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这篇文章将为大家详细讲解有关Uber jvm profiler如何使用，小编觉得挺实用的，因此分享给大家做个参考，希望大家阅读完这篇文章后可以有所收获。
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背景
uber jvm profiler是用于在分布式监控收集jvm 相关指标，如:cpu/memory/io/gc信息等
安装
确保安装了maven和JDK>=8前提下，直接mvn clean package
java application
说明
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选项解释
| 参数 | 说明 |
|---|---|
| reporter | reporter类别, 此处直接默认为com.uber.profiling.reporters.KafkaOutputReporter就可以 |
| 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，会输出到reporter中 |
| metricInterval | metric report的频率，根据实际情况设置，单位为ms |
| sampleInterval | jvm堆栈metrics report的频率，根据实际情况设置，单位为ms |
结果展示
"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
说明
和java应用不同，需要把jvm-profiler.jar分发到各个节点上
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
选项解释
| 参数 | 说明 |
|---|---|
| reporter | reporter类别, 此处直接默认为com.uber.profiling.reporters.KafkaOutputReporter就可以 |
| 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，会输出到reporter中 |
| metricInterval | metric report的频率，根据实际情况设置，单位为ms |
| sampleInterval | jvm堆栈metrics report的频率，根据实际情况设置，单位为ms |
结果展示
"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 | 说明 |
|---|---|
| ConsoleOutputReporter | 默认的repoter，一般用于调试 |
| FileOutputReporter | 基于文件的reporter,分布式环境下不适用，得设置outputDir |
| KafkaOutputReporter | 基于kafka的reporter，正式环境用的多，得设置brokerList，topicPrefix |
| GraphiteOutputReporter | 基于Graphite的reporter,需设置graphite.host等配置 |
| redisOutputReporter | 基于redis的reporter，构建命令 mvn -P redis clean package |
| InfluxDBOutputReporter | 基于InfluxDB的reporter，构建命令mvn -P influxdb clean package，需设置influxdb.host等配置 |
建议在生产环境下使用KafkaOutputReporter，操作灵活性高，可以结合clickhouse grafana进行指标展示
源码分析
该jvm-profiler整体是基于java agent实现,项目pom文件 指定了MANIFEST.MF中的Premain-Class项和Agent-Class为com.uber.profiling.Agent 具体的实现类为AgentImpl
就具体的AgentImpl类的run方法来进行分析public void run(Arguments arguments, Instrumentation instrumentation, Collection
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 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() 获取reporter，如果没有设置则设置为reporterConstructor,否则设置为指定的reporter
String appId ,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应用则取spark.app.id的值
List
profilers = createProfilers(reporter, arguments, processUuid, appId)，创建profilers,默认有CpuAndMemoryProfiler，ThreadInfoProfiler，ProcessInfoProfiler ；
1.其中CpuAndMemoryProfiler，ThreadInfoProfiler，ProcessInfoProfiler是从JMX中读取数据，ProcessInfoProfiler还会从 /pro读取数据；
2.如果设置了durationProfiling，argumentProfiling，sampleInterval，ioProfiling，则会增加对应的MethodDurationProfiler(输出方法调用花费的时间)，MethodArgumentProfiler(输出方法参数的值)，StacktraceReporterProfiler，IOProfiler；
3.MethodArgumentProfiler和MethodDurationProfiler利用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
4.StacktraceReporterProfiler从JMX中读取数据
5.IOProfiler则是读取本地机器上的/pro文件对应的目录的数据ProfilerGroup profilerGroup = startProfilers(profilers) 开始进行profiler的定时report
其中还会区分oneTimeProfilers和periodicProfilers，ProcessInfoProfiler就属于oneTimeProfilers，因为process的信息，在运行期间是不会变的，不需要周期行的reporter
至此，整个流程结束
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文章标题：Uberjvmprofiler如何使用
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