使用Ganglia进行监控实战

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"Monitoring with Ganglia" Ganglia是一款开源的分布式监控系统，它被设计用来实时监控Linux、Unix以及Windows系统的性能数据。这个系统能够收集并聚合来自多个节点的数据，提供对网络中所有计算机资源的全面视图，包括CPU使用率、内存使用情况、磁盘I/O和网络流量等关键指标。 Ganglia的核心组件包括Gmetad（全局元数据服务器）、Gmond（监控代理）和Web界面。Gmond在每个要监控的节点上运行，负责收集本地系统的性能数据，并将这些数据发送到Gmetad。Gmetad接收并存储这些数据，然后创建一个全局的监控视图，允许用户通过Web界面查看整个集群的状态。在部署Ganglia时，你需要配置Gmond来适应你的环境，包括设置要监控的度量、报告的频率以及数据传输的目标（通常是Gmetad）。Gmetad的配置则涉及数据源、时间戳同步和数据存储策略。Web界面通常基于PHP和CGI，通过Web服务器展示Gmetad收集的数据，以图形化的方式显示各种性能指标。使用Ganglia可以实现以下功能： 1. **实时监控**：Ganglia提供了实时监控系统性能的能力，帮助管理员快速识别并解决问题。 2. **报警和通知**：你可以设置阈值，当特定指标超过这些阈值时，Ganglia可以发送报警，帮助预防潜在的问题。 3. **历史数据分析**：Ganglia存储性能数据，允许分析过去的时间段，找出性能瓶颈或趋势。 4. **可扩展性**：Ganglia设计为水平扩展，可以轻松添加更多监控节点，适应不断增长的集群规模。 5. **跨平台支持**：Ganglia支持多种操作系统，包括Linux、Unix和Windows，使其成为多平台环境的理想选择。在《Monitoring with Ganglia》这本书中，作者详细介绍了如何安装、配置和使用Ganglia，包括最佳实践、故障排查和案例研究。书中还涵盖了与其他监控工具（如Nagios）集成的可能性，以及如何自定义Ganglia以满足特定需求。无论你是初次接触Ganglia的新手，还是寻求优化现有部署的高级用户，这本书都能提供宝贵的指导。通过学习这本书，读者将了解到如何利用Ganglia进行系统优化，提高集群的稳定性和效率。此外，书中还包含了Ganglia的最新发展和更新，确保读者掌握的技能与当前技术保持同步。

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xiv | Preface

CHAPTER 1

Introducing Ganglia

Dave Josephsen

you’re reading this, odds are you have a problem to solve. I won’t presume to guess

the particulars, but I’m willing to bet that the authors of this book have shared your

pain at one time or another, so if you’re in need of a monitoring and metrics collection

engine, you’ve come to the right place. We created Ganglia for the same reason you’ve

picked up this book: we had a problem to solve.

If you’ve looked at other monitoring tools, or have already implemented a few, you’ll

find that Ganglia is as powerful as it is conceptually and operationally different from

any monitoring system you’re likely to have previously encountered. It runs on every

popular OS out there, scales easily to very large networks, and is resilient by design to

node failures. In the real world, Ganglia routinely provides near real-time monitoring

and performance metrics data for computer networks that are simply too large for more

traditional monitoring systems to handle, and it integrates seamlessly with any tradi-

tional monitoring systems you may happen to be using.

In this chapter, we’d like to introduce you to Ganglia and help you evaluate whether

it’s a good fit for your environment. Because Ganglia is a product of the labor of systems

guys—like you—who were trying to solve a problem, our introduction begins with a

description of the environment in which Ganglia was born and the problem it was

intended to solve.

It’s a Problem of Scale

Say you have a lot of machines. I’m not talking a few hundred, I mean metric oodles of

servers, stacked floor to ceiling as far as the eye can see. Servers so numerous that they

put to shame swarms of locusts, outnumber the snowflakes in Siberia, and must be

expressed in scientific notation, or as some multiple of Avogadro’s number.

Okay, maybe not quite that numerous, but the point is, if you had lots of machines,

how would you go about gathering a metric—the CPU utilization, say—from every

host every 10 seconds? Assuming 20,000 hosts, for example, your monitoring system

would need to poll 2,000 hosts per second to achieve a 10-second resolution for that

singular metric. It would also need to store, graph, and present that data quickly and

efficiently. This is the problem domain for which Ganglia was designed; to monitor

and collect massive quantities of system metrics in near real time for Large installations.

Large. With a capital L.

Large installations are interesting because they force us to reinvent or at least reevaluate

every problem we thought we’d already solved as systems administrators. The prospect

of firing up rsync or kludging together some Perl is altogether different when 20,000

hosts are involved. As the machines become more numerous, we’re more likely to care

about the efficiency of the polling protocol, we’re more likely to encounter exceptions,

and we’re less likely to interact directly with every machine. That’s not even mentioning

the quadratic curve towards infinity that describes the odds of some subset of our hosts

going offline as the total number grows.

I don’t mean to imply that Ganglia can’t be used in smaller networks—swarms of

locusts would laugh at my own puny corporate network and I couldn’t live without

Ganglia—but it’s important to understand the design characteristics from which Gan-

glia was derived, because as I mentioned, Ganglia operates quite differently from other

monitoring systems because of them. The most influential consideration shaping Gan-

glia’s design is certainly the problem of scale.

Hosts ARE the Monitoring System

The problem of scale also changes how we think about systems management, some-

times in surprising or counterintuitive ways. For example, an admin over 20,000

systems is far more likely to be running a configuration management engine such as

Puppet/Chef or CFEngine and will therefore have fewer qualms about host-centric

configuration. The large installation administrator knows that he can make configu-

ration changes to all of the hosts centrally. It’s no big deal. Smaller installations instead

tend to favor tools that minimize the necessity to configure individual hosts.

Large installation admin are rarely concerned about individual node failures. Designs

that incorporate single points of failure are generally to be avoided in large application

frameworks where it can be safely assumed, given the sheer amount of hardware in-

volved, that some percentage of nodes are always going to be on the fritz. Smaller

installations tend to favor monitoring tools that strictly define individual hosts centrally

and alert on individual host failures. This sort of behavior quickly becomes unwieldy

and annoying in larger networks.

If you think about it, the monitoring systems we’re used to dealing with all work the

way they do because of this “little network” mind set. This tendency to centralize and

strictly define the configuration begets a central daemon that sits somewhere on the

network and polls every host every so often for status. These systems are easy to use in

small environments: just install the (usually bloated) agent on every system and

2 | Chapter 1: Introducing Ganglia

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