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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services

Using the Flink cluster UI


Using the Flink cluster UI, you can understand and monitor what's running in your cluster and dig deeply into various jobs and tasks. You can monitor the job statuses, cancel jobs, or debug any problems with the jobs. By looking at logs, you can also diagnose problems with your code, and fix them.

The following is a list of Completed Jobs:

You can drill down into any particular job to see more details about the job's execution:

Figure: Drilling down a particular job to see job's execution

You can look at the Timeline of the job to get more details:

Figure: Screenshot to see Timeline of a job

The following screenshot shows the Task Managers tab, showing all of the task managers. This helps you understand the number and status of the task managers:

You can also check the Logs, as shown in the following screenshot:

The Metrics tab gives you details of the memory and CPU resources:

Figure: Screenshot showing details of the memory and CPU resources in Metrics tab

You can also...

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