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You can use different ways to monitor a Pulsar cluster, exposing both metrics related to the usage of topics and the overall health of the individual components of the cluster.

Collect metrics

You can collect broker stats, ZooKeeper stats, and BookKeeper stats.

Broker stats

You can collect Pulsar broker metrics from brokers and export the metrics in JSON format. The Pulsar broker metrics mainly have two types:

  • Destination dumps, which contain stats for each topic. You can fetch the destination dumps using the command below:

    bin/pulsar-admin broker-stats destinations
  • Broker metrics, which contain the broker information and topics stats aggregated at the namespace level. You can fetch the broker metrics by using the following command:

    bin/pulsar-admin broker-stats monitoring-metrics

All the message rates are updated every minute.

The aggregated broker metrics are also exposed in the Prometheus format at:


ZooKeeper stats

The local ZooKeeper, configuration store server and clients that are shipped with Pulsar can expose detailed stats through Prometheus.


The default port of local ZooKeeper is 8000 and the default port of the configuration store is 8001. You can use a different stats port by configuring metricsProvider.httpPort in the conf/zookeeper.conf file.

BookKeeper stats

You can configure the stats frameworks for BookKeeper by modifying the statsProviderClass in the conf/bookkeeper.conf file.

The default BookKeeper configuration enables the Prometheus exporter. The configuration is included with Pulsar distribution.


The default port for bookie is 8000. You can change the port by configuring prometheusStatsHttpPort in the conf/bookkeeper.conf file.

Managed cursor acknowledgment state

The acknowledgment state is persistent to the ledger first. When the acknowledgment state fails to be persistent to the ledger, they are persistent to ZooKeeper. To track the stats of acknowledgment, you can configure the metrics for the managed cursor.

pulsar_ml_cursor_persistLedgerSucceed(namespace=", ledger_name="", cursor_name:")
pulsar_ml_cursor_persistLedgerErrors(namespace="", ledger_name="", cursor_name:"")
pulsar_ml_cursor_persistZookeeperSucceed(namespace="", ledger_name="", cursor_name:"")
pulsar_ml_cursor_persistZookeeperErrors(namespace="", ledger_name="", cursor_name:"")
pulsar_ml_cursor_nonContiguousDeletedMessagesRange(namespace="", ledger_name="", cursor_name:"")

Those metrics are added in the Prometheus interface, you can monitor and check the metrics stats in Grafana.

Function and connector stats

You can collect functions worker stats from functions-worker and export the metrics in JSON formats, which contain functions worker JVM metrics.

pulsar-admin functions-worker monitoring-metrics

You can collect functions and connectors metrics from functions-worker and export the metrics in JSON formats.

pulsar-admin functions-worker function-stats

The aggregated functions and connectors metrics can be exposed in Prometheus formats as below. You can get FUNCTIONS_WORKER_ADDRESS and WORKER_PORT from the functions_worker.yml file.


Configure Prometheus

You can use Prometheus to collect all the metrics exposed for Pulsar components and set up Grafana dashboards to display the metrics and monitor your Pulsar cluster. For details, refer to Prometheus guide.

When you run Pulsar on bare metal, you can provide the list of nodes to be probed. When you deploy Pulsar in a Kubernetes cluster, the monitoring is set up automatically. For details, refer to Kubernetes instructions.


When you collect time-series statistics, the major problem is to make sure the number of dimensions attached to the data does not explode. Thus you only need to collect time series of metrics aggregated at the namespace level.

Pulsar per-topic dashboard

The per-topic dashboard instructions are available at Pulsar manager.


You can use Grafana to create a dashboard driven by the data that is stored in Prometheus.

When you deploy Pulsar on Kubernetes with the Pulsar Helm Chart, a pulsar-grafana Docker image is enabled by default. You can use the docker image with the principal dashboards.

The following are some Grafana dashboards examples:

  • pulsar-grafana: a Grafana dashboard that displays metrics collected in Prometheus for Pulsar clusters running on Kubernetes.
  • apache-pulsar-grafana-dashboard: a collection of Grafana dashboard templates for different Pulsar components running on both Kubernetes and on-premise machines.

Alerting rules

You can set alerting rules according to your Pulsar environment. To configure alerting rules for Apache Pulsar, refer to alerting rules.



Pulsar emits OpenTelemetry metrics starting from version 3.3.0. OpenTelemetry log and trace signals are not exposed by Pulsar. OpenTelemetry support is currently experimental and complements the pre-existing Prometheus metric system, with the goal of eventually replacing it. The metrics it exposes are semantically equivalent to the Prometheus metrics.

For a detailed list of OpenTelemetry metrics exposed by Pulsar, refer to OpenTelemetry Metrics.


Pulsar OpenTelemetry metrics are gradually being added for the broker only. Support for the proxy and function worker is planned for a future release.

OpenTelemetry Configuration

Pulsar natively supports OpenTelemetry via manual instrumentation, instead of relying on the OpenTelemetry automatic instrumentation agent. Pulsar uses the auto-configuration extension of OpenTelemetry to manage the SDK configuration. The extension allows parameter input from environment variables and Java system properties. The instructions below rely on environment variables, but can be adapted to use system properties too. These variables must be exposed to the Pulsar process via the respective deployment method.

Note that the experimental file based configuration is not currently supported by Pulsar.

Telemetry Enablement

The experimental OpenTelemetry feature is explicitly disabled by default in Pulsar. Set environment variable OTEL_SDK_DISABLED=false to enable the SDK. When disabled, metrics will not be collected nor exported.

Exporter Configuration

Exporters using the native OpenTelemetry Protocol and Prometheus are included in the Pulsar distribution assembly by default and can be used out-of-the-box. Other exporters are not currently supported.


The native OTLP exporter is the recommended way to obtain metrics out of Pulsar as the Apache Pulsar community is working on modifying it (and not Prometheus) to be highly performant. Pulsar defaults to using the OTLP exporter unless otherwise overridden by environment variable OTEL_METRICS_EXPORTER.

To use the exporter, set environment variable OTEL_EXPORTER_OTLP_ENDPOINT to the respective URL endpoint. This should represent the location of the OpenTelemetry Collector. Pulsar supports both gRPC and HTTP endpoints.

The exporter periodically collects and sends metrics. This process occurs every 60 seconds by default, and can be controlled by changing environment variable OTEL_METRIC_EXPORT_INTERVAL.

Additional parameters that can be configured, such as authentication, compression, and timeout, are described in the exporter documentation.

Remote Collector Considerations

If the remote OTLP collector sends data downstream to Prometheus or a Prometheus like-system, it is recommended to copy OpenTelemetry resource attribute pulsar.cluster to Prometheus labels on each time-series (metric). This can be done using collector transformations.

The example below leverages the OpenTelemetry Transformation Language and the transform processor to achieve this.

set(attributes["pulsar_cluster"], resource.attributes["pulsar.cluster"])

Pulsar supports exporting OpenTelemetry metrics in Prometheus format. This exporter is pull based and operates by opening up a server in the local Pulsar process. To use it, set OTEL_METRICS_EXPORTER=prometheus and the Prometheus listener details using the following environment variables:


This endpoint must be accessible by the remote Prometheus scrape server. Note that the exporter is less resource efficient than the OTLP exporter.

Prometheus currently exports the resource attributes in metric target_info. In practice, if you have more than one cluster, it forces you to use PromQL joins to obtain the cluster ID label.

The Pulsar community has added the option to the OpenTelemetry Java SDK Prometheus Exporter to embed (copy) the cluster ID label (pulsar.cluster) to each outgoing time series labels. Once this is finalized it will be added by default into Pulsar.

For further configuration details, refer to the exporter documentation.

Resource Attributes Configuration

Pulsar automatically sets the following resource attributes:

pulsar.clusterThe name of the Pulsar cluster.
service.nameThe name of the Pulsar service. For the broker, this defaults to pulsar-broker.
service.versionThe version of the Pulsar service.

Any of these attributes can be overridden by means of environment variable OTEL_RESOURCE_ATTRIBUTES. Additional attributes can be added too. For example:


For further details on configuring resource attributes, refer to the SDK documentation.

Additional runtime resource attributes, such as hostname, process ID, or operating system, are automatically inferred by the SDK using Resource Providers. For a description of these attributes, refer to the respective documentation. Further details regarding the configuration of Resource Providers can be obtained via the documentation.

Attribute Cardinality Configuration

OpenTelemetry provides an experimental mechanism to control the maximum cardinality of attributes. This is useful for limiting the resource usage of the exporter. Pulsar sets the value to 10000 attributes by default. For brokers with a large number of topics, this can prove insufficient. The value is controlled by environment variable OTEL_EXPERIMENTAL_METRICS_CARDINALITY_LIMIT.