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 a topic

So far, we have processed the data and printed the results in real time. To send these results to another topic, we use a CREATE command modality, where it is specified from a SELECT.

Let's start by writing the uptime as a string and writing the data in a comma-delimited format, shown as follows (remember that KSQL supports comma-delimited, JSON, and Avro formats). At the moment, it's enough because we're only writing one value:

ksql> CREATE STREAM uptimes WITH (kafka_topic='uptimes', value_format='delimited') AS SELECT CAST((STRINGTOTIMESTAMP('2017-11-18','yyyy-MM-dd''T''HH:mm:ss.SSSZ')-STRINGTOTIMESTAMP(lastStartedAt,'yyyy-MM-dd'))/86400/1000 AS string) AS uptime FROM healthchecks;

The output is similar to this:

Stream created and running...