Spring Bootlog and monitor

LearningSpring Boot logconfiguration and monitormechanism, includingLogback, Actuator and Prometheusetc.tool using

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1. Spring Bootlogconfiguration

Spring Boot默认usingSLF4Jserving aslog门面, Logbackserving aslogimplementation.

1.1 log级别

log级别 from low to high 依次 for :

  • TRACE: 最详细 loginformation
  • DEBUG: debuginformation
  • INFO: 一般information
  • WARN: warninginformation
  • ERROR: errorinformation

1.2 basiclogconfiguration

in application.propertiesinconfigurationlog级别: 

# 根log级别
logging.level.root=INFO
# specificpackage log级别
logging.level.com.example=DEBUG
# specificclass log级别
logging.level.org.springframework.web=WARN
logging.level.org.hibernate=ERROR

1.3 log输出格式

configurationlog输出格式: 

# 控制台输出格式
logging.pattern.console=%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n
# file输出格式
logging.pattern.file=%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n

1.4 logfileconfiguration

configurationlogfile: 

# logfilepath
logging.file.name=logs/myapp.log
# logfile big  small 限制
logging.logback.rollingpolicy.max-file-size=10MB
# 保留 logfile数量
logging.logback.rollingpolicy.max-history=7

1.5 自定义Logbackconfiguration

in src/main/resourcesTable of Contents under creationlogback-spring.xmlfile, 自定义Logbackconfiguration: 

<configuration>
    <!-- application名称 -->
    <property name="APP_NAME" value="myapp" />
    
    <!-- logfilepath -->
    <property name="LOG_PATH" value="logs" />
    
    <!-- 控制台输出 -->
    <appender name="CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
        <encoder>
            <pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n</pattern>
            <charset>UTF-8</charset>
        </encoder>
    </appender>
    
    <!-- file输出 (INFO级别)  -->
    <appender name="FILE_INFO" class="ch.qos.logback.core.rolling.RollingFileAppender">
        <filter class="ch.qos.logback.classic.filter.ThresholdFilter">
            <level>INFO</level>
        </filter>
        <encoder>
            <pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n</pattern>
            <charset>UTF-8</charset>
        </encoder>
        <rollingPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedRollingPolicy">
            <fileNamePattern>${LOG_PATH}/${APP_NAME}_info_%d{yyyy-MM-dd}.%i.log</fileNamePattern>
            <maxFileSize>10MB</maxFileSize>
            <maxHistory>7</maxHistory>
            <totalSizeCap>1GB</totalSizeCap>
        </rollingPolicy>
    </appender>
    
    <!-- file输出 (ERROR级别)  -->
    <appender name="FILE_ERROR" class="ch.qos.logback.core.rolling.RollingFileAppender">
        <filter class="ch.qos.logback.classic.filter.ThresholdFilter">
            <level>ERROR</level>
        </filter>
        <encoder>
            <pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n</pattern>
            <charset>UTF-8</charset>
        </encoder>
        <rollingPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedRollingPolicy">
            <fileNamePattern>${LOG_PATH}/${APP_NAME}_error_%d{yyyy-MM-dd}.%i.log</fileNamePattern>
            <maxFileSize>10MB</maxFileSize>
            <maxHistory>7</maxHistory>
            <totalSizeCap>1GB</totalSizeCap>
        </rollingPolicy>
    </appender>
    
    <!-- 根logconfiguration -->
    <root level="INFO">
        <appender-ref ref="CONSOLE" />
        <appender-ref ref="FILE_INFO" />
        <appender-ref ref="FILE_ERROR" />
    </root>
    
    <!-- specificpackage logconfiguration -->
    <logger name="com.example" level="DEBUG" additivity="false">
        <appender-ref ref="CONSOLE" />
        <appender-ref ref="FILE_INFO" />
        <appender-ref ref="FILE_ERROR" />
    </logger>
</configuration>

2. Spring Boot Actuator

Spring Boot Actuatorproviding了一系列produce级functions, such ashealthycheck, 指标monitoretc..

2.1 添加Actuator依赖

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>

2.2 configurationActuator

in application.propertiesinconfigurationActuator: 

# 暴露所 has 端点
management.endpoints.web.exposure.include=*
# 暴露healthycheck详细information
management.endpoint.health.show-details=always
# 设置端点 before 缀
management.endpoints.web.base-path=/actuator

2.3 常用Actuator端点

  • /actuator/health: healthycheck
  • /actuator/info: applicationinformation
  • /actuator/metrics: application指标
  • /actuator/env: environmentvariable
  • /actuator/configprops: configurationproperty
  • /actuator/loggers: logconfiguration
  • /actuator/beans: Spring Beaninformation
  • /actuator/mappings: requestmap
  • /actuator/threaddump: thread转储
  • /actuator/heapdump: 堆转储

2.4 自定义healthycheck

creation自定义healthycheckcomponent: 

@Component
public class CustomHealthIndicator implements HealthIndicator {
    
    @Override
    public Health health() {
        // check自定义healthystatus
        boolean isHealthy = checkCustomHealth();
        
        if (isHealthy) {
            return Health.up()
                    .withDetail("custom", "service正常")
                    .withDetail("timestamp", System.currentTimeMillis())
                    .build();
        } else {
            return Health.down()
                    .withDetail("custom", "serviceexception")
                    .withDetail("timestamp", System.currentTimeMillis())
                    .build();
        }
    }
    
    private boolean checkCustomHealth() {
        // implementation自定义healthycheck逻辑
        return true;
    }
}

2.5 自定义Infoinformation

in application.propertiesinconfiguration自定义Infoinformation: 

info.app.name=My Application
info.app.version=1.0.0
info.app.description=Spring Boot Actuator Demo
info.build.artifact=@project.artifactId@
info.build.version=@project.version@

3. Prometheus and Grafanamonitor

Prometheus用于收集指标, Grafana用于visualization指标.

3.1 添加Micrometer依赖

<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-registry-prometheus</artifactId>
</dependency>

3.2 configurationPrometheus

configurationPrometheus收集Spring Boot指标: 

# configurationMicrometer
management.metrics.export.prometheus.enabled=true
# configurationapplication名称 (用于Prometheustag) 
spring.application.name=myapp

3.3 installationPrometheus

  1. from Prometheus官网 under 载Prometheus
  2. 解压并creationprometheus.ymlconfigurationfile: 
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'spring-boot'
    metrics_path: '/actuator/prometheus'
    static_configs:
      - targets: ['localhost:8080']

启动Prometheus: 

./prometheus --config.file=prometheus.yml

访问http://localhost:9090查看Prometheus界面.

3.4 installationGrafana

  1. from Grafana官网 under 载Grafana
  2. 启动Grafana: 
# Linux
sudo systemctl start grafana-server

# Windows
启动grafana-server.exe

访问http://localhost:3000 (默认user名/password: admin/admin) .

3.5 configurationGrafanadatasources

  1. loginGrafana after , 点击"Configuration" -> "Data Sources"
  2. 点击"Add data source", 选择"Prometheus"
  3. 设置URL for http://localhost:9090, 点击"Save & Test"

3.6 importGrafana仪表板

  1. 点击"Create" -> "Import"
  2. 输入仪表板ID (such as10280, Spring Boot 2.x 官方仪表板)
  3. 选择Prometheusdatasources, 点击"Import"

4. log收集 and analysis

for 于distributedsystem, 我们需要集in收集 and analysislog.

4.1 ELK Stack

ELK StackincludingElasticsearch, Logstash and Kibana:

  • Elasticsearch: distributed搜index擎, 用于store and indexlog
  • Logstash: log收集 and processingtool
  • Kibana: logvisualizationtool

4.2 usingFilebeat收集log

Filebeat is a 轻量级 log收集tool: 

# installationFilebeat
sudo apt-get install filebeat

# configurationFilebeat (/etc/filebeat/filebeat.yml) 
filebeat.inputs:
- type: log
  enabled: true
  paths:
    - /path/to/logs/*.log

output.elasticsearch:
  hosts: ["localhost:9200"]

setup.kibana:
  host: "localhost:5601"

# 启动Filebeat
sudo systemctl start filebeat

4.3 usingLogstashprocessinglog

configurationLogstashprocessinglog: 

# configurationLogstash (logstash.conf) 
input {
  beats {
    port => 5044
  }
}

filter {
  grok {
    match => { "message" => "%{TIMESTAMP_ISO8601:timestamp} \[%{DATA:thread}\] %{LOGLEVEL:level} %{DATA:logger} - %{GREEDYDATA:message}" }
  }
  date {
    match => [ "timestamp", "yyyy-MM-dd HH:mm:ss.SSS" ]
    target => "@timestamp"
  }
}

output {
  elasticsearch {
    hosts => ["localhost:9200"]
    index => "myapp-logs-%{+YYYY.MM.dd}"
  }
  stdout {
    codec => rubydebug
  }
}

5. distributed追踪

distributed追踪用于跟踪request in distributedsystemin 流转.

5.1 添加Sleuth and Zipkin依赖

<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-sleuth</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-zipkin</artifactId>
</dependency>

5.2 configurationSleuth and Zipkin

in application.propertiesinconfiguration: 

# configuration采样率 (1.0表示100%采样) 
spring.sleuth.sampler.rate=1.0
# Zipkinserver地址
spring.zipkin.base-url=http://localhost:9411/

5.3 启动Zipkin

# usingDocker启动Zipkin
docker run -d -p 9411:9411 openzipkin/zipkin

访问http://localhost:9411查看Zipkin界面.

6. monitorbest practices

6.1 选择合适 monitor指标

  • Basics设施指标: CPU, memory, disk, networketc.
  • application指标: request数, response时间, error率etc.
  • 业务指标: 订单数, user数, 销售额etc.

6.2 设置合理 告警规则

根据指标设置合理 告警阈值, such as:

  • CPUusing率超过80%持续5分钟
  • memoryusing率超过90%持续10分钟
  • error率超过5%持续1分钟
  • requestresponse时间超过2秒持续5分钟

6.3 定期analysismonitordata

定期analysismonitordata, 找出system瓶颈 and optimization点.

6.4 monitordata持久化

将monitordata持久化 to datalibrary, 用于趋势analysis and 容量planning.

6.5 security考虑

  • 限制Actuator端点 访问
  • for monitorsystem添加authentication and authorization
  • encryptionmonitordata 传输

7. logbest practices

7.1 log级别usingprinciples

  • TRACE/DEBUG: 仅 in Development and debug时using
  • INFO: 记录 important 业务operation and systemevent
  • WARN: 记录可能导致issues circumstances
  • ERROR: 记录error and exception

7.2 log in 容best practices

  • log in 容应package含足够 on under 文information
  • 避免记录敏感information (such aspassword, Tokenetc.)
  • usingstructure化log, 便于analysis and 搜索
  • 统一log格式, 便于集in收集 and analysis

7.3 logperformance考虑

  • 避免 in 循环in记录 big 量log
  • using条件log, such aslogger.isDebugEnabled()
  • 合理设置log级别, 避免记录过 many 无用log
  • usingasynchronouslog, improvingapplicationperformance

7.4 asynchronouslogconfiguration

in Logbackinconfigurationasynchronouslog: 

<appender name="ASYNC_FILE" class="ch.qos.logback.classic.AsyncAppender">
    <discardingThreshold>0</discardingThreshold>
    <queueSize>512</queueSize>
    <appender-ref ref="FILE_INFO" />
</appender>
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