日志收集系统架构
1.项目背景
a. 每个系统都有日志,当系统出现问题时,需要通过日志解决问题
b. 当系统机器比较少时,登陆到服务器上查看即可满足
c. 当系统机器规模巨大,登陆到机器上查看几乎不现实
2.解决方案
a. 把机器上的日志实时收集,统一的存储到中心系统
b. 然后再对这些日志建立索引,通过搜索即可以找到对应日志
c. 通过提供界面友好的web界面,通过web即可以完成日志搜索
面临的问题
a. 实时日志量非常大,每天几十亿条
b. 日志准实时收集,延迟控制在分钟级别
c. 能够水平可扩展
ELK介绍
• 中文指南https://www.gitbook.com/book/chenryn/elk-stack-guide-cn/details
• ELKStack (5.0版本之后)--> ElasticStack == (ELKStack + Beats)
• ELK Stack包含:ElasticSearch、Logstash、Kibana
• ElasticSearch是一个搜索引擎,用来搜索、分析、存储日志。它是分布式的,也就是说可以横向扩容,可以自动发现,索引自动分片,总之很强大。文档https://www.elastic.co/guide/cn/elasticsearch/guide/current/index.html
• Logstash用来采集日志,把日志解析为json格式交给ElasticSearch。
• Kibana是一个数据可视化组件,把处理后的结果通过web界面展示
• Beats在这里是一个轻量级日志采集器,其实Beats家族有5个成员
• 早期的ELK架构中使用Logstash收集、解析日志,但是Logstash对内存、cpu、io等资源消耗比较高。相比 Logstash,Beats所占系统的CPU和内存几乎可以忽略不计
• x-pack对ElasticStack提供了安全、警报、监控、报表、图表于一身的扩展包,是收费的
elk方案问题
a. 运维成本高,每增加一个日志收集,都需要手动修改配置
b. 监控缺失,无法准确获取logstash的状态
c. 无法做定制化开发以及维护
日志收集系统设计
Kafka消息队列
数据解耦
a. Log Agent,日志收集客户端,用来收集服务器上的日志
b. Kafka,高吞吐量的分布式队列,linkin开发,apache顶级开源项目
c. ES,elasticsearch,开源的搜索引擎,提供基于http restful的web接口
d. Hadoop,分布式计算框架,能够对大量数据进行分布式处理的平台
zookeeper
Zookeeper 作为一个分布式的服务框架,主要用来解决分布式集群中应用系统的一致性问题,它能提供基于类似于文件系统的目录节点树方式的数据存储, Zookeeper 作用主要是用来维护和监控存储的数据的状态变化,通过监控这些数据状态的变化,从而达到基于数据的集群管理
简单的说,zookeeper=文件系统+通知机制
a. 安装JDK,从oracle下载最新的SDK安装
b. 安装zookeeper3.3.6,下载地址:http://apache.fayea.com/zookeeper/
1)mv conf/zoo_sample.cfg conf/zoo.cfg
2)编辑 conf/zoo.cfg,修改dataDir
1# the directory where the snapshot is stored. 2dataDir=/tmp/zookeeper/data 3# the port at which the clients will connect 4clientPort=2181 5dataLogDir=/tmp/zookeeper/log
3)vim /etc/profile
export PATH=$PATH:/usr/local/zookeeper/bin
source /etc/profile
运行:
1[root@greg02 zookeeper]#zkServer.sh start 2JMX enabled by default 3Using config: /usr/local/zookeeper/bin/../conf/zoo.cfg 4Starting zookeeper ... STARTED
kafka
1.打开链接:http://kafka.apache.org/downloads.html
下载https://www.apache.org/dyn/closer.cgi?path=/kafka/0.11.0.2/kafka\_2.12-0.11.0.2.tgz
2.打开config目录下的server.properties, 修改log.dirs为D:kafka_logs,修改advertised.host.name=服务器ip
3.启动kafka
[root@greg02 kafka]#kafka-server-start.sh config/server.properties
kafka消费者开启
1[root@greg02 kafka]#kafka-console-consumer.sh --topic nginx_log --zookeeper 127.0.0.1 2181 2Using the ConsoleConsumer with old consumer is deprecated and will be removed in a future major release. Consider using the new consumer by passing [bootstrap-server] instead of [zookeeper]. 3[2018-02-05 18:30:22,451] WARN Connected to an old server; r-o mode will be unavailable (org.apache.zookeeper.ClientCnxnSocket) 4[2018-02-05 18:30:22,597] WARN Connected to an old server; r-o mode will be unavailable (org.apache.zookeeper.ClientCnxnSocket)
go kafka
1package main 2 3import ( 4 "fmt" 5 "time" 6 "github.com/Shopify/sarama" 7) 8 9func main() { 10 config := sarama.NewConfig() 11 config.Producer.RequiredAcks = sarama.WaitForAll 12 config.Producer.Partitioner = sarama.NewRandomPartitioner 13 config.Producer.Return.Successes = true 14 15 client, err := sarama.NewSyncProducer([]string{"192.168.179.130:9092"}, config) 16 if err != nil { 17 fmt.Println("producer close, err:", err) 18 return 19 } 20 21 defer client.Close() 22 msg := &sarama.ProducerMessage{} 23 msg.Topic = "nginx_log" 24 msg.Value = sarama.StringEncoder("this is a good test, my message is good") 25 26 pid, offset, err := client.SendMessage(msg) 27 if err != nil { 28 fmt.Println("send message failed,", err) 29 return 30 } 31 32 fmt.Printf("pid:%v offset:%v 33", pid, offset) 34 time.Sleep(10 * time.Millisecond) 35}
linux tail命令
-f 用于循环读取文件的内容,监视文件的增长
-F 与-f类似,区别在于当将监视的文件删除重建后-F仍能监视该文件内容-f则不行,-F有重试的功能,会不断重试
1package main 2 3import ( 4 "fmt" 5 "github.com/hpcloud/tail" 6 "time" 7) 8func main() { 9 filename := "/root/passwd" 10 tails, err := tail.TailFile(filename, tail.Config{ 11 ReOpen: true, 12 Follow: true, 13 //Location: &tail.SeekInfo{Offset: 0, Whence: 2}, 14 MustExist: false, 15 Poll: true, 16 }) 17 if err != nil { 18 fmt.Println("tail file err:", err) 19 return 20 } 21 var msg *tail.Line 22 var ok bool 23 for true { 24 msg, ok = <-tails.Lines 25 if !ok { 26 fmt.Printf("tail file close reopen, filename:%s 27", tails.Filename) 28 time.Sleep(100 * time.Millisecond) 29 continue 30 } 31 fmt.Println("msg:", msg) 32 } 33}
配置文件库使用
-
初始化配置库
1iniconf, err := NewConfig("ini", "testini.conf") 2if err != nil { 3 t.Fatal(err) 4} -
读取配置项
1 • String(key string) string 2 • Int(key string) (int, error) 3 • Int64(key string) (int64, error) 4 • Bool(key string) (bool, error) 5 • Float(key string) (float64, error)
cofig的go实现
1package main 2 3import ( 4 "fmt" 5 "github.com/astaxie/beego/config" 6) 7 8func main() { 9 conf, err := config.NewConfig("ini", "./logagent.conf") 10 if err != nil { 11 fmt.Println("new config failed, err:", err) 12 return 13 } 14 15 port, err := conf.Int("server::port") 16 if err != nil { 17 fmt.Println("read server:port failed, err:", err) 18 return 19 } 20 21 fmt.Println("Port:", port) 22 log_level := conf.String("logs::log_level") 23 if len(log_level) == 0 { 24 log_level = "debug" 25 } 26 27 fmt.Println("log_level:", log_level) 28 29 log_path := conf.String("logs::log_path") 30 fmt.Println("log_path:", log_path) 31}
日志库的使用
-
配置log组件
1 config := make(map[string]interface{}) 2 config["filename"] = "./logs/logcollect.log" 3 config["level"] = logs.LevelDebug 4 5 configStr, err := json.Marshal(config) 6 if err != nil { 7 fmt.Println("marshal failed, err:", err) 8 return 9 } -
初始化日志组件
logs.SetLogger(“file”, string(configStr))写日志
1package main 2 3import ( 4 "encoding/json" 5 "fmt" 6 "github.com/astaxie/beego/logs" 7) 8 9func main() { 10 config := make(map[string]interface{}) 11 config["filename"] = "/root/logs/logcollect.log" 12 config["level"] = logs.LevelDebug 13 14 configStr, err := json.Marshal(config) 15 if err != nil { 16 fmt.Println("marshal failed, err:", err) 17 return 18 } 19 20 logs.SetLogger(logs.AdapterFile, string(configStr)) 21 22 logs.Debug("this is a test, my name is %s", "stu01") 23 logs.Trace("this is a trace, my name is %s", "stu02") 24 logs.Warn("this is a warn, my name is %s", "stu03") 25}