Introduction: IoT Data Analytics With Apache Spark and Thingsboard

Picture of IoT Data Analytics With Apache Spark and Thingsboard

Thingsboard is an open-source server-side platform that allows you to monitor and control IoT devices. It is free for both personal and commercial usage and you can deploy it anywhere. If this is your first experience with the platform I recommend to review what-is-thingsboard page and getting-started guide.

Thingsboard rule engine supports basic analysis of incoming telemetry data, for example, threshold crossing. The idea behind rule engine is to provide functionality to route data from IoT Devices to different plugins, based on device attributes or the data itself.

However, most of the real-life use cases also require support of advanced analytics: machine learning, predictive analytics, etc.

This tutorial will demonstrate how you can:

  • route telemetry device data from Thingsboard to Kafka topic using built-in plugin.
  • aggregate data from multiple devices using simple Apache Spark application.
  • push results of the analytics back to Thingsboard for persistence and visualization.

Of course, analytics in this tutorial is quite simple, our goal is to highlight the integration steps.

Overview

Let’s assume we have big amount of weather stations that are located in different geo-location zones. Thingsboard is used to collect, store and visualize wind speed from this stations, but we are also interested in average wind speed in each geo-location zone. Once again, this is completely fake scenario just to demonstrate the integration of all components.

In this scenario we are going to upload wind speed as a telemetry reading, however, the geo-location zone will be a static attribute of the weather station device. This is logical, since telemetry readings are going to change often, and the geo-location is static.

We will analyze real-time data from multiple devices using Spark Streaming job with 10 seconds batch window.

In order to store and visualize results of the analytics we are going to create one virtual device for each geo-location zone. This is possible using special Thingsboard MQTT Gateway API. This API allows to efficiently stream data from multiple devices using single MQTT session. So, in our case, the Spark Job itself acts as a gateway that publish data on behalf of several virtual devices. Let’s name this gateway as an Analytics Gateway.

Step 1: Prerequisites

We assume you have Thingsboard instance is up and running. We also assume you are familiar with Kafka and Spark and have also prepared those environments for this tutorial.

Step 2: Configuration of Thingsboard Kafka Plugin

Picture of Configuration of Thingsboard Kafka Plugin

We need to configure Kafka Plugin that will be used to push telemetry data to Kafka. You can find detail description of Kafka Plugin here.

Download the json with plugin descriptor and use this instructions to import it to your instance.

Please note that the plugin configuration expects Kafka to be running on the localhost with port 9092.

Don’t forget to activate your new plugin instance by clicking on corresponding button in plugin details!

Step 3: Configuration of Telemetry Forwarding Rule

Picture of Configuration of Telemetry Forwarding Rule

Now we need to configure the Rule that will be used to push wind speed data from the weather stations to Kafka.

Download the json with plugin descriptor and use this instructions to import it to your instance.

Don’t forget to activate your new rule instance by clicking on corresponding button in rule details!

Let’s review main rule configuration below.

Attributes filter

Thingsboard may process data from completely different devices. We will use filter by device attributes in order to filter out data that belongs to Weather Station devices.
The filter expression in the first screen-shot above validates that two attributes are set for particular device: deviceType and geoZone. You may notice that we check that deviceType is equal to “WeatherStation”. The cs variable is a map that contains all client-side attributes. See corresponding filter documentation for more details.

Timeseries data filter

Each device connected to Thingsboard may upload multiple telemetry keys simultaneously on independently. In some use cases you may need to process only certain sub-set of the data. We will use telemetry data filter to achieve this.
The filter expression in the second screen-shot above validates that windSpeed telemetry is present in the processed message.

See corresponding filter documentation for more details.

Kafka plugin action

Topic name in our case is ‘weather-stations-data’ and the message is a valid JSON that uses client-side attribute geoZone and windSpeed telemetry value (see third screen-shot above).

Step 4: Configuration of the Analytics Gateway Device

Picture of Configuration of the Analytics Gateway Device

Let’s create device that we define ‘Analytics Gateway’ and we’ll send average temperature from Spark Application to this device (see first screen-shot above).

Once added, open the ‘Analytics Gateway’ device card and click on copy ‘Access Token’ from this device and store it somewhere (see second screen-shot above).

We’ll use this token later in Spark Application for sending analytics results back to Thingsboard and will refer to it as $GATEWAY_ACCESS_TOKEN.

Step 5: Download the Sample Spark Application Source Code

Feel free to grab the code from this sample Thingsboard repository and follow along.

Step 6: Dependencies Review

Sample application was developed using Spark version 2.1.0. Please consider this if you’ll use different version of Spark because in this case you may need to use different version of Kafka Streaming API.

Dependencies that are used in sample project:

<!-- Spark, Spark Streaming and Kafka Dependencies -->

<dependency>

<groupId>org.apache.spark</groupId>

<artifactId>spark-core_2.11</artifactId>

<version></version>

</dependency>

<dependency>

<groupId>org.apache.spark</groupId>

<artifactId>spark-streaming_2.11</artifactId>

<version>${spark-version}</version>

</dependency>

<dependency>

<groupId>org.apache.spark</groupId>

<artifactId>spark-streaming-kafka-0-10_2.11</artifactId>

<version>${spark-version}</version>

</dependency>

<!-- MQTT client dependency to send messages to Thingsboard -->

<dependency>

<groupId>org.eclipse.paho</groupId>

<artifactId>org.eclipse.paho.client.mqttv3</artifactId>

<version>${paho.client.version}</version>

</dependency>

Step 7: Source Code Review

Here is a description of particular code snippet from SparkKafkaStreamingDemoMain class. Main constants are listed below:

// Access token for 'Analytics Gateway' Device.
private static final String GATEWAY_ACCESS_TOKEN = "$GATEWAY_ACCESS_TOKEN"; // Kafka brokers URL for Spark Streaming to connect and fetched messages from. private static final String KAFKA_BROKER_LIST = "localhost:9092"; // URL of Thingsboard MQTT endpoint private static final String THINGSBOARD_MQTT_ENDPOINT = "tcp://localhost:1883"; // Time interval in milliseconds of Spark Streaming Job, 10 seconds by default. private static final int STREAM_WINDOW_MILLISECONDS = 10000; // 10 seconds // Kafka telemetry topic to subscribe to. This should match to the topic in the rule action. private static final Collection TOPICS = Arrays.asList("weather-stations-data");

Main processing logic is listed below:

try (JavaStreamingContext ssc = new JavaStreamingContext(conf, new Duration(STREAM_WINDOW_MILLISECONDS))) {
connectToThingsboard(); JavaInputDStream> stream = KafkaUtils.createDirectStream( ssc, LocationStrategies.PreferConsistent(), ConsumerStrategies.Subscribe(TOPICS, getKafkaParams()) ); stream.foreachRDD(rdd -> { // Map incoming JSON to WindSpeedData objects JavaRDD windRdd = rdd.map(new WeatherStationDataMapper()); // Map WindSpeedData objects by GeoZone JavaPairRDD windByZoneRdd = windRdd.mapToPair(d -> new Tuple2<>(d.getGeoZone(), new AvgWindSpeedData(d.getWindSpeed()))); // Reduce all data volume by GeoZone key windByZoneRdd = windByZoneRdd.reduceByKey((a, b) -> AvgWindSpeedData.sum(a, b)); // Map back to WindSpeedData List aggData = windByZoneRdd.map(t -> new WindSpeedData(t._1, t._2.getAvgValue())).collect(); // Push aggregated data to Thingsboard using Gateway MQTT API publishTelemetryToThingsboard(aggData); }); ssc.start(); ssc.awaitTermination(); }

The following method is responsbile for publishing the telemetry data:

private void publishTelemetryToThingsboard(List aggData) throws Exception {
    if (!aggData.isEmpty()) {
        for (WindSpeedData d : aggData) {
            MqttMessage connectMsg = new MqttMessage(toConnectJson(d.getGeoZone()).getBytes(StandardCharsets.UTF_8));
            client.publish("v1/gateway/connect", connectMsg, null, getCallback());
        }
        MqttMessage dataMsg = new MqttMessage(toDataJson(aggData).getBytes(StandardCharsets.UTF_8));
        client.publish("v1/gateway/telemetry", dataMsg, null, getCallback());
    }
}

Now let’s run SparkKafkaStreamingDemoMain class from the IDE or submit it to Spark cluster. Sample app will be fetching all the messages from Kafka topic and send average temperature telemetry to appropriate ‘Average Temperature Device’ in Thingsboard.

Step 8: Dry Run

Picture of Dry Run

Once Kafka Plugin is configured, ‘Analytics Gateway device’ is provisioned and Spark Streaming Application is running please start sending windSpeed telemetry from different devices.

The following command will provision deviceType and geoZone attributes. You may change zone to different values for different devices.

mosquitto_pub -d -h "localhost" -p 1883 -t "v1/devices/me/attributes" -u "$YOUR_DEVICE_ACCESS_TOKEN" -m '{"deviceType":"WeatherStation", "geoZone":"Zone A"}'

The following command will report windSpeed telemetry for particular device.

mosquitto_pub -d -h "localhost" -p 1883 -t "v1/devices/me/telemetry" -u "$YOUR_DEVICE_ACCESS_TOKEN" -m '{"windSpeed":42}'

Once you sent the telemetry data to Thingsboard, wait up to 10 seconds for new device that identifies your geoZone to become available.

Comments

MiretteH (author)2017-04-10

it didn't work for me !! any help

Swansong (author)2017-03-29

Thanks for sharing :)

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