hadoop 权威指南（第三版）英文版

hadoop权威指南第三版（英文版）。 Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv 1. Meet Hadoop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Data! Data Storage and Analysis Comparison with Other Systems RDBMS Grid Computing Volunteer Computing A Brief History of Hadoop Apache Hadoop and the Hadoop Ecosystem Hadoop Releases What’s Covered in this Book Compatibility 2. MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 A Weather Dataset Data Format Analyzing the Data with Unix Tools Analyzing the Data with Hadoop Map and Reduce Java MapReduce Scaling Out Data Flow Combiner Functions Running a Distributed MapReduce Job Hadoop Streaming Ruby Python iii Hadoop Pipes Compiling and Running 3. The Hadoop Distributed Filesystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 The Design of HDFS HDFS Concepts Blocks Namenodes and Datanodes HDFS Federation HDFS High-Availability The Command-Line Interface Basic Filesystem Operations Hadoop Filesystems Interfaces The Java Interface Reading Data from a Hadoop URL Reading Data Using the FileSystem API Writing Data Directories Querying the Filesystem Deleting Data Data Flow Anatomy of a File Read Anatomy of a File Write Coherency Model Parallel Copying with distcp Keeping an HDFS Cluster Balanced Hadoop Archives Using Hadoop Archives Limitations 4. Hadoop I/O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Data Integrity Data Integrity in HDFS LocalFileSystem ChecksumFileSystem Compression Codecs Compression and Input Splits Using Compression in MapReduce Serialization The Writable Interface Writable Classes iv | Table of Contents Implementing a Custom Writable Serialization Frameworks Avro File-Based Data Structures SequenceFile MapFile 5. Developing a MapReduce Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 The Configuration API Combining Resources Variable Expansion Configuring the Development Environment Managing Configuration GenericOptionsParser, Tool, and ToolRunner Writing a Unit Test Mapper Reducer Running Locally on Test Data Running a Job in a Local Job Runner Testing the Driver Running on a Cluster Packaging Launching a Job The MapReduce Web UI Retrieving the Results Debugging a Job Hadoop Logs Remote Debugging Tuning a Job Profiling Tasks MapReduce Workflows Decomposing a Problem into MapReduce Jobs JobControl Apache Oozie 6. How MapReduce Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Anatomy of a MapReduce Job Run Classic MapReduce (MapReduce 1) YARN (MapReduce 2) Failures Failures in Classic MapReduce Failures in YARN Job Scheduling Table of Contents | v The Fair Scheduler The Capacity Scheduler Shuffle and Sort The Map Side The Reduce Side Configuration Tuning Task Execution The Task Execution Environment Speculative Execution Output Committers Task JVM Reuse Skipping Bad Records 7. MapReduce Types and Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 MapReduce Types The Default MapReduce Job Input Formats Input Splits and Records Text Input Binary Input Multiple Inputs Database Input (and Output) Output Formats Text Output Binary Output Multiple Outputs Lazy Output Database Output