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Apache Kafka Quick Start Guide

You're reading from   Apache Kafka Quick Start Guide Leverage Apache Kafka 2.0 to simplify real-time data processing for distributed applications

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781788997829
Length 186 pages
Edition 1st Edition
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Author (1):
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Raúl Estrada Raúl Estrada
Author Profile Icon Raúl Estrada
Raúl Estrada
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Table of Contents (10) Chapters Close

Preface 1. Configuring Kafka FREE CHAPTER 2. Message Validation 3. Message Enrichment 4. Serialization 5. Schema Registry 6. Kafka Streams 7. KSQL 8. Kafka Connect 9. Other Books You May Enjoy

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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