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Storm Blueprints: Patterns for Distributed Real-time Computation. One of the best ways of getting to grips with the world’s most popular framework for real-time processing is to study real-world projects. This books lets you do just that, resulting in a sound understanding of the fundamentals eBook -- spis treÅ›ci

• Storm Blueprints: Patterns for Distributed Real-time Computation
  • Table of Contents
  • Storm Blueprints: Patterns for Distributed Real-time Computation
  • Credits
  • About the Authors
  • About the Reviewers
  • www.PacktPub.com
    • Support files, eBooks, discount offers and more
      • Why Subscribe?
      • Free Access for Packt account holders
  • Preface
    • What this book covers
    • What you need for this book
    • Who this book is for
    • Conventions
    • Reader feedback
    • Customer support
      • Downloading the example code
      • Errata
      • Piracy
      • Questions
  • 1. Distributed Word Count
    • Introducing elements of a Storm topology streams, spouts, and bolts
      • Streams
      • Spouts
      • Bolts
    • Introducing the word count topology data flow
      • Sentence spout
        • Introducing the split sentence bolt
        • Introducing the word count bolt
        • Introducing the report bolt
    • Implementing the word count topology
      • Setting up a development environment
      • Implementing the sentence spout
      • Implementing the split sentence bolt
      • Implementing the word count bolt
      • Implementing the report bolt
      • Implementing the word count topology
    • Introducing parallelism in Storm
      • WordCountTopology parallelism
        • Adding workers to a topology
        • Configuring executors and tasks
    • Understanding stream groupings
    • Guaranteed processing
      • Reliability in spouts
      • Reliability in bolts
      • Reliable word count
    • Summary
  • 2. Configuring Storm Clusters
    • Introducing the anatomy of a Storm cluster
      • Understanding the nimbus daemon
      • Working with the supervisor daemon
      • Introducing Apache ZooKeeper
      • Working with Storms DRPC server
      • Introducing the Storm UI
    • Introducing the Storm technology stack
      • Java and Clojure
      • Python
    • Installing Storm on Linux
      • Installing the base operating system
      • Installing Java
      • ZooKeeper installation
      • Storm installation
      • Running the Storm daemons
      • Configuring Storm
      • Mandatory settings
      • Optional settings
      • The Storm executable
      • Setting up the Storm executable on a workstation
      • The daemon commands
        • Nimbus
        • Supervisor
        • UI
        • DRPC
      • The management commands
        • Jar
        • Kill
        • Deactivate
        • Activate
        • Rebalance
        • Remoteconfvalue
      • Local debug/development commands
        • REPL
        • Classpath
        • Localconfvalue
    • Submitting topologies to a Storm cluster
    • Automating the cluster configuration
    • A rapid introduction to Puppet
      • Puppet manifests
      • Puppet classes and modules
      • Puppet templates
      • Managing environments with Puppet Hiera
      • Introducing Hiera
    • Summary
  • 3. Trident Topologies and Sensor Data
    • Examining our use case
    • Introducing Trident topologies
    • Introducing Trident spouts
    • Introducing Trident operations filters and functions
      • Introducing Trident filters
      • Introducing Trident functions
    • Introducing Trident aggregators Combiners and Reducers
      • CombinerAggregator
      • ReducerAggregator
      • Aggregator
    • Introducing the Trident state
      • The Repeat Transactional state
      • The Opaque state
    • Executing the topology
    • Summary
  • 4. Real-time Trend Analysis
    • Use case
    • Architecture
      • The source application
      • The logback Kafka appender
      • Apache Kafka
      • Kafka spout
      • The XMPP server
    • Installing the required software
      • Installing Kafka
      • Installing OpenFire
    • Introducing the sample application
      • Sending log messages to Kafka
    • Introducing the log analysis topology
      • Kafka spout
      • The JSON project function
      • Calculating a moving average
      • Adding a sliding window
      • Implementing the moving average function
      • Filtering on thresholds
      • Sending notifications with XMPP
    • The final topology
    • Running the log analysis topology
    • Summary
  • 5. Real-time Graph Analysis
    • Use case
    • Architecture
      • The Twitter client
      • Kafka spout
      • A titan-distributed graph database
    • A brief introduction to graph databases
      • Accessing the graph the TinkerPop stack
      • Manipulating the graph with the Blueprints API
      • Manipulating the graph with the Gremlin shell
    • Software installation
      • Titan installation
    • Setting up Titan to use the Cassandra storage backend
      • Installing Cassandra
      • Starting Titan with the Cassandra backend
    • Graph data model
    • Connecting to the Twitter stream
      • Setting up the Twitter4J client
      • The OAuth configuration
        • The TwitterStreamConsumer class
        • The TwitterStatusListener class
    • Twitter graph topology
      • The JSONProjectFunction class
    • Implementing GraphState
      • GraphFactory
      • GraphTupleProcessor
      • GraphStateFactory
      • GraphState
      • GraphUpdater
    • Implementing GraphFactory
    • Implementing GraphTupleProcessor
    • Putting it all together the TwitterGraphTopology class
      • The TwitterGraphTopology class
    • Querying the graph with Gremlin
    • Summary
  • 6. Artificial Intelligence
    • Designing for our use case
    • Establishing the architecture
      • Examining the design challenges
      • Implementing the recursion
        • Accessing the function's return values
        • Immutable tuple field values
        • Upfront field declaration
        • Tuple acknowledgement in recursion
        • Output to multiple streams
        • Read-before-write
      • Solving the challenges
    • Implementing the architecture
      • The data model
      • Examining the recursive topology
      • The queue interaction
      • Functions and filters
      • Examining the Scoring Topology
        • Addressing read-before-write
          • Distributed locking
          • Retry when stale
          • Executing the topology
        • Enumerating the game tree
      • Distributed Remote Procedure Call (DRPC)
        • Remote deployment
    • Summary
  • 7. Integrating Druid for Financial Analytics
    • Use case
    • Integrating a non-transactional system
    • The topology
      • The spout
      • The filter
      • The state design
    • Implementing the architecture
      • DruidState
      • Implementing the StormFirehose object
      • Implementing the partition status in ZooKeeper
    • Executing the implementation
    • Examining the analytics
    • Summary
  • 8. Natural Language Processing
    • Motivating a Lambda architecture
    • Examining our use case
    • Realizing a Lambda architecture
    • Designing the topology for our use case
    • Implementing the design
      • TwitterSpout/TweetEmitter
      • Functions
        • TweetSplitterFunction
        • WordFrequencyFunction
        • PersistenceFunction
    • Examining the analytics
    • Batch processing / historical analysis
    • Hadoop
      • An overview of MapReduce
      • The Druid setup
        • HadoopDruidIndexer
    • Summary
  • 9. Deploying Storm on Hadoop for Advertising Analysis
    • Examining the use case
    • Establishing the architecture
      • Examining HDFS
      • Examining YARN
    • Configuring the infrastructure
      • The Hadoop infrastructure
      • Configuring HDFS
        • Configuring the NameNode
        • Configuring the DataNode
        • Configuring YARN
          • Configuring the ResourceManager
        • Configuring the NodeManager
    • Deploying the analytics
      • Performing a batch analysis with the Pig infrastructure
      • Performing a real-time analysis with the Storm-YARN infrastructure
    • Performing the analytics
      • Executing the batch analysis
      • Executing real-time analysis
    • Deploying the topology
    • Executing the topology
    • Summary
  • 10. Storm in the Cloud
    • Introducing Amazon Elastic Compute Cloud (EC2)
      • Setting up an AWS account
      • The AWS Management Console
        • Creating an SSH key pair
      • Launching an EC2 instance manually
        • Logging in to the EC2 instance
    • Introducing Apache Whirr
      • Installing Whirr
    • Configuring a Storm cluster with Whirr
      • Launching the cluster
    • Introducing Whirr Storm
      • Setting up Whirr Storm
        • Cluster configuration
        • Customizing Storm's configuration
        • Customizing firewall rules
    • Introducing Vagrant
      • Installing Vagrant
      • Launching your first virtual machine
        • The Vagrantfile and shared filesystem
        • Vagrant provisioning
        • Configuring multimachine clusters with Vagrant
    • Creating Storm-provisioning scripts
      • ZooKeeper
      • Storm
      • Supervisord
        • The Storm Vagrantfile
        • Launching the Storm cluster
    • Summary
  • Index