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Apache Kafka Quick Start Guide

Apache Kafka Quick Start Guide

By : Estrada
3.5 (2)
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Apache Kafka Quick Start Guide

Apache Kafka Quick Start Guide

3.5 (2)
By: 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)
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Data processing

Now, what we are going to do is to calculate the uptimes. As is to be expected, Spark does not have a built-in function to calculate the number of days between two dates, so we are going to create a user-defined function.

If we remember the KSQL chapter, it is also possible to build and use new UDFs in KSQL.

To achieve this, the first thing we do is build a function that receives as input a java.sql.Timestamp, as shown in the following code (this is how timestamps are represented in the Spark DataSets) and returns an integer with the number of days from that date:

private final int uptimeFunc(Timestamp date) {
LocalDate localDate = date.toLocalDateTime().toLocalDate();
return Period.between(localDate, LocalDate.now()).getDays();
}

The next step is to generate a Spark UDF as follows:

Dataset<Row> processedDs = healthCheckDs
.withColumn( "lastStartedAt...
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