Important

You are viewing documentation for an older version of Confluent Platform. For the latest, click here.

Quick Start using Community Components (Local)¶

This quick start demonstrates how to get up and running using Confluent Platform with Confluent Community components. It demonstrates the basic and most powerful capabilities, including creating topics, adding and modifying data, and stream processing by using KSQL. In this quick start you will create Kafka topics and streaming queries on these topics by using KSQL.

This quick start leverages the Confluent Platform CLI, the Apache Kafka® CLI, and the KSQL CLI. For a rich UI-based experience, try out the Confluent Platform quick start with commercial components.

You can also run an automated version of this quick start designed for Confluent Platform local installs.

Important

Java 1.8 is supported in this version of Confluent Platform (Java 1.9 and 1.10 are currently not supported). For more information, see Java supported versions.

Prerequisites:

Internet connectivity.
Ensure you are on an Operating System currently supported by Confluent Platform.
wget to get the connector configuration file.

Step 1: Download and Install¶

Go to the downloads page and choose Download Confluent Community.
Provide your name and email and select Download.

Decompress the file. You should have these directories:

Folder	Description
/bin/	Driver scripts for starting and stopping services
/etc/	Configuration files
/lib/	Systemd services
/logs/	Log files
/share/	Jars and licenses
/src/	Source files that require a platform-dependent build

Install the Confluent Hub client. This is used in the next step to install the free and open source kafka-source-datagen connector.
- Install on MacOS
- Install on Linux
Install the Kafka Connect Datagen source connector using the Confluent Hub client. This connector generates mock data for demonstration purposes and is not suitable for production. Confluent Hub is an online library of pre-packaged and ready-to-install extensions or add-ons for Confluent Platform and Kafka.
```
<path-to-confluent>/bin/confluent-hub install \
--no-prompt confluentinc/kafka-connect-datagen:0.1.0
```
Your output should resemble:
```
Running in a "--no-prompt" mode
...
Completed
```
Start Confluent Platform using the Confluent CLI. This command will start all of the Confluent Platform components, including Kafka, ZooKeeper, Schema Registry, HTTP REST Proxy for Kafka, Kafka Connect, and KSQL. You can add Confluent’s bin directory to your PATH by running: export PATH=<path-to-confluent>/bin:$PATH

Important

The Confluent CLI is meant for development purposes only and is not suitable for a production environment. The data that are produced are transient and are intended to be temporary. For production-ready workflows, see Install and Upgrade.
```
<path-to-confluent>/bin/confluent start
```
Your output should resemble:
```
Starting zookeeper
zookeeper is [UP]
Starting kafka
kafka is [UP]
Starting schema-registry
schema-registry is [UP]
Starting kafka-rest
kafka-rest is [UP]
Starting connect
connect is [UP]
Starting ksql-server
ksql-server is [UP]
```

Step 2: Create Kafka Topics¶

In this step Kafka topics are created in Confluent Platform by using the Kafka CLI.

Run this command to create a topic named users.

<path-to-confluent>/bin/kafka-topics --create --zookeeper localhost:2181 \
--replication-factor 1 --partitions 1 --topic users

Your output should resemble:

Created topic "users".

Run this command to create a topic named pageviews.

<path-to-confluent>/bin/kafka-topics --create --zookeeper localhost:2181   \
--replication-factor 1 --partitions 1 --topic pageviews

Your output should resemble:

Created topic "pageviews".

Step 3: Install a Kafka Connector and Generate Sample Data¶

In this step, you use Kafka Connect to run a demo source connector called kafka-connect-datagen that creates sample data for the Kafka topics pageviews and users.

Run one instance of the Kafka Connect Datagen connector to produce Kafka data to the pageviews topic in AVRO format.

wget https://github.com/confluentinc/kafka-connect-datagen/raw/master/config/connector_pageviews_cos.config
curl -X POST -H "Content-Type: application/json" --data @connector_pageviews_cos.config http://localhost:8083/connectors

Run another instance of the Kafka Connect Datagen connector to produce Kafka data to the users topic in AVRO format.

wget https://github.com/confluentinc/kafka-connect-datagen/raw/master/config/connector_users_cos.config
curl -X POST -H "Content-Type: application/json" --data @connector_users_cos.config http://localhost:8083/connectors

Tip

The Kafka Connect Datagen connector was installed manually in Step 1: Download and Install. If you encounter issues locating the Datagen Connector, refer to the Issue: Cannot locate the Datagen Connector in the Troubleshooting section.

Step 4: Create and Write to a Stream and Table using KSQL¶

In this step, KSQL queries are run on the pageviews and users topics that were created in the previous step. The following KSQL commands are run from the KSQL CLI. Enter these commands in your terminal and press Enter.

Important

Confluent Platform must be installed and running.
To try out the preview KSQL web interface, see the Confluent Platform quick start with commercial components.
All KSQL commands must end with a closing semicolon (;).

Create Streams and Tables¶

Start the KSQL CLI in your terminal with this command.
```
LOG_DIR=./ksql_logs <path-to-confluent>/bin/ksql
```
Important

By default KSQL 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, <path-to-confluent>/bin, you must override this default behavior by using the LOG_DIR variable.

Create a stream pageviews from the Kafka topic pageviews, specifying the value_format of AVRO.

CREATE STREAM pageviews (viewtime BIGINT, userid VARCHAR, pageid VARCHAR) \
WITH (KAFKA_TOPIC='pageviews', VALUE_FORMAT='AVRO');

Tip: Enter the SHOW STREAMS; command to view your streams. For example:

 Stream Name      | Kafka Topic      | Format
-------------------------------------------------
 PAGEVIEWS        | pageviews        | AVRO
-------------------------------------------------

Create a table users with several columns from the Kafka topic users, with the value_format of AVRO.

CREATE TABLE users (registertime BIGINT, gender VARCHAR, regionid VARCHAR,  \
userid VARCHAR) \
WITH (KAFKA_TOPIC='users', VALUE_FORMAT='AVRO', KEY = 'userid');

Tip: Enter the SHOW TABLES; query to view your tables.

 Table Name        | Kafka Topic       | Format    | Windowed
--------------------------------------------------------------
 USERS             | users             | AVRO      | false
--------------------------------------------------------------

Write Queries¶

These examples write queries using KSQL. The following KSQL commands are run from the KSQL CLI. Enter these commands in your terminal and press Enter.

Add the custom query property earliest for the auto.offset.reset parameter. This instructs KSQL queries to read all available topic data from the beginning. This configuration is used for each subsequent query. For more information, see the KSQL Configuration Parameter Reference.
```
SET 'auto.offset.reset'='earliest';
```
Your output should resemble:
```
Successfully changed local property 'auto.offset.reset' from 'null' to 'earliest'
```

Create a query that returns data from a stream with the results limited to three rows.

SELECT pageid FROM pageviews LIMIT 3;

Your output should resemble:

Page_45
Page_38
Page_11
LIMIT reached for the partition.
Query terminated

Create a persistent query that filters for female users. The results from this query are written to the Kafka PAGEVIEWS_FEMALE topic. This query enriches the pageviews STREAM by doing a LEFT JOIN with the users TABLE on the user ID, where a condition (gender = 'FEMALE') is met.

CREATE STREAM pageviews_female AS SELECT users.userid AS userid, pageid, \
regionid, gender FROM pageviews LEFT JOIN users ON pageviews.userid = users.userid \
WHERE gender = 'FEMALE';

Your output should resemble:

 Message
----------------------------
 Stream created and running
----------------------------

Create a persistent query where a condition (regionid) is met, using LIKE. Results from this query are written to a Kafka topic named pageviews_enriched_r8_r9.

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

Your output should resemble:

 Message
----------------------------
 Stream created and running
----------------------------

Create a 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. Because the procedure is grouping and counting, the result is now a table, rather than a stream. Results from this query are written to a Kafka topic called 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;
```
Your output should resemble:
```
 Message
---------------------------
 Table created and running
---------------------------
```

Step 5: Monitor Streaming Data¶

Now that your streams are running you can monitor them.

View the details for your stream or table with the DESCRIBE EXTENDED command. For example, run this command to view the pageviews_female_like_89 stream:

DESCRIBE EXTENDED pageviews_female_like_89;

Your output should look like this:

Type                 : STREAM
Key field            : PAGEVIEWS.USERID
Timestamp field      : Not set - using <ROWTIME>
Key format           : STRING
Value format         : AVRO
Kafka output topic   : pageviews_enriched_r8_r9 (partitions: 4, replication: 1)

 Field    | Type
--------------------------------------
 ROWTIME  | BIGINT           (system)
 ROWKEY   | VARCHAR(STRING)  (system)
 USERID   | VARCHAR(STRING)  (key)
 PAGEID   | VARCHAR(STRING)
 REGIONID | VARCHAR(STRING)
 GENDER   | VARCHAR(STRING)
--------------------------------------

Queries that write into this STREAM
-----------------------------------
id:CSAS_PAGEVIEWS_FEMALE_LIKE_89 - CREATE STREAM pageviews_female_like_89 WITH (kafka_topic='pageviews_enriched_r8_r9', value_format='AVRO') AS SELECT * FROM pageviews_female WHERE regionid LIKE '%_8' OR regionid LIKE '%_9';

For query topology and execution plan please run: EXPLAIN <QueryId>

Local runtime statistics
------------------------
messages-per-sec:      2.01   total-messages:     10515     last-message: 3/14/18 2:25:40 PM PDT
 failed-messages:         0 failed-messages-per-sec:         0      last-failed:       n/a
(Statistics of the local KSQL server interaction with the Kafka topic pageviews_enriched_r8_r9)

Discover the query execution plan with the EXPLAIN command. For example, run this command to view the query execution plan for CTAS_PAGEVIEWS_REGIONS:

EXPLAIN CTAS_PAGEVIEWS_REGIONS;

Your should look like this:

Type                 : QUERY
SQL                  : 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;


Local runtime statistics
------------------------
messages-per-sec:      1.42   total-messages:     13871     last-message: 3/14/18 2:50:02 PM PDT
 failed-messages:         0 failed-messages-per-sec:         0      last-failed:       n/a
(Statistics of the local KSQL server interaction with the Kafka topic PAGEVIEWS_REGIONS)

Execution plan
--------------
 > [ PROJECT ] Schema: [GENDER : STRING , REGIONID : STRING , NUMUSERS : INT64].
         > [ FILTER ] Schema: [PAGEVIEWS_FEMALE.GENDER : STRING , PAGEVIEWS_FEMALE.REGIONID : STRING , PAGEVIEWS_FEMALE.ROWTIME : INT64 , KSQL_AGG_VARIABLE_0 : INT64 , KSQL_AGG_VARIABLE_1 : INT64].
                 > [ AGGREGATE ] Schema: [PAGEVIEWS_FEMALE.GENDER : STRING , PAGEVIEWS_FEMALE.REGIONID : STRING , PAGEVIEWS_FEMALE.ROWTIME : INT64 , KSQL_AGG_VARIABLE_0 : INT64 , KSQL_AGG_VARIABLE_1 : INT64].
                         > [ PROJECT ] Schema: [PAGEVIEWS_FEMALE.GENDER : STRING , PAGEVIEWS_FEMALE.REGIONID : STRING , PAGEVIEWS_FEMALE.ROWTIME : INT64].
                                 > [ SOURCE ] Schema: [PAGEVIEWS_FEMALE.ROWTIME : INT64 , PAGEVIEWS_FEMALE.ROWKEY : STRING , PAGEVIEWS_FEMALE.USERID : STRING , PAGEVIEWS_FEMALE.PAGEID : STRING , PAGEVIEWS_FEMALE.REGIONID : STRING , PAGEVIEWS_FEMALE.GENDER : STRING].


Processing topology
-------------------
Topologies:
   Sub-topology: 0
    Source: KSTREAM-SOURCE-0000000000 (topics: [PAGEVIEWS_FEMALE])
      --> KSTREAM-MAPVALUES-0000000001
    Processor: KSTREAM-MAPVALUES-0000000001 (stores: [])
      --> KSTREAM-TRANSFORMVALUES-0000000002
      <-- KSTREAM-SOURCE-0000000000
    ...

  Sub-topology: 1
    Source: KSTREAM-SOURCE-0000000008 (topics: [KSQL_Agg_Query_1521052072079-repartition])
      --> KSTREAM-AGGREGATE-0000000005
    Processor: KSTREAM-AGGREGATE-0000000005 (stores: [KSQL_Agg_Query_1521052072079])
      --> KTABLE-FILTER-0000000009
      <-- KSTREAM-SOURCE-0000000008
    ...

For more information about KSQL syntax, see KSQL Syntax Reference.

Step 6: Stop Confluent Platform¶

When you are done working with the local install, you can stop Confluent Platform.

Stop Confluent Platform using the Confluent CLI stop command.
```
<path-to-confluent>/bin/confluent stop
```
Destroy the data in the Confluent Platform instance with the destroy command.
```
<path-to-confluent>/bin/confluent destroy
```

You can start the local install of Confluent Platform again with the start command.

Troubleshooting¶

If you encountered any issues, review the following resolutions before trying the steps again.

Issue: Cannot locate the Datagen Connector¶

Resolution: Verify the DataGen Connector is installed and running.

Ensure that the kafka-connect-datagen is installed and running as described in Step 1: Download and Install.

<path-to-confluent>/bin/confluent-hub install --no-prompt confluentinc/kafka-connect-datagen:0.1.0

Your output should resemble:

Running in a "--no-prompt" mode
...
Completed

Resolution: Check the connect logs for Datagen.

<path-to-confluent>/bin/confluent log connect | grep -i Datagen

Your output should resemble:

[2019-04-18 14:21:08,840] INFO Loading plugin from: /Users/user.name/Confluent/confluent-version/share/confluent-hub-components/confluentinc-kafka-connect-datagen (org.apache.kafka.connect.runtime.isolation.DelegatingClassLoader:215)
[2019-04-18 14:21:08,894] INFO Registered loader: PluginClassLoader{pluginLocation=file:/Users/user.name/Confluent/confluent-version/share/confluent-hub-components/confluentinc-kafka-connect-datagen/} (org.apache.kafka.connect.runtime.isolation.DelegatingClassLoader:238)
[2019-04-18 14:21:08,894] INFO Added plugin 'io.confluent.kafka.connect.datagen.DatagenConnector' (org.apache.kafka.connect.runtime.isolation.DelegatingClassLoader:167)
[2019-04-18 14:21:09,882] INFO Added aliases 'DatagenConnector' and 'Datagen' to plugin 'io.confluent.kafka.connect.datagen.DatagenConnector' (org.apache.kafka.connect.runtime.isolation.DelegatingClassLoader:386)

Resolution: Verify the .jar file for kafka-connect-datagen has been added and is present in the lib subfolder.

ls <path-to-confluent>/share/confluent-hub-components/confluentinc-kafka-connect-datagen/lib/

Your output should resemble:

...
kafka-connect-datagen-0.1.0.jar
...

Resolution: Verify the plugin exists in the connector path.

When you installed the kafka-connect-datagen file from Confluent hub, the installation directory is added to the plugin path of several properties files:

 Adding installation directory to plugin path in the following files:
/Users/user.name/Confluent/confluent-version/etc/kafka/connect-distributed.properties
/Users/user.name/Confluent/confluent-version/etc/kafka/connect-standalone.properties
/Users/user.name/Confluent/confluent-version/etc/schema-registry/connect-avro-distributed.properties
/Users/user.name/Confluent/confluent-version/etc/schema-registry/connect-avro-standalone.properties
...

You can use any of them to check the connector path. This example uses the connect-avro-distributed.properties file.

grep plugin.path <path-to-confluent>/etc/schema-registry/connect-avro-distributed.properties

Your output should resemble:

plugin.path=share/java,/Users/user.name/Confluent/confluent-version/share/confluent-hub-components

Confirm its contents are present:

ls <path-to-confluent>/share/confluent-hub-components/confluentinc-kafka-connect-datagen

Your output should resemble:

assets   doc  lib  manifest.json

Issue: Stream-Stream joins error¶

An error states Stream-Stream joins must have a WITHIN clause specified. This error can occur if you created streams for both pageviews and users by mistake.

Resolution: Ensure that you created a stream for pageviews, and a table for users in Step 4: Create and Write to a Stream and Table using KSQL.

Issue: Unable to successfully complete KSQL query steps¶

Java errors or other severe errors were encountered.

Resolution: Ensure you are on an Operating System currently supported by Confluent Platform.

KSQL errors were encountered.

Resolution: Review the help in the KSQL terminal for successful command tips and links to more documentation.

ksql> help

Next Steps¶

Learn more about the components shown in this quick start:

KSQL documentation Learn about processing your data with KSQL for use cases such as streaming ETL, real-time monitoring, and anomaly detection. You can also learn how to use KSQL with this collection of scripted demos.
Stream Processing Cookbook Try out in-depth KSQL tutorials and recommended deployment scenarios.
Kafka Streams documentation Learn how to build stream processing applications in Java or Scala.
Kafka Connect documentation Learn how to integrate Kafka with other systems and download ready-to-use connectors to easily ingest data in and out of Kafka in real-time.
Kafka Clients documentation Learn how to read and write data to and from Kafka using programming languages such as Go, Python, .NET, C/C++.
Tutorials and Demos Try out the Confluent Platform tutorials and examples, watch demos and screencasts, and learn with white papers and blogs.