Book Image

Apache Kafka Quick Start Guide

By : Raúl Estrada
Book Image

Apache Kafka Quick Start Guide

By: Raúl Estrada

Overview of this book

Apache Kafka is a great open source platform for handling your real-time data pipeline to ensure high-speed filtering and pattern matching on the ?y. In this book, you will learn how to use Apache Kafka for efficient processing of distributed applications and will get familiar with solving everyday problems in fast data and processing pipelines. This book focuses on programming rather than the configuration management of Kafka clusters or DevOps. It starts off with the installation and setting up the development environment, before quickly moving on to performing fundamental messaging operations such as validation and enrichment. Here you will learn about message composition with pure Kafka API and Kafka Streams. You will look into the transformation of messages in different formats, such asext, binary, XML, JSON, and AVRO. Next, you will learn how to expose the schemas contained in Kafka with the Schema Registry. You will then learn how to work with all relevant connectors with Kafka Connect. While working with Kafka Streams, you will perform various interesting operations on streams, such as windowing, joins, and aggregations. Finally, through KSQL, you will learn how to retrieve, insert, modify, and delete data streams, and how to manipulate watermarks and windows.
Table of Contents (10 chapters)

Writing to Kafka from Spark

As we already processed the data and calculated the uptime, now all we need to do is to write these values in the Kafka topic called uptimes.

Kafka's connector allows us to write values to Kafka. The requirement is that the Dataset to write must have a column called key and another column called value; each one can be of the type String or binary.

Since we want the machine serial number to be the key, there is no problem if it is already of String type. Now, we just have to convert the uptime column from binary into String.

We use the select() method of the Dataset class to calculate these two columns and assign them new names using the as() method, shown as follows (to do this, we could also use the alias() method of that class):

Dataset<Row> resDf = processedDs.select(
(new Column("serialNumber")).as("key"),
processedDs...