November 10, 2021

Monitoring Rust web application with Prometheus and Grafana


In this article, I’ll show you how to set up monitoring of a Rust web application. The application will expose Prometheus metrics that will be visualized using Grafana. The monitored application is mongodb-redis demo described in detail here. Finally, the following architecture will be obtained:


The monitoring system includes:

  • Prometheus — monitoring platform which collects real-time metrics and records them in a time series database

  • Grafana — observability and data visualization platform

  • AlertManager — an application that handles alerts sent by the Prometheus server (for example, when something goes wrong in your application) and notifies an end user through email, Slack, Telegram, or other channels

  • cAdvisor — a platform providing container users an understanding of the resource usage and performance characteristics of their running containers. (Actually, it gathers information on all Docker containers on the scheme)

To start all the tools at once you can use the following:

version: '3.8'

    image: prom/prometheus:latest
    container_name: prometheus
    restart: always
      - '9090:9090'
      - ./monitoring/prometheus:/etc/prometheus
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--web.external-url=http://localhost:9090'

    image: grafana/grafana:latest
    container_name: grafana
    restart: always
      - '3000:3000'
      - ./monitoring/grafana/data:/var/lib/grafana
      - ./monitoring/grafana/provisioning:/etc/grafana/provisioning

    image: prom/alertmanager:latest
    container_name: alertmanager
      - '9093:9093'
      - ./monitoring/alertmanager:/etc/alertmanager
      - '--config.file=/etc/alertmanager/alertmanager.yml'
      - '--web.external-url=http://localhost:9093'

    container_name: cadvisor
    restart: always
      - '8080:8080'
      - /:/rootfs:ro
      - /var/run:/var/run:rw
      - /sys:/sys:ro
      - /var/lib/docker/:/var/lib/docker:ro

Exposing Prometheus metrics in a Rust application

Metrics exposition is implemented with prometheus crate.

There are four core types of Prometheus metrics: counter, gauge, histogram, and summary. The first three of them will be described in the article (the crate currently doesn’t support summary type).

Metrics definition

Metrics can be created and registered in this way:

lazy_static! {
    pub static ref HTTP_REQUESTS_TOTAL: IntCounterVec = register_int_counter_vec!(
        opts!("http_requests_total", "HTTP requests total"),
        &["method", "path"]
    .expect("Can't create a metric");
    pub static ref HTTP_CONNECTED_SSE_CLIENTS: IntGauge =
        register_int_gauge!(opts!("http_connected_sse_clients", "Connected SSE clients"))
            .expect("Can't create a metric");
    pub static ref HTTP_RESPONSE_TIME_SECONDS: HistogramVec = register_histogram_vec!(
        "HTTP response times",
        &["method", "path"],
    .expect("Can't create a metric");

In the code snippet above, custom metrics are added to the default registry; it is also possible to register them in a custom registry (see example).

Further, the purpose and usage of the metrics will be described in detail.


If we want to count all incoming HTTP requests, it is possible to use IntCounter type. But it is more useful to see not just the total number of requests, but also some other dimensions, such as a path and a method of a request. That can be done with IntCounterVec; HTTP_REQUESTS_TOTAL of the type is used in custom Actix middleware as follows:

Listing 3. Usage of HTTP_REQUESTS_TOTAL metric
let request_path = req.path();
let is_registered_resource = req.resource_map().has_resource(request_path);
// this check prevents possible DoS attacks that can be done by flooding the application
// using requests to different unregistered paths. That can cause high memory consumption
// of the application and Prometheus server and also overflow Prometheus's TSDB
if is_registered_resource {
    let request_method = req.method().to_string();
        .with_label_values(&[&request_method, request_path])

After making some requests to API, something like the following will be produced:

Listing 4. Output of HTTP_REQUESTS_TOTAL metric
# HELP http_requests_total HTTP requests total
# TYPE http_requests_total counter
http_requests_total{method="GET",path="/"} 1
http_requests_total{method="GET",path="/events"} 1
http_requests_total{method="GET",path="/metrics"} 22
http_requests_total{method="GET",path="/planets"} 20634

Each sample of the metric has method and path labels (the metric’s attributes), so Prometheus can differentiate the samples.

As is shown in the previous snippet, requests to GET /metrics (endpoint from which Prometheus server collects metrics of an application) are also observed.


Gauge differs from the counter by the fact that its value can go down. The example of the gauge shows how many clients at present are connected using SSE. The gauge is used as follows:

Listing 5. Usage of HTTP_CONNECTED_SSE_CLIENTS metric;

crate::metrics::HTTP_CONNECTED_SSE_CLIENTS.set(broadcaster_mutex.clients.len() as i64)

If you navigate to http://localhost:9000 in your browser, connection over SSE will be established and the metric will be incremented. After that, its output will look like this:

Listing 6. Output of HTTP_CONNECTED_SSE_CLIENTS metric
# HELP http_connected_sse_clients Connected SSE clients
# TYPE http_connected_sse_clients gauge
http_connected_sse_clients 1


To implement SSE clients gauge it was needed to refactor the code of the application and implement broadcaster. It stores all connected (using sse function) clients in a vector and periodically pings each of them (in remove_stale_clients function) to make sure that the connection is still alive otherwise removing disconnected clients from the vector. The broadcaster allows opening just one Redis Pub/Sub connection; messages from it are sent (broadcasted) to all the clients.


In this guide, histogram is used to collect data on response times. As in the case of request counter, tracking is done in Actix middleware; it is implemented using the following code:


histogram_timer = Some(
        .with_label_values(&[&request_method, request_path])


if let Some(histogram_timer) = histogram_timer {

I think this approach is not precise (the question is how less is this time than the actual response time) but nevertheless the observation data will be useful as an example of a histogram and for its further visualization in Grafana.

A histogram samples observations and counts them in configurable buckets (there are default buckets but most likely you will be required to define buckets customized to your use case); to configure them it is nice to know approximate spread of a metric’s values. In this application response time is quite small so the following configuration is used:

    0.0005, 0.0008, 0.00085, 0.0009, 0.00095, 0.001, 0.00105, 0.0011, 0.00115, 0.0012, 0.0015,
    0.002, 0.003, 1.0,

The output will look like this (only a part of data is shown to save space):

Listing 9. Output of HTTP_RESPONSE_TIME_SECONDS metric
# HELP http_response_time_seconds HTTP response times
# TYPE http_response_time_seconds histogram
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.0005"} 0
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.0008"} 6
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.00085"} 1307
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.0009"} 10848
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.00095"} 22334
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.001"} 31698
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.00105"} 38973
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.0011"} 44619
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.00115"} 48707
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.0012"} 51495
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.0015"} 57066
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.002"} 59542
http_response_time_seconds_bucket{method="GET",path="/planets",le="0.003"} 60532
http_response_time_seconds_bucket{method="GET",path="/planets",le="1"} 60901
http_response_time_seconds_bucket{method="GET",path="/planets",le="+Inf"} 60901
http_response_time_seconds_sum{method="GET",path="/planets"} 66.43133770000004
http_response_time_seconds_count{method="GET",path="/planets"} 60901

The data show numbers of observations falling into particular buckets of observation values. The output also provides information on total count and sum of observations.

System metrics

process feature enables exporting of process metrics such as CPU or memory usage. All you need is just to specify the feature in Cargo.toml. After that, you will get something like this:

Listing 10. Output of process metrics
# HELP process_cpu_seconds_total Total user and system CPU time spent in seconds.
# TYPE process_cpu_seconds_total counter
process_cpu_seconds_total 134.49
# HELP process_max_fds Maximum number of open file descriptors.
# TYPE process_max_fds gauge
process_max_fds 1048576
# HELP process_open_fds Number of open file descriptors.
# TYPE process_open_fds gauge
process_open_fds 37
# HELP process_resident_memory_bytes Resident memory size in bytes.
# TYPE process_resident_memory_bytes gauge
process_resident_memory_bytes 15601664
# HELP process_start_time_seconds Start time of the process since unix epoch in seconds.
# TYPE process_start_time_seconds gauge
process_start_time_seconds 1636309802.38
# HELP process_threads Number of OS threads in the process.
# TYPE process_threads gauge
process_threads 6
# HELP process_virtual_memory_bytes Virtual memory size in bytes.
# TYPE process_virtual_memory_bytes gauge
process_virtual_memory_bytes 439435264

Note that prometheus crate supports exporting process metrics of an application running on Linux (for example in such a Docker container).

Endpoint for metrics exposition

Actix is configured to process GET /metrics requests using this handler:

Listing 11. Metrics handler
pub async fn metrics() -> Result<HttpResponse, CustomError> {
    let encoder = TextEncoder::new();
    let mut buffer = vec![];
        .encode(&prometheus::gather(), &mut buffer)
        .expect("Failed to encode metrics");

    let response = String::from_utf8(buffer.clone()).expect("Failed to convert bytes to string");


Now, after the successful configuration of the application, you can get all previously described metrics by executing GET http://localhost:9000/metrics request. The endpoint is used by the Prometheus server to collect metrics of the application.

Metrics are exposed in a simple text-based format.

Prometheus setup for metrics gathering

Prometheus collects (scrapes) metrics using the following config:

Listing 12. Prometheus config for metrics gathering

  - job_name: mongodb_redis_web_app
    scrape_interval: 5s
      - targets: ['host.docker.internal:9000']

  - job_name: cadvisor
    scrape_interval: 5s
      - targets: ['cadvisor:8080']

In the config, two jobs are specified. The former job collects previously described metrics of the application, the latter collects resource usage and performance metrics of running containers (that will be described in detail in the section about cAdvisor). scrape_interval specifies how frequently to scrape a target. metrics_path parameter is not specified so Prometheus expects metrics to be available on targets on a path of /metrics.

Expression browser and graphing interface

To use Prometheus’s built-in expression browser, navigate to http://localhost:9090/graph and try to query any of the previously described metrics, for example, http_requests_total. Use the "Graph" tab to visualize the data.

PromQL allows you to make more complex queries; let’s consider a couple of examples.

  • return all time series with the metric http_requests_total and the given job:


    job and instance labels are attached automatically to the scraped time series by the Prometheus server.

  • return per-second rate of incoming requests, as measured over the last 5 minutes:


You can find more examples here.

Grafana setup for metrics visualization

In this project, Grafana is configured with the following:

  • data sources (from where Grafana will request data)

    Listing 13. Data sources config of Grafana
    apiVersion: 1
      - name: Prometheus
        type: prometheus
        access: proxy
        url: prometheus:9090
        isDefault: true
  • dashboards provider (from where Grafana will load dashboards)

    Listing 14. Dashboards config of Grafana
    apiVersion: 1
      - name: 'default'
        folder: 'default'
        type: file
        allowUiUpdates: true
        updateIntervalSeconds: 30
          path: /etc/grafana/provisioning/dashboards
          foldersFromFilesStructure: true

After launching the project as was specified in Docker Compose file, you can go to http://localhost:3000/, log in with admin/admin credentials, and find webapp_metrics dashboard. A little bit later, it will look like this:


The dashboard displays the state of the application under easy load test. (If you will launch some load test, for better visibility of the graphs (particularly the histogram) you will need to disable somehow MAX_REQUESTS_PER_MINUTE restriction, for example, by a sharp increase of that number.)

To visualize the data, the dashboard uses PromQL queries that utilize previously described metrics, for example:

  • rate(http_response_time_seconds_sum[5m]) / rate(http_response_time_seconds_count[5m])

    Shows average response time for the last five minutes

  • sum(increase(http_response_time_seconds_bucket{path="/planets"}[30s])) by (le)

    Is used to visualize response time distribution in the form of a heatmap. A heatmap is like a histogram, but over time, where each time slice represents its own histogram

  • rate(process_cpu_seconds_total{job="mongodb_redis_web_app"}[1m]), sum(rate(container_cpu_usage_seconds_total{name='mongodb-redis'}[1m])) by (name)

    Shows CPU usage for the last five minutes. Requested data are coming from two different sources and show CPU usage of the process and the container respectively. The two graphs are almost identical. (sum is used because container_cpu_usage_seconds_total provides information on the usage of each core.)


"Memory usage" graph shows memory used by:

  • the process (process_resident_memory_bytes{job="mongodb_redis_web_app"} / 1024 / 1024)

  • the container (container_memory_usage_bytes{name="mongodb-redis"} / 1024 / 1024)

For an unknown reason, the graph shows that the process consumes much more memory than the whole container. Also, I created an issue on this question. Please, let me know if there’s something wrong with that comparison or if you know how this can be explained.

Monitoring metrics of application container using cAdvisor

In addition to system metrics of a process (was shown previously), system metrics of the Docker container can also be exported. This can be done using cAdvisor.

Web UI of cAdvisor is accessible via http://localhost:8080/. All running Docker containers are shown at http://localhost:8080/docker/:

cadvisor docker containers

You can get information on resource usage of any container:

cadvisor container info

Metrics are gathered by the Prometheus server from http://localhost:8080/metrics.

Metrics exported by cAdvisor are listed here.

System metrics of machines can be exported with Node exporter or Windows exporter.

Setup of alert notifications using rules and AlertManager

In this project, the following part of Prometheus config is responsible for alerting:

Listing 15. Prometheus config for alerting
  - 'rules.yml'

    - static_configs:
        - targets: ['alertmanager:9093']

The alerting section defines the AlertManager instance Prometheus server communicates with.

Alerting rules allow you to define some conditions based on PromQL expressions:

Listing 16. Example of an alerting rule in rules.yml
- name: default
  - alert: SseClients
    expr:  http_connected_sse_clients > 0
    for: 1m
      severity: high
      summary: Too many SSE clients
  • alert – a name of the alert

  • expr – the actual rule definition in the form of Prometheus expression

  • for – how long the rule needs to be broken before raising an alert. In our case, if the number of SSE clients remains greater than 0 for 1 minute, an alert will be raised

  • labels – extra information that can be attached to the alert, e.g. severity

  • annotations – extra descriptions that can be attached to the alert, e.g. summary

This rule of SSE clients number greater than 0 isn’t something you would typically setup for your application. It is used as an example as it will be easy for us to break the rule by making just one request.

If the rule is broken, the Prometheus server will send an alert to the AlertManager instance. It provides numerous features, such as alerts deduplication, grouping, silencing, and dispatching notifications to an end user. We will consider only routing ability: the alert will be dispatched to an email.

AlertManager is configured as follows:

  receiver: gmail

- name: gmail
  - to:
    auth_password: password

In this project, AlertManager is configured with a Gmail account. To generate an app password, you can use this guide.

To trigger SseClients alerting rule, you just need to navigate to http://localhost:9000 in your browser. This will increase http_connected_sse_clients metric by 1. You can track the status of the SseClients alert on http://localhost:9090/alerts. After the triggering, the alert will go to the Pending status. After the for interval defined in rules.yml has passed (in our case 1 minute), the alert will go to the Firing status.

prometheus alert

That will cause the Prometheus server sending the alert to AlertManager. It will decide what to do with the alert, in our case sending an email:

gmail alert

Monitoring third-party systems using Prometheus exporters

For third-party tools, such as MongoDB, Redis, and many others, it is possible to set up monitoring using Prometheus exporters.


docker compose up --build


In this article, I showed how to setup metrics exposition in a Rust web application, their gathering with Prometheus, and data visualization with Grafana. Also, it was shown how to start working with cAdvisor to gather containers' metrics and with alerting using AlertManager. Feel free to contact me if you have found any mistakes in the article or the source code. Thanks for reading!

© Roman Kudryashov 2019-2020

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