Che can expose certain data as metrics, that can be processed by Prometheus and Grafana stack. Prometheus is a monitoring system, that maintains the collection of metrics - time series key-value data which can represent consumption of resources like CPU and memory, amount of processed HTTP queries and their execution time, and Che specific resources, such as number of users and workspaces, the start and shutdown of workspaces, information about JsonRPC stack.

Prometheus is powered with a special query language, that allows manipulating the collected data, and perform various binary, vector and aggregation operations with it, to help create a more refined view on data.

While Prometheus is the central piece, responsible for scraping and storing the metrics, while Grafana offers a front-end "facade" with tools to create a various visual representation in the form of dashboards with various panels and graph types.

Note that this monitoring stack is not an official production-ready solution, but rather has an introduction purpose.

monitoring che stack structure
Figure 1. The structure of Che monitoring stack

Enabling Che metrics collections

Prerequisites
Procedure

  1. Set the CHE_METRICS_ENABLED=true environment variable

  2. Expose the 8087 port as a service on the che-master host

  3. Configure Prometheus to scrape metrics from the 8087 port

  4. Configure a Prometheus data source on Grafana

  5. Deploy Che-specific dashboards on Grafana

Collecting Che metrics with Prometheus

Prometheus is a monitoring system that collects metrics in real time and stores them in a time series database.

Prometheus comes with a console accessible at the 9090 port of the application pod. By default, a template provides an existing service and a route to access it. It can be used to query and view metrics.

monitoring che prometheus console

Prometheus terminology

Prometheus offers:

counter

the simplest numerical type of metric whose value can be only increased. A typical example is counting the amount of HTTP requests that go through the system.

gauge

numerical value that can be increased or decreased. Best suited for representing values of objects.

histogram

a more complex metric that is suited for performing observations. Metrics are collected and grouped in configurable buckets, which allwos to present the results, for instance, in a form of a heatmap.

Configuring Prometheus

Prometheus configuration
- apiVersion: v1
  data:
    prometheus.yml: |-
      global:
        scrape_interval:     5s           (1)
        evaluation_interval: 5s           (2)
      scrape_configs:
        - job_name: 'che'
          static_configs:
            - targets: ['che-host:8087']
  kind: ConfigMap
  metadata:
    name: prometheus-config
1 rate, at which a target is scraped
2 rate, at which recording and alerting rules are re-checked (not used in our system at the moment)

Viewing Che metrics on Grafana dashboards

Grafana is used for informative representation of Prometheus metrics. Providing visibility for OpenShift, Grafana’s deployment configuration and ConfigMaps are located in the che-monitoring.yaml configuration file.

Configuring and deploying Grafana

Grafana is run on port 3000 with a corresponding service and route.

Three ConfigMaps are used to configure Grafana:

  • grafana-datasources — configuration for Grafana datasource, a Prometheus endpoint

  • grafana-dashboards — configuration of Grafana dashboards and panels

  • grafana-dashboard-provider  — configuration of the Grafana dashboard provider API object, which tells Grafana where to look in the file system for pre-provisioned dashboards

Grafana dashboards overview

Che provides several types of dashboards.

Che server dashboard

Use case: Che server-specific metrics related to Che components, such as workspaces or users.

monitoring che che server dashboard general panel
Figure 2. The General panel

The General panel contains basic information, such as the total number of users and workspaces in the Che database.

monitoring che che server dashboard workspace panel
Figure 3. The Workspaces panel

  • Workspace start rate — the ratio between successful and failed started workspaces

  • Workspace stop rate — the ratio between successful and failed stopped workspaces

  • Workspace Failures — the number of workspace failures shown on the graph

  • Starting Workspaces — the gauge that shows the number of currently starting workspaces

  • Average Workspace Start Time — 1-hour average of workspace starts or fails

  • Average Workspace Stop Time — 1-hour average of workspace stops

  • Running Workspaces — the gauge that shows the number of currently running workspaces

  • Stopping Workspaces — the gauge that shows the number of currently stopping workspaces

  • Workspaces started under 60 seconds — the percentage of workspaces started under 60 seconds

  • Number of Workspaces — the number of workspaces created over time

monitoring che che server dashboard users panel
Figure 4. The Users panel

  • Number of Users — the number of users known to Che over time

monitoring che che server dashboard tomcat panel
Figure 5. The Tomcat panel

  • Max number of active sessions — the max number of active sessions that have been active at the same time

  • Number of current active sessions — the number of currently active sessions

  • Total sessions — the total number of sessions

  • Expired sessions — the number of sessions that have expired

  • Rejected sessions — the number of sessions that were not created because the maximum number of active sessions was reached

  • Longest time of an expired session — the longest time (in seconds) that an expired session had been alive

monitoring che che server dashboard requests panel
Figure 6. The Request panel

The Requests panel displays HTTP requests in a graph that shows the average number of requests per minute.

monitoring che che server dashboard executors panel 1
Figure 7. The Executors panel, part 1

  • Threads running - the number of threads that are not terminated aka alive. May include threads that are in a waiting or blocked state.

  • Threads terminated - the number of threads that was finished its execution.

  • Threads created - number of threads created by thread factory for given executor service.

  • Created thread/minute - Speed of thread creating for the given executor service.

monitoring che che server dashboard executors panel 2
Figure 8. The Executors panel, part 2

  • Executor threads active - number of threads that actively execute tasks.

  • Executor pool size - number of threads that actively execute tasks.

  • Queued task - the approximate number of tasks that are queued for execution

  • Queued occupancy - the percent of the queue used by the tasks that is waining for execution.

monitoring che che server dashboard executors panel 3
Figure 9. The Executors panel, part 3

  • Rejected task - the number of tasks that were rejected from execution.

  • Rejected task/minute - the speed of task rejections

  • Completed tasks - the number of completed tasks

  • Completed tasks/minute - the speed of task execution

monitoring che che server dashboard executors panel 4
Figure 10. The Executors panel, part 4

  • Task execution seconds max - 5min moving maximum of task execution

  • Tasks execution seconds avg - 1h moving average of task execution

  • Executor idle seconds max - 5min moving maximum of executor idle state.

  • Executor idle seconds avg - 1h moving average of executor idle state.

monitoring che che server dashboard trace panel 1
Figure 11. The Traces panel, part 1

  • Workspace start Max - maximum workspace start time

  • Workspace start Avg - 1h moving average of the workspace start time components

  • Workspace stop Max - maximum of workspace stop time

  • Workspace stop Avg - 1h moving average of the workspace stop time components

monitoring che che server dashboard trace panel 2
Figure 12. The Traces panel, part 2

  • OpenShiftInternalRuntime#start Max - maximum time of OpenShiftInternalRuntime#start operation

  • OpenShiftInternalRuntime#start Avg - 1h moving average time of OpenShiftInternalRuntime#start operation

  • Plugin Brokering Execution Max - maximum time of PluginBrokerManager#getTooling operation

  • Plugin Brokering Execution Avg - 1h moving average of PluginBrokerManager#getTooling operation

monitoring che che server dashboard trace panel 3
Figure 13. The Traces panel, part 3

  • OpenShiftEnvironmentProvisioner#provision Max - maximum time of OpenShiftEnvironmentProvisioner#provision operation

  • OpenShiftEnvironmentProvisioner#provision Avg -1h moving average of OpenShiftEnvironmentProvisioner#provision operation

  • Plugin Brokering Execution Max - maximum time of PluginBrokerManager#getTooling components execution time

  • Plugin Brokering Execution Avg - 1h moving average of time of PluginBrokerManager#getTooling components execution time

monitoring che che server dashboard trace panel 4
Figure 14. The Traces panel, part 4

  • WaitMachinesStart Max - maximim time of WaitMachinesStart operations

  • WaitMachinesStart Avg - 1h moving average time of WaitMachinesStart operations

  • OpenShiftInternalRuntime#startMachines Max - maximim time of OpenShiftInternalRuntime#startMachines operations

  • OpenShiftInternalRuntime#startMachines Avg - 1h moving average of the time of OpenShiftInternalRuntime#startMachines operations

monitoring che che server dashboard workspace detailed panel
Figure 15. The Workspace detailed panel

The Workspace Detailed panel contains heat maps, which illustrate the average time of workspace starts or fails. The row shows some period of time.

Che server JVM dashboard

Use case: JVM metrics of the Che server, such as JVM memory or classloading.

monitoring che che server jvm dashboard
Figure 16. Che server JVM dashboard
monitoring che che server jvm dashboard quick facts
Figure 17. Quick Facts
monitoring che che server jvm dashboard jvm memory
Figure 18. JVM Memory
monitoring che che server jvm dashboard jvm misc
Figure 19. JVM Misc
monitoring che che server jvm dashboard jvm memory pools heap
Figure 20. JVM Memory Pools (heap)
monitoring che che server jvm dashboard jvm memory pools non heap
Figure 21. JVM Memory Pools (Non-Heap)
monitoring che che server jvm dashboard garbage collection
Figure 22. Garbage Collection
monitoring che che server jvm dashboard classloading
Figure 23. Classloading
monitoring che che server jvm dashboard buffer pools
Figure 24. Buffer Pools

Developing Grafana dashboards

Grafana offers the possibility to add custom panels.

Procedure

To add a custom panel, use the New dashboard view.

  1. In the first section, define Queries to. Use the Prometheus Query Language to construct a specific metric, as well as to modify it with various aggregation operators.

    monitoring che new grafana dashboard queries
    Figure 25. New Grafana dashboard: Queries to

  2. In the Visualisation section, choose a metric to be shown in the following visual in the form of a graph, gauge, heatmap, or others.

    monitoring che new grafana dashboard visualization
    Figure 26. New Grafana dashboard: Visualization

  3. Save changes to the dashboard by clicking the Save button, and copy and paste the JSON code to the deployment.

  4. Load changes in the configuration of a running Grafana deployment. First remove the deployment:

    $ oc process -f che-monitoring.yaml | oc delete -f -

    Then redeploy your Grafana with the new configuration:

    $ oc process -f che-monitoring.yaml | oc apply -f - | oc rollout latest grafana

Extending Che monitoring metrics

There are two major modules for metrics:

  • che-core-metrics-core — contains core metrics module

  • che-core-api-metrics — contains metrics that are dependent on core Che components, such as workspace or user managers

Procedure

To create a metric or a group of metrics, you need a class that extends the MeterBinder class. This allows to register the created metric in the overriden bindTo(MeterRegistry registry) method.

The following is an example of a metric that has a function that supplies the value for it:

Example metric
public class UserMeterBinder implements MeterBinder {

  private final UserManager userManager;

  @Inject
  public UserMeterBinder(UserManager userManager) {
    this.userManager = userManager;
  }

  @Override
  public void bindTo(MeterRegistry registry) {
    Gauge.builder("che.user.total", this::count)
        .description("Total amount of users")
        .register(registry);
  }

  private double count() {
    try {
      return userManager.getTotalCount();
    } catch (ServerException e) {
      return Double.NaN;
    }
  }

Alternatively, the metric can be stored with a reference and updated manually in some other place in the code.

Additional resources

For more information about the types of metrics and naming conventions, visit Prometheus documentation:

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