High Performance in-memory computing with Apache Ignite (The Book with code samples)
High Performance in-memory computing with Apache Ignite
Building low latency, near real time application
About the Book
A new title "The Apache Ignite Book" is published and available at LeanPub.
"As one of the original creators of the Apache Ignite project, I really enjoyed reading this book. The coding examples are nice and descriptive. Ignite components are described in an easy to understand language. Great work by the authors!" -- Dmitriy Setrakyan, founder of the Apache Ignite project.
About the Editors
Table of Contents
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Preface
- What this book covers
- Code Samples
- Support
- About the authors
- Acknowledgments
-
Introduction
- What is Apache Ignite?
- Modern application architecture with Apache Ignite
- Who uses Apache Ignite?
- Why Ignite instead of others?
- Our Hope
-
Chapter one: Installation and the first Ignite application
- Pre-requisites
- Installation
- Run multiple instances of Apache Ignite in a single host
- Configure a multi-node cluster in different host
- Rest client to manipulate with the Apache Ignite
- Java client
- SQL client
- Conclusion
- What’s Next
-
Chapter two: Architecture overview
- Functional overview
-
Cluster Topology
- Client and Server
- Embedded with the application
- Server in separate JVM (real cluster topology)
- Client and Server in separate JVM on single host
-
Caching Topology
- Partitioned caching topology
- Replicated caching topology
- Local mode
-
Caching strategy
- Cache-aside
- Read-through and Write-through
- Write behind
- Data model
- CAP theorem and where does Ignite stand in?
-
Clustering
- Cluster group
- Data collocation
- Compute collocation with Data
- Zero SPOF
- How SQL queries works in Ignite
- Multi-datacenter replication
- Asynchronous support
- Resilience
- Security
- Key API
- Conclusion
- What’s next
-
Chapter three: In-memory caching
-
Apache Ignite as a 2 level cache
- MyBatis 2 level cache
- Hibernate 2 level cache
- Java method caching
- Web session clustering with Apache Ignite
- Apache Ignite as a big memory, off-heap memory
- Conclusion
- What’s next
-
Apache Ignite as a 2 level cache
-
Chapter four: Persistence
-
Persistence Ignite’s cache
- Persistence in RDBMS (PostgreSQL)
- Persistence in MongoDB
-
Cache queries
- Scan queries
- Text queries
-
SQL queries
- Projection and indexing with annotations
- Query API
- Collocated distributed Joins
- Non-collocated distributed joins
- Performance tuning SQL queries
- Apache Ignite with JPA
- Apache Ignite with Spring Data
-
Expiration & Eviction of cache entries
- Expiration
- Eviction
-
Transaction
- Ignite transactions
- Transaction commit protocols
- Optimistic Transactions
- Pessimistic Transactions
- Performance impact on transaction
- Conclusion
- What’s next
-
Persistence Ignite’s cache
-
Chapter five: Accelerating Big Data computing
-
Hadoop accelerator
- In-memory Map/Reduce
- Using Apache Pig for data analysis
- Near real-time data analysis with Hive
- Replace HDFS by Ignite In-memory File System (IGFS)
- Hadoop file system cache
-
Ignite for Apache Spark
- Apache Spark – an introduction
- IgniteContext
- IgniteRDD
- Preparing the sandbox
- Spark-shell to run Spark jobs
- Spark application example in Scala to share states
- Conclusion
- What’s next
-
Hadoop accelerator
-
Chapter six: Streaming and complex event processing
- Introducing data streamer
-
IgniteDataStreamer
- StreamReceiver
- StreamVisitor
-
Camel data streamer
- Direct Ingestion
- Mediated Ingestion
- Flume streamer
- Storm data streamer
- Conclusion
- What’s next
-
Chapter seven: 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 Ignite
- Conclusion
-
Compute grid
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