聊聊storm的LoggingMetricsConsumer
序
本文主要研究一下storm的LoggingMetricsConsumer
LoggingMetricsConsumer
storm-2.0.0/storm-client/src/jvm/org/apache/storm/metric/LoggingMetricsConsumer.java
public class LoggingMetricsConsumer implements IMetricsConsumer {
public static final Logger LOG = LoggerFactory.getLogger(LoggingMetricsConsumer.class);
static private String padding = " ";
@Override
public void prepare(Map<String, Object> topoConf, Object registrationArgument, TopologyContext context, IErrorReporter errorReporter) {
}
@Override
public void handleDataPoints(TaskInfo taskInfo, Collection<DataPoint> dataPoints) {
StringBuilder sb = new StringBuilder();
String header = String.format("%d\t%15s:%-4d\t%3d:%-11s\t",
taskInfo.timestamp,
taskInfo.srcWorkerHost, taskInfo.srcWorkerPort,
taskInfo.srcTaskId,
taskInfo.srcComponentId);
sb.append(header);
for (DataPoint p : dataPoints) {
sb.delete(header.length(), sb.length());
sb.append(p.name)
.append(padding).delete(header.length() + 23, sb.length()).append("\t")
.append(p.value);
LOG.info(sb.toString());
}
}
@Override
public void cleanup() {
}
}- LoggingMetricsConsumer实现了IMetricsConsumer接口，在handleDataPoints方法将taskInfo及dataPoints打印到log；具体打印到哪个log呢，这个需要看storm的log4j2的配置
log4j2/worker.xml
<?xml version="1.0" encoding="UTF-8"?>
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this work for additional information regarding copyright ownership.
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limitations under the License.
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<configuration monitorInterval="60" shutdownHook="disable">
<properties>
<property name="pattern">%d{yyyy-MM-dd HH:mm:ss.SSS} %c{1.} %t [%p] %msg%n</property>
<property name="patternNoTime">%msg%n</property>
<property name="patternMetrics">%d %-8r %m%n</property>
</properties>
<appenders>
<RollingFile name="A1"
fileName="${sys:workers.artifacts}/${sys:storm.id}/${sys:worker.port}/${sys:logfile.name}"
filePattern="${sys:workers.artifacts}/${sys:storm.id}/${sys:worker.port}/${sys:logfile.name}.%i.gz">
<PatternLayout>
<pattern>${pattern}</pattern>
</PatternLayout>
<Policies>
<SizeBasedTriggeringPolicy size="100 MB"/> <!-- Or every 100 MB -->
</Policies>
<DefaultRolloverStrategy max="9"/>
</RollingFile>
<RollingFile name="STDOUT"
fileName="${sys:workers.artifacts}/${sys:storm.id}/${sys:worker.port}/${sys:logfile.name}.out"
filePattern="${sys:workers.artifacts}/${sys:storm.id}/${sys:worker.port}/${sys:logfile.name}.out.%i.gz">
<PatternLayout>
<pattern>${patternNoTime}</pattern>
</PatternLayout>
<Policies>
<SizeBasedTriggeringPolicy size="100 MB"/> <!-- Or every 100 MB -->
</Policies>
<DefaultRolloverStrategy max="4"/>
</RollingFile>
<RollingFile name="STDERR"
fileName="${sys:workers.artifacts}/${sys:storm.id}/${sys:worker.port}/${sys:logfile.name}.err"
filePattern="${sys:workers.artifacts}/${sys:storm.id}/${sys:worker.port}/${sys:logfile.name}.err.%i.gz">
<PatternLayout>
<pattern>${patternNoTime}</pattern>
</PatternLayout>
<Policies>
<SizeBasedTriggeringPolicy size="100 MB"/> <!-- Or every 100 MB -->
</Policies>
<DefaultRolloverStrategy max="4"/>
</RollingFile>
<RollingFile name="METRICS"
fileName="${sys:workers.artifacts}/${sys:storm.id}/${sys:worker.port}/${sys:logfile.name}.metrics"
filePattern="${sys:workers.artifacts}/${sys:storm.id}/${sys:worker.port}/${sys:logfile.name}.metrics.%i.gz">
<PatternLayout>
<pattern>${patternMetrics}</pattern>
</PatternLayout>
<Policies>
<SizeBasedTriggeringPolicy size="2 MB"/>
</Policies>
<DefaultRolloverStrategy max="9"/>
</RollingFile>
<Syslog name="syslog" format="RFC5424" charset="UTF-8" host="localhost" port="514"
protocol="UDP" appName="[${sys:storm.id}:${sys:worker.port}]" mdcId="mdc" includeMDC="true"
facility="LOCAL5" enterpriseNumber="18060" newLine="true" exceptionPattern="%rEx{full}"
messageId="[${sys:user.name}:${sys:logging.sensitivity}]" id="storm" immediateFail="true" immediateFlush="true"/>
</appenders>
<loggers>
<root level="info"> <!-- We log everything -->
<appender-ref ref="A1"/>
<appender-ref ref="syslog"/>
</root>
<Logger name="org.apache.storm.metric.LoggingMetricsConsumer" level="info" additivity="false">
<appender-ref ref="METRICS"/>
</Logger>
<Logger name="STDERR" level="INFO">
<appender-ref ref="STDERR"/>
<appender-ref ref="syslog"/>
</Logger>
<Logger name="STDOUT" level="INFO">
<appender-ref ref="STDOUT"/>
<appender-ref ref="syslog"/>
</Logger>
</loggers>
</configuration>- 以worker.xml为例，这里对name为org.apache.storm.metric.LoggingMetricsConsumer的logger配置了info级别的输出，additivity为false
- METRICS的appender指定了文件名为${sys:workers.artifacts}/${sys:storm.id}/${sys:worker.port}/${sys:logfile.name}.metrics，比如workers-artifacts/tickDemo-1-1541070680/6700/worker.log.metrics
- METRCIS配置的是RollingFile，SizeBasedTriggeringPolicy的大小为2MB
配置
topology配置
conf.registerMetricsConsumer(org.apache.storm.metric.LoggingMetricsConsumer.class, 1);
- 可以在topology提交的时候，在conf注册LoggingMetricsConsumer；这种配置只有该topology的worker生效，即有指标数据的话，会写入topology的worker.log.metrics文件
storm.yaml配置
topology.metrics.consumer.register:
- class: "org.apache.storm.metric.LoggingMetricsConsumer"
max.retain.metric.tuples: 100
parallelism.hint: 1
- class: "org.apache.storm.metric.HttpForwardingMetricsConsumer"
parallelism.hint: 1
argument: "http://example.com:8080/metrics/my-topology/"- storm.yaml配置是作用于所有的topology，注意这里配置的是topology.metrics.consumer.register，是topology级别的，数据是写入worker.log.metrics文件
- 如果是cluster级别的话，配置的是storm.cluster.metrics.consumer.register，而且只能使用storm.yaml的配置方式，开启这个的话，有指标数据会写入nimbus.log.metrics以及supervisor.log.metrics文件
- 启动nimbus以及supervisor采用的log4j配置参数为-Dlog4j.configurationFile=/apache-storm/log4j2/cluster.xml；而启动woker采用的log4j配置参数为-Dlog4j.configurationFile=/apache-storm/log4j2/worker.xml；各个组件的-Dlogfile.name参数分别为nimbus.log、supervisor.log、worker.log
MetricsConsumerBolt
storm-2.0.0/storm-client/src/jvm/org/apache/storm/metric/MetricsConsumerBolt.java
public class MetricsConsumerBolt implements IBolt {
public static final Logger LOG = LoggerFactory.getLogger(MetricsConsumerBolt.class);
private final int _maxRetainMetricTuples;
private final Predicate<IMetricsConsumer.DataPoint> _filterPredicate;
private final DataPointExpander _expander;
private final BlockingQueue<MetricsTask> _taskQueue;
IMetricsConsumer _metricsConsumer;
String _consumerClassName;
OutputCollector _collector;
Object _registrationArgument;
private Thread _taskExecuteThread;
private volatile boolean _running = true;
public MetricsConsumerBolt(String consumerClassName, Object registrationArgument, int maxRetainMetricTuples,
Predicate<IMetricsConsumer.DataPoint> filterPredicate, DataPointExpander expander) {
_consumerClassName = consumerClassName;
_registrationArgument = registrationArgument;
_maxRetainMetricTuples = maxRetainMetricTuples;
_filterPredicate = filterPredicate;
_expander = expander;
if (_maxRetainMetricTuples > 0) {
_taskQueue = new LinkedBlockingDeque<>(_maxRetainMetricTuples);
} else {
_taskQueue = new LinkedBlockingDeque<>();
}
}
@Override
public void prepare(Map<String, Object> topoConf, TopologyContext context, OutputCollector collector) {
try {
_metricsConsumer = (IMetricsConsumer) Class.forName(_consumerClassName).newInstance();
} catch (Exception e) {
throw new RuntimeException("Could not instantiate a class listed in config under section " +
Config.TOPOLOGY_METRICS_CONSUMER_REGISTER + " with fully qualified name " + _consumerClassName, e);
}
_metricsConsumer.prepare(topoConf, _registrationArgument, context, collector);
_collector = collector;
_taskExecuteThread = new Thread(new MetricsHandlerRunnable());
_taskExecuteThread.setDaemon(true);
_taskExecuteThread.start();
}
@Override
public void execute(Tuple input) {
IMetricsConsumer.TaskInfo taskInfo = (IMetricsConsumer.TaskInfo) input.getValue(0);
Collection<IMetricsConsumer.DataPoint> dataPoints = (Collection) input.getValue(1);
Collection<IMetricsConsumer.DataPoint> expandedDataPoints = _expander.expandDataPoints(dataPoints);
List<IMetricsConsumer.DataPoint> filteredDataPoints = getFilteredDataPoints(expandedDataPoints);
MetricsTask metricsTask = new MetricsTask(taskInfo, filteredDataPoints);
while (!_taskQueue.offer(metricsTask)) {
_taskQueue.poll();
}
_collector.ack(input);
}
private List<IMetricsConsumer.DataPoint> getFilteredDataPoints(Collection<IMetricsConsumer.DataPoint> dataPoints) {
return Lists.newArrayList(Iterables.filter(dataPoints, _filterPredicate));
}
@Override
public void cleanup() {
_running = false;
_metricsConsumer.cleanup();
_taskExecuteThread.interrupt();
}
static class MetricsTask {
private IMetricsConsumer.TaskInfo taskInfo;
private Collection<IMetricsConsumer.DataPoint> dataPoints;
public MetricsTask(IMetricsConsumer.TaskInfo taskInfo, Collection<IMetricsConsumer.DataPoint> dataPoints) {
this.taskInfo = taskInfo;
this.dataPoints = dataPoints;
}
public IMetricsConsumer.TaskInfo getTaskInfo() {
return taskInfo;
}
public Collection<IMetricsConsumer.DataPoint> getDataPoints() {
return dataPoints;
}
}
class MetricsHandlerRunnable implements Runnable {
@Override
public void run() {
while (_running) {
try {
MetricsTask task = _taskQueue.take();
_metricsConsumer.handleDataPoints(task.getTaskInfo(), task.getDataPoints());
} catch (InterruptedException e) {
break;
} catch (Throwable t) {
LOG.error("Exception occurred during handle metrics", t);
}
}
}
}
}- MetricsConsumerBolt在构造器里头创建了_taskQueue，如果_maxRetainMetricTuples大于0，则创建的是有界队列，否则创建的是无界队列；读取的是topology.metrics.consumer.register下面的max.retain.metric.tuples值，读取不到默认为100
- MetricsConsumerBolt在prepare的时候启动了MetricsHandlerRunnable线程，该线程从_taskQueue取出MetricsTask，然后调用_metricsConsumer.handleDataPoints(task.getTaskInfo(), task.getDataPoints());
- MetricsConsumerBolt的execute方法，在接收到tuple的时候，就会往_taskQueue添加数据，如果添加不进去，则poll掉一个再添加
StormCommon.systemTopologyImpl
storm-2.0.0/storm-client/src/jvm/org/apache/storm/daemon/StormCommon.java
protected StormTopology systemTopologyImpl(Map<String, Object> topoConf, StormTopology topology) throws InvalidTopologyException {
validateBasic(topology);
StormTopology ret = topology.deepCopy();
addAcker(topoConf, ret);
if (hasEventLoggers(topoConf)) {
addEventLogger(topoConf, ret);
}
addMetricComponents(topoConf, ret);
addSystemComponents(topoConf, ret);
addMetricStreams(ret);
addSystemStreams(ret);
validateStructure(ret);
return ret;
}
public static void addMetricComponents(Map<String, Object> conf, StormTopology topology) {
Map<String, Bolt> metricsConsumerBolts = metricsConsumerBoltSpecs(conf, topology);
for (Map.Entry<String, Bolt> entry : metricsConsumerBolts.entrySet()) {
topology.put_to_bolts(entry.getKey(), entry.getValue());
}
}
public static void addMetricStreams(StormTopology topology) {
for (Object component : allComponents(topology).values()) {
ComponentCommon common = getComponentCommon(component);
StreamInfo streamInfo = Thrift.outputFields(Arrays.asList("task-info", "data-points"));
common.put_to_streams(Constants.METRICS_STREAM_ID, streamInfo);
}
}
public static Map<String, Bolt> metricsConsumerBoltSpecs(Map<String, Object> conf, StormTopology topology) {
Map<String, Bolt> metricsConsumerBolts = new HashMap<>();
Set<String> componentIdsEmitMetrics = new HashSet<>();
componentIdsEmitMetrics.addAll(allComponents(topology).keySet());
componentIdsEmitMetrics.add(Constants.SYSTEM_COMPONENT_ID);
Map<GlobalStreamId, Grouping> inputs = new HashMap<>();
for (String componentId : componentIdsEmitMetrics) {
inputs.put(Utils.getGlobalStreamId(componentId, Constants.METRICS_STREAM_ID), Thrift.prepareShuffleGrouping());
}
List<Map<String, Object>> registerInfo = (List<Map<String, Object>>) conf.get(Config.TOPOLOGY_METRICS_CONSUMER_REGISTER);
if (registerInfo != null) {
Map<String, Integer> classOccurrencesMap = new HashMap<String, Integer>();
for (Map<String, Object> info : registerInfo) {
String className = (String) info.get(TOPOLOGY_METRICS_CONSUMER_CLASS);
Object argument = info.get(TOPOLOGY_METRICS_CONSUMER_ARGUMENT);
Integer maxRetainMetricTuples = ObjectReader.getInt(info.get(
TOPOLOGY_METRICS_CONSUMER_MAX_RETAIN_METRIC_TUPLES), 100);
Integer phintNum = ObjectReader.getInt(info.get(TOPOLOGY_METRICS_CONSUMER_PARALLELISM_HINT), 1);
Map<String, Object> metricsConsumerConf = new HashMap<String, Object>();
metricsConsumerConf.put(Config.TOPOLOGY_TASKS, phintNum);
List<String> whitelist = (List<String>) info.get(
TOPOLOGY_METRICS_CONSUMER_WHITELIST);
List<String> blacklist = (List<String>) info.get(
TOPOLOGY_METRICS_CONSUMER_BLACKLIST);
FilterByMetricName filterPredicate = new FilterByMetricName(whitelist, blacklist);
Boolean expandMapType = ObjectReader.getBoolean(info.get(
TOPOLOGY_METRICS_CONSUMER_EXPAND_MAP_TYPE), false);
String metricNameSeparator = ObjectReader.getString(info.get(
TOPOLOGY_METRICS_CONSUMER_METRIC_NAME_SEPARATOR), ".");
DataPointExpander expander = new DataPointExpander(expandMapType, metricNameSeparator);
MetricsConsumerBolt boltInstance = new MetricsConsumerBolt(className, argument,
maxRetainMetricTuples, filterPredicate, expander);
Bolt metricsConsumerBolt = Thrift.prepareSerializedBoltDetails(inputs,
boltInstance, null, phintNum, metricsConsumerConf);
String id = className;
if (classOccurrencesMap.containsKey(className)) {
// e.g. [\"a\", \"b\", \"a\"]) => [\"a\", \"b\", \"a#2\"]"
int occurrenceNum = classOccurrencesMap.get(className);
occurrenceNum++;
classOccurrencesMap.put(className, occurrenceNum);
id = Constants.METRICS_COMPONENT_ID_PREFIX + className + "#" + occurrenceNum;
} else {
id = Constants.METRICS_COMPONENT_ID_PREFIX + className;
classOccurrencesMap.put(className, 1);
}
metricsConsumerBolts.put(id, metricsConsumerBolt);
}
}
return metricsConsumerBolts;
}- StormCommon在创建systemTopologyImpl的时候，会添加添加一些系统的components，这里就调用了addMetricComponents以及addMetricStreams
- addMetricComponents根据conf创建MetricsConsumerBolt，并使用shuffle以及Constants.METRICS_STREAM_ID指定所有的component为输入源
- addMetricStreams给每个component配置了输出数据到Constants.METRICS_STREAM_ID，且输出的字段为Arrays.asList("task-info", "data-points")
Executor.setupMetrics
storm-2.0.0/storm-client/src/jvm/org/apache/storm/executor/Executor.java
protected final Map<Integer, Map<Integer, Map<String, IMetric>>> intervalToTaskToMetricToRegistry;
protected void setupMetrics() {
for (final Integer interval : intervalToTaskToMetricToRegistry.keySet()) {
StormTimer timerTask = workerData.getUserTimer();
timerTask.scheduleRecurring(interval, interval,
() -> {
TupleImpl tuple =
new TupleImpl(workerTopologyContext, new Values(interval), Constants.SYSTEM_COMPONENT_ID,
(int) Constants.SYSTEM_TASK_ID, Constants.METRICS_TICK_STREAM_ID);
AddressedTuple metricsTickTuple = new AddressedTuple(AddressedTuple.BROADCAST_DEST, tuple);
try {
receiveQueue.publish(metricsTickTuple);
receiveQueue.flush(); // avoid buffering
} catch (InterruptedException e) {
LOG.warn("Thread interrupted when publishing metrics. Setting interrupt flag.");
Thread.currentThread().interrupt();
return;
}
}
);
}
}
public Map<Integer, Map<Integer, Map<String, IMetric>>> getIntervalToTaskToMetricToRegistry() {
return intervalToTaskToMetricToRegistry;
}- Executor在setupMetrics方法里头，设置了定时任务，采用BROADCAST_DEST的方式定时向METRICS_TICK_STREAM_ID发射metricsTickTuple
- 这里是依据intervalToTaskToMetricToRegistry来配置的，其key为interval
- intervalToTaskToMetricToRegistry在Executor构造器中初始化：intervalToTaskToMetricToRegistry = new HashMap<>()
Task.mkTopologyContext
storm-2.0.0/storm-client/src/jvm/org/apache/storm/daemon/Task.java
private TopologyContext mkTopologyContext(StormTopology topology) throws IOException {
Map<String, Object> conf = workerData.getConf();
return new TopologyContext(
topology,
workerData.getTopologyConf(),
workerData.getTaskToComponent(),
workerData.getComponentToSortedTasks(),
workerData.getComponentToStreamToFields(),
// This is updated by the Worker and the topology has shared access to it
workerData.getBlobToLastKnownVersion(),
workerData.getTopologyId(),
ConfigUtils.supervisorStormResourcesPath(
ConfigUtils.supervisorStormDistRoot(conf, workerData.getTopologyId())),
ConfigUtils.workerPidsRoot(conf, workerData.getWorkerId()),
taskId,
workerData.getPort(), workerData.getLocalTaskIds(),
workerData.getDefaultSharedResources(),
workerData.getUserSharedResources(),
executor.getSharedExecutorData(),
executor.getIntervalToTaskToMetricToRegistry(),
executor.getOpenOrPrepareWasCalled());
}- mkTopologyContext方法在创建TopologyContext的时候，传递进去了executor.getIntervalToTaskToMetricToRegistry()
TopologyContext
storm-2.0.0/storm-client/src/jvm/org/apache/storm/task/TopologyContext.java
public class TopologyContext extends WorkerTopologyContext implements IMetricsContext {
private Integer _taskId;
private Map<String, Object> _taskData = new HashMap<>();
private List<ITaskHook> _hooks = new ArrayList<>();
private Map<String, Object> _executorData;
private Map<Integer, Map<Integer, Map<String, IMetric>>> _registeredMetrics;
public <T extends IMetric> T registerMetric(String name, T metric, int timeBucketSizeInSecs) {
if (_openOrPrepareWasCalled.get()) {
throw new RuntimeException("TopologyContext.registerMetric can only be called from within overridden " +
"IBolt::prepare() or ISpout::open() method.");
}
if (metric == null) {
throw new IllegalArgumentException("Cannot register a null metric");
}
if (timeBucketSizeInSecs <= 0) {
throw new IllegalArgumentException("TopologyContext.registerMetric can only be called with timeBucketSizeInSecs " +
"greater than or equal to 1 second.");
}
if (getRegisteredMetricByName(name) != null) {
throw new RuntimeException("The same metric name `" + name + "` was registered twice.");
}
Map<Integer, Map<Integer, Map<String, IMetric>>> m1 = _registeredMetrics;
if (!m1.containsKey(timeBucketSizeInSecs)) {
m1.put(timeBucketSizeInSecs, new HashMap<Integer, Map<String, IMetric>>());
}
Map<Integer, Map<String, IMetric>> m2 = m1.get(timeBucketSizeInSecs);
if (!m2.containsKey(_taskId)) {
m2.put(_taskId, new HashMap<String, IMetric>());
}
Map<String, IMetric> m3 = m2.get(_taskId);
if (m3.containsKey(name)) {
throw new RuntimeException("The same metric name `" + name + "` was registered twice.");
} else {
m3.put(name, metric);
}
return metric;
}
//......
}- Executor的intervalToTaskToMetricToRegistry最后传递给了TopologyContext的_registeredMetrics
- registerMetric方法会往_registeredMetrics添加值，其key为timeBucketSizeInSecs
- 内置metrics的timeBucketSizeInSecs读取的是Config.TOPOLOGY_BUILTIN_METRICS_BUCKET_SIZE_SECS(
topology.builtin.metrics.bucket.size.secs)值，在defaults.yaml中默认为60，即Executor每隔60秒发射一次metricsTickTuple，其streamId为Constants.METRICS_TICK_STREAM_ID
Executor.metricsTick
storm-2.0.0/storm-client/src/jvm/org/apache/storm/executor/Executor.java
public void metricsTick(Task task, TupleImpl tuple) {
try {
Integer interval = tuple.getInteger(0);
int taskId = task.getTaskId();
Map<Integer, Map<String, IMetric>> taskToMetricToRegistry = intervalToTaskToMetricToRegistry.get(interval);
Map<String, IMetric> nameToRegistry = null;
if (taskToMetricToRegistry != null) {
nameToRegistry = taskToMetricToRegistry.get(taskId);
}
if (nameToRegistry != null) {
IMetricsConsumer.TaskInfo taskInfo = new IMetricsConsumer.TaskInfo(
hostname, workerTopologyContext.getThisWorkerPort(),
componentId, taskId, Time.currentTimeSecs(), interval);
List<IMetricsConsumer.DataPoint> dataPoints = new ArrayList<>();
for (Map.Entry<String, IMetric> entry : nameToRegistry.entrySet()) {
IMetric metric = entry.getValue();
Object value = metric.getValueAndReset();
if (value != null) {
IMetricsConsumer.DataPoint dataPoint = new IMetricsConsumer.DataPoint(entry.getKey(), value);
dataPoints.add(dataPoint);
}
}
if (!dataPoints.isEmpty()) {
task.sendUnanchored(Constants.METRICS_STREAM_ID,
new Values(taskInfo, dataPoints), executorTransfer, pendingEmits);
executorTransfer.flush();
}
}
} catch (Exception e) {
throw Utils.wrapInRuntime(e);
}
}- SpoutExecutor以及BoltExecutor在tupleActionFn中接收到streamId为Constants.METRICS_TICK_STREAM_ID的tuple的时候，会调用父类Executor.metricsTick方法
- metricsTick采用task.sendUnanchored(Constants.METRICS_STREAM_ID, new Values(taskInfo, dataPoints), executorTransfer, pendingEmits);发射数据，发射到Constants.METRICS_STREAM_ID中，values为taskInfo及dataPoints；dataPoints的数据从TopologyContext的_registeredMetrics中读取(
这个使用的是旧版的metrics，非V2版本) - MetricsConsumerBolt接收到数据之后，就是放入_taskQueue队列；与此同时MetricsHandlerRunnable线程会阻塞从_taskQueue中取数据，然后回调_metricsConsumer.handleDataPoints方法来消费数据
小结
- LoggingMetricsConsumer是storm metric提供的，metrics2中没有；nimbus及supervisor使用的是-Dlog4j.configurationFile=/apache-storm/log4j2/cluster.xml；worker使用的是-Dlog4j.configurationFile=/apache-storm/log4j2/worker.xml；各个组件的-Dlogfile.name分别为nimbus.log、supervisor.log、worker.log
- storm在构建topology的时候会添加系统的component，其中就包括添加metricsConsumerBolt以及metricStreams；同时Executor在init方法中会setupMetrics，定时发射metricsTickTuple；SpoutExecutor以及BoltExecutor在tupleActionFn接收到metricsTickTuple的时候，会调用metricsTick方法来生产数据发射到Constants.METRICS_STREAM_ID中，之后MetricsConsumerBolt就可以接收数据，然后回调_metricsConsumer.handleDataPoints方法来消费数据
- 这里要注意两个参数，一个是MetricsConsumerBolt里头用到的max.retain.metric.tuples，它是配置在topology.metrics.consumer.register下面的，如果没有配置默认为100；它是MetricsConsumerBolt里头_taskQueue队列的大小，如果设置为0，则表示无界；内置metric的interval读取的是Config.TOPOLOGY_BUILTIN_METRICS_BUCKET_SIZE_SECS(
topology.builtin.metrics.bucket.size.secs)参数，默认为60，即60秒发射一次metricsTickTuple
doc
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