Book Image

Learning Spark SQL

By : Aurobindo Sarkar
Book Image

Learning Spark SQL

By: Aurobindo Sarkar

Overview of this book

In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems. This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL. It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Implementing a scalable monitoring solution


Building a scalable monitoring function for large-scale deployments can be challenging as there could be billions of data points captured each day. Additionally, the volume of and the number of metrics can be difficult to manage without a suitable big data platform with streaming and visualization support.

Voluminous logs collected from applications, servers, network devices, and so on are processed to provide real-time monitoring that help detect errors, warnings, failures, and other issues. Typically, various daemons, services, and tools are used to collect/send log records to the monitoring system. For example, log entries in the JSON format can be sent to Kafka queues or Amazon Kinesis. These JSON records can then be stored on S3 as files and/or streamed to be analyzed in real time (in a Lambda architecture implementation). Typically, an ETL pipeline is run to cleanse the log data, transform it into a more structured form, and then it into...