Hadoop Application Architectures

Spis treści książki

• Foreword
• Preface
  • A Note About the Code Examples
  • Who Should Read This Book
  • Why We Wrote This Book
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • Safari Books Online
  • How to Contact Us
  • Acknowledgments
    • Mark Grovers Acknowledgements
    • Ted Malaskas Acknowledgements
    • Jonathan Seidmans Acknowledgements
    • Gwen Shapiras Acknowledgements
• I. Architectural Considerations for Hadoop Applications
• 1. Data Modeling in Hadoop
  • Data Storage Options
    • Standard File Formats
      • Text data
      • Structured text data
      • Binary data
    • Hadoop File Types
      • File-based data structures
    • Serialization Formats
      • Thrift
      • Protocol Buffers
      • Avro
    • Columnar Formats
      • RCFile
      • ORC
      • Parquet
        • Avro and Parquet
    • Compression
      • Snappy
      • LZO
      • Gzip
      • bzip2
      • Compression recommendations
  • HDFS Schema Design
    • Location of HDFS Files
    • Advanced HDFS Schema Design
      • Partitioning
      • Bucketing
      • Denormalizing
    • HDFS Schema Design Summary
  • HBase Schema Design
    • Row Key
      • Record retrieval
      • Distribution
      • Block cache
      • Ability to scan
      • Size
      • Readability
      • Uniqueness
    • Timestamp
    • Hops
    • Tables and Regions
      • Put performance
      • Compaction time
    • Using Columns
    • Using Column Families
    • Time-to-Live
  • Managing Metadata
    • What Is Metadata?
    • Why Care About Metadata?
    • Where to Store Metadata?
    • Examples of Managing Metadata
    • Limitations of the Hive Metastore and HCatalog
    • Other Ways of Storing Metadata
      • Embedding metadata in file paths and names
      • Storing the metadata in HDFS
  • Conclusion
• 2. Data Movement
  • Data Ingestion Considerations
    • Timeliness of Data Ingestion
    • Incremental Updates
    • Access Patterns
    • Original Source System and Data Structure
      • Read speed of the devices on source systems
      • Original file type
      • Compression
      • Relational database management systems
      • Streaming data
      • Logfiles
    • Transformations
      • Interceptors
      • Selectors
    • Network Bottlenecks
    • Network Security
    • Push or Pull
      • Sqoop
      • Flume
    • Failure Handling
    • Level of Complexity
  • Data Ingestion Options
    • File Transfers
      • HDFS client commands
      • Mountable HDFS
    • Considerations for File Transfers versus Other Ingest Methods
    • Sqoop: Batch Transfer Between Hadoop and Relational Databases
      • Choosing a split-by column
      • Using database-specific connectors whenever available
      • Using the Goldilocks method of Sqoop performance tuning
      • Loading many tables in parallel with fair scheduler throttling
      • Diagnosing bottlenecks
      • Keeping Hadoop updated
    • Flume: Event-Based Data Collection and Processing
      • Flume architecture
      • Flume patterns
      • File formats
      • Recommendations
        • Flume sources
        • Flume sinks
        • Flume interceptors
        • Flume memory channels
        • Flume file channels
        • Sizing Channels
      • Finding Flume bottlenecks
    • Kafka
      • Kafka fault tolerance
      • Kafka and Hadoop
  • Data Extraction
  • Conclusion
• 3. Processing Data in Hadoop
  • MapReduce
    • MapReduce Overview
      • Map phase
        • InputFormat
        • RecordReader
        • Mapper.setup()
        • Mapper.map
        • Partitioner
        • Mapper.cleanup()
        • Combiner
      • Reducer
        • Shuffle
        • Reducer.setup()
        • Reducer.reduce()
        • Reducer.cleanup()
        • OutputFormat
    • Example for MapReduce
    • When to Use MapReduce
  • Spark
    • Spark Overview
      • DAG Model
    • Overview of Spark Components
    • Basic Spark Concepts
      • Resilient Distributed Datasets
      • Shared variables
      • SparkContext
      • Transformations
      • Action
    • Benefits of Using Spark
      • Simplicity
      • Versatility
      • Reduced disk I/O
      • Storage
      • Multilanguage
      • Resource manager independence
      • Interactive shell (REPL)
    • Spark Example
    • When to Use Spark
  • Abstractions
    • Pig
    • Pig Example
    • When to Use Pig
  • Crunch
    • Crunch Example
    • When to Use Crunch
  • Cascading
    • Cascading Example
    • When to Use Cascading
  • Hive
    • Hive Overview
    • Example of Hive Code
    • When to Use Hive
  • Impala
    • Impala Overview
    • Speed-Oriented Design
      • Efficient use of memory
      • Long running daemons
      • Efficient execution engine
      • Use of LLVM
    • Impala Example
    • When to Use Impala
  • Conclusion
• 4. Common Hadoop Processing Patterns
  • Pattern: Removing Duplicate Records by Primary Key
    • Data Generation for Deduplication Example
    • Code Example: Spark Deduplication in Scala
    • Code Example: Deduplication in SQL
  • Pattern: Windowing Analysis
    • Data Generation for Windowing Analysis Example
    • Code Example: Peaks and Valleys in Spark
    • Code Example: Peaks and Valleys in SQL
  • Pattern: Time Series Modifications
    • Use HBase and Versioning
    • Use HBase with a RowKey of RecordKey and StartTime
    • Use HDFS and Rewrite the Whole Table
    • Use Partitions on HDFS for Current and Historical Records
    • Data Generation for Time Series Example
    • Code Example: Time Series in Spark
    • Code Example: Time Series in SQL
  • Conclusion
• 5. Graph Processing on Hadoop
  • What Is a Graph?
  • What Is Graph Processing?
  • How Do You Process a Graph in a Distributed System?
    • The Bulk Synchronous Parallel Model
    • BSP by Example
  • Giraph
    • Read and Partition the Data
    • Batch Process the Graph with BSP
    • Write the Graph Back to Disk
    • Putting It All Together
    • When Should You Use Giraph?
  • GraphX
    • Just Another RDD
    • GraphX Pregel Interface
    • vprog()
    • sendMessage()
    • mergeMessage()
  • Which Tool to Use?
  • Conclusion
• 6. Orchestration
  • Why We Need Workflow Orchestration
  • The Limits of Scripting
  • The Enterprise Job Scheduler and Hadoop
  • Orchestration Frameworks in the Hadoop Ecosystem
  • Oozie Terminology
  • Oozie Overview
  • Oozie Workflow
  • Workflow Patterns
    • Point-to-Point Workflow
    • Fan-Out Workflow
    • Capture-and-Decide Workflow
  • Parameterizing Workflows
  • Classpath Definition
  • Scheduling Patterns
    • Frequency Scheduling
    • Time and Data Triggers
  • Executing Workflows
  • Conclusion
• 7. Near-Real-Time Processing with Hadoop
  • Stream Processing
  • Apache Storm
    • Storm High-Level Architecture
    • Storm Topologies
    • Tuples and Streams
    • Spouts and Bolts
    • Stream Groupings
    • Reliability of Storm Applications
    • Exactly-Once Processing
    • Fault Tolerance
    • Integrating Storm with HDFS
    • Integrating Storm with HBase
    • Storm Example: Simple Moving Average
    • Evaluating Storm
      • Support for aggregation and windowing
      • Enrichment and alerting
      • Lamdba Architecture
  • Trident
    • Trident Example: Simple Moving Average
    • Evaluating Trident
      • Support for counting and windowing
      • Enrichment and alerting
      • Lamdba Architecture
  • Spark Streaming
    • Overview of Spark Streaming
    • Spark Streaming Example: Simple Count
    • Spark Streaming Example: Multiple Inputs
    • Spark Streaming Example: Maintaining State
    • Spark Streaming Example: Windowing
    • Spark Streaming Example: Streaming versus ETL Code
    • Evaluating Spark Streaming
      • Support for counting and windowing
      • Enrichment and alerting
      • Lambda Architecture
  • Flume Interceptors
  • Which Tool to Use?
    • Low-Latency Enrichment, Validation, Alerting, and Ingestion
      • Solution One: Flume
      • Solution Two: Kafka and Storm
    • NRT Counting, Rolling Averages, and Iterative Processing
    • Complex Data Pipelines
  • Conclusion
• II. Case Studies
• 8. Clickstream Analysis
  • Defining the Use Case
  • Using Hadoop for Clickstream Analysis
  • Design Overview
  • Storage
  • Ingestion
    • The Client Tier
    • The Collector Tier
  • Processing
    • Data Deduplication
      • Deduplication in Hive
      • Deduplication in Pig
    • Sessionization
      • Sessionization in Spark
      • Sessionization in MapReduce
      • Sessionization in Pig
      • Sessionization in Hive
  • Analyzing
  • Orchestration
  • Conclusion
• 9. Fraud Detection
  • Continuous Improvement
  • Taking Action
  • Architectural Requirements of Fraud Detection Systems
  • Introducing Our Use Case
  • High-Level Design
  • Client Architecture
  • Profile Storage and Retrieval
    • Caching
      • Distributed memory caching
      • HBase with BlockCache
    • HBase Data Definition
      • Columns (combined or atomic)
      • Event counting using HBase increment or put
      • Event history using HBase put
    • Delivering Transaction Status: Approved or Denied?
  • Ingest
    • Path Between the Client and Flume
      • Client push
      • Logfile pull
      • Message queue or Kafka in the middle
  • Near-Real-Time and Exploratory Analytics
  • Near-Real-Time Processing
  • Exploratory Analytics
  • What About Other Architectures?
    • Flume Interceptors
    • Kafka to Storm or Spark Streaming
    • External Business Rules Engine
  • Conclusion
• 10. Data Warehouse
  • Using Hadoop for Data Warehousing
  • Defining the Use Case
  • OLTP Schema
  • Data Warehouse: Introduction and Terminology
  • Data Warehousing with Hadoop
  • High-Level Design
    • Data Modeling and Storage
      • Choosing a storage engine
      • Denormalizing
      • Tracking updates in Hadoop
      • Selecting storage format and compression
      • Partitioning
    • Ingestion
    • Data Processing and Access
      • Partitioning
      • Merge/update
    • Aggregations
    • Data Export
    • Orchestration
  • Conclusion
• A. Joins in Impala
  • Broadcast Joins
  • Partitioned Hash Join
• Index

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