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本文主要研究一下micrometer的HistogramGauges
AutoConfiguration
针对springboot应用,配备有各种export的AutoConfiguration,详见org.springframework.boot.actuate.autoconfigure.metrics.export包,2.0.1版本目前支持了如下类型的export:
atlas、datadog、ganglia、graphite、influx、jmx、newrelic、prometheus、properties、signalfx、simple、statsd、wavefront这里看下statsd及prometheus的AutoConfiguration
StatsdMetricsExportAutoConfiguration
spring-boot-actuator-autoconfigure-2.0.1.RELEASE-sources.jar!/org/springframework/boot/actuate/autoconfigure/metrics/export/statsd/StatsdMetricsExportAutoConfiguration.java
@Configuration
@AutoConfigureBefore({ CompositeMeterRegistryAutoConfiguration.class,
SimpleMetricsExportAutoConfiguration.class })
@AutoConfigureAfter(MetricsAutoConfiguration.class)
@ConditionalOnBean(Clock.class)
@ConditionalOnClass(StatsdMeterRegistry.class)
@ConditionalOnProperty(prefix = "management.metrics.export.statsd", name = "enabled", havingValue = "true", matchIfMissing = true)
@EnableConfigurationProperties(StatsdProperties.class)
public class StatsdMetricsExportAutoConfiguration {
@Bean
@ConditionalOnMissingBean
public StatsdConfig statsdConfig(StatsdProperties statsdProperties) {
return new StatsdPropertiesConfigAdapter(statsdProperties);
}
@Bean
@ConditionalOnMissingBean
public StatsdMeterRegistry statsdMeterRegistry(StatsdConfig statsdConfig,
Clock clock) {
return new StatsdMeterRegistry(statsdConfig, clock);
}
@Bean
public StatsdMetrics statsdMetrics() {
return new StatsdMetrics();
}
}
可以看到,创建了StatsdMeterRegistry
PrometheusMetricsExportAutoConfiguration
spring-boot-actuator-autoconfigure-2.0.1.RELEASE-sources.jar!/org/springframework/boot/actuate/autoconfigure/metrics/export/prometheus/PrometheusMetricsExportAutoConfiguration.java
@Configuration
@AutoConfigureBefore({ CompositeMeterRegistryAutoConfiguration.class,
SimpleMetricsExportAutoConfiguration.class })
@AutoConfigureAfter(MetricsAutoConfiguration.class)
@ConditionalOnBean(Clock.class)
@ConditionalOnClass(PrometheusMeterRegistry.class)
@ConditionalOnProperty(prefix = "management.metrics.export.prometheus", name = "enabled", havingValue = "true", matchIfMissing = true)
@EnableConfigurationProperties(PrometheusProperties.class)
public class PrometheusMetricsExportAutoConfiguration {
@Bean
@ConditionalOnMissingBean
public PrometheusConfig prometheusConfig(PrometheusProperties prometheusProperties) {
return new PrometheusPropertiesConfigAdapter(prometheusProperties);
}
@Bean
@ConditionalOnMissingBean
public PrometheusMeterRegistry prometheusMeterRegistry(
PrometheusConfig prometheusConfig, CollectorRegistry collectorRegistry,
Clock clock) {
return new PrometheusMeterRegistry(prometheusConfig, collectorRegistry, clock);
}
@Bean
@ConditionalOnMissingBean
public CollectorRegistry collectorRegistry() {
return new CollectorRegistry(true);
}
@ManagementContextConfiguration
public static class PrometheusScrapeEndpointConfiguration {
@Bean
@ConditionalOnEnabledEndpoint
@ConditionalOnMissingBean
public PrometheusScrapeEndpoint prometheusEndpoint(
CollectorRegistry collectorRegistry) {
return new PrometheusScrapeEndpoint(collectorRegistry);
}
}
}
可以看到创建了PrometheusMeterRegistry
Timer.register
micrometer-core-1.0.3-sources.jar!/io/micrometer/core/instrument/Timer.java
/**
* Add the timer to a single registry, or return an existing timer in that registry. The returned
* timer will be unique for each registry, but each registry is guaranteed to only create one timer
* for the same combination of name and tags.
*
* @param registry A registry to add the timer to, if it doesn't already exist.
* @return A new or existing timer.
*/
public Timer register(MeterRegistry registry) {
// the base unit for a timer will be determined by the monitoring system implementation
return registry.timer(new Meter.Id(name, tags, null, description, Type.TIMER), distributionConfigBuilder.build(),
pauseDetector == null ? registry.config().pauseDetector() : pauseDetector);
}
可以看到该register委托给了registry.timer方法
MeterRegistry
micrometer-core-1.0.3-sources.jar!/io/micrometer/core/instrument/MeterRegistry.java
/**
* Only used by {@link Timer#builder(String)}.
*
* @param id The identifier for this timer.
* @param distributionStatisticConfig Configuration that governs how distribution statistics are computed.
* @return A new or existing timer.
*/
Timer timer(Meter.Id id, DistributionStatisticConfig distributionStatisticConfig, PauseDetector pauseDetectorOverride) {
return registerMeterIfNecessary(Timer.class, id, distributionStatisticConfig, (id2, filteredConfig) -> {
Meter.Id withUnit = id2.withBaseUnit(getBaseTimeUnitStr());
return newTimer(withUnit, filteredConfig.merge(defaultHistogramConfig()), pauseDetectorOverride);
}, NoopTimer::new);
}
/**
* Build a new timer to be added to the registry. This is guaranteed to only be called if the timer doesn't already exist.
*
* @param id The id that uniquely identifies the timer.
* @param distributionStatisticConfig Configuration for published distribution statistics.
* @param pauseDetector The pause detector to use for coordinated omission compensation.
* @return A new timer.
*/
protected abstract Timer newTimer(Meter.Id id, DistributionStatisticConfig distributionStatisticConfig, PauseDetector pauseDetector);
这里有调用了newTimer抽象方法
StatsdMeterRegistry.newTimer
micrometer-registry-statsd-1.0.3-sources.jar!/io/micrometer/statsd/StatsdMeterRegistry.java
@SuppressWarnings("ConstantConditions")
@Override
protected Timer newTimer(Meter.Id id, DistributionStatisticConfig distributionStatisticConfig, PauseDetector
pauseDetector) {
Timer timer = new StatsdTimer(id, lineBuilder(id), publisher, clock, distributionStatisticConfig, pauseDetector, getBaseTimeUnit(),
statsdConfig.step().toMillis());
HistogramGauges.registerWithCommonFormat(timer, this);
return timer;
}
可以看到newTimer操作里头调用了HistogramGauges.registerWithCommonFormat(timer, this);
HistogramGauges.registerWithCommonFormat
micrometer-core-1.0.3-sources.jar!/io/micrometer/core/instrument/distribution/HistogramGauges.java
/**
* Register a set of gauges for percentiles and histogram buckets that follow a common format when
* the monitoring system doesn't have an opinion about the structure of this data.
*/
public static HistogramGauges registerWithCommonFormat(Timer timer, MeterRegistry registry) {
Meter.Id id = timer.getId();
return HistogramGauges.register(timer, registry,
percentile -> id.getName() + ".percentile",
percentile -> Tags.concat(id.getTags(), "phi", DoubleFormat.decimalOrNan(percentile.percentile())),
percentile -> percentile.value(timer.baseTimeUnit()),
bucket -> id.getName() + ".histogram",
bucket -> Tags.concat(id.getTags(), "le", DoubleFormat.decimalOrWhole(bucket.bucket(timer.baseTimeUnit()))));
}
可以看到这里使用HistogramGauges进行注册,percentileName的名称为id.getName() + ".percentile",bucketName的名称为id.getName() + ".histogram"
HistogramGauges
micrometer-core-1.0.3-sources.jar!/io/micrometer/core/instrument/distribution/HistogramGauges.java
private HistogramGauges(HistogramSupport meter, MeterRegistry registry,
Function<ValueAtPercentile, String> percentileName,
Function<ValueAtPercentile, Iterable<Tag>> percentileTags,
Function<ValueAtPercentile, Double> percentileValue,
Function<CountAtBucket, String> bucketName,
Function<CountAtBucket, Iterable<Tag>> bucketTags) {
this.meter = meter;
HistogramSnapshot initialSnapshot = meter.takeSnapshot();
this.snapshot = initialSnapshot;
ValueAtPercentile[] valueAtPercentiles = initialSnapshot.percentileValues();
CountAtBucket[] countAtBuckets = initialSnapshot.histogramCounts();
this.totalGauges = valueAtPercentiles.length + countAtBuckets.length;
// set to zero initially, so the first polling of one of the gauges on each publish cycle results in a
// new snapshot
this.polledGaugesLatch = new CountDownLatch(0);
for (int i = 0; i < valueAtPercentiles.length; i++) {
final int index = i;
ToDoubleFunction<HistogramSupport> percentileValueFunction = m -> {
snapshotIfNecessary();
polledGaugesLatch.countDown();
return percentileValue.apply(snapshot.percentileValues()[index]);
};
Gauge.builder(percentileName.apply(valueAtPercentiles[i]), meter, percentileValueFunction)
.tags(percentileTags.apply(valueAtPercentiles[i]))
.register(registry);
}
for (int i = 0; i < countAtBuckets.length; i++) {
final int index = i;
ToDoubleFunction<HistogramSupport> bucketCountFunction = m -> {
snapshotIfNecessary();
polledGaugesLatch.countDown();
return snapshot.histogramCounts()[index].count();
};
Gauge.builder(bucketName.apply(countAtBuckets[i]), meter, bucketCountFunction)
.tags(bucketTags.apply(countAtBuckets[i]))
.register(registry);
}
}
可以看到这里针对HistogramSnapshot取了percentileValues注册了Gauge,然后针对HistogramSnapshot的CountAtBucket[]注册了对应的Gauge
实例
SimpleMeterRegistry simpleMeterRegistry = new SimpleMeterRegistry();
@Test
public void testHistogramGauges() throws InterruptedException {
Timer timer = Timer.builder("api-cost")
.publishPercentileHistogram()
.publishPercentiles(0.95,0.99)
.register(simpleMeterRegistry);
IntStream.rangeClosed(1,1000)
.forEach(i -> {
timer.record(Duration.ofMillis(ThreadLocalRandom.current().nextInt(200)));
simpleMeterRegistry.getMeters()
.stream()
.forEach(m -> {
System.out.println(m.getId() + "-->" + m.measure());
});
});
TimeUnit.MINUTES.sleep(5);
}
输出实例
MeterId{name='api-cost.percentile', tags=[ImmutableTag{key='phi', value='0.95'}]}-->[Measurement{statistic='VALUE', value=0.192905216}]
MeterId{name='api-cost.percentile', tags=[ImmutableTag{key='phi', value='0.99'}]}-->[Measurement{statistic='VALUE', value=0.201293824}]
MeterId{name='api-cost', tags=[]}-->[Measurement{statistic='COUNT', value=999.0}, Measurement{statistic='TOTAL_TIME', value=97.158}, Measurement{statistic='MAX', value=0.199}]
MeterId{name='api-cost.percentile', tags=[ImmutableTag{key='phi', value='0.95'}]}-->[Measurement{statistic='VALUE', value=0.192905216}]
MeterId{name='api-cost.percentile', tags=[ImmutableTag{key='phi', value='0.99'}]}-->[Measurement{statistic='VALUE', value=0.201293824}]
MeterId{name='api-cost', tags=[]}-->[Measurement{statistic='COUNT', value=1000.0}, Measurement{statistic='TOTAL_TIME', value=97.348}, Measurement{statistic='MAX', value=0.199}]
小结
目前只有Prometheus和Atlas支持Percentile histograms,不过micrometer在client端简单支持了下percentile,不过不像server端支持那么灵活,不能跨tag进行聚合,目前是把tag作为meter id的一部分,一起上报。针对qps的计算,可以使用Timer类型来计量,然后通过percentile指标,根据时间间隔进行group来统计。
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