Prometheus监控与Grafana展示实战教程

需积分: 10 182 浏览量更新于2024-07-18 收藏 4.31MB PDF 举报

"Prometheus Up and Running 是一本深入介绍Prometheus监控系统及其配套工具Grafana的教程。这本书提供了一条清晰的学习路径，从基础知识到实际应用，帮助读者理解和掌握Prometheus的全貌。" 在《Prometheus Up and Running》中，作者首先介绍了监控的重要性，监控是现代软件系统不可或缺的一部分，它有助于及时发现并解决问题，确保服务的稳定运行。书中通过简短的历史回顾，展示了监控技术的发展，从而引出Prometheus这个时下流行的开源监控解决方案。 Prometheus的架构设计是其核心优势之一。书中详细讲解了Prometheus的工作方式，包括客户端库、导出器、服务发现、抓取数据、存储、仪表板、记录规则、警报管理等关键组件。Prometheus采用时间序列数据库，支持高效的查询和分析，并允许用户通过配置规则定义报警条件，实现自动化的问题发现。在实际操作部分，读者将学习如何启动Prometheus服务器，使用表达式浏览器进行数据查询，以及通过NodeExporter监控节点状态。此外，书中还涉及了Alertmanager的设置，用于处理和传递警报，确保及时响应系统异常。第二部分主要围绕应用监控展开，介绍了如何对代码进行合适的度量（Instrumentation）。书中通过示例程序讲解了计数器、 Gauge、Summary和Histogram等不同类型的指标，以及如何在各种编程语言如Python、Go、Java中实现这些度量。特别强调了度量命名的重要性，以及如何权衡度量的数量和粒度，以达到最佳监控效果。第三部分则探讨了如何将这些度量暴露（Exposition）出来，让Prometheus能够收集。书中涵盖了多种服务器框架和语言的集成方法，如Python的WSGI和Twisted，Go的HTTP服务器，Java的Servlet，以及Pushgateway的使用，使得各种应用程序都能够与Prometheus无缝对接。同时，书里还详细讲解了Prometheus的exposition格式、指标类型、标签以及相关的编码规则。《Prometheus Up and Running》是一本全面的教程，不仅适合初学者了解Prometheus和Grafana的基础知识，也适合有一定经验的开发者深入学习监控系统的高级用法，提升运维效率。

Prometheus has integrations with many common service discovery mechanisms, such as Kubernetes, EC2,

and Consul. There is also a generic integration for those whose setup is a little oﬀ the beaten path (see

“File”).

This still leaves a problem though. Just because Prometheus has a list of machines and services doesn’t

mean we know how they ﬁt into your architecture. For example, you might be using the EC2 Name tag

indicate what application runs on a machine, whereas others might use a tag called app .

As every organisation does it slightly diﬀerently, Prometheus allows you to conﬁgure how metadata from

service discovery is mapped to monitoring targets and their labels using relabelling.

Scraping

Service discovery and relabelling give us a list of targets to be monitored. Now Prometheus needs to fetch

the metrics. Prometheus does this by sending a HTTP request called a scrape. The response to the scrape is

parsed and ingested into storage. Several useful metrics are also added in, such as if the scrape succeeded

and how long it took. Scrapes happen regularly; usually you would conﬁgure it to happen every 10 to 60

seconds for each target.

PULL VERSUS PUSHPrometheus is a pull-based system. It decides when and what to scrape, based on its

conﬁguration. There are also push-based systems, where the monitoring target decides if it is going to be

monitored and how often.There is vigorous debate online about the two designs, which often bears

similarities to debates around Vim versus EMACS. Suﬃce to say both have pros and cons, and overall it

doesn’t matter much.As a Prometheus user you should understand that pull is ingrained in the core of

Prometheus, and attempting to make it do push instead is at best unwise.

Storage

Prometheus stores data locally in a custom database. Distributed systems are challenging to make reliable,

so Prometheus does not attempt to do any form of clustering. In addition to reliability, this makes

Prometheus easier to run.

Over the years, storage has gone through a number of redesigns, with the storage system in Prometheus

2.0 being the third iteration. The storage system can handle ingesting millions of samples per second,

making it possible to monitor thousands of machines with a single Prometheus server. The compression

algorithm used can achieve 1.3 bytes per sample on real-world data. An SSD is recommended, but not

strictly required.

Dashboards

Prometheus has a number of HTTP APIs that allow you to both request raw data and evaluate PromQL

queries. These can be used to produce graphs and dashboards. Out of the box, Prometheus provides the

expression browser. It uses these APIs and is suitable for ad hoc querying and data exploration, but it is not a

general dashboard system.

It is recommended that you use Grafana for dashboards. It has a wide variety of features, including oﬃcial

support for Prometheus as a data source. It can produce a wide variety of dashboards, such as the one in

Figure 1-2. Grafana supports talking to multiple Prometheus servers, even within a single dashboard panel.

Figure 1-2. A Grafana dashboard

TIP

A basic setup similar to the one used in this chapter is publicly available athttp://demo.robustperception.io/.

Prometheus does not oﬀer a clustered storage solution to store data across multiple machines, but there

are remote read and write APIs that allow other systems to hook in and take on this role. These allow

PromQL queries to be transparently run against both local and remote data.

What Prometheus Is Not

Now that you have an idea of where Prometheus ﬁts in the broader monitoring landscape and what its

major components are, let’s look at some use cases for which Prometheus is not a particularly good choice.

As a metrics-based system, Prometheus is not suitable for storing event logs or individual events. Nor is it

the best choice for high cardinality data, such as email addresses or usernames.

Prometheus is designed for operational monitoring, where small inaccuracies and race conditions due to

factors like kernel scheduling and failed scrapes are a fact of life. Prometheus makes tradeoﬀs and prefers

giving you data that is 99.9% correct over your monitoring breaking while waiting for perfect data. Thus in

applications involving money or billing, Prometheus should be used with caution.

In the next chapter I will show you how to run Prometheus and do some basic monitoring.

Chapter 2. Getting Started with Prometheus

In this chapter you will set up and run Prometheus, the Node exporter, and the Alertmanager. This simple

example will monitor a single machine and give you a small taste of what a full Prometheus deployment

looks like. Later chapters will look at each aspect of this setup in detail.

This chapter requires a machine running any reasonable, modern version of Linux. Either bare metal or a

virtual machine will do. You will use the command line and access services on the machine using a web

browser. For simplicity I will assume that everything is running on localhost; if this is not the case, adjust the

URLs as appropriate.

Running Prometheus

Prebuilt versions of Prometheus and other components are available from the Prometheuswebsite at http

s://prometheus.io/download/. Go to that page and download the latest version of Prometheus for the Linux

OS with Arch amd64; the download page will look something like Figure 2-1.

Figure 2-1. Part of the Prometheus download page. The Linux/amd64 version is in the middle.

STABILITY GUARANTEES

Prometheus upgrades are intended to be safe between minor versions, such as from 2.0.0 to 2.0.1,

2.1.0, or 2.3.1. Even so, as with all software it is wise to read through the changelog.

Any 2.x.x version of Prometheus should suﬃce for this chapter.

YAML

The Prometheus ecosystem uses Yet Another Markup Language (YAML) for its conﬁguration ﬁles, as it is

both approachable to humans and can be processed by tools. The format is sensitive to whitespace

though, so make sure to copy examples exactly and use spaces rather than tabs.

Here I am using Prometheus 2.2.1, so prometheus-2.2.1.linux-amd64.tar.gz is the ﬁlename.

Extract the tarball on the command line and change into its directory:

Now change the ﬁle called prometheus.yml to contain the following text:

By default Prometheus runs on TCP port 9090, so this conﬁguration instructs to scrape itself every 10

seconds. You can now run the Prometheus binary with ./prometheus.

As you can see, Prometheus logs various useful information at startup, including its exact version and

details of the machine it is running on. Now you can access the Prometheus UI in your browser at http://local

host:9090/, which will look like Figure 2-2.

hostname $ tar -xzf prometheus-*.linux-amd64.tar.gz

hostname $ cd prometheus-*.linux-amd64/

global:

scrape_interval: 10s

scrape_configs:

- job_name: prometheus

static_configs:

- targets:

- localhost:9090

hostname $ ./prometheus

level=info ... msg="Starting Prometheus" version="(version=2.2.1, branch=HEAD,

revision=bc6058c81272a8d938c05e75607371284236aadc)"

level=info ... build_context="(go=go1.10, user=root@149e5b3f0829,

date=20180314-14:15:45)"

level=info ... host_details="(Linux 4.4.0-98-generic #121-Ubuntu..."

level=info ... fd_limits="(soft=1024, hard=1048576)"

level=info ... msg="Start listening for connections" address=0.0.0.0:9090

level=info ... msg="Starting TSDB ..."

level=info ... msg="TSDB started"

level=info ... msg="Loading configuration file" filename=prometheus.yml

level=info ... msg="Server is ready to receive web requests."

剩余278页未读，继续阅读

程序员学编程

粉丝: 352
资源: 14

Prometheus监控与Grafana展示实战教程

Prometheus - Up & Running

Prometheus 监控

Prometheus - Up & Running: Infrastructure and Application Performance Monitoring

Kubernetes : Up and Running: Dive into the Future of Infrastructure

【Practical Exercise】Deployment and Optimization of Web Crawler Project: Container Orchestration ...

Integrating Docker in VSCode for Fast Deployment and Debugging

Real-time Monitoring and Alerting Mechanism of Doris Database

Monitoring MySQL Database Performance with Python: Essential Tools and Professional Techniques

【Basics】Getting Started with the Scrapy Web Scraping Framework: Structure and Basic Usage

【Advanced Chapter】Deploying Advanced Web Crawlers and Building a Cluster: Using Docker to ...

最新资源