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README.md

Quick Start

Overview Quick Start Concepts Syntax Reference Demo Examples FAQ Roadmap

Welcome to the quick start for KSQL!

The goal of this quick start is to demonstrate a simple workflow using KSQL to write streaming queries against data in Kafka.

Table of Contents

Setup

Because KSQL queries data in a Kafka cluster, you will need to bring up a Kafka cluster, including ZooKeeper and a Kafka broker. Do not run KSQL against a production Kafka cluster while KSQL is in developer preview.

  1. Bring up a Kafka cluster and start KSQL.

  1. After you have successfully started the Kafka cluster and started KSQL, you will see the KSQL prompt:

                       ======================================
                   =      _  __ _____  ____  _          =
                   =     | |/ // ____|/ __ \| |         =
                   =     | ' /| (___ | |  | | |         =
                   =     |  <  \___ \| |  | | |         =
                   =     | . \ ____) | |__| | |____     =
                   =     |_|\_\_____/ \___\_\______|    =
                   =                                    =
                   =   Streaming SQL Engine for Kafka   =
Copyright 2017 Confluent Inc.                         

CLI v0.1, Server v0.1 located at http://localhost:9098

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

ksql>

  2. KSQL provides a structured query language to query Kafka data, so you need some data to query. For this quick start, you will produce mock streams to the Kafka cluster.

    • If you are using our Docker Compose files, a Docker container is already running with a data generator that is continuously producing Kafka messages to the Kafka cluster. No further action is required.
    • If you are not using our Docker environment, then follow these instructions to generate data to the Kafka cluster.

Create a Stream and Table

This KSQL quick start shows examples querying data from Kafka topics called pageviews and users using the following schemas:

image

Before proceeding, please confirm:

  • In the terminal window where you started KSQL, you see the ksql> prompt.
  • If you are not using Docker, you must have manually run the data generator to produce topics called pageviews and users. If you haven't done this, please follow these instructions to manually generate data. (If you are using Docker this is done automatically for you.)

  1. Create a STREAM pageviews_original from the Kafka topic pageviews, specifying the value_format of DELIMITED. Then DESCRIBE the new STREAM. Notice that KSQL created additional columns called ROWTIME, which corresponds to the Kafka message timestamp, and ROWKEY, which corresponds to the Kafka message key.

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

ksql> DESCRIBE pageviews_original;

 Field    | Type            
----------------------------
 ROWTIME  | BIGINT          
 ROWKEY   | VARCHAR(STRING) 
 VIEWTIME | BIGINT          
 USERID   | VARCHAR(STRING) 
 PAGEID   | VARCHAR(STRING)

  2. Create a TABLE users_original from the Kafka topic users, specifying the value_format of JSON. Then DESCRIBE the new TABLE.

    ksql> CREATE TABLE users_original (registertime bigint, gender varchar, regionid varchar, userid varchar) WITH (kafka_topic='users', value_format='JSON');

ksql> DESCRIBE users_original;

 Field        | Type            
--------------------------------
 ROWTIME      | BIGINT          
 ROWKEY       | VARCHAR(STRING) 
 REGISTERTIME | BIGINT          
 GENDER       | VARCHAR(STRING) 
 REGIONID     | VARCHAR(STRING) 
 USERID       | VARCHAR(STRING)

  3. Show all STREAMS and TABLES.

    ksql> SHOW STREAMS;

 Stream Name              | Kafka Topic              | Format    
-----------------------------------------------------------------
 PAGEVIEWS_ORIGINAL       | pageviews                | DELIMITED 

ksql> SHOW TABLES;

 Table Name        | Kafka Topic       | Format    | Windowed 
--------------------------------------------------------------
 USERS_ORIGINAL    | users             | JSON      | false

Write Queries

Note: By default KSQL reads the topics for streams and tables from the latest offset.

  1. Use SELECT to create a query that returns data from a STREAM. To stop viewing the data, press <ctrl-c>. You may optionally include the LIMIT keyword to limit the number of rows returned in the query result. Note that exact data output may vary because of the randomness of the data generation.

    ksql> SELECT pageid FROM pageviews_original LIMIT 3;
Page_24
Page_73
Page_78
LIMIT reached for the partition.
Query terminated
ksql>

  2. Create a persistent query by using the CREATE STREAM keywords to precede the SELECT statement. Unlike the non-persistent query above, results from this query are written to a Kafka topic PAGEVIEWS_FEMALE. The query below enriches the pageviews STREAM by doing a LEFT JOIN with the users_original TABLE on the user ID, where a condition is met.

    ksql> CREATE STREAM pageviews_female AS SELECT users_original.userid AS userid, pageid, regionid, gender FROM pageviews_original LEFT JOIN users_original ON pageviews_original.userid = users_original.userid WHERE gender = 'FEMALE';

ksql> DESCRIBE pageviews_female;
 Field    | Type            
----------------------------
 ROWTIME  | BIGINT          
 ROWKEY   | VARCHAR(STRING) 
 USERID   | VARCHAR(STRING) 
 PAGEID   | VARCHAR(STRING) 
 REGIONID | VARCHAR(STRING) 
 GENDER   | VARCHAR(STRING)

  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 underyling KSQL application.

    ksql> SELECT * FROM pageviews_female;
1502477856762 | User_2 | User_2 | Page_55 | Region_9 | FEMALE
1502477857946 | User_5 | User_5 | Page_14 | Region_2 | FEMALE
1502477858436 | User_3 | User_3 | Page_60 | Region_3 | FEMALE
^CQuery terminated
ksql>

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

    ksql> CREATE STREAM pageviews_female_like_89 WITH (kafka_topic='pageviews_enriched_r8_r9', value_format='DELIMITED') AS SELECT * FROM pageviews_female WHERE regionid LIKE '%_8' OR regionid LIKE '%_9';

  5. 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 1. Results from this query are written to a Kafka topic called PAGEVIEWS_REGIONS.

    ksql> CREATE TABLE pageviews_regions AS SELECT gender, regionid , COUNT(*) AS numusers FROM pageviews_female WINDOW TUMBLING (size 30 second) GROUP BY gender, regionid HAVING COUNT(*) > 1;

ksql> DESCRIBE pageviews_regions;

 Field    | Type            
----------------------------
 ROWTIME  | BIGINT          
 ROWKEY   | VARCHAR(STRING) 
 GENDER   | VARCHAR(STRING) 
 REGIONID | VARCHAR(STRING) 
 NUMUSERS | BIGINT

  6. Use SELECT to view results from the above query.

    ksql> SELECT regionid, numusers FROM pageviews_regions LIMIT 5;
Region_3 | 4
Region_3 | 5
Region_6 | 5
Region_6 | 6
Region_3 | 8
LIMIT reached for the partition.
Query terminated
ksql>

  7. Show all persistent queries.

    ksql> SHOW QUERIES;

 Query ID | Kafka Topic              | Query String                                                                                                                                                                                                                      
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 1        | PAGEVIEWS_FEMALE         | CREATE STREAM pageviews_female AS SELECT users_original.userid AS userid, pageid, regionid, gender FROM pageviews_original LEFT JOIN users_original ON pageviews_original.userid = users_original.userid WHERE gender = 'FEMALE'; 
 2        | pageviews_enriched_r8_r9 | CREATE STREAM pageviews_female_like_89 WITH (kafka_topic='pageviews_enriched_r8_r9', value_format='DELIMITED') AS SELECT * FROM pageviews_female WHERE regionid LIKE '%_8' OR regionid LIKE '%_9';                                
 3        | PAGEVIEWS_REGIONS        | CREATE TABLE pageviews_regions AS SELECT gender, regionid , COUNT(*) AS numusers FROM pageviews_female WINDOW TUMBLING (size 30 second) GROUP BY gender, regionid HAVING COUNT(*) > 1;

Terminate and Exit

KSQL

Important: Queries will continuously run as KSQL applications until they are manually terminated. Exiting KSQL 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 2:

    ksql> TERMINATE 2;

  2. To exit from KSQL, type 'exit'.

    ksql> exit

Docker

If you are running Docker Compose, you must explicitly shut down Docker Compose. For more information, see the docker-compose down documentation.

Important: This command will delete all KSQL queries and topic data.

$ docker-compose down

Confluent Platform

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

$ confluent stop

Next steps

Try the end-to-end Clickstream Analysis demo, which shows how to build an application that performs real-time user analytics.