Get Started with Atlas Stream Processing

In Atlas, go to the Stream Processing page for your project.

WARNING: Navigation Improvements In Progress

We're currently rolling out a new and improved navigation experience. If the following steps don't match your view in the Atlas UI, see the preview documentation.

1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

3. In the sidebar, click Stream Processing under the Services heading.

   The Stream Processing page displays.

Create a Stream Processing Instance.

1. Click Get Started in the lower-right corner. Atlas provides a brief explanation of core Atlas Stream Processing components.

2. Click the Create instance button.

3. On the Create a stream processing instance page, configure your instance as follows:

   • Tier: SP30

   • Provider: AWS

   • Region: us-east-1

   • Instance Name: tutorialInstance

4. Click Create.

Get the stream processing instance connection string.

1. Locate the overview panel of your stream processing instance and click Connect.

2. Select I have the MongoDB shell installed.

3. From the Select your mongo shell version dropdown menu, select the latest version of mongosh.

4. Copy the connection string provided under Run your connection string in your command line. You will need this in a later step.

5. Click Close.

Add a MongoDB Atlas connection to the connection registry.

This connection serves as our streaming data sink.

1. In the pane for your stream processing instance, click Configure.

2. In the Connection Registry tab, click + Add Connection in the upper right.

3. Click Atlas Database. In the Connection Name field, enter mongodb1. From the Atlas Cluster drop down, select an Atlas cluster without any data stored on it.

4. Click Add connection.

Verify that your streaming data source emits messages.

Your stream processing instance comes preconfigured with a connection to a sample data source called sample_stream_solar. This source generates a stream of reports from various solar power devices. Each report describes the observed wattage and temperature of a single solar device at a specific point in time, as well as that device's maximum wattage.

The following document is a representative example.

{
  device_id: 'device_8',
  group_id: 7,
  timestamp: '2024-08-12T21:41:01.788+00:00',
  max_watts: 450,
  event_type: 0,
  obs: {
    watts: 252,
    temp: 17
  }
}

To verify that this source emits messages, create a stream processor interactively.

1. Open a terminal application of your choice.

2. Connect to your stream processing instance with mongosh.

   Paste the mongosh connection string that you copied in a previous step into your terminal, where <atlas-stream-processing-url> is the URL of your stream processing instance and <username> is a user with the atlasAdmin role.

   mongosh "mongodb://<atlas-stream-processing-url>/"
   --tls --authenticationDatabase admin --username <username>

   Enter your password when prompted.

3. Create the stream processor.

   Copy the following code into your mongosh prompt:

   sp.process([{"$source": {
      "connectionName": "sample_stream_solar"
   }}])

   Verify that data from the sample_stream_solar connection displays to the console, and terminate the process.

   Stream processors you create with sp.process() don't persist after you terminate them.

Create a persistent stream processor.

Using an aggregation pipeline, you can transform each document as it is ingested. The following aggregation pipeline derives the maximum temperature and the average, median, maximum, and minimum wattages of each solar device at one-second intervals.

1. Configure a $source stage.

   The following $source stage ingests data from the sample_stream_solar source.

   let s = {
      $source: {
         connectionName: "sample_stream_solar"
      }
   }

2. Configure a $group stage.

   The following $group stage organizes all incoming data according to their group_id, accumulates the values of the obs.temp and obs.watts fields of all documents for each group_id, then derives the desired data.

   let g = {
      $group: {
         _id: "$group_id",
         max_temp: {
            $avg: "$obs.temp"
         },
         avg_watts: {
            $min: "$obs.watts"
         },
         median_watts: {
            $min: "$obs.watts"
         },
         max_watts: {
            $max: "$obs.watts"
         },
         min_watts: {
            $min: "$obs.watts"
         }
      }
   }

3. Configure a $tumblingWindow stage.

   In order to perform accumulations such as $group on streaming data, Atlas Stream Processing uses windows to bound the data set. The following $tumblingWindow stage separates the stream into consecutive 10-second intervals.

   This means, for example, that when the $group stage computes a value for median_watts, it takes the obs.watts values for all documents with a given group_id ingested in the previous 10 seconds.

    let t = {
    $tumblingWindow: {
      interval: {
        size: NumberInt(10),
        unit: "second"
      },
       pipeline: [g]
     }
   }

4. Configure a $merge stage.

   $merge allows you to write your processed streaming data to an Atlas database.

   let m = {
      $merge: {
         into: {
            connectionName: "mongodb1",
            db: "solarDb",
            coll: "solarColl"
         }
      }
   }

5. Create the stream processor.

   Assign a name to your new stream processor, and declare its aggregation pipeline by listing each stage in order. The $group stage belongs to the nested pipeline of the $tumblingWindow, and you must not include it in the processor pipeline definition.

   sp.createStreamProcessor("solarDemo", [s, t, m])

This creates a stream processor named solarDemo that applies the previously defined query and writes the processed data to the solarColl collection of the solarDb database on the cluster you connected to. It returns various measurements derived from 10-second intervals of observations from your solar devices.

To learn more about how Atlas Stream Processing writes to at-rest databases, see $merge (Stream Processing).

Start the stream processor.

Run the following command in mongosh:

sp.solarDemo.start()

Verify the output of the stream processor.

To verify that the processor is active, run the following command in mongosh:

sp.solarDemo.stats()

This command reports operational statistics of the solarDemo stream processor.

To verify that the stream processor is writing data to your Atlas cluster:

1. In Atlas, go to the Clusters page for your project.

   WARNING: Navigation Improvements In Progress

   We're currently rolling out a new and improved navigation experience. If the following steps don't match your view in the Atlas UI, see the preview documentation.

   1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

   2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

   3. If it's not already displayed, click Clusters in the sidebar.

      The Clusters page displays.

2. Click the Browse Collections button for your cluster.

   The Data Explorer displays.

3. View the MySolar collection.

Alternatively, you can display a sampling of processed documents in the terminal using mongosh:

sp.solarDemo.sample()

{
  _id: 10,
  max_watts: 136,
  min_watts: 130,
  avg_watts: 133,
  median_watts: 130,
  max_temp: 7
}

Drop the stream processor.

Run the following command in mongosh:

sp.solarDemo.drop()

To confirm that you have dropped avgWatts, list all your available stream processors:

sp.listStreamProcessors()