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)

Processing data with KSQL

In previous chapters, we took the data from the healthchecks topic, calculated the uptimes of the machines, and pushed this data into a topic called uptimes. Now, we are going to do this with KSQL.

At the time of writing, KSQL does not yet have a function to compare two dates, so we have the following two options:

  • Code a user-defined function (UDF) for KSQL in Java
  • Use the existing functions to make our calculation

As creating a new UDF is out of scope for now, let's go for the second option: use the existing functions to make our calculation.

The first step is to parse the startup time using the STRINGTOTIMESTAMP function, shown as follows (remember that we declared the date in string format, because KSQL doesn't yet have a DATE type):

ksql> SELECT event, factory, serialNumber, type, status, lastStartedAt, temperature, ipAddress, STRINGTOTIMESTAMP...