关于spark:Uber-jvm-profiler-使用

背景

uber jvm profiler是用于在分布式监控收集jvm 相干指标，如:cpu/memory/io/gc信息等

装置

确保装置了maven和JDK>=8前提下，间接mvn clean package

java application

阐明
间接以java agent的部署就能够应用
应用
java -javaagent:jvm-profiler-1.0.0.jar=reporter=com.uber.profiling.reporters.KafkaOutputReporter,brokerList='kafka1:9092',topicPrefix=demo_,tag=tag-demo,metricInterval=5000,sampleInterval=0 -cp target/jvm-profiler-1.0.0.jar
选项解释

|参数|阐明|
|——|—–|
|reporter|reporter类别, 此处间接默认为com.uber.profiling.reporters.KafkaOutputReporter就能够|
|brokerList|如reporter为com.uber.profiling.reporters.KafkaOutputReporter,则brokerList为kafka列表,以逗号分隔|
|topicPrefix|如reporter为com.uber.profiling.reporters.KafkaOutputReporter,则topicPrefix为kafka topic的前缀|
|tag|key为tag的metric，会输入到reporter中|
|metricInterval|metric report的频率，依据理论状况设置，单位为ms|
|sampleInterval|jvm堆栈metrics report的频率，依据理论状况设置，单位为ms|

后果展现

  "nonHeapMemoryTotalUsed": 11890584.0,
  "bufferPools": [
      {
          "totalCapacity": 0,
          "name": "direct",
          "count": 0,
          "memoryUsed": 0
      },
      {
          "totalCapacity": 0,
          "name": "mapped",
          "count": 0,
          "memoryUsed": 0
      }
  ],
  "heapMemoryTotalUsed": 24330736.0,
  "epochMillis": 1515627003374,
  "nonHeapMemoryCommitted": 13565952.0,
  "heapMemoryCommitted": 257425408.0,
  "memoryPools": [
      {
          "peakUsageMax": 251658240,
          "usageMax": 251658240,
          "peakUsageUsed": 1194496,
          "name": "Code Cache",
          "peakUsageCommitted": 2555904,
          "usageUsed": 1173504,
          "type": "Non-heap memory",
          "usageCommitted": 2555904
      },
      {
          "peakUsageMax": -1,
          "usageMax": -1,
          "peakUsageUsed": 9622920,
          "name": "Metaspace",
          "peakUsageCommitted": 9830400,
          "usageUsed": 9622920,
          "type": "Non-heap memory",
          "usageCommitted": 9830400
      },
      {
          "peakUsageMax": 1073741824,
          "usageMax": 1073741824,
          "peakUsageUsed": 1094160,
          "name": "Compressed Class Space",
          "peakUsageCommitted": 1179648,
          "usageUsed": 1094160,
          "type": "Non-heap memory",
          "usageCommitted": 1179648
      },
      {
          "peakUsageMax": 1409286144,
          "usageMax": 1409286144,
          "peakUsageUsed": 24330736,
          "name": "PS Eden Space",
          "peakUsageCommitted": 67108864,
          "usageUsed": 24330736,
          "type": "Heap memory",
          "usageCommitted": 67108864
      },
      {
          "peakUsageMax": 11010048,
          "usageMax": 11010048,
          "peakUsageUsed": 0,
          "name": "PS Survivor Space",
          "peakUsageCommitted": 11010048,
          "usageUsed": 0,
          "type": "Heap memory",
          "usageCommitted": 11010048
      },
      {
          "peakUsageMax": 2863661056,
          "usageMax": 2863661056,
          "peakUsageUsed": 0,
          "name": "PS Old Gen",
          "peakUsageCommitted": 179306496,
          "usageUsed": 0,
          "type": "Heap memory",
          "usageCommitted": 179306496
      }
  ],
  "processCpuLoad": 0.0008024004394748531,
  "systemCpuLoad": 0.23138430784607697,
  "processCpuTime": 496918000,
  "appId": null,
  "name": "24103@machine01",
  "host": "machine01",
  "processUuid": "3c2ec835-749d-45ea-a7ec-e4b9fe17c23a",
  "tag": "mytag",
  "gc": [
      {
          "collectionTime": 0,
          "name": "PS Scavenge",
          "collectionCount": 0
      },
      {
          "collectionTime": 0,
          "name": "PS MarkSweep",
          "collectionCount": 0
      }

}


## spark application
 - 阐明     

 和java利用不同，须要把jvm-profiler.jar散发到各个节点上
 - 应用

  --jars hdfs:///public/libs/jvm-profiler-1.0.0.jar   
  --conf spark.driver.extraJavaOptions=-javaagent:jvm-profiler-1.0.0.jar=reporter=com.uber.profiling.reporters.KafkaOutputReporter,brokerList='kafka1:9092',topicPrefix=demo_,tag=tag-demo,metricInterval=5000,sampleInterval=0 
  --conf spark.executor.extraJavaOptions=-javaagent:jvm-profiler-1.0.0.jar=reporter=com.uber.profiling.reporters.KafkaOutputReporter,brokerList='kafka1:9092',topicPrefix=demo_,tag=tag-demo,metricInterval=5000,sampleInterval=0

  - 选项解释    

  
|参数|阐明|
|---|---|
|reporter|reporter类别, 此处间接默认为com.uber.profiling.reporters.KafkaOutputReporter就能够|
|brokerList|如reporter为com.uber.profiling.reporters.KafkaOutputReporter,则brokerList为kafka列表,以逗号分隔|
|topicPrefix|如reporter为com.uber.profiling.reporters.KafkaOutputReporter,则topicPrefix为kafka topic的前缀|
|tag|key为tag的metric，会输入到reporter中|
|metricInterval|metric report的频率，依据理论状况设置，单位为ms|
|sampleInterval|jvm堆栈metrics report的频率，依据理论状况设置，单位为ms|

  - 后果展现

"nonHeapMemoryTotalUsed": 11890584.0,
"bufferPools": [
    {
        "totalCapacity": 0,
        "name": "direct",
        "count": 0,
        "memoryUsed": 0
    },
    {
        "totalCapacity": 0,
        "name": "mapped",
        "count": 0,
        "memoryUsed": 0
    }
],
"heapMemoryTotalUsed": 24330736.0,
"epochMillis": 1515627003374,
"nonHeapMemoryCommitted": 13565952.0,
"heapMemoryCommitted": 257425408.0,
"memoryPools": [
    {
        "peakUsageMax": 251658240,
        "usageMax": 251658240,
        "peakUsageUsed": 1194496,
        "name": "Code Cache",
        "peakUsageCommitted": 2555904,
        "usageUsed": 1173504,
        "type": "Non-heap memory",
        "usageCommitted": 2555904
    },
    {
        "peakUsageMax": -1,
        "usageMax": -1,
        "peakUsageUsed": 9622920,
        "name": "Metaspace",
        "peakUsageCommitted": 9830400,
        "usageUsed": 9622920,
        "type": "Non-heap memory",
        "usageCommitted": 9830400
    },
    {
        "peakUsageMax": 1073741824,
        "usageMax": 1073741824,
        "peakUsageUsed": 1094160,
        "name": "Compressed Class Space",
        "peakUsageCommitted": 1179648,
        "usageUsed": 1094160,
        "type": "Non-heap memory",
        "usageCommitted": 1179648
    },
    {
        "peakUsageMax": 1409286144,
        "usageMax": 1409286144,
        "peakUsageUsed": 24330736,
        "name": "PS Eden Space",
        "peakUsageCommitted": 67108864,
        "usageUsed": 24330736,
        "type": "Heap memory",
        "usageCommitted": 67108864
    },
    {
        "peakUsageMax": 11010048,
        "usageMax": 11010048,
        "peakUsageUsed": 0,
        "name": "PS Survivor Space",
        "peakUsageCommitted": 11010048,
        "usageUsed": 0,
        "type": "Heap memory",
        "usageCommitted": 11010048
    },
    {
        "peakUsageMax": 2863661056,
        "usageMax": 2863661056,
        "peakUsageUsed": 0,
        "name": "PS Old Gen",
        "peakUsageCommitted": 179306496,
        "usageUsed": 0,
        "type": "Heap memory",
        "usageCommitted": 179306496
    }
],
"processCpuLoad": 0.0008024004394748531,
"systemCpuLoad": 0.23138430784607697,
"processCpuTime": 496918000,
"appId": null,
"name": "24103@machine01",
"host": "machine01",
"processUuid": "3c2ec835-749d-45ea-a7ec-e4b9fe17c23a",
"tag": "mytag",
"gc": [
    {
        "collectionTime": 0,
        "name": "PS Scavenge",
        "collectionCount": 0
    },
    {
        "collectionTime": 0,
        "name": "PS MarkSweep",
        "collectionCount": 0
    }
]

}

## 剖析

 - 已有的reporter   
 
 
|reporter|阐明|
|---|---|
|[ConsoleOutputReporter](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/reporters/ConsoleOutputReporter.java#L25)|默认的repoter，个别用于调试|
|[FileOutputReporter](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/reporters/FileOutputReporter.java#L34)|基于文件的reporter,分布式环境下不实用，得设置outputDir|
|[KafkaOutputReporter](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/reporters/KafkaOutputReporter.java#L36)|基于kafka的reporter，正式环境用的多，得设置brokerList，topicPrefix|
|[GraphiteOutputReporter](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/reporters/GraphiteOutputReporter.java#L34)|基于Graphite的reporter,需设置graphite.host等配置|
|[RedisOutputReporter](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java_redis/com/uber/profiling/RedisOutputReporter.java#L16)|基于redis的reporter，构建命令 `mvn -P redis clean package`|
|[InfluxDBOutputReporter](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java_influxdb/com/uber/profiling/reporters/InfluxDBOutputReporter.java#L43)|基于InfluxDB的reporter，构建命令`mvn -P influxdb clean package`，需设置influxdb.host等配置|

倡议在生产环境下应用KafkaOutputReporter，操作灵活性高，能够联合clickhouse grafana进行指标展现


- 源码剖析   

  该jvm-profiler整体是基于[java agent](https://www.developer.com/java/data/what-is-java-agent.html)实现,我的项目[pom文件](https://github.com/uber-common/jvm-profiler/blob/master/pom.xml#L105) 指定了MANIFEST.MF中的Premain-Class项和Agent-Class为[com.uber.profiling.Agent](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/Agent.java#L32)
  具体的实现类为[AgentImpl](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/AgentImpl.java#L47)   
  就具体的AgentImpl类的run办法来进行剖析

public void run(Arguments arguments, Instrumentation instrumentation, Collection<AutoCloseable> objectsToCloseOnShutdown) {

    if (arguments.isNoop()) {
        logger.info("Agent noop is true, do not run anything");
        return;
    }
    
    Reporter reporter = arguments.getReporter();

    String processUuid = UUID.randomUUID().toString();

    String appId = null;
    
    String appIdVariable = arguments.getAppIdVariable();
    if (appIdVariable != null && !appIdVariable.isEmpty()) {
        appId = System.getenv(appIdVariable);
    }
    
    if (appId == null || appId.isEmpty()) {
        appId = SparkUtils.probeAppId(arguments.getAppIdRegex());
    }

    if (!arguments.getDurationProfiling().isEmpty()
            || !arguments.getArgumentProfiling().isEmpty()) {
        instrumentation.addTransformer(new JavaAgentFileTransformer(arguments.getDurationProfiling(), arguments.getArgumentProfiling()));
    }

    List<Profiler> profilers = createProfilers(reporter, arguments, processUuid, appId);
    
    ProfilerGroup profilerGroup = startProfilers(profilers);

    Thread shutdownHook = new Thread(new ShutdownHookRunner(profilerGroup.getPeriodicProfilers(), Arrays.asList(reporter), objectsToCloseOnShutdown));
    Runtime.getRuntime().addShutdownHook(shutdownHook);
}


- [arguments.getReporter()](arguments.getReporter()) 获取reporter，如果没有设置则设置为[reporterConstructor](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/Arguments.java#L264),否则设置为指定的reporter    
- [String appId](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/AgentImpl.java#L66) ,设置appId，首先从配置中查找，如果没有设置，再从env中查找，对于spark利用则取[spark.app.id](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/util/SparkUtils.java#L26)的值   
- [List<Profiler> profilers = createProfilers(reporter, arguments, processUuid, appId)](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/AgentImpl.java#L133)，创立profilers,默认有CpuAndMemoryProfiler，ThreadInfoProfiler，ProcessInfoProfiler ；   
1.其中[CpuAndMemoryProfiler](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/profilers/CpuAndMemoryProfiler.java#L39)，[ThreadInfoProfiler](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/profilers/ThreadInfoProfiler.java#L15)，[ProcessInfoProfiler](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/profilers/ProcessInfoProfiler.java#L33)是从JMX中读取数据，ProcessInfoProfiler还会从 [/pro](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/profilers/IOProfiler.java#L55)读取数据；     
2.如果设置了durationProfiling，argumentProfiling，sampleInterval，ioProfiling，则会减少对应的MethodDurationProfiler(输入办法调用破费的工夫)，MethodArgumentProfiler(输入办法参数的值)，StacktraceReporterProfiler，IOProfiler；  
3.[MethodArgumentProfiler](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/profilers/MethodDurationProfiler.java#L29)和[MethodDurationProfiler](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/profilers/MethodArgumentProfiler.java#L29)利用[javassist](https://github.com/jboss-javassist/javassist)第三方字节码编译工具来改写对应的类，具体实现参照[JavaAgentFileTransformer](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/transformers/JavaAgentFileTransformer.java#L35)   
4.[StacktraceReporterProfiler](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/profilers/StacktraceReporterProfiler.java#L35)从JMX中读取数据    
5.[IOProfiler](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/profilers/IOProfiler.java#L27)则是读取本地机器上的[/pro](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/profilers/IOProfiler.java#L55)文件对应的目录的数据    
- [ProfilerGroup profilerGroup = startProfilers(profilers)](https://github.com/uber-common/jvm-profiler/blob/master/src/main/java/com/uber/profiling/AgentImpl.java#L90)   开始进行profiler的定时report    
其中还会辨别oneTimeProfilers和periodicProfilers，ProcessInfoProfiler就属于oneTimeProfilers，因为process的信息，在运行期间是不会变的，不须要周期行的reporter   
至此，整个流程完结

关于spark:Uber-jvm-profiler-使用

背景

装置

java application

评论

发表回复取消回复

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关于spark:Uber-jvm-profiler-使用

背景

装置

java application

评论

发表回复 取消回复

更多文章

DDN HPC 存储硬件架构设计深度分析

探秘IO500：从Lustre并行文件系统出发，开启HPC存储性能新征程

苹果iOS打包的ipa应用无法安装？一篇文章带你了解可能的原因及排查方法

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发表回复取消回复