Hadoop Operations：中文版缺失的经典指南

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"《Hadoop Operations》是一本由Eric Sammer编写的关于Hadoop运维的书籍，专注于介绍在实际生产环境中如何有效地管理和维护Hadoop集群。该书由O'Reilly Media出版，适合教育、商业和销售推广使用。" 《Hadoop Operations》这本书详细探讨了Hadoop生态系统中的关键组件和最佳实践，旨在帮助读者理解和掌握Hadoop集群的运维工作。书中涵盖了以下几个重要的知识点： 1. **Hadoop架构**：Hadoop是由Apache软件基金会开发的开源分布式计算框架，主要由HDFS（Hadoop Distributed File System）和MapReduce两部分组成。HDFS提供高容错性的数据存储，而MapReduce则用于处理和分析大规模数据。 2. **集群规划与部署**：书中会讲解如何根据业务需求规划Hadoop集群的规模，包括节点数量、硬件配置以及网络设计。同时，还会详细介绍如何安装和配置Hadoop组件，确保集群稳定运行。 3. **数据管理**：讨论Hadoop中的数据生命周期管理，包括数据的上传、存储、备份和恢复策略。此外，还会涉及数据的压缩和分块，以优化存储和计算效率。 4. **性能调优**：针对MapReduce作业进行性能优化是运维工作的重要部分。书中会介绍监控工具和指标，帮助识别瓶颈并提升处理速度。 5. **安全性与访问控制**：Hadoop的安全性包括身份验证、授权和审计，如Kerberos认证和Hadoop的权限模型。这部分内容将帮助读者理解如何保护数据安全，防止未授权访问。 6. **故障排查与容错**：Hadoop设计中有大量的容错机制，但实际操作中仍可能出现问题。书中会讲述如何识别、诊断和解决Hadoop集群中的常见故障，以保持服务的连续性。 7. **扩展与升级**：随着业务的增长，可能需要扩展集群或升级硬件。这部分内容将指导读者如何平滑地进行这些操作，避免数据丢失。 8. **Hadoop生态组件**：除了核心的HDFS和MapReduce，Hadoop生态系统还包括Hive、Pig、HBase、Spark等工具。书中可能会介绍这些组件的使用和它们在大数据处理中的角色。 9. **监控与日志管理**：高效的运维离不开良好的监控系统。书中会介绍如何设置和使用Hadoop的监控工具，如Ambari、Ganglia和Nagios，以及日志收集和分析的最佳实践。 10. **运维自动化**：通过Shell脚本、 Puppet或Chef等自动化工具，可以简化Hadoop集群的日常维护工作，提高效率。《Hadoop Operations》这本书对于那些希望深入了解Hadoop运维的读者来说，是一份宝贵的资源。它不仅提供了理论知识，还包含了许多来自实际经验的技巧和建议，帮助读者应对各种挑战，成功地管理复杂的大数据环境。

and generally improve in quality with more of it. Knowing more about a problem space

generally leads to better decisions (or algorithm efficacy), which in turn leads to happier

users, more money, reduced fraud, healthier people, safer conditions, or whatever the

desired result might be.

Apache Hadoop is a platform that provides pragmatic, cost-effective, scalable infra-

structure for building many of the types of applications described earlier. Made up of

a distributed filesystem called the Hadoop Distributed Filesystem (HDFS) and a com-

putation layer that implements a processing paradigm called MapReduce, Hadoop is

an open source, batch data processing system for enormous amounts of data. We live

in a flawed world, and Hadoop is designed to survive in it by not only tolerating hard-

ware and software failures, but also treating them as first-class conditions that happen

regularly. Hadoop uses a cluster of plain old commodity servers with no specialized

hardware or network infrastructure to form a single, logical, storage and compute plat-

form, or cluster, that can be shared by multiple individuals or groups. Computation in

Hadoop MapReduce is performed in parallel, automatically, with a simple abstraction

for developers that obviates complex synchronization and network programming. Un-

like many other distributed data processing systems, Hadoop runs the user-provided

processing logic on the machine where the data lives rather than dragging the data

across the network; a huge win for performance.

For those interested in the history, Hadoop was modeled after two papers produced

by Google, one of the many companies to have these kinds of data-intensive processing

problems. The first, presented in 2003, describes a pragmatic, scalable, distributed

filesystem optimized for storing enormous datasets, called the Google Filesystem, or

GFS. In addition to simple storage, GFS was built to support large-scale, data-intensive,

distributed processing applications. The following year, another paper, titled "Map-

Reduce: Simplified Data Processing on Large Clusters," was presented, defining a pro-

gramming model and accompanying framework that provided automatic paralleliza-

tion, fault tolerance, and the scale to process hundreds of terabytes of data in a single

job over thousands of machines. When paired, these two systems could be used to build

large data processing clusters on relatively inexpensive, commodity machines. These

papers directly inspired the development of HDFS and Hadoop MapReduce, respec-

tively.

Interest and investment in Hadoop has led to an entire ecosystem of related software

both open source and commercial. Within the Apache Software Foundation alone,

projects that explicitly make use of, or integrate with, Hadoop are springing up regu-

larly. Some of these projects make authoring MapReduce jobs easier and more acces-

sible, while others focus on getting data in and out of HDFS, simplify operations, enable

deployment in cloud environments, and so on. Here is a sampling of the more popular

projects with which you should familiarize yourself:

Apache Hive

Hive creates a relational database−style abstraction that allows developers to write

a dialect of SQL, which in turn is executed as one or more MapReduce jobs on the

2 | Chapter 1: Introduction

cluster. Developers, analysts, and existing third-party packages already know and

speak SQL (Hive’s dialect of SQL is called HiveQL and implements only a subset

of any of the common standards). Hive takes advantage of this and provides a quick

way to reduce the learning curve to adopting Hadoop and writing MapReduce jobs.

For this reason, Hive is by far one of the most popular Hadoop ecosystem projects.

Hive works by defining a table-like schema over an existing set of files in HDFS

and handling the gory details of extracting records from those files when a query

is run. The data on disk is never actually changed, just parsed at query time. HiveQL

statements are interpreted and an execution plan of prebuilt map and reduce

classes is assembled to perform the MapReduce equivalent of the SQL statement.

Apache Pig

Like Hive, Apache Pig was created to simplify the authoring of MapReduce jobs,

obviating the need to write Java code. Instead, users write data processing jobs in

a high-level scripting language from which Pig builds an execution plan and exe-

cutes a series of MapReduce jobs to do the heavy lifting. In cases where Pig doesn’t

support a necessary function, developers can extend its set of built-in operations

by writing user-defined functions in Java (Hive supports similar functionality as

well). If you know Perl, Python, Ruby, JavaScript, or even shell script, you can learn

Pig’s syntax in the morning and be running MapReduce jobs by lunchtime.

Apache Sqoop

Not only does Hadoop not want to replace your database, it wants to be friends

with it. Exchanging data with relational databases is one of the most popular in-

tegration points with Apache Hadoop. Sqoop, short for “SQL to Hadoop,” per-

forms bidirectional data transfer between Hadoop and almost any database with

a JDBC driver. Using MapReduce, Sqoop performs these operations in parallel

with no need to write code.

For even greater performance, Sqoop supports database-specific plug-ins that use

native features of the RDBMS rather than incurring the overhead of JDBC. Many

of these connectors are open source, while others are free or available from com-

mercial vendors at a cost. Today, Sqoop includes native connectors (called direct

support) for MySQL and PostgreSQL. Free connectors exist for Teradata, Netezza,

SQL Server, and Oracle (from Quest Software), and are available for download

from their respective company websites.

Apache Flume

Apache Flume is a streaming data collection and aggregation system designed to

transport massive volumes of data into systems such as Hadoop. It supports native

connectivity and support for writing directly to HDFS, and simplifies reliable,

streaming data delivery from a variety of sources including RPC services, log4j

appenders, syslog, and even the output from OS commands. Data can be routed,

load-balanced, replicated to multiple destinations, and aggregated from thousands

of hosts by a tier of agents.

Introduction | 3

Apache Oozie

It’s not uncommon for large production clusters to run many coordinated Map-

Reduce jobs in a workfow. Apache Oozie is a workflow engine and scheduler built

specifically for large-scale job orchestration on a Hadoop cluster. Workflows can

be triggered by time or events such as data arriving in a directory, and job failure

handling logic can be implemented so that policies are adhered to. Oozie presents

a REST service for programmatic management of workflows and status retrieval.

Apache Whirr

Apache Whirr was developed to simplify the creation and deployment of ephem-

eral clusters in cloud environments such as Amazon’s AWS. Run as a command-

line tool either locally or within the cloud, Whirr can spin up instances, deploy

Hadoop, configure the software, and tear it down on demand. Under the hood,

Whirr uses the powerful jclouds library so that it is cloud provider−neutral. The

developers have put in the work to make Whirr support both Amazon EC2 and

Rackspace Cloud. In addition to Hadoop, Whirr understands how to provision

Apache Cassandra, Apache ZooKeeper, Apache HBase, ElasticSearch, Voldemort,

and Apache Hama.

Apache HBase

Apache HBase is a low-latency, distributed (nonrelational) database built on top

of HDFS. Modeled after Google’s Bigtable, HBase presents a flexible data model

with scale-out properties and a very simple API. Data in HBase is stored in a semi-

columnar format partitioned by rows into regions. It’s not uncommon for a single

table in HBase to be well into the hundreds of terabytes or in some cases petabytes.

Over the past few years, HBase has gained a massive following based on some very

public deployments such as Facebook’s Messages platform. Today, HBase is used

to serve huge amounts of data to real-time systems in major production deploy-

ments.

Apache ZooKeeper

A true workhorse, Apache ZooKeeper is a distributed, consensus-based coordina-

tion system used to support distributed applications. Distributed applications that

require leader election, locking, group membership, service location, and config-

uration services can use ZooKeeper rather than reimplement the complex coordi-

nation and error handling that comes with these functions. In fact, many projects

within the Hadoop ecosystem use ZooKeeper for exactly this purpose (most no-

tably, HBase).

Apache HCatalog

A relatively new entry, Apache HCatalog is a service that provides shared schema

and data access abstraction services to applications with the ecosystem. The

long-term goal of HCatalog is to enable interoperability between tools such as

Apache Hive and Pig so that they can share dataset metadata information.

The Hadoop ecosystem is exploding into the commercial world as well. Vendors such

as Oracle, SAS, MicroStrategy, Tableau, Informatica, Microsoft, Pentaho, Talend, HP,

4 | Chapter 1: Introduction

Dell, and dozens of others have all developed integration or support for Hadoop within

one or more of their products. Hadoop is fast becoming (or, as an increasingly growing

group would believe, already has become) the de facto standard for truly large-scale

data processing in the data center.

If you’re reading this book, you may be a developer with some exposure to Hadoop

looking to learn more about managing the system in a production environment. Alter-

natively, it could be that you’re an application or system administrator tasked with

owning the current or planned production cluster. Those in the latter camp may be

rolling their eyes at the prospect of dealing with yet another system. That’s fair, and we

won’t spend a ton of time talking about writing applications, APIs, and other pesky

code problems. There are other fantastic books on those topics, especially Hadoop: The

Definitive Guide by Tom White (O’Reilly). Administrators do, however, play an abso-

lutely critical role in planning, installing, configuring, maintaining, and monitoring

Hadoop clusters. Hadoop is a comparatively low-level system, leaning heavily on the

host operating system for many features, and it works best when developers and ad-

ministrators collaborate regularly. What you do impacts how things work.

It’s an extremely exciting time to get into Apache Hadoop. The so-called big data space

is all the rage, sure, but more importantly, Hadoop is growing and changing at a stag-

gering rate. Each new version—and there have been a few big ones in the past year or

two—brings another truckload of features for both developers and administrators

alike. You could say that Hadoop is experiencing software puberty; thanks to its rapid

growth and adoption, it’s also a little awkward at times. You’ll find, throughout this

book, that there are significant changes between even minor versions. It’s a lot to keep

up with, admittedly, but don’t let it overwhelm you. Where necessary, the differences

are called out, and a section in Chapter 4 is devoted to walking you through the most

commonly encountered versions.

This book is intended to be a pragmatic guide to running Hadoop in production. Those

who have some familiarity with Hadoop may already know alternative methods for

installation or have differing thoughts on how to properly tune the number of map slots

based on CPU utilization.

That’s expected and more than fine. The goal is not to

enumerate all possible scenarios, but rather to call out what works, as demonstrated

in critical deployments.

Chapters 2 and 3 provide the necessary background, describing what HDFS and Map-

Reduce are, why they exist, and at a high level, how they work. Chapter 4 walks you

through the process of planning for an Hadoop deployment including hardware selec-

tion, basic resource planning, operating system selection and configuration, Hadoop

distribution and version selection, and network concerns for Hadoop clusters. If you

are looking for the meat and potatoes, Chapter 5 is where it’s at, with configuration

and setup information, including a listing of the most critical properties, organized by

2. We also briefly cover the flux capacitor and discuss the burn rate of energon cubes during combat.

Introduction | 5

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