Stream Spark Data into Apache Kafka Topics

Apache Kafka is an open-source stream processing platform that is primarily used for building real-time data pipelines and event-driven applications. When paired with the CData JDBC Driver for Apache Spark, Kafka can work with live Spark data. This article describes how to connect, access and stream Spark data into Apache Kafka Topics and to start Confluent Control Center to help users secure, manage, and monitor the Spark data received using Kafka infrastructure in the Confluent Platform.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Spark data. When you issue complex SQL queries to Spark, the driver pushes supported SQL operations, like filters and aggregations, directly to Spark and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Spark data using native data types.

Prerequisites

Before connecting the CData JDBC Driver for streaming Spark data in Apache Kafka Topics, install and configure the following in the client Linux-based system.

1. Confluent Platform for Apache Kafka
2. Confluent Hub CLI Installation
3. Self-Managed Kafka JDBC Source Connector for Confluent Platform

Define a New JDBC Connection to Spark data

1. Download CData JDBC Driver for Apache Spark on a Linux-based system
2. Follow the given instructions to create a new directory extract all the driver contents into it:
   1. Create a new directory named Spark mkdir SparkSQL
   2. Move the downloaded driver file (.zip) into this new directory mv SparkSQLJDBCDriver.zip SparkSQL/
   3. Unzip the CData SparkSQLJDBCDriver contents into this new directory unzip SparkSQLJDBCDriver.zip
3. Open the Spark directory and navigate to the lib folder ls cd lib/
4. Copy the contents of the lib folder of Spark into the lib folder of Kafka Connect JDBC. Check the Kafka Connect JDBC folder contents to confirm that the cdata.jdbc.sparksql.jar file is successfully copied into the lib folder cp * ../../confluent-7.5.0/share/confluent-hub-components/confluentinc-kafka-connect-jdbc/lib/ cd ../../confluent-7.5.0/share/confluent-hub-components/confluentinc-kafka-connect-jdbc/lib/
5. Install the CData Spark JDBC driver license using the given command, followed by your Name and Email ID java -jar cdata.jdbc.sparksql.jar -l
6. Enter the product key or "TRIAL" (In the scenarios of license expiry, please contact our CData Support team)
7. Start the Confluent local services using the command: confluent local services start

   This starts all the Confluent Services like Zookeeper, Kafka, Schema Registry, Kafka REST, Kafka CONNECT, ksqlDB and Control Center. You are now ready to use the CData JDBC driver for Spark to stream messages using Kafka Connect Driver into Kafka Topics on ksqlDB.

8. Create the Kafka topics manually using a POST HTTP API Request: curl --location 'server_address:8083/connectors' --header 'Content-Type: application/json' --data '{ "name": "jdbc_source_cdata_sparksql_01", "config": { "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector", "connection.url": "jdbc:sparksql:Server=127.0.0.1;", "topic.prefix": "sparksql-01-", "mode": "bulk" } }'

   Let us understand the fields used in the HTTP POST body (shown above):

   • connector.class: Specifies the Java class of the Kafka Connect connector to be used.
   • connection.url: The JDBC connection URL to connect with Spark data.

     Built-in Connection String Designer

     For assistance in constructing the JDBC URL, use the connection string designer built into the Spark JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

     java -jar cdata.jdbc.sparksql.jar

     Fill in the connection properties and copy the connection string to the clipboard.

     Set the Server, Database, User, and Password connection properties to connect to SparkSQL.

   • topic.prefix: A prefix that will be added to the Kafka topics created by the connector. It's set to "sparksql-01-".
   • mode: Specifies the mode in which the connector operates. In this case, it's set to "bulk", which suggests that the connector is configured to perform bulk data transfer.

   This request adds all the tables/contents from Spark as Kafka Topics.

   Note: The IP Address (server) to POST the request (shown above) is the Linux Network IP Address.

9. Run ksqlDB and list the topics. Use the commands: ksql list topics;
10. To view the data inside the topics, type the SQL Statement: PRINT topic FROM BEGINNING;

Connecting with the Confluent Control Center

To access the Confluent Control Center user interface, ensure to run the "confluent local services" as described in the above section and type http://<server address>:9021/clusters/ on your local browser.

Get Started Today

Download a free, 30-day trial of the CData JDBC Driver for Apache Spark and start streaming Spark data into Apache Kafka. Reach out to our Support Team if you have any questions.