"User_6","pageid":"Page_40"}, partition: 0
rowtime: 2021/05/06 21:40:26.044 Z, key: 1620337226044, value: {"viewtime":1620337226044,"userid":"User_7","pageid":"Page_83"}, partition: 0
rowtime: 2021/05/06 21:40:26.243 Z, key: 1620337226243, value: {"viewtime":1620337226243,"userid":"User_1","pageid":"Page_63"}, partition: 0
^CTopic printing ceased

Press CTRL+C to stop printing messages.

For more information, see ksqlDB Syntax Reference.

Create a Stream and Table

These examples query messages from Kafka topics called pageviews and users using the following schemas:

../../_images/ksql-quickstart-schemas.jpg
  1. Create a stream, named pageviews_original, from the pageviews Kafka topic, specifying the value_format of JSON.

    CREATE STREAM pageviews_original (viewtime bigint, userid varchar, pageid varchar) WITH
        (kafka_topic='pageviews', value_format='JSON');
    

    Your output should resemble:

     Message
    ---------------
     Stream created
    ---------------
    

    Tip

    You can run DESCRIBE pageviews_original; to see the schema for the stream. Notice that ksqlDB created an additional column, named ROWTIME, which corresponds with the Kafka message timestamp.

  2. Create a table, named users_original, from the users Kafka topic, specifying the value_format of AVRO.

    CREATE TABLE users_original (id VARCHAR PRIMARY KEY) WITH
        (kafka_topic='users', value_format='AVRO');
    

    Your output should resemble:

     Message
    ---------------
     Table created
    ---------------
    

    Tip

    You can run DESCRIBE users_original; to see the schema for the table.

    Note

    You may have noticed the CREATE TABLE did not define the set of columns like the CREATE STREAM statement did. This is because the value format is Avro, and the DataGen tool publishes the Avro schema to Schema Registry. ksqlDB retrieves the schema from Schema Registry and uses this to build the SQL schema for the table. You may still provide the schema if you wish.

  3. Optional: Show all streams and tables.

    ksql> SHOW STREAMS;
    
      Stream Name         | Kafka Topic                 | Key Format | Value Format | Windowed
     ------------------------------------------------------------------------------------------
      KSQL_PROCESSING_LOG | default_ksql_processing_log | KAFKA      | JSON         | false
      PAGEVIEWS_ORIGINAL  | pageviews                   | KAFKA      | JSON         | false
     ------------------------------------------------------------------------------------------
    
    ksql> SHOW TABLES;
    
     Table Name     | Kafka Topic | Key Format | Value Format | Windowed
    ---------------------------------------------------------------------
     USERS_ORIGINAL | users       | KAFKA      | AVRO         | false
    ---------------------------------------------------------------------
    

    Tip

    Notice the KSQL_PROCESSING_LOG stream listed in the SHOW STREAMS output? ksqlDB appends messages that describe any issues it encountered while processing your data. If things aren’t working as you expect, check the contents of this stream to see if ksqlDB is encountering data errors.

View your data

  1. Use SELECT to create a query that returns data from a TABLE. This query includes the LIMIT keyword to limit the number of rows returned in the query result, and the EMIT CHANGES keywords to indicate we wish to stream results back. This is known as a pull query. for an explanation of the different query types, see Queries. Note that exact data output may vary because of the randomness of the data generation.

    SELECT * FROM users_original EMIT CHANGES LIMIT 5;
    

    Your output should resemble:

    +---------------+---------------+---------------+---------------+---------------+
    |ID             |REGISTERTIME   |USERID         |REGIONID       |GENDER         |
    +---------------+---------------+---------------+---------------+---------------+
    |User_2         |1502155111606  |User_2         |Region_8       |OTHER          |
    |User_1         |1499783711681  |User_1         |Region_3       |OTHER          |
    |User_9         |1504556621362  |User_9         |Region_5       |FEMALE         |
    |User_6         |1488869543103  |User_6         |Region_4       |OTHER          |
    |User_3         |1512248344223  |User_3         |Region_9       |FEMALE         |
    Limit Reached
    Query terminated
    

    Note

    Push queries on tables output the full history of the table that is stored in the Kafka changelog topic, which means that it outputs historic data, followed by the stream of updates to the table. It is therefore likely that rows with matching ID are output as existing rows in the table are updated.

  2. View the data in your pageviews_original stream by issuing the following push query:

    SELECT viewtime, userid, pageid FROM pageviews_original emit changes LIMIT 3;
    

    Your output should resemble:

    +--------------+--------------+--------------+
    |VIEWTIME      |USERID        |PAGEID        |
    +--------------+--------------+--------------+
    |1581078296791 |User_1        |Page_54       |
    |1581078297792 |User_8        |Page_93       |
    |1581078298792 |User_6        |Page_26       |
    Limit Reached
    Query terminated
    

    Note

    By default, push queries on streams output only changes that occur after the query is started, which means that historic data is not included. Run set 'auto.offset.reset'='earliest'; to update your session properties if you want to see the historic data.

Write Queries

These examples write queries using ksqlDB.

Note

By default ksqlDB reads the topics for streams and tables from the latest offset.

  1. Create a query that enriches the pageviews data with the user’s gender and regionid from the users table. The following query enriches the pageviews_original stream by doing a LEFT JOIN with the users_original table on the userid column.

    SELECT users_original.id AS userid, pageid, regionid, gender
        FROM pageviews_original
        LEFT JOIN users_original
          ON pageviews_original.userid = users_original.id
        EMIT CHANGES
        LIMIT 5;
    

    Your output should resemble:

    +-------------------+-------------------+-------------------+-------------------+
    |ID                 |PAGEID             |REGIONID           |GENDER             |
    +-------------------+-------------------+-------------------+-------------------+
    |User_7             |Page_23            |Region_2           |OTHER              |
    |User_3             |Page_42            |Region_2           |MALE               |
    |User_7             |Page_87            |Region_2           |OTHER              |
    |User_2             |Page_57            |Region_5           |FEMALE             |
    |User_9             |Page_59            |Region_1           |OTHER              |
    Limit Reached
    Query terminated
    
  2. Create a persistent query by using the CREATE STREAM keywords to precede the SELECT statement. The results from this query are written to the PAGEVIEWS_ENRICHED Kafka topic. The following query enriches the pageviews_original STREAM by doing a LEFT JOIN with the users_original TABLE on the user ID.

    CREATE STREAM pageviews_enriched AS
      SELECT users_original.id AS userid, pageid, regionid, gender
      FROM pageviews_original
      LEFT JOIN users_original
        ON pageviews_original.userid = users_original.id
      EMIT CHANGES;
    

    Your output should resemble:

     Message
    --------------------------------------------------
     Created query with ID CSAS_PAGEVIEWS_ENRICHED_33
    --------------------------------------------------
    

    Tip

    You can run DESCRIBE pageviews_enriched; to describe the stream.

  3. Use SELECT to view query results as they come in. To stop viewing the query results, press Ctrl+C. This stops printing to the console but it does not terminate the actual query. The query continues to run in the underlying ksqlDB application.

    SELECT * FROM pageviews_enriched EMIT CHANGES;
    

    Your output should resemble:

    +---------------------+---------------------+---------------------+---------------------+
    |ID                   |PAGEID               |REGIONID             |GENDER               |
    +---------------------+---------------------+---------------------+---------------------+
    |User_8               |Page_41              |Region_4             |FEMALE               |
    |User_2               |Page_87              |Region_3             |OTHER                |
    |User_3               |Page_84              |Region_8             |FEMALE               |
    ^CQuery terminated
    

    Use Ctrl+C to terminate the query.

  4. Create a new persistent query where a condition limits the streams content, using WHERE. Results from this query are written to a Kafka topic named PAGEVIEWS_FEMALE.

    CREATE STREAM pageviews_female AS
      SELECT * FROM pageviews_enriched
      WHERE gender = 'FEMALE'
      EMIT CHANGES;
    

    Your output should resemble:

     Message
    ------------------------------------------------
     Created query with ID CSAS_PAGEVIEWS_FEMALE_35
    ------------------------------------------------
    

    Tip

    You can run DESCRIBE pageviews_female; to describe the stream.

  5. Create a new persistent query where another condition is met, using LIKE. Results from this query are written to the pageviews_enriched_r8_r9 Kafka topic.

    CREATE STREAM pageviews_female_like_89
      WITH (kafka_topic='pageviews_enriched_r8_r9') AS
      SELECT * FROM pageviews_female
      WHERE regionid LIKE '%_8' OR regionid LIKE '%_9'
      EMIT CHANGES;
    

    Your output should resemble:

     Message
    --------------------------------------------------------
     Created query with ID CSAS_PAGEVIEWS_FEMALE_LIKE_89_37
    --------------------------------------------------------
    
  6. Create a new persistent query that counts the pageviews for each region and gender combination in a tumbling window of 30 seconds when the count is greater than one. Results from this query are written to the PAGEVIEWS_REGIONS Kafka topic in the Avro format. ksqlDB registers the Avro schema with the configured Schema Registry when it writes the first message to the PAGEVIEWS_REGIONS topic.

    CREATE TABLE pageviews_regions
      WITH (KEY_FORMAT='json') AS
    SELECT gender, regionid , COUNT(*) AS numusers
    FROM pageviews_enriched
      WINDOW TUMBLING (size 30 second)
    GROUP BY gender, regionid
    EMIT CHANGES;
    

    Your output should resemble:

     Message
    -------------------------------------------------
     Created query with ID CTAS_PAGEVIEWS_REGIONS_39
    -------------------------------------------------
    

    Tip

    You can run DESCRIBE pageviews_regions; to describe the table.

  7. Optional: View results from the above queries by using a push query.

    SELECT * FROM pageviews_regions EMIT CHANGES LIMIT 5;
    

    Your output should resemble:

    +----------------------+----------------------+----------------------+----------------------+----------------------+
    |GENDER                |REGIONID              |WINDOWSTART           |WINDOWEND             |NUMUSERS              |
    +----------------------+----------------------+----------------------+----------------------+----------------------+
    |OTHER                 |Region_1              |1620681750000         |1620681780000         |3                     |
    |FEMALE                |Region_5              |1620681750000         |1620681780000         |3                     |
    |OTHER                 |Region_3              |1620681750000         |1620681780000         |5                     |
    |MALE                  |Region_4              |1620681750000         |1620681780000         |7                     |
    |FEMALE                |Region_9              |1620681750000         |1620681780000         |5                     |
    Limit Reached
    Query terminated
    

    Note

    Notice the addition of the WINDOWSTART and WINDOWEND columns. These are available because pageviews_regions is aggregating data per 30 second window. ksqlDB automatically adds these system columns for windowed results.

  8. Optional: View results from the previous queries by using pull query.

    When a CREATE TABLE statement contains a GROUP BY clause, ksqlDB internally builds a table that contains the results of the aggregation. ksqlDB supports pull queries against such aggregation results.

    Unlike the push query used in the previous step, which pushes a stream of results to you, pull queries pull a result set and automatically terminate.

    Pull queries do not have the EMIT CHANGES clause.

    View all of the windows and user counts available for a specific gender and region by using a pull query:

    SELECT * FROM pageviews_regions WHERE gender='FEMALE' AND regionid='Region_4';
    

    Your output should resemble:

    +----------------------+----------------------+----------------------+----------------------+----------------------+
    |GENDER                |REGIONID              |WINDOWSTART           |WINDOWEND             |NUMUSERS              |
    +----------------------+----------------------+----------------------+----------------------+----------------------+
    |FEMALE                |Region_4              |1620681780000         |1620681810000         |17                    |
    |FEMALE                |Region_4              |1620681810000         |1620681840000         |19                    |
    |FEMALE                |Region_4              |1620681840000         |1620681870000         |19                    |
    Query terminated
    

    Pull queries on windowed tables like pageviews_regions also support querying a single window’s result:

    SELECT NUMUSERS FROM pageviews_regions WHERE
      gender='FEMALE' AND regionid='Region_4' AND WINDOWSTART=1620681780000;
    

    Note

    You must change the value of WINDOWSTART in the previous SQL to match one of the window boundaries in your data. Otherwise, no results are returned.

    Your output should resemble:

    +----------+
    |NUMUSERS  |
    +----------+
    |17        |
    Query terminated
    

    To query a range of windows:

    SELECT WINDOWSTART, WINDOWEND, NUMUSERS FROM pageviews_regions WHERE
      gender='FEMALE' AND regionid='Region_4' AND 1620681780000 <= WINDOWSTART AND WINDOWSTART <= 1620681840000;
    

    Note

    You must change the value of WINDOWSTART in the previous SQL to match one of the window boundaries in your data. Otherwise, no results are returned.

    Your output should resemble:

    +--------------------------------------+--------------------------------------+--------------------------------------+
    |WINDOWSTART                           |WINDOWEND                             |NUMUSERS                              |
    +--------------------------------------+--------------------------------------+--------------------------------------+
    |1620681780000                         |1620681810000                         |17                                    |
    |1620681810000                         |1620681840000                         |19                                    |
    |1620681840000                         |1620681870000                         |19                                    |
    Query terminated
    
  9. Optional: Show all persistent queries.

    SHOW QUERIES;
    

    Your output should resemble:

    Query ID                         | Query Type | Status    | Sink Name                | Sink Kafka Topic         | Query String
    
    ------------------------------------------------------------------------------------------------------------------------ CSAS_PAGEVIEWS_ENRICHED_85       | PERSISTENT | RUNNING:1 | PAGEVIEWS_ENRICHED       | PAGEVIEWS_ENRICHED       | CREATE STREAM PAGEVIEWS_ENRICHED WITH (KAFKA_TOPIC='PAGEVIEWS_ENRICHED', PARTITIONS=1, REPLICAS=1) AS SELECT   USERS_ORIGINAL.ID USERID,   PAGEVIEWS_ORIGINAL.PAGEID PAGEID,   USERS_ORIGINAL.REGIONID REGIONID,   USERS_ORIGINAL.GENDER GENDER FROM PAGEVIEWS_ORIGINAL PAGEVIEWS_ORIGINAL LEFT OUTER JOIN USERS_ORIGINAL USERS_ORIGINAL ON ((PAGEVIEWS_ORIGINAL.USERID = USERS_ORIGINAL.ID)) EMIT CHANGES;
    CSAS_PAGEVIEWS_FEMALE_LIKE_89_89 | PERSISTENT | RUNNING:1 | PAGEVIEWS_FEMALE_LIKE_89 | pageviews_enriched_r8_r9 | CREATE STREAM PAGEVIEWS_FEMALE_LIKE_89 WITH (KAFKA_TOPIC='pageviews_enriched_r8_r9', PARTITIONS=1, REPLICAS=1) AS SELECT * FROM PAGEVIEWS_FEMALE PAGEVIEWS_FEMALE WHERE ((PAGEVIEWS_FEMALE.REGIONID LIKE '%_8') OR (PAGEVIEWS_FEMALE.REGIONID LIKE '%_9')) EMIT CHANGES;
    
    CSAS_PAGEVIEWS_FEMALE_87         | PERSISTENT | RUNNING:1 | PAGEVIEWS_FEMALE         | PAGEVIEWS_FEMALE         | CREATE STREAM PAGEVIEWS_FEMALE WITH (KAFKA_TOPIC='PAGEVIEWS_FEMALE', PARTITIONS=1, REPLICAS=1) AS SELECT * FROM PAGEVIEWS_ENRICHED PAGEVIEWS_ENRICHED WHERE (PAGEVIEWS_ENRICHED.GENDER = 'FEMALE') EMIT CHANGES;
    
    CTAS_PAGEVIEWS_REGIONS_91        | PERSISTENT | RUNNING:1 | PAGEVIEWS_REGIONS        | PAGEVIEWS_REGIONS        | CREATE TABLE PAGEVIEWS_REGIONS WITH (KAFKA_TOPIC='PAGEVIEWS_REGIONS', KEY_FORMAT='json', PARTITIONS=1, REPLICAS=1) AS SELECT   PAGEVIEWS_ENRICHED.GENDER GENDER,   PAGEVIEWS_ENRICHED.REGIONID REGIONID,   COUNT(*) NUMksql> FROM PAGEVIEWS_ENRICHED PAGEVIEWS_ENRICHED WINDOW TUMBLING ( SIZE 30 SECONDS )  GROUP BY PAGEVIEWS_ENRICHED.GENDER, PAGEVIEWS_ENRICHED.REGIONID EMIT CHANGES;
    ------------------------------------------------------------------------------------------------------------------------
    For detailed information on a Query run: EXPLAIN <Query ID>;
    
  10. Optional: Examine query run-time metrics and details. Information including the target Kafka topic is available, as well as throughput figures for the messages being processed.

    DESCRIBE pageviews_regions EXTENDED;
    

    Your output should resemble:

    Name                 : PAGEVIEWS_REGIONS
    Type                 : TABLE
    Timestamp field      : Not set - using <ROWTIME>
    Key format           : KAFKA
    Value format         : AVRO
    Kafka topic          : PAGEVIEWS_REGIONS (partitions: 1, replication: 1)
    Statement            : CREATE TABLE PAGEVIEWS_REGIONS WITH (KAFKA_TOPIC='PAGEVIEWS_REGIONS', PARTITIONS=1, REPLICAS=1, VALUE_FORMAT='avro') AS SELECT
    PAGEVIEWS_ENRICHED.GENDER GENDER,
    PAGEVIEWS_ENRICHED.REGIONID REGIONID,
    COUNT(*) NUMUSERS
    FROM PAGEVIEWS_ENRICHED PAGEVIEWS_ENRICHED
    WINDOW TUMBLING ( SIZE 30 SECONDS )
    GROUP BY PAGEVIEWS_ENRICHED.GENDER, PAGEVIEWS_ENRICHED.REGIONID
    EMIT CHANGES;
    
    Field      | Type
    ---------------------------------------------------------------------
    KSQL_COL_0 | VARCHAR(STRING)  (primary key) (Window type: TUMBLING)
    NUMUSERS   | BIGINT
    ---------------------------------------------------------------------
    
    Queries that write from this TABLE
    -----------------------------------
    CTAS_PAGEVIEWS_REGIONS_39 (RUNNING) : CREATE TABLE PAGEVIEWS_REGIONS WITH (KAFKA_TOPIC='PAGEVIEWS_REGIONS', PARTITIONS=1, REPLICAS=1, VALUE_FORMAT='avro') AS SELECT   PAGEVIEWS_ENRICHED.GENDER GENDER,   PAGEVIEWS_ENRICHED.REGIONID REGIONID,   COUNT(*) NUMUSERS FROM PAGEVIEWS_ENRICHED PAGEVIEWS_ENRICHED WINDOW TUMBLING ( SIZE 30 SECONDS )  GROUP BY PAGEVIEWS_ENRICHED.GENDER, PAGEVIEWS_ENRICHED.REGIONID EMIT CHANGES;
    
    For query topology and execution plan please run: EXPLAIN <QueryId>
    
    Local runtime statistics
    ------------------------
    messages-per-sec:      2.89   total-messages:      3648     last-message: 2021-01-27T19:36:11.197Z
    
    (Statistics of the local KSQL server interaction with the Kafka topic PAGEVIEWS_REGIONS)
    
    Consumer Groups summary:
    
    Consumer Group       : _confluent-ksql-default_query_CTAS_PAGEVIEWS_REGIONS_39
    
    Kafka topic          : PAGEVIEWS_ENRICHED
    Max lag              : 5
    
    Partition | Start Offset | End Offset | Offset | Lag
    ------------------------------------------------------
    0         | 0            | 7690       | 7685   | 5
    ------------------------------------------------------
    
    Kafka topic          : _confluent-ksql-default_query_CTAS_PAGEVIEWS_REGIONS_39-Aggregate-GroupBy-repartition
    Max lag              : 5
    
    Partition | Start Offset | End Offset | Offset | Lag
    ------------------------------------------------------
    0         | 6224         | 6229       | 6224   | 5
    ------------------------------------------------------
    

Using Nested Schemas (STRUCT) in ksqlDB

Struct support enables the modeling and access of nested data in Kafka topics, from both JSON and Avro.

Here we’ll use the ksql-datagen tool to create some sample data which includes a nested address field. Run this in a new window, and leave it running.

$CONFLUENT_HOME/bin/ksql-datagen  \
     quickstart=orders \
     format=avro \
     topic=orders \
     msgRate=1

From the ksqlDB command prompt, register the topic in ksqlDB:

CREATE STREAM orders
(
    ordertime BIGINT,
    orderid INT,
    itemid STRING,
    orderunits DOUBLE,
    address STRUCT<city STRING, state STRING, zipcode BIGINT>
)
WITH (KAFKA_TOPIC='orders', VALUE_FORMAT='avro');

Your output should resemble:

 Message
----------------
 Stream created
----------------

Use the DESCRIBE function to observe the schema, which includes a STRUCT:

DESCRIBE orders;

Your output should resemble:

 Field      | Type
----------------------------------------------------------------------------------
 ORDERTIME  | BIGINT
 ORDERID    | INTEGER
 ITEMID     | VARCHAR(STRING)
 ORDERUNITS | DOUBLE
 ADDRESS    | STRUCT<CITY VARCHAR(STRING), STATE VARCHAR(STRING), ZIPCODE BIGINT>
----------------------------------------------------------------------------------
For runtime statistics and query details run: DESCRIBE <Stream,Table> EXTENDED;

Query the data by using -> notation to access the Struct contents:

SELECT ORDERID, ADDRESS->CITY FROM ORDERS EMIT CHANGES LIMIT 5;

Your output should resemble:

+-----------------------------------+-----------------------------------+
|ORDERID                            |ADDRESS__CITY                      |
+-----------------------------------+-----------------------------------+
|1188                               |City_95                            |
|1189                               |City_24                            |
|1190                               |City_57                            |
|1191                               |City_37                            |
|1192                               |City_82                            |
Limit Reached
Query terminated

INSERT INTO

You can use the INSERT INTO syntax to merge the contents of multiple streams. An example of this could be where the same event type is coming from different sources.

Open two new terminals and run two Datagen processes, each writing to a different topic. This simulates order data arriving from a local installation and from a third-party.

Tip

Each of these commands should be run in a separate window. When the exercise is finished, exit them by pressing Ctrl-C.

$CONFLUENT_HOME/bin/ksql-datagen \
     quickstart=orders \
     format=json \
     topic=orders_local \
     msgRate=2

$CONFLUENT_HOME/bin/ksql-datagen \
     quickstart=orders \
     format=json \
     topic=orders_3rdparty \
     msgRate=2

In the ksqlDB CLI, register the source topic for each:

CREATE STREAM orders_src_local
(
  ordertime BIGINT,
  orderid INT,
  itemid STRING,
  orderunits DOUBLE,
  address STRUCT<city STRING, state STRING, zipcode BIGINT>
)
WITH (KAFKA_TOPIC='orders_local', VALUE_FORMAT='JSON');

CREATE STREAM orders_src_3rdparty
(
  ordertime BIGINT,
  orderid INT,
  itemid STRING,
  orderunits DOUBLE,
  address STRUCT<city STRING, state STRING, zipcode BIGINT>
)
WITH (KAFKA_TOPIC='orders_3rdparty', VALUE_FORMAT='JSON');

After each CREATE STREAM statement you should get the message:

 Message
----------------
 Stream created
----------------

Create the output stream, using the standard CREATE STREAM AS syntax. Because multiple sources of data are being joined into a common target, it is useful to add in lineage information. This can be done by simply including it as part of the SELECT:

CREATE STREAM all_orders AS SELECT 'LOCAL' AS SRC, * FROM orders_src_local EMIT CHANGES;

Your output should resemble:

 Message
------------------------------------------
 Created query with ID CSAS_ALL_ORDERS_71
------------------------------------------

Use the DESCRIBE command to observe the schema of the target stream.

DESCRIBE all_orders;

Your output should resemble:

Name                 : ALL_ORDERS
 Field      | Type
----------------------------------------------------------------------------------
 SRC        | VARCHAR(STRING)
 ORDERTIME  | BIGINT
 ORDERID    | INTEGER
 ITEMID     | VARCHAR(STRING)
 ORDERUNITS | DOUBLE
 ADDRESS    | STRUCT<CITY VARCHAR(STRING), STATE VARCHAR(STRING), ZIPCODE BIGINT>
----------------------------------------------------------------------------------
 For runtime statistics and query details run: DESCRIBE <Stream,Table> EXTENDED;

Add stream of third-party orders into the existing output stream:

INSERT INTO all_orders SELECT '3RD PARTY' AS SRC, * FROM orders_src_3rdparty EMIT CHANGES;

Your output should resemble:

 Message
--------------------------------------
 Created query with ID INSERTQUERY_73
--------------------------------------

Query the output stream to verify that data from each source is being written to it:

SELECT * FROM all_orders EMIT CHANGES;

Your output should resemble the following. Note that there are messages from both source topics (denoted by LOCAL and 3RD PARTY respectively).

+---------------------+---------------------+---------------------+---------------------+---------------------+---------------------+
|SRC                  |ORDERTIME            |ORDERID              |ITEMID               |ORDERUNITS           |ADDRESS              |
+---------------------+---------------------+---------------------+---------------------+---------------------+---------------------+
|3RD PARTY            |1491006356222        |1583                 |Item_169             |2.091966572094054    |{CITY=City_91, STATE=|
|                     |                     |                     |                     |                     |State_51, ZIPCODE=184|
|                     |                     |                     |                     |                     |74}                  |
|LOCAL                |1504382324241        |1630                 |Item_410             |0.6462658185260942   |{CITY=City_55, STATE=|
|                     |                     |                     |                     |                     |State_38, ZIPCODE=372|
|                     |                     |                     |                     |                     |44}                  |
|3RD PARTY            |1512567250385        |1584                 |Item_357             |7.205193136057381    |{CITY=City_91, STATE=|
|                     |                     |                     |                     |                     |State_19, ZIPCODE=457|
|                     |                     |                     |                     |                     |45}                  |
^CQuery terminated

Press Ctrl+C to cancel the SELECT query and return to the ksqlDB prompt.

Terminate and Exit

ksqlDB

Note

Persisted queries will continuously run as ksqlDB applications until they are manually terminated. Exiting ksqlDB CLI does not terminate persistent queries.

  1. From the output of SHOW QUERIES; identify a query ID you would like to terminate. For example, if you wish to terminate query ID CTAS_PAGEVIEWS_REGIONS_15:

    TERMINATE CTAS_PAGEVIEWS_REGIONS_15;
    

    Tip

    The actual name of the query running may vary; refer to the output of SHOW QUERIES;.

  2. Run the exit command to leave the ksqlDB CLI.

    ksql> exit
    Exiting ksqlDB.
    

Confluent CLI

If you are running Confluent Platform using the CLI, you can stop it with this command.

$CONFLUENT_HOME/bin/confluent local services stop