Observing Your Application with Prometheus, Grafana, and Jaeger

Observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs.

Providing Observability in Ballerina

Monitoring, logging, and distributed tracing are key methods that reveal the internal state of the system to provide observability. Ballerina becomes fully observable by exposing itself via these three methods to various external systems allowing to monitor metrics such as request count and response time statistics, analyze logs, and perform distributed tracing.

HTTP/HTTPS based Ballerina services and any client connectors are observable by default. HTTP/HTTPS and SQL client connectors use semantic tags to make tracing and metrics monitoring more informative.

This guide focuses on enabling Ballerina service observability with some of its supported systems. Prometheus and Grafana are used for metrics monitoring, and Jaeger is used for distributed tracing. Ballerina logs can be fed to any external log monitoring system like Elastic Stack to perform log monitoring and analysis.

Observing a Ballerina Service

Follow the steps below to observe a sample Ballerina service.

Step 1 - Setting up the Prerequisites

Make sure you have already installed Docker to set up external products such as Jaeger, Prometheus, etc. You can follow Docker documentation to install Docker.

Step 2 - Installing and Configuring the External Systems

Step 3 - Creating a ‘Hello World’ Ballerina Service

Create a Service as shown below and save it as hello_world_service.bal.

import ballerina/http;
import ballerina/log;
import ballerinax/prometheus as _;
import ballerinax/jaeger as _;

service /hello on new http:Listener(9090) {
    resource function get sayHello(http:Caller caller, http:Request req) returns error? {
        log:printInfo("This is a test Info log");
        log:printError("This is a test Error log");
        http:Response res = new;
        res.setPayload("Hello, World!");
        check caller->respond(res);

Step 4 - Observing the ‘Hello World’ Ballerina Service

By default, observability is not included in the executable created by Ballerina. It can be added by using the –observability-included build flag or by adding the following section to the Ballerina.toml file.


To include the Prometheus and Jaeger extensions into the executable, the ballerinax/prometheus and ballerinax/jaeger modules need to be imported in your Ballerina code.

import ballerinax/prometheus as _;
import ballerinax/jaeger as _;

Observability is disabled by default at runtime as well and it can be enabled selectively for metrics and tracing by adding the following runtime configurations to the Config.toml file.


The created configuration file can be passed to the Ballerina program with the BAL_CONFIG_FILES environment variable along with the path of the configuration file.

$ BAL_CONFIG_FILES=<path-to-conf>/Config.toml bal run --observability-included hello_world_service.bal

[ballerina/http] started HTTP/WS listener
ballerina: started Prometheus HTTP listener
ballerina: started publishing traces to Jaeger on localhost:55680
[ballerina/http] started HTTP/WS listener

When Ballerina observability is enabled, the Ballerina runtime exposes internal metrics via an HTTP endpoint for metrics monitoring and traces will be published to Jaeger. Prometheus should be configured to scrape metrics from the metrics HTTP endpoint in Ballerina.

Ballerina logs are logged on the console. Therefore, the logs need to be redirected to a file, which can then be pushed to Elastic Stack to perform the log analysis.

Therefore, redirect the standard output to a file if you want to monitor logs.

$ BAL_CONFIG_FILES=<path-to-conf>/Config.toml nohup bal run --observability-included hello_world_service.bal > ballerina.log &

Step 5 - Sending Few Requests

Send few requests to http://localhost:9090/hello/sayHello

Example cURL command:

$ curl http://localhost:9090/hello/sayHello

Step 6 - Viewing Tracing and Metrics in the Dashboard

View the tracing information on Jaeger via http://localhost:16686/ and view metrics information from the Grafana dashboard on http://localhost:3000/.

Sample view of Jaeger dashboard for hello_world_service.bal is shown below. Jaeger Sample Dashboard

Sample view of Grafana dashboard for hello_world_service.bal is shown below. Grafana Sample Dashboard

Step 7 - Visualizing the Logs

If you have configured log analytics, view the logs in Kibana via http://localhost:5601

Kibana Sample Dashboard

Monitoring Metrics

Metrics help to monitor the runtime behavior of a service. Therefore, metrics are a vital part of monitoring Ballerina services. However, metrics are not the same as analytics. For example, you should not use metrics to do something like per-request billing. Metrics are used to measure what Ballerina service does at runtime to make better decisions using the numbers. The code generates business value when it continuously runs in production. Therefore, it is imperative to continuously measure the code in production.

In order to support Prometheus as the metrics reporter, an HTTP endpoint starts with the context of /metrics in default port 9797 when starting the Ballerina service.

Configuring Advanced Metrics for Ballerina

This section focuses on the Ballerina configurations that are available for metrics monitoring with Prometheus, and the sample configuration is provided below.



The descriptions of each configuration above are provided below with possible alternate options.

Configuration Key Description Default Value Possible Values
ballerina.observe. metricsEnabled Whether metrics monitoring is enabled (true) or disabled (false) false true or false
ballerina.observe. metricsReporter Reporter name that reports the collected Metrics to the remote metrics server. This is only required to be modified if a custom reporter is implemented and needs to be used. choreo prometheus or if any custom implementation, the name of the reporter.
ballerinax.prometheus. port The value of the port in which the service ‘/metrics’ will bind to. This service will be used by Prometheus to scrape the information of the Ballerina service. 9797 Any suitable value for port 0 - 0 - 65535. However, within that range, ports 0 - 1023 are generally reserved for specific purposes, therefore it is advisable to select a port without that range.
ballerinax.prometheus. host The name of the host in which the service ‘/metrics’ will bind to. This service will be used by Prometheus to scrape the information of the Ballerina service. IP or Hostname or of the node in which the Ballerina service is running.

Setting Up the External Systems for Metrics

There are mainly two systems involved in collecting and visualizing the metrics. Prometheus is used to collect the metrics from the Ballerina service and Grafana can connect to Prometheus and visualize the metrics in the dashboard.

Setting Up Prometheus

Prometheus is used as the monitoring system, which pulls out the metrics collected from the Ballerina service ‘/metrics’. This section focuses on the quick installation of Prometheus with Docker and configures it to collect metrics from the Ballerina service with default configurations. Follow the steps below to configure Prometheus.

Tip: There are many other ways to install the Prometheus and you can find possible options from installation guide.

  1. Create a prometheus.yml file in the /tmp/ directory.

  2. Add the following content to /tmp/prometheus.yml.

  scrape_interval:     15s
  evaluation_interval: 15s

  - job_name: 'prometheus'
      - targets: ['a.b.c.d:9797']

Here the targets 'a.b.c.d:9797' should contain the host and port of the /metrics service that’s exposed from Ballerina for metrics collection. Add the IP of the host in which the Ballerina service is running as a.b.c.d and its port (default 9797). If you need more information, go to the Prometheus Documentation.

If your Ballerina service is running on localhost and Prometheus in a Docker container, add the target as host.docker.internal:9797 to access the localhost from Docker.

  1. Start the Prometheus server in a Docker container with the command below.
$ docker run -p 19090:9090 -v /tmp/prometheus.yml:/etc/prometheus/prometheus.yml prom/prometheus
  1. Go to http://localhost:19090/ and check whether you can see the Prometheus graph. Ballerina metrics should appear in Prometheus graph’s metrics list when Ballerina service is started.

Setting Up Grafana

Let’s use Grafana to visualize metrics in a dashboard. For this, we need to install Grafana, and configure Prometheus as a data source. Follow the steps below to configure Grafana.

  1. Start Grafana as a Docker container with the command below.
$ docker run -d --name=grafana -p 3000:3000 grafana/grafana

For more information, go to Grafana in Docker Hub.

  1. Go to http://localhost:3000/ to access the Grafana dashboard running on Docker.

  2. Log in to the dashboard with the default user, username: admin and password: admin

  3. Add Prometheus as a data source with Browser access configuration as provided below.

Grafana Prometheus Datasource

  1. Import the Grafana dashboard designed to visualize Ballerina metrics from https://grafana.com/dashboards/5841. This dashboard consists of service and client invocation level metrics in near real-time view.

Ballerina HTTP Service Metrics Dashboard Panel will be as below. Ballerina Service Metrics

Ballerina HTTP Client Metrics Dashboard Panel will be as below. Ballerina Client Metrics

Ballerina SQL Client Metrics Dashboard Panel will be as below. Ballerina SQL Client Metrics

Distributed Tracing

Tracing provides information regarding the roundtrip of a service invocation based on the concept of spans, which are structured in a hierarchy based on the cause and effect concept. A trace can spread across several services that can be deployed in several nodes, depicting a high-level view of interconnections among services as well, hence coining the term distributed tracing.

A span is a logical unit of work, which encapsulates a start and end time as well as metadata to give more meaning to the unit of work being completed. For example, a span representing a client call to an HTTP endpoint would give the user the latency of the client call and metadata like the HTTP URL being called and HTTP method used. If the span represents an SQL client call, the metadata would include the query being executed.

Tracing gives the user a high-level view of how a single service invocation is processed across several distributed microservices.

  • Identify service bottlenecks - The user can monitor the latencies and identify when a service invocation slows down, pinpoint where the slowing down happens (by looking at the span latencies) and take action to improve the latency.
  • Error identification - If an error occurs during the service invocation, it will show up in the list of traces. The user can easily identify where the error occurred and information of the error will be attached to the relevant span as metadata.

Ballerina supports OpenTelemetry standards by default. This means that Ballerina services can be traced using OpenTelemetry implementations like Jaeger.

Configuring Advanced Tracing for Ballerina

Tracing can be enabled in Ballerina with the few configurations as mentioned in the Observing a Ballerina Service. This section mainly focuses on the configuration options with the description and possible values.

The sample configuration that enables tracing and uses Jaeger as the tracer as provided below.


The table below provides the descriptions of each configuration option and possible values that can be assigned.

Configuration Key Description Default Value Possible Values
ballerina.observe.tracingEnabled Whether tracing is enabled (true) or disabled (false) false true or false
ballerina.observe.tracingProvider The tracer name, which implements the tracer interface. choreo jaeger or the name of the tracer of any custom implementation.

Using the Jaeger Client

The below are the sample configuration options that are available to support Jaeger as the tracer provider in Ballerina.



The table below provides the descriptions of each configuration option and possible values that can be assigned.

Configuration Key Description Default Value Possible Values
ballerina.observe. agentHostname Hostname of the Jaeger agent localhost IP or hostname of the Jaeger agent. If it is running on the same node as Ballerina, it can be localhost.
ballerina.observe. agentPort Port of the Jaeger agent 55680 The port on which the Jaeger agent is listening.
ballerina.observe. samplerType Type of the sampling methods used in the Jaeger tracer. const const, probabilistic, or ratelimiting.
ballerina.observe. samplerParam It is a floating value. Based on the sampler type, the effect of the sampler param varies 1.0 For const 0 (no sampling) or 1 (sample all spans), for probabilistic 0.0 to 1.0, for ratelimiting any positive integer (rate per second).
ballerina.observe. reporterFlushInterval The Jaeger client will be sending the spans to the agent at this interval. 2000 Any positive integer value.
ballerina.observe. reporterBufferSize Queue size of the Jaeger client. 2000 Any positive integer value.

Setting Up the External Systems for Tracing

You can configure Ballerina to support distributed tracing with Jaeger. This section focuses on configuring Jaeger with Docker as a quick installation.

Setting Up the Jaeger Server

There are many possible ways to deploy Jaeger. For more information, see Deployment. This focuses on an all-in-one deployment with Docker.

  1. Install Jaeger via Docker and start the Docker container by executing command below.
$ docker run -d -p 13133:13133 -p 16686:16686 -p 55680:55680 jaegertracing/opentelemetry-all-in-one
  1. Go to http://localhost:16686 and load the web UI of the Jaeger to make sure it is functioning properly.

The image below is the sample tracing information you can see from Jaeger.

Jaeger Tracing Dashboard

Distributed Logging

Ballerina distributed logging and analysis is supported by Elastic Stack. Ballerina has a log module for logging in to the console. In order to monitor the logs, the Ballerina standard output needs to be redirected to a file.

This can be done by running the Ballerina service as below.

$ nohup bal run hello_world_service.bal > ballerina.log &

You can view the logs with the command below.

$ tail -f ~/wso2-ballerina/workspace/ballerina.log

Setting Up the External Systems for Log Analytics

Setting Up Elastic Stack

The elastic stack comprises of the following components.

  1. Beats - Multiple agents that ship data to Logstash or Elasticsearch. In our context, Filebeat will ship the Ballerina logs to Logstash. Filebeat should be a container running on the same host as the Ballerina service. This is so that the log file (ballerina.log) can be mounted to the Filebeat container.
  2. Logstash - Used to process and structure the log files received from Filebeat and send them to Elasticsearch.
  3. Elasticsearch - Storage and indexing of the logs received by Logstash.
  4. Kibana - Visualizes the data stored in Elasticsearch

Elasticsearch and Kibana are provided as Cloud Services Alternatively, Docker containers can be used to set up Elasticsearch and Kibana as well.

  1. Download the Docker images using the following commands.
# Elasticsearch Image
$ docker pull docker.elastic.co/elasticsearch/elasticsearch:6.5.1
# Kibana Image
$ docker pull docker.elastic.co/kibana/kibana:6.5.1
# Filebeat Image
$ docker pull docker.elastic.co/beats/filebeat:6.5.1
# Logstash Image
$ docker pull docker.elastic.co/logstash/logstash:6.5.1
  1. Start Elasticsearch and Kibana containers by executing the following commands.
$ docker run -p 9200:9200 -p 9300:9300 -it -h elasticsearch --name elasticsearch docker.elastic.co/elasticsearch/elasticsearch:6.5.1
$ docker run -p 5601:5601 -h kibana --name kibana --link elasticsearch:elasticsearch docker.elastic.co/kibana/kibana:6.5.1

If you run on Linux you may have to increase the vm.max_map_count for the Elasticsearch container to start. Execute the following command to do that.

$ sudo sysctl -w vm.max_map_count=262144
  1. Create a logstash.conf file in the /tmp/pipeline/ directory and include the following content in the file.
input {
  beats {
    port => 5044
filter {
  grok  {
    match => { "message" => "%{TIMESTAMP_ISO8601:date}%{SPACE}%{WORD:logLevel}%{SPACE}\[%{GREEDYDATA:module}\]%{SPACE}\-%{SPACE}%{GREEDYDATA:logMessage}"}
output {
    elasticsearch {
        hosts => "elasticsearch:9200"
        index => "ballerina"
      document_type => "ballerina_logs"

Here the 3 stages are specified in the pipeline. Input is specified as beats and listens to port 5044. A grok filter is used to structure the Ballerina logs and the output is specified to push to Elasticsearch on elasticsearch:9200.

  1. Start the Logstash container by the following command.
$ docker run -h logstash --name logstash --link elasticsearch:elasticsearch -it --rm -v /tmp/pipeline:/usr/share/logstash/pipeline/ -p 5044:5044 docker.elastic.co/logstash/logstash:6.5.1
  1. Configure Filebeat to ship the Ballerina logs. Create a filebeat.yml file in the /tmp/ directory and include the following content in the file.
- type: log
    - /usr/share/filebeat/ballerina.log
  hosts: ["logstash:5044"]
  1. Start the Filebeat container with the following command.

The -v flag is used for bind mounting, where the container will read the file from the host machine. Provide the path to the ballerina.log file, to be bind-mounted to the filebeat container.

$ docker run -v /tmp/filebeat.yml:/usr/share/filebeat/filebeat.yml -v /<path-to-ballerina.log>/ballerina.log:/usr/share/filebeat/ballerina.log --link logstash:logstash docker.elastic.co/beats/filebeat:6.5.1
  1. Access Kibana to visualize the logs at http://localhost:5601. Add an index named ballerina and click on Discover to visualize the logs.