grafana/docs/sources/developers/plugins/build-a-streaming-data-source-plugin.md

title

title
Build a streaming data source plugin

Build a streaming data source plugin

In Grafana, you can set your dashboards to automatically refresh at a certain interval, no matter what data source you use. Unfortunately, this means that your queries are requesting all the data to be sent again, regardless of whether the data has actually changed. Adding streaming to a plugin helps reduce queries so your dashboard is only updated when new data becomes available.

Before you begin

This guide assumes that you're already familiar with how to Build a data source plugin.

Grafana uses RxJS to continuously send data from a data source to a panel visualization.

Note: To learn more about RxJs, refer to the RxJS documentation.

Add streaming to your data source

Enable streaming for your data source plugin to update your dashboard when new data becomes available.

For example, a streaming data source plugin can connect to a websocket, or subscribe to a message bus, and update the visualization whenever a new message is available.

Step 1: Edit the plugin.json file

Enable streaming for your data source in the plugin.json file.

{
  "streaming": true
}

Step 2: Change the signature of the query method

Modify the signature of the query method to return an Observable from the rxjs package. Make sure you remove the async keyword.

import { Observable } from 'rxjs';

query(options: DataQueryRequest<MyQuery>): Observable<DataQueryResponse> {
  // ...
}

Step 3: Create an Observable instance for each query

Create an Observable instance for each query, and then combine them all using the merge function from the rxjs package.

import { Observable, merge } from 'rxjs';

const observables = options.targets.map((target) => {
  return new Observable<DataQueryResponse>((subscriber) => {
    // ...
  });
});

return merge(...observables);

Step 4: Create a CircularDataFrame instance

In the subscribe function, create a CircularDataFrame instance.

import { CircularDataFrame } from '@grafana/data';

const frame = new CircularDataFrame({
  append: 'tail',
  capacity: 1000,
});

frame.refId = query.refId;
frame.addField({ name: 'time', type: FieldType.time });
frame.addField({ name: 'value', type: FieldType.number });

Circular data frames have a limited capacity. When a circular data frame reaches its capacity, the oldest data point is removed.

Step 5: Send the updated data frame

Use subscriber.next() to send the updated data frame whenever you receive new updates.

import { LoadingState } from '@grafana/data';

const intervalId = setInterval(() => {
  frame.add({ time: Date.now(), value: Math.random() });

  subscriber.next({
    data: [frame],
    key: query.refId,
    state: LoadingState.Streaming,
  });
}, 500);

return () => {
  clearInterval(intervalId);
};

Note: In practice, you'd call subscriber.next as soon as you receive new data from a websocket or a message bus. In the example above, data is being received every 500 milliseconds.

Example code for final query method

query(options: DataQueryRequest<MyQuery>): Observable<DataQueryResponse> {
  const streams = options.targets.map(target => {
    const query = defaults(target, defaultQuery);

    return new Observable<DataQueryResponse>(subscriber => {
      const frame = new CircularDataFrame({
        append: 'tail',
        capacity: 1000,
      });

      frame.refId = query.refId;
      frame.addField({ name: 'time', type: FieldType.time });
      frame.addField({ name: 'value', type: FieldType.number });

      const intervalId = setInterval(() => {
        frame.add({ time: Date.now(), value: Math.random() });

        subscriber.next({
          data: [frame],
          key: query.refId,
          state: LoadingState.Streaming,
        });
      }, 100);

      return () => {
        clearInterval(intervalId);
      };
    });
  });

  return merge(...streams);
}

One limitation with this example is that the panel visualization is cleared every time you update the dashboard. If you have access to historical data, you can add it, or backfill it, to the data frame before the first call to subscriber.next().

For another example of a streaming plugin, refer to the streaming websocket example on GitHub.