1. Indicator access instructions

2. Indicator system design

Dubbo’s indicator system involves three parts in total, indicator collection, local aggregation, and indicator push Indicator collection: Push the indicators that need to be monitored inside Dubbo to a unified Collector for storage Local Aggregation: All indicators collected are basic indicators, and some quantile indicators need to be calculated through local aggregation Indicator push: The collected and aggregated indicators are pushed to third-party servers in a certain way, currently only involving Prometheus

3. Structural Design

  • Remove the original classes related to Metrics
  • Create new modules dubbo-metrics/dubbo-metrics-api, dubbo-metrics/dubbo-metrics-prometheus, MetricsConfig as the configuration class of the module
  • Use micrometer, use basic types in Collector to represent indicators, such as Long, Double, etc., and introduce micrometer in dubbo-metrics-api, and use micrometer to convert internal indicators

4. data flow


5. Goals

The indicator interface will provide a MetricsService, which not only provides interface-level data of the flexible service, but also provides query methods for all indicators. The interface for querying method-level indicators can be declared as follows

public interface MetricsService {

     * Default {@link MetricsService} extension name.
    String DEFAULT_EXTENSION_NAME = "default";

     * The contract version of {@link MetricsService}, the future update must make sure compatible.
    String VERSION = "1.0.0";

     * Get metrics by prefixes
     * @param categories categories
     * @return metrics - key=MetricCategory value=MetricsEntityList
    Map<MetricsCategory, List<MetricsEntity>> getMetricsByCategories(List<MetricsCategory> categories);

     * Get metrics by interface and prefixes
     * @param serviceUniqueName serviceUniqueName (
     * @param categories categories
     * @return metrics - key=MetricCategory value=MetricsEntityList
    Map<MetricsCategory, List<MetricsEntity>> getMetricsByCategories(String serviceUniqueName, List<MetricsCategory> categories);

     * Get metrics by interface、method and prefixes
     * @param serviceUniqueName serviceUniqueName (
     * @param methodName methodName
     * @param parameterTypes method parameter types
     * @param categories categories
     * @return metrics - key=MetricCategory value=MetricsEntityList
    Map<MetricsCategory, List<MetricsEntity>> getMetricsByCategories(String serviceUniqueName, String methodName, Class<?>[] parameterTypes, List<MetricsCategory> categories);

Among them, MetricsCategory is designed as follows:

public enum MetricsCategory {

MetricsEntity is designed as follows

public class MetricsEntity {
    private String name;
    private Map<String, String> tags;
    private MetricsCategory category;
    private Object value;

metrics collection

1. Embedding position

The Dubbo architecture diagram is as follows img.png

Add a layer of MetricsFilter to the provider, rewrite the invoke method, embed the call link to collect metrics, and use try-catch-finally for processing. The core code is as follows

@Activate(group = PROVIDER, order = -1)
public class MetricsFilter implements Filter, ScopeModelAware {
    public Result invoke(Invoker<?> invoker, Invocation invocation) throws RpcException {
        collector.increaseTotalRequests(interfaceName, methodName, group, version);
        collector.increaseProcessingRequests(interfaceName, methodName, group, version);
        Long startTime = System.currentTimeMillis();
        try {
            Result invoke = invoker.invoke(invocation);
            collector.increaseSucceedRequests(interfaceName, methodName, group, version);
            return invoke;
        } catch (RpcException e) {
            collector.increaseFailedRequests(interfaceName, methodName, group, version);
            throw e;
        } finally {
            Long endTime = System.currentTimeMillis();
            Long rt = endTime - startTime;
            collector.addRT(interfaceName, methodName, group, version, rt);
            collector.decreaseProcessingRequests(interfaceName, methodName, group, version);

2. Indicator identification

Use the following five attributes as the isolation level to distinguish and identify different methods, which are also the keys of each ConcurrentHashMap

public class MethodMetric {
    private String applicationName;
    private String interfaceName;
    private String methodName;
    private String group;
    private String version;

3. Basic indicators

Metrics store all metrics data through MetricsCollector under the common module

public class DefaultMetricsCollector implements MetricsCollector {
    private Boolean collectEnabled = false;
    private final List<MetricsListener> listeners = new ArrayList<>();
    private final ApplicationModel applicationModel;
    private final String applicationName;

    private final Map<MethodMetric, AtomicLong> totalRequests = new ConcurrentHashMap<>();
    private final Map<MethodMetric, AtomicLong> succeedRequests = new ConcurrentHashMap<>();
    private final Map<MethodMetric, AtomicLong> failedRequests = new ConcurrentHashMap<>();
    private final Map<MethodMetric, AtomicLong> processingRequests = new ConcurrentHashMap<>();

    private final Map<MethodMetric, AtomicLong> lastRT = new ConcurrentHashMap<>();
    private final Map<MethodMetric, LongAccumulator> minRT = new ConcurrentHashMap<>();
    private final Map<MethodMetric, LongAccumulator> maxRT = new ConcurrentHashMap<>();
    private final Map<MethodMetric, AtomicLong> avgRT = new ConcurrentHashMap<>();
    private final Map<MethodMetric, AtomicLong> totalRT = new ConcurrentHashMap<>();
    private final Map<MethodMetric, AtomicLong> rtCount = new ConcurrentHashMap<>();

local aggregation

Local aggregation refers to the process of obtaining quantile indicators by calculating some simple indicators

1. Parameter Design

When collecting indicators, only the basic indicators are collected by default, and some stand-alone aggregation indicators need to enable service flexibility or start a new thread calculation after local aggregation. If service flexibility is enabled here, local aggregation is enabled by default

1.1 How to enable local aggregation

  <dubbo:aggregation enable="true" />

1.2 Index Aggregation Parameters

  <dubbo:aggregation enable="true" bucket-num="5" time-window-seconds="10"/>

2. Specific indicators

Four key metrics to monitor. They call it the “four golden signals”: Latency, Traffic, Errors, and Saturation. Dubbo mainly includes the following monitoring indicators:

infrastructurebusiness monitoring
LatencyIO wait; RPC Latency;Interface, average service time, TP90, TP99, TP999, etc.
Trafficnetwork and disk IO;QPS at the service level
ErrorsDowntime; disk (bad disk or file system error); process or port hang; network packet loss;Error log; business status code, error code trend;
SaturationSystem resource utilization: CPU, memory, disk, network, etc.; Saturation: number of waiting threads, queue backlog length;This mainly includes JVM, thread pool, etc.
  • qps: Get dynamic qps based on sliding window
  • rt: Get dynamic rt based on sliding window
  • Number of failed requests: Get the number of failed requests in the latest time based on the sliding window
  • Number of successful requests: Get the number of successful requests in the latest time based on the sliding window
  • The number of processing requests: Add Filter simple statistics before and after
  • Specific indicators rely on sliding windows, and additionally use AggregateMetricsCollector to collect

The relevant indicators output to Prometheus can be referred to as follows:

# HELP jvm_gc_live_data_size_bytes Size of long-lived heap memory pool after reclamation
# TYPE jvm_gc_live_data_size_bytes gauge
jvm_gc_live_data_size_bytes 1.6086528E7
# HELP requests_succeed_aggregate Aggregated Succeed Requests
# TYPE requests_succeed_aggregate gauge
requests_succeed_aggregate{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 39.0
# HELP jvm_buffer_memory_used_bytes An estimate of the memory that the Java virtual machine is using for this buffer pool
# TYPE jvm_buffer_memory_used_bytes gauge
jvm_buffer_memory_used_bytes{id="direct",} 1.679975E7
jvm_buffer_memory_used_bytes{id="mapped",} 0.0
# HELP jvm_gc_memory_allocated_bytes_total Incremented for an increase in the size of the (young) heap memory pool after one GC to before the next
# TYPE jvm_gc_memory_allocated_bytes_total counter
jvm_gc_memory_allocated_bytes_total 2.9884416E9
# HELP requests_total_aggregate Aggregated Total Requests
# TYPE requests_total_aggregate gauge
requests_total_aggregate{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 39.0
# HELP system_load_average_1m The sum of the number of runnable entities queued to available processors and the number of runnable entities running on the available processors averaged over a period of time
# TYPE system_load_average_1m gauge
system_load_average_1m 0.0
# HELP system_cpu_usage The "recent cpu usage" for the whole system
# TYPE system_cpu_usage gauge
system_cpu_usage 0.015802269043760128
# HELP jvm_threads_peak_threads The peak live thread count since the Java virtual machine started or peak was reset
# TYPE jvm_threads_peak_threads gauge
jvm_threads_peak_threads 40.0
# HELP requests_processing Processing Requests
# TYPE requests_processing gauge
requests_processing{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 0.0
# HELP jvm_memory_max_bytes The maximum amount of memory in bytes that can be used for memory management
# TYPE jvm_memory_max_bytes gauge
jvm_memory_max_bytes{area="nonheap",id="CodeHeap 'profiled nmethods'",} 1.22912768E8
jvm_memory_max_bytes{area="heap",id="G1 Survivor Space",} -1.0
jvm_memory_max_bytes{area="heap",id="G1 Old Gen",} 9.52107008E8
jvm_memory_max_bytes{area="nonheap",id="Metaspace",} -1.0
jvm_memory_max_bytes{area="heap",id="G1 Eden Space",} -1.0
jvm_memory_max_bytes{area="nonheap",id="CodeHeap 'non-nmethods'",} 5828608.0
jvm_memory_max_bytes{area="nonheap",id="Compressed Class Space",} 1.073741824E9
jvm_memory_max_bytes{area="nonheap",id="CodeHeap 'non-profiled nmethods'",} 1.22916864E8
# HELP jvm_threads_states_threads The current number of threads having BLOCKED state
# TYPE jvm_threads_states_threads gauge
jvm_threads_states_threads{state="blocked",} 0.0
jvm_threads_states_threads{state="runnable",} 10.0
jvm_threads_states_threads{state="waiting",} 16.0
jvm_threads_states_threads{state="timed-waiting",} 13.0
jvm_threads_states_threads{state="new",} 0.0
jvm_threads_states_threads{state="terminated",} 0.0
# HELP jvm_buffer_total_capacity_bytes An estimate of the total capacity of the buffers in this pool
# TYPE jvm_buffer_total_capacity_bytes gauge
jvm_buffer_total_capacity_bytes{id="direct",} 1.6799749E7
jvm_buffer_total_capacity_bytes{id="mapped",} 0.0
# HELP rt_p99 Response Time P99
# TYPE rt_p99 gauge
rt_p99{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 1.0
# HELP jvm_memory_used_bytes The amount of used memory
# TYPE jvm_memory_used_bytes gauge
jvm_memory_used_bytes{area="heap",id="G1 Survivor Space",} 1048576.0
jvm_memory_used_bytes{area="nonheap",id="CodeHeap 'profiled nmethods'",} 1.462464E7
jvm_memory_used_bytes{area="heap",id="G1 Old Gen",} 1.6098728E7
jvm_memory_used_bytes{area="nonheap",id="Metaspace",} 4.0126952E7
jvm_memory_used_bytes{area="heap",id="G1 Eden Space",} 8.2837504E7
jvm_memory_used_bytes{area="nonheap",id="CodeHeap 'non-nmethods'",} 1372032.0
jvm_memory_used_bytes{area="nonheap",id="Compressed Class Space",} 4519248.0
jvm_memory_used_bytes{area="nonheap",id="CodeHeap 'non-profiled nmethods'",} 5697408.0
# HELP qps Query Per Seconds
# TYPE qps gauge
qps{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 0.3333333333333333
# HELP rt_min Min Response Time
# TYPE rt_min gauge
rt_min{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 0.0
# HELP jvm_buffer_count_buffers An estimate of the number of buffers in the pool
# TYPE jvm_buffer_count_buffers gauge
jvm_buffer_count_buffers{id="mapped",} 0.0
jvm_buffer_count_buffers{id="direct",} 10.0
# HELP system_cpu_count The number of processors available to the Java virtual machine
# TYPE system_cpu_count gauge
system_cpu_count 2.0
# HELP jvm_classes_loaded_classes The number of classes that are currently loaded in the Java virtual machine
# TYPE jvm_classes_loaded_classes gauge
jvm_classes_loaded_classes 7325.0
# HELP rt_total Total Response Time
# TYPE rt_total gauge
rt_total{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 2783.0
# HELP rt_last Last Response Time
# TYPE rt_last gauge
rt_last{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 0.0
# HELP jvm_gc_memory_promoted_bytes_total Count of positive increases in the size of the old generation memory pool before GC to after GC
# TYPE jvm_gc_memory_promoted_bytes_total counter
jvm_gc_memory_promoted_bytes_total 1.4450952E7
# HELP jvm_gc_pause_seconds Time spent in GC pause
# TYPE jvm_gc_pause_seconds summary
jvm_gc_pause_seconds_count{action="end of minor GC",cause="Metadata GC Threshold",} 2.0
jvm_gc_pause_seconds_sum{action="end of minor GC",cause="Metadata GC Threshold",} 0.026
jvm_gc_pause_seconds_count{action="end of minor GC",cause="G1 Evacuation Pause",} 37.0
jvm_gc_pause_seconds_sum{action="end of minor GC",cause="G1 Evacuation Pause",} 0.156
# HELP jvm_gc_pause_seconds_max Time spent in GC pause
# TYPE jvm_gc_pause_seconds_max gauge
jvm_gc_pause_seconds_max{action="end of minor GC",cause="Metadata GC Threshold",} 0.0
jvm_gc_pause_seconds_max{action="end of minor GC",cause="G1 Evacuation Pause",} 0.0
# HELP rt_p95 Response Time P95
# TYPE rt_p95 gauge
rt_p95{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 0.0
# HELP requests_total Total Requests
# TYPE requests_total gauge
requests_total{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 27738.0
# HELP process_cpu_usage The "recent cpu usage" for the Java Virtual Machine process
# TYPE process_cpu_usage gauge
process_cpu_usage 8.103727714748784E-4
# HELP rt_max Max Response Time
# TYPE rt_max gauge
rt_max{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 4.0
# HELP jvm_gc_max_data_size_bytes Max size of long-lived heap memory pool
# TYPE jvm_gc_max_data_size_bytes gauge
jvm_gc_max_data_size_bytes 9.52107008E8
# HELP jvm_threads_live_threads The current number of live threads including both daemon and non-daemon threads
# TYPE jvm_threads_live_threads gauge
jvm_threads_live_threads 39.0
# HELP jvm_threads_daemon_threads The current number of live daemon threads
# TYPE jvm_threads_daemon_threads gauge
jvm_threads_daemon_threads 36.0
# HELP jvm_classes_unloaded_classes_total The total number of classes unloaded since the Java virtual machine has started execution
# TYPE jvm_classes_unloaded_classes_total counter
jvm_classes_unloaded_classes_total 0.0
# HELP jvm_memory_committed_bytes The amount of memory in bytes that is committed for the Java virtual machine to use
# TYPE jvm_memory_committed_bytes gauge
jvm_memory_committed_bytes{area="nonheap",id="CodeHeap 'profiled nmethods'",} 1.4680064E7
jvm_memory_committed_bytes{area="heap",id="G1 Survivor Space",} 1048576.0
jvm_memory_committed_bytes{area="heap",id="G1 Old Gen",} 5.24288E7
jvm_memory_committed_bytes{area="nonheap",id="Metaspace",} 4.1623552E7
jvm_memory_committed_bytes{area="heap",id="G1 Eden Space",} 9.0177536E7
jvm_memory_committed_bytes{area="nonheap",id="CodeHeap 'non-nmethods'",} 2555904.0
jvm_memory_committed_bytes{area="nonheap",id="Compressed Class Space",} 5111808.0
jvm_memory_committed_bytes{area="nonheap",id="CodeHeap 'non-profiled nmethods'",} 5701632.0
# HELP requests_succeed Succeed Requests
# TYPE requests_succeed gauge
requests_succeed{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 27738.0
# HELP rt_avg Average Response Time
# TYPE rt_avg gauge
rt_avg{application_name="metrics-provider",group="",hostname="iZ8lgm9icspkthZ",interface="org.apache.dubbo.samples.metrics.prometheus.api.DemoService",ip="",method="sayHello",version="",} 0.0

aggregate collector

public class AggregateMetricsCollector implements MetricsCollector, MetricsListener {
    private int bucketNum;
    private int timeWindowSeconds;

    private final Map<MethodMetric, TimeWindowCounter> totalRequests = new ConcurrentHashMap<>();
    private final Map<MethodMetric, TimeWindowCounter> succeedRequests = new ConcurrentHashMap<>();
    private final Map<MethodMetric, TimeWindowCounter> failedRequests = new ConcurrentHashMap<>();
    private final Map<MethodMetric, TimeWindowCounter> qps = new ConcurrentHashMap<>();
    private final Map<MethodMetric, TimeWindowQuantile> rt = new ConcurrentHashMap<>();

    private final ApplicationModel applicationModel;

    private static final Integer DEFAULT_COMPRESSION = 100;
    private static final Integer DEFAULT_BUCKET_NUM = 10;
    private static final Integer DEFAULT_TIME_WINDOW_SECONDS = 120;


    public AggregateMetricsCollector(ApplicationModel applicationModel) {
        this.applicationModel = applicationModel;
        ConfigManager configManager = applicationModel.getApplicationConfigManager();
        MetricsConfig config = configManager.getMetrics().orElse(null);
        if (config != null && config.getAggregation() != null && Boolean.TRUE.equals(config.getAggregation().getEnabled())) {
            // only registered when aggregation is enabled.

            AggregationConfig aggregation = config.getAggregation();
            this.bucketNum = aggregation.getBucketNum() == null ? DEFAULT_BUCKET_NUM : aggregation.getBucketNum();
            this.timeWindowSeconds = aggregation.getTimeWindowSeconds() == null ? DEFAULT_TIME_WINDOW_SECONDS : aggregation.getTimeWindowSeconds();

If local aggregation is enabled, listeners are added through spring’s BeanFactory, and AggregateMetricsCollector is bound to DefaultMetricsCollector to implement a survivor-consumer model. DefaultMetricsCollector uses a list of listeners for easy expansion

private void registerListener() {

3. Index Aggregation

sliding window Suppose we initially have 6 buckets, and each window time is set to 2 minutes Every time the index data is written, the data will be written into 6 buckets respectively, and a bucket will be moved every two minutes and the data in the original bucket will be cleared When reading the indicator, read the bucket pointed to by the current current to achieve the effect of sliding window Specifically, as shown in the figure below, the current bucket stores the data within the bucket life cycle set in the configuration, that is, the recent data img_1.png

In each bucket, use the TDigest algorithm to calculate the quantile index

TDigest algorithm (extreme quantile accuracy is high, such as p1 p99, middle quantile accuracy is low, such as p50), the relevant information is as follows>

The code is implemented as follows, in addition to TimeWindowQuantile is used to calculate quantile indicators, and TimeWindowCounter is provided to collect the number of indicators in the time interval

public class TimeWindowQuantile {
    private final double compression;
    private final TDigest[] ringBuffer;
    private int currentBucket;
    private long lastRotateTimestampMillis;
    private final long durationBetweenRotatesMillis;

    public TimeWindowQuantile(double compression, int bucketNum, int timeWindowSeconds) {
        this.compression = compression;
        this.ringBuffer = new TDigest[bucketNum];
        for (int i = 0; i < bucketNum; i++) {
            this.ringBuffer[i] = TDigest.createDigest(compression);

        this.currentBucket = 0;
        this.lastRotateTimestampMillis = System.currentTimeMillis();
        this.durationBetweenRotatesMillis = TimeUnit.SECONDS.toMillis(timeWindowSeconds) / bucketNum;

    public synchronized double quantile(double q) {
        TDigest currentBucket = rotate();
        return currentBucket.quantile(q);

    public synchronized void add(double value) {
        for (TDigest bucket : ringBuffer) {

    private TDigest rotate() {
        long timeSinceLastRotateMillis = System.currentTimeMillis() - lastRotateTimestampMillis;
        while (timeSinceLastRotateMillis > durationBetweenRotatesMillis) {
            ringBuffer[currentBucket] = TDigest.createDigest(compression);
            if (++currentBucket >= ringBuffer.length) {
                currentBucket = 0;
            timeSinceLastRotateMillis -= durationBetweenRotatesMillis;
            lastRotateTimestampMillis += durationBetweenRotatesMillis;
        return ringBuffer[currentBucket];

Indicator push

Metrics push can only be enabled after the user has set <dubbo:metrics /> configuration and configured protocol parameters. If only metrics aggregation is enabled, metrics will not be pushed by default.

1. Promehteus Pull Service Discovery

Use dubbo-admin and other similar middle layers, and push the local IP, Port, and MetricsURL address information to dubbo-admin (or any middle layer) according to the configuration at startup, and expose HTTP ServiceDiscovery for prometheus to read. The configuration method is as follows: dubbo:metrics protocol=“prometheus” mode=“pull” address="${dubbo-admin.address}" port=“20888” url="/metrics"/>, where address is an optional parameter in pull mode, If not filled, the user needs to manually configure the address in the Prometheus configuration file

private void exportHttpServer() {
    boolean exporterEnabled = url.getParameter(PROMETHEUS_EXPORTER_ENABLED_KEY, false);
    if (exporterEnabled) {
        if (!path.startsWith("/")) {
            path = "/" + path;

        try {
            prometheusExporterHttpServer = HttpServer.create(new InetSocketAddress(port), 0);
            prometheusExporterHttpServer.createContext(path, httpExchange -> {
                String response = prometheusRegistry.scrape();
                httpExchange.sendResponseHeaders(200, response.getBytes().length);
                try (OutputStream os = httpExchange.getResponseBody()) {

            httpServerThread = new Thread(prometheusExporterHttpServer::start);
        } catch (IOException e) {
            throw new RuntimeException(e);

2. Prometheus Push Pushgateway

Users can directly configure the address of Prometheus Pushgateway in the Dubbo configuration file, such as <dubbo:metrics protocol=“prometheus” mode=“push” address="${prometheus.pushgateway-url}" interval=“5” />, Where interval represents the push interval

private void schedulePushJob() {
    boolean pushEnabled = url.getParameter(PROMETHEUS_PUSHGATEWAY_ENABLED_KEY, false);
    if (pushEnabled) {
        String baseUrl = url.getParameter(PROMETHEUS_PUSHGATEWAY_BASE_URL_KEY);
        String username = url.getParameter(PROMETHEUS_PUSHGATEWAY_USERNAME_KEY);
        String password = url.getParameter(PROMETHEUS_PUSHGATEWAY_PASSWORD_KEY);

        NamedThreadFactory threadFactory = new NamedThreadFactory("prometheus-push-job", true);
        pushJobExecutor = Executors.newScheduledThreadPool(1, threadFactory);
        PushGateway pushGateway = new PushGateway(baseUrl);
        if (!StringUtils.isBlank(username)) {
            pushGateway.setConnectionFactory(new BasicAuthHttpConnectionFactory(username, password));

        pushJobExecutor.scheduleWithFixedDelay(() -> push(pushGateway, job), pushInterval, pushInterval, TimeUnit.SECONDS);

protected void push(PushGateway pushGateway, String job) {
    try {
        pushGateway.pushAdd(prometheusRegistry.getPrometheusRegistry(), job);
    } catch (IOException e) {
        logger.error("Error occurred when pushing metrics to prometheus: ", e);

Last modified January 10, 2023: Meter design doc (#1815) (d0ba76bd7dd)