Preface

What this book covers

Code Samples

Readership

Conventions

Reader feedback

About the authors

Acknowledgments

Chapter 1. Introduction

Chapter 2. Getting started with Apache Ignite

Installing and setting up Apache Ignite

Building from source code

Running multiple instances of the Apache Ignite in a single host

Running Apache Ignite in Docker containers

Using Apache Ignite SQLLINE CLI

Meet with Apache Ignite SQL engine: H2 database

Using a universal SQL client IDE to work with Apache Ignite

First Java application

Apache Ignite thin client

Using REST API for manipulating the Apache Ignite caches

Configuring a multi-node Ignite cluster in different hosts

A simple checklist for beginners

Summary

What’s next?

Chapter 3. Apache Ignite use cases

Caching for fast data access

HTAP

High-volume transaction processing

Fast data processing

Lambda architecture

Resilient web acceleration

Microservices in distributed fashion

Cache as a service

Big Data accelerations

In-memory machine learning

In-memory geospatial

Cluster management

Summary

What’s next?

Chapter 4. Architecture deep dive

Functional overview

Understanding the cluster topology: shared-nothing architecture

Client and server node

Embedded with the application

Client and the server nodes in the same host

Running multiple nodes within single JVM

Real cluster topology

Data partitioning in Ignite

Understanding data distribution: DHT

Rendezvous hashing

Replication

Master-Slave replication

Peer-to-peer replication

Partitioned mode

Replicated mode

Local mode

Near cache

Partition loss policies

Caching strategy

Cache a-side

Read through and write through

Write behind

Ignite data model

CAP theorem and where does Ignite stand in?

Apache Ignite life cycle

Memory-Centric storage

Durable memory architecture

Page

Data Page

Index pages and B+ trees

Segments

Region

Data eviction

Page based eviction

Entry based eviction

Data expiration

Ignite read/write path

Native persistence

Write-Ahead-Log (WAL)

Checkpointing

Baseline topology

Automatic cluster activation

Split-brain protection

Fast rebalancing and it’s pitfalls

Tool to control baseline topology

Automate the rebalancing

Discovery and communication mechanisms

Discovery

Communication

Cluster groups

Predefined cluster group

Cluster group with node attributes

Custom cluster group

Data collocation

Compute collocation with data

Protocols and clients

Resilience & Automatic failover

Ignite client node

Ignite thin client

JDBC client

Ignite Rest client

Multi data center replication

Key API’s

Summary

What’s next?

Chapter 5. Intelligent caching

Smart caching

Caching best practices

Design patterns

Basic terms

Database caching

Hibernate caching

MyBatis caching

Memoization

Web session clustering

Classic solution 1: using a sticky session

Classic solution 2: store sessions in DB

Classic solution 3: use only stateless service

Distributed web session clustering

Prepare the caches correctly

Summary

What’s next?

Chapter 6. Database

Ignite tables & indexes configuration

Ignite queries

SQL queries

Query API

SqlFieldsQuery

SqlQuery

How SQL queries works in Ignite

Cache queries

Scan queries

Text queries

Affinity collocation based data modeling

Collocated distributed joins

Non-collocated distributed joins

Spring Data integration

Apache Ignite with JPA

Persistence

Native persistence

Persistence in 3 party database (MongoDB)

Transaction

Ignite transactions

Transaction commit protocols

Node types (NearNode/Remote/DHT)

Concurrency Modes and Isolation Levels

MVCC

Performance impact on transaction

Summary

What’s next?

Chapter 7. Distributed computing

Compute grid

Distributed Closures

MapReduce and Fork-join

Per-Node share state

Distributed task session

Fault tolerance and checkpointing

Collocation of computation and data

Job scheduling

Service Grid

Developing services

Cluster singleton

Service management and configuration

Developing microservices in Apache Ignite

Summary

What’s next?

Chapter 8. Streaming and complex event processing

Kafka Streamer

IgniteSinkConnector

IgniteSourceConnector

Camel Streamer

Direct Ingestion

Mediated Ingestion

Flume sink

Storm streamer

Summary

What’s next?

Chapter 9. Accelerating Big data computing

Ignite for Apache Spark

Apache Spark – a short history

Ignite RDD

Ignite DataFrame

Summary

What’s next?

Chapter 10. Management and monitoring

Managing Ignite cluster

Configuring Zookeeper discovery

Managing Baseline topology

Monitoring Ignite cluster

VisualVM

Grafana

Summary