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Uber jvm profiler如何使用

發(fā)布時間：2022-01-05 14:52:41 來源：億速云 閱讀：126 作者：小新 欄目：大數(shù)據(jù)

這篇文章將為大家詳細講解有關Uber jvm profiler如何使用，小編覺得挺實用的，因此分享給大家做個參考，希望大家閱讀完這篇文章后可以有所收獲。

背景

uber jvm profiler是用于在分布式監(jiān)控收集jvm 相關指標，如:cpu/memory/io/gc信息等

安裝

確保安裝了maven和JDK>=8前提下，直接mvn clean package

java application

  • 說明

    直接以java agent的部署就可以使用

  • 使用

    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

  • 選項解釋

參數(shù)說明
reporterreporter類別, 此處直接默認為com.uber.profiling.reporters.KafkaOutputReporter就可以
brokerList如reporter為com.uber.profiling.reporters.KafkaOutputReporter,則brokerList為kafka列表,以逗號分隔
topicPrefix如reporter為com.uber.profiling.reporters.KafkaOutputReporter,則topicPrefix為kafka topic的前綴
tagkey為tag的metric，會輸出到reporter中
metricIntervalmetric report的頻率，根據(jù)實際情況設置，單位為ms
sampleIntervaljvm堆棧metrics report的頻率，根據(jù)實際情況設置，單位為ms

  • 結(jié)果展示

  "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

  • 說明

    和java應用不同，需要把jvm-profiler.jar分發(fā)到各個節(jié)點上

  • 使用

       --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


  • 選項解釋

參數(shù)說明
reporterreporter類別, 此處直接默認為com.uber.profiling.reporters.KafkaOutputReporter就可以
brokerList如reporter為com.uber.profiling.reporters.KafkaOutputReporter,則brokerList為kafka列表,以逗號分隔
topicPrefix如reporter為com.uber.profiling.reporters.KafkaOutputReporter,則topicPrefix為kafka topic的前綴
tagkey為tag的metric，會輸出到reporter中
metricIntervalmetric report的頻率，根據(jù)實際情況設置，單位為ms
sampleIntervaljvm堆棧metrics report的頻率，根據(jù)實際情況設置，單位為ms

  • 結(jié)果展示

  "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
      }
  ]
}

分析

  • 已有的reporter

reporter說明
ConsoleOutputReporter默認的repoter，一般用于調(diào)試
FileOutputReporter基于文件的reporter,分布式環(huán)境下不適用，得設置outputDir
KafkaOutputReporter基于kafka的reporter，正式環(huán)境用的多，得設置brokerList，topicPrefix
GraphiteOutputReporter基于Graphite的reporter,需設置graphite.host等配置
RedisOutputReporter基于redis的reporter，構(gòu)建命令 mvn -P redis clean package
InfluxDBOutputReporter基于InfluxDB的reporter，構(gòu)建命令mvn -P influxdb clean package，需設置influxdb.host等配置
建議在生產(chǎn)環(huán)境下使用KafkaOutputReporter，操作靈活性高，可以結(jié)合clickhouse grafana進行指標展示

  • 源碼分析

    該jvm-profiler整體是基于java agent實現(xiàn),項目pom文件 指定了MANIFEST.MF中的Premain-Class項和Agent-Class為com.uber.profiling.Agent 具體的實現(xiàn)類為AgentImpl
    就具體的AgentImpl類的run方法來進行分析

    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() 獲取reporter，如果沒有設置則設置為reporterConstructor,否則設置為指定的reporter

    • String appId ,設置appId，首先從配置中查找，如果沒有設置，再從env中查找，對于spark應用則取spark.app.id的值

    • List<Profiler> profilers = createProfilers(reporter, arguments, processUuid, appId)，創(chuàng)建profilers,默認有CpuAndMemoryProfiler，ThreadInfoProfiler，ProcessInfoProfiler ；
      1.其中CpuAndMemoryProfiler，ThreadInfoProfiler，ProcessInfoProfiler是從JMX中讀取數(shù)據(jù)，ProcessInfoProfiler還會從 /pro讀取數(shù)據(jù)；
      2.如果設置了durationProfiling，argumentProfiling，sampleInterval，ioProfiling，則會增加對應的MethodDurationProfiler(輸出方法調(diào)用花費的時間)，MethodArgumentProfiler(輸出方法參數(shù)的值)，StacktraceReporterProfiler，IOProfiler；
      3.MethodArgumentProfiler和MethodDurationProfiler利用javassist第三方字節(jié)碼編譯工具來改寫對應的類，具體實現(xiàn)參照JavaAgentFileTransformer
      4.StacktraceReporterProfiler從JMX中讀取數(shù)據(jù)
      5.IOProfiler則是讀取本地機器上的/pro文件對應的目錄的數(shù)據(jù)

    • ProfilerGroup profilerGroup = startProfilers(profilers) 開始進行profiler的定時report
      其中還會區(qū)分oneTimeProfilers和periodicProfilers，ProcessInfoProfiler就屬于oneTimeProfilers，因為process的信息，在運行期間是不會變的，不需要周期行的reporter
      至此，整個流程結(jié)束

關于“Uber jvm profiler如何使用”這篇文章就分享到這里了，希望以上內(nèi)容可以對大家有一定的幫助，使各位可以學到更多知識，如果覺得文章不錯，請把它分享出去讓更多的人看到。

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