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The full table of contents of the book High Performance in-memory computing with Apache Ignite

The book High Performance in-memory computing with Apache Ignite has been completed and available at LeanPub.


Table of contents:

  • Introduction
    • What is Apache Ignite
    • Who uses Apache Ignite
    • Why Ignite instead of others
    • Our Hope
  • Chapter one: Installation and the first Ignite application
    • Pre-requirities
    • Installation
    • Run multiple instances of Ignite in a single host
    • Configure a multi-node cluster in different host
    • Rest client to manipulate with Ignite
    • Java client
    • SQL client
    • Conclusion
    • What's Next
  • Chapter two: Architecture overview
    • Functional overview
    • ClusterTopology
      • 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
      • ZeroSPOF
    • How SQL queries works in Ignite
    • Multi-data center replication
    • Asynchronous support
    • Resilience
    • Security
    • KeyAPI
    • Conclusion
    • What's next
  • Chapter three: In-memory caching
    • Apache Ignite as a 2nd level cache
      • MyBatis 2nd level cache
      • Hibernate 2nd level cache
    • Java method caching
    • Web session clustering with Apache Ignite
    • Apache Ignite as a big memory, off-heap memory
    • Conclusion
    • What’s next
  • 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
    • Expiration & Eviction of cache entries in Ignite
      • Expiration
      • Eviction
    • Transaction
      • Ignite transactions
      • Transaction commit protocols
      • Optimistic Transactions
      • Pessimistic Transactions
      • Performance impact on transaction
    • Conclusion
    • What’s next
  • Chapter five: Accelerating BigData 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
    • Conclusion
    • What’s next
  • Chapter six: Streaming and complex event processing
    • Introducing data streamer
      • StreamReceiver
      • StreamVisitor
    • IgniteDataStreamer
      • Direct Ingestion
      • Mediated Ingestion
    • Camel data streamer
    • 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 & checkpointing
      • Collocation of compute and data
      • Job scheduling
    • Service Grid
      • Developing services
      • Cluster singleton
      • Service management & configuration
    • Developing microservices in Ignite
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