Preface

Apache Ignite is one of the most widely used open source memory-centric distributed, caching, and processing platform. This allows the users to use the platform as an in-memory computing framework or a full functional persistence data stores with SQL and ACID transaction support. On the other hand, Apache Ignite can be used for accelerating existing Relational and NoSQL databases, processing events & streaming data or developing Microservices in fault-tolerant fashion.

This book addressed anyone interested in learning in-memory computing and distributed database. This book intends to provide someone with little to no experience of Apache Ignite with an opportunity to learn how to use this platform effectively from scratch taking a practical hands-on approach to learning.

What this book covers

Chapter 1. Introduction: gives an overview of the trends that have made in-memory computing such important technology today. By the end of this chapter, you will have a clear idea of what Apache Ignite is and why use Apache Ignite instead of others frameworks like HazelCast, Ehcache?

Chapter 2. Getting started with Apache Ignite: is about getting excited. This chapter walks you through the initial setup of an Ignite database and running of some sample application. You will implement your first Ignite application to read and write entries from the Cache at the end of the chapter. Also, you will learn how to install and configure an SQL IDE to run SQL queries against Ignite caches and use Apache Ignite Thin client to working with the Ignite database.

Chapter 3. Apache Ignite use cases: discusses various design decisions and use cases where Ignite can be deployed. These use cases detailed and explained through the rest of the book.

Chapter 4. Architecture deep dive: covers Ignite’s internal plumbing. This chapter has a lot of useful design concepts if you have never worked with a distributed system. This chapter introduces Ignite shared nothing architecture, cluster topology, distributed hashing, Ignite replication strategy and durable memory architecture. It is a theoretical chapter; you may skip (not recommended) it and come back later.

Chapter 5. Intelligent caching: presents Ignite smart caching capabilities, Memoization, and Web-session clustering. This chapter covers developments and techniques to improve the performance of your existing web applications without changing any code.

Chapter 6. Database: guides you through the Ignite database features. This massive chapter explores: Ignite tables and index configurations, different Ignite queries, how SQL works under the cover, collocated/Non-collocated distributed joins, Spring data integration, using Ignite with JPA and Ignite native persistence. This chapter is for you if you are planning to use Ignite as a database.

Chapter 7. Distributed computing: focuses on more advanced Ignite features such as distributed computing and how Ignite can help you develop Micro-service like application, which will be performed in parallel fashion to gain high performance, low latency, and linear scalability. You will learn about Ignite inline MapReduce & ForkJoin, distributed closure execution, continuous mapping for data processing across multiple nodes in the cluster.

Chapter 8. Streaming and complex event processing: takes the next step and goes beyond using Apache Ignite to solve complex real-time event processing problem. This chapter covers how Ignite can be used easily with other Big data technologies such as Kafka, flume, storm, and camel to solve various business problems. We will guide you through with complete examples for developing real-time data processing on Apache Ignite.

Chapter 9. Accelerating Big data computing: is a full chapter about how to use Apache Spark Dataframe and RDD for processing massive datasets. We detailed by examples of how to share the application states in memory across multiple Spark jobs by using Ignite.

Chapter 10. Management and monitoring: explain the various tools that you can use to monitor and manage the Ignite cluster. For instance, configuring Zookeeper discovery, scaling up a cluster with Baseline topology. We provide a complete example of using Grafana for monitoring Ignite cluster at the end of this chapter.

Code Samples

All code samples, scripts, and more in-depth examples can be found on the GitHub repository.

Readership

The target audiences of this book are IT architect, team leaders or programmer with minimum programming knowledge. No excessive knowledge is required, though it would be good to be familiar with Java, Spring framework and tools like Maven. The book is also useful for any reader, who already familiar with Oracle Coherence, Hazelcast, Infinispan or Memcached.

Conventions

The following typographical conventions are used in this book:

Italic and Bold indicates new terms, important words, URL’s, filenames, and file extensions.

A block code is set as follows:

public class MySuperExtractor implements StreamSingleTupleExtractor<SinkRecord, String, S\
tring> {

  @Override public Map.Entry<String, String> extract(SinkRecord msg) {
      String[] parts = ((String)msg.value()).split("_");
      return new AbstractMap.SimpleEntry<String, String>(parts[1], parts[2]+":"+parts[3]);
  }
}

Any command-line input or output is written as follows:

[2018-09-30 15:39:04,479] INFO Kafka version : 2.0.0 (org.apache.kafka.common.utils.AppIn\
foParser)
[2018-09-30 15:39:04,479] INFO Kafka commitId : 3402a8361b734732 (org.apache.kafka.common\
.utils.AppInfoParser)
[2018-09-30 15:39:04,480] INFO [KafkaServer id=0] started (kafka.server.KafkaServer)

Reader feedback

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