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Apache Spark 2: Data Processing and Real-Time Analytics

You're reading from Apache Spark 2: Data Processing and Real-Time Analytics Master complex big data processing, stream analytics, and machine learning with Apache Spark

Product type Course

Published in Dec 2018

Publisher Packt

ISBN-13 9781789959208

Length 616 pages

Edition 1st Edition

Languages

Processing

Tools

Apache Spark

Concepts

Big Data

Authors (7):

Sridhar Alla

Romeo Kienzler

Siamak Amirghodsi

Broderick Hall

Md. Rezaul Karim

Meenakshi Rajendran

Shuen Mei

+3 more

View More author details

Table of Contents (23) Chapters

Title Page

Copyright

About Packt

Contributors

Preface

1. A First Taste and What's New in Apache Spark V2 FREE CHAPTER

2. Apache Spark Streaming

3. Structured Streaming

4. Apache Spark MLlib

5. Apache SparkML

6. Apache SystemML

7. Apache Spark GraphX

8. Spark Tuning

9. Testing and Debugging Spark

10. Practical Machine Learning with Spark Using Scala

11. Spark's Three Data Musketeers for Machine Learning - Perfect Together

12. Common Recipes for Implementing a Robust Machine Learning System

13. Recommendation Engine that Scales with Spark

14. Unsupervised Clustering with Apache Spark 2.0

15. Implementing Text Analytics with Spark 2.0 ML Library

16. Spark Streaming and Machine Learning Library

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Index

Debugging Spark applications

In this section, we will see how to debug Spark applications that are running locally (on Eclipse or IntelliJ), standalone or cluster mode in YARN or Mesos. However, before diving deeper, it is necessary to know about logging in the Spark application.

Logging with log4j with Spark recap

As stated earlier, Spark uses log4j for its own logging. If you configured Spark properly, Spark gets logged all the operation to the shell console. A sample snapshot of the file can be seen from the following figure:

Figure 16: A snap of the log4j.properties file

Set the default spark-shell log level to WARN. When running the spark-shell, the log level for this class is used to overwrite the root logger's log level so that the user can have different defaults for the shell and regular Spark apps. We also need to append JVM arguments when launching a job executed by an executor and managed by the driver. For this, you should edit the conf/spark-defaults.conf. In short, the following...

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Authors (7)

Romeo Kienzler

Romeo Kienzler

Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics.

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Shuen Mei

Shuen Mei

Shuen Mei is a big data analytic platforms expert with 15+ years of experience in designing, building, and executing large-scale, enterprise-distributed financial systems with mission-critical low-latency requirements. He is certified in the Apache Spark, Cloudera Big Data platform, including Developer, Admin, and HBase. He is also a certified AWS solutions architect with emphasis on peta-byte range real-time data platform systems.

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Broderick Hall

Broderick Hall

Broderick Hall is a hands-on big data analytics expert and holds a masters degree in computer science with 20 years of experience in designing and developing complex enterprise-wide software applications with real-time and regulatory requirements at a global scale. He is a deep learning early adopter and is currently working on a large-scale cloud-based data platform with deep learning net augmentation.

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Meenakshi Rajendran

Meenakshi Rajendran

Meenakshi Rajendran is experienced in the end-to-end delivery of data analytics and data science products for leading financial institutions. Meenakshi holds a master's degree in business administration and is a certified PMP with over 13 years of experience in global software delivery environments. Her areas of research and interest are Apache Spark, cloud, regulatory data governance, machine learning, Cassandra, and managing global data teams at scale.

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Siamak Amirghodsi

Siamak Amirghodsi

Siamak Amirghodsi (Sammy) is interested in building advanced technical teams, executive management, Spark, Hadoop, big data analytics, AI, deep learning nets, TensorFlow, cognitive models, swarm algorithms, real-time streaming systems, quantum computing, financial risk management, trading signal discovery, econometrics, long-term financial cycles, IoT, blockchain, probabilistic graphical models, cryptography, and NLP.

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Sridhar Alla

Sridhar Alla

Sridhar?Alla?is the co-founder and CTO of Blue Whale Consulting and is expert at helping companies (big and small) define their vision for systems and capabilities that will allow them to establish a strategic execution plan to deal with the ever-growing data collected to support analytics and product teams. He has very experienced at dealing with all aspects of data collection, security, governance, and processing as part of end-to-end big data analytics and machine learning initiatives (including predictive modeling, deep learning, and ML automation). Sridhar?is a published book author and an avid presenter at numerous conferences, including Strata, Hadoop World, and Spark Summit.? He also has several patents filed with the US PTO on large-scale computing and distributed systems.? He has over 18 years' experience writing code in Scala, Java, C, C++, Python, R, and Go, and has extensive hands-on knowledge of Spark, Flink, TensorFlow, Keras, Hadoop, Cassandra, HBase, MongoDB, Riak, Redis, Zeppelin, Mesos, Docker, Kafka, ElasticSearch, Solr, H2O, machine learning, text analytics, distributed computing, and high-performance computing. Sridhar lives with his wife and daughter in New Jersey and in his spare time loves blogging and coaching organizations on next-generation advancements in technology and their alignment with business goals.

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Md. Rezaul Karim

Md. Rezaul Karim

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.

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