Description: This tutorial is an introduction to the FIWARE Cosmos Orion Flink Connector, which facilitates Big Data analysis of context data, through an integration with Apache Flink, one of the most popular Big Data platforms. Apache Flink is a framework and distributed processing engine for stateful computations both over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.

The tutorial uses cUrl commands throughout, but is also available as Postman documentation

Real-time Processing and Big Data Analysis

"Who controls the past controls the future: who controls the present controls the past."

— George Orwell. "1984"

Smart solutions based on FIWARE are architecturally designed around microservices. They are therefore are designed to scale-up from simple applications (such as the Supermarket tutorial) through to city-wide installations base on a large array of IoT sensors and other context data providers.

The massive amount of data involved eventually becomes too much for a single machine to analyse, process and store, and therefore the work must be delegated to additional distributed services. These distributed systems form the basis of so-called Big Data Analysis. The distribution of tasks allows developers to be able to extract insights from huge data sets which would be too complex to be dealt with using traditional methods. and uncover hidden patterns and correlations.

As we have seen, context data is core to any Smart Solution, and the Context Broker is able to monitor changes of state and raise subscription events as the context changes. For smaller installations, each subscription event can be processed one-by-one by a single receiving endpoint, however as the system grows, another technique will be required to avoid overwhelming the listener, potentially blocking resources and missing updates.

Apache Flink is a Java/Scala based stream-processing framework which enables the delegation of data-flow processes. Therefore, additional computational resources can be called upon to deal with data as events arrive. The Cosmos Flink connector allows developers write custom business logic to listen for context data subscription events and then process the flow of the context data. Flink is able to delegate these actions to other workers where they will be acted upon either in sequentially or in parallel as required. The data flow processing itself can be arbitrarily complex.

Obviously, in reality, our existing Supermarket scenario is far too small to require the use of a Big Data solution, but will serve as a basis for demonstrating the type of real-time processing which may be required in a larger solution which is processing a continuous stream of context-data events.

Architecture

This application builds on the components and dummy IoT devices created in previous tutorials. It will make use of three FIWARE components - the Orion Context Broker, the IoT Agent for Ultralight 2.0, and the Cosmos Orion Flink Connector for connecting Orion to an Apache Flink cluster. The Flink cluster itself will consist of a single JobManager master to coordinate execution and a single TaskManager worker to execute the tasks.

Both the Orion Context Broker and the IoT Agent rely on open source MongoDB technology to keep persistence of the information they hold. We will also be using the dummy IoT devices created in the previous tutorial.

Therefore, the overall architecture will consist of the following elements:

Two FIWARE Generic Enablers as independent microservices:
- The Orion Context Broker which will receive requests using NGSI-LD.
- The FIWARE IoT Agent for UltraLight 2.0 which will receive southbound requests using NGSI-LD and convert them to UltraLight 2.0 commands for the devices.
An Apache Flink cluster consisting of a single JobManager and a single TaskManager:
- The FIWARE Cosmos Orion Flink Connector will be deployed as part of the dataflow which will subscribe to context changes and make operations on them in real-time.
One MongoDB database:
- Used by the Orion Context Broker to hold context data information such as data entities, subscriptions and registrations.
- Used by the IoT Agent to hold device information such as device URLs and Keys.
An HTTP Web-Server which offers static @context files defining the context entities within the system.
The Tutorial Application does the following:
- Acts as set of dummy agricultural IoT devices using the UltraLight 2.0 protocol running over HTTP.

The overall architecture can be seen below:

Since all interactions between the elements are initiated by HTTP requests, the entities can be containerized and run from exposed ports.

The configuration information of the Apache Flink cluster can be seen in the jobmanager and taskmanager sections of the associated docker-compose.yml file:

Flink Cluster Configuration

jobmanager:
    image: flink:1.9.0-scala_2.11
    hostname: jobmanager
    container_name: flink-jobmanager
    expose:
        - "8081"
        - "9001"
    ports:
        - "6123:6123"
        - "8081:8081"
    command: jobmanager
    environment:
        - JOB_MANAGER_RPC_ADDRESS=jobmanager

taskmanager:
    image: flink:1.9.0-scala_2.11
    hostname: taskmanager
    container_name: flink-taskmanager
    ports:
        - "6121:6121"
        - "6122:6122"
        - "9001:9001"
    depends_on:
        - jobmanager
    command: taskmanager
    links:
        - "jobmanager:jobmanager"
    environment:
        - JOB_MANAGER_RPC_ADDRESS=jobmanager

The jobmanager container is listening on three ports:

Port 8081 is exposed therefore we can see the web frontend of the Apache Flink Dashboard.
Port 6123 is the standard JobManager RPC port, used for internal communications.

The taskmanager container is listening on two ports:

Ports 6121 and 6122 are used and RPC ports by the TaskManager, used for internal communications.
Port 9001 is exposed so that the installation can receive context data subscriptions.

The containers within the flink cluster are driven by a single environment variable as shown:

Key	Value	Description
JOB_MANAGER_RPC_ADDRESS	`jobmanager`	URL of the master Job Manager which coordinates the task processing

Start Up

Before you start, you should ensure that you have obtained or built the necessary Docker images locally. Please clone the repository and create the necessary images by running the commands shown below. Note that you might need to run some commands as a privileged user:

#!/bin/bash
git clone https://github.com/FIWARE/tutorials.Big-Data-Flink.git
cd tutorials.Big-Data-Flink
git checkout NGSI-LD
./services create

This command will also import seed data from the previous tutorials and provision the dummy IoT sensors on startup.

To start the system with your preferred context broker, run the following command:

./services [orion|scorpio|stellio]

Note: If you want to clean up and start over again you can do so with the following command:

./services stop

Real-time Processing Operations

Dataflow within Apache Flink is defined within the Flink documentation as follows:

"The basic building blocks of Flink programs are streams and transformations. Conceptually a stream is a (potentially never-ending) flow of data records, and a transformation is an operation that takes one or more streams as input, and produces one or more output streams as a result.

When executed, Flink programs are mapped to streaming dataflows, consisting of streams and transformation operators. Each dataflow starts with one or more sources and ends in one or more sinks. The dataflows resemble arbitrary directed acyclic graphs (DAGs). Although special forms of cycles are permitted via iteration constructs, for the most part this can be glossed over this for simplicity."

This means that to create a streaming data flow we must supply the following:

A mechanism for reading Context data as a Source Operator.
Business logic to define the transform operations.
A mechanism for pushing Context data back to the context broker as a Sink Operator.

The orion.flink.connector-1.2.4.jar offers both Source and Sink operations. It therefore only remains to write the necessary Scala code to connect the streaming dataflow pipeline operations together. The processing code can be complied into a JAR file which can be uploaded to the flink cluster. Two examples will be detailed below, all the source code for this tutorial can be found within the cosmos-examples directory.

Further Flink processing examples can be found on the Apache Flink site and Flink Connector Examples.

Compiling a JAR file for Flink

An existing pom.xml file has been created which holds the necessary prerequisites to build the examples JAR file.

In order to use the Orion Flink Connector we first need to manually install the connector JAR as an artifact using Maven:

cd cosmos-examples
curl -LO https://github.com/ging/fiware-cosmos-orion-flink-connector/releases/download/1.2.4/orion.flink.connector-1.2.4.jar
mvn install:install-file \
  -Dfile=./orion.flink.connector-1.2.4.jar \
  -DgroupId=org.fiware.cosmos \
  -DartifactId=orion.flink.connector \
  -Dversion=1.2.4 \
  -Dpackaging=jar

Thereafter, the source code can be compiled by running the mvn package command within the same directory (cosmos-examples):

mvn package

A new JAR file called cosmos-examples-1.2.jar will be created within the cosmos-examples/target directory.

Generating a stream of Context Data

For the purpose of this tutorial, we must be monitoring a system in which the context is periodically being updated. The dummy IoT Sensors can be used to do this. Open the device monitor page at http://localhost:3000/device/monitor and start a tractor moving. This can be done by selecting an appropriate the command from the drop-down list and pressing the send button. The stream of measurements coming from the devices can then be seen on the same page:

Logger - Reading Context Data Streams

The first example makes use of the NGSILDSource operator in order to receive notifications from the Orion-LD Context Broker. Specifically, the example counts the number notifications that each type of device sends in one minute. You can find the source code of the example in org/fiware/cosmos/tutorial/LoggerLD.scala.

Logger - Installing the JAR

Open the browser and access http://localhost:8081/#/submit:

Submit new job:

Filename: cosmos-examples-1.2.jar.
Entry Class: org.fiware.cosmos.tutorial.LoggerLD.

An alternative would be to use curl on the command-line as shown:

curl -X POST -H "Expect:" -F "jarfile=@/cosmos-examples-1.2.jar" http://localhost:8081/jars/upload

Logger - Subscribing to context changes

Once a dynamic context system is up and running (we have deployed the Logger job in the Flink cluster), we need to inform Flink of changes in context.

This is done by making a POST request to the /ngsi-ld/v1/subscriptions endpoint of the Orion Context Broker:

The NGSILD-Tenant header is used to filter the subscription to only listen to measurements from the attached IoT Sensors, since they had been provisioned using these settings.
The notification uri must match the one our Flink program is listening to.
The throttling value defines the rate that changes are sampled.

1 Request:

curl -L -X POST 'http://localhost:1026/ngsi-ld/v1/subscriptions/' \
-H 'Content-Type: application/ld+json' \
-H 'NGSILD-Tenant: openiot' \
--data-raw '{
  "description": "Notify Flink of all animal and farm vehicle movements",
  "type": "Subscription",
  "entities": [{"type": "Tractor"}, {"type": "Device"}],
  "watchedAttributes": ["location"],
  "notification": {
    "attributes": ["location"],
    "format": "normalized",
    "endpoint": {
      "uri": "http://taskmanager:9001",
      "accept": "application/json"
    }
  },
   "@context": "http://context/user-context.jsonld"
}'

The response will be **201 - Created**.

If a subscription has been created, we can check to see if it is firing by making a GET request to the /ngsi-ld/v1/subscriptions/ endpoint.

2 Request:

curl -X GET \
'http://localhost:1026/ngsi-ld/v1/subscriptions/' \
-H 'Link: <http://context/user-context.jsonld>; rel="http://www.w3.org/ns/json-ld#context"; type="application/ld+json"' \
-H 'NGSILD-Tenant: openiot'

Response:

Tip: Use jq to format the JSON responses in this tutorial. Pipe the result by appending

| jq '.'

[
    {
        "id": "urn:ngsi-ld:Subscription:60216f404dae3a1f22b705e6",
        "type": "Subscription",
        "description": "Notify Flink of all animal and farm vehicle movements",
        "entities": [{ "type": "Tractor" }, { "type": "Device" }],
        "watchedAttributes": ["location"],
        "notification": {
            "attributes": ["location"],
            "format": "normalized",
            "endpoint": {
                "uri": "http://taskmanager:9001",
                "accept": "application/json"
            },
            "timesSent": 74,
            "lastNotification": "2021-02-08T17:06:06.043Z"
        }
    }
]

Within the notification section of the response, you can see several additional attributes which describe the health of the subscription.

If the criteria of the subscription have been met, timesSent should be greater than 0. A zero value would indicate that the subject of the subscription is incorrect or the subscription has created with the wrong fiware-service-path or fiware-service header.

The lastNotification should be a recent timestamp - if this is not the case, then the devices are not regularly sending data. Remember to activate the smart farm by moving a Tractor.

The lastSuccess should match the lastNotification date - if this is not the case then Cosmos is not receiving the subscription properly. Check that the hostname and port are correct.

Finally, check that the status of the subscription is active - an expired subscription will not fire.

Logger - Checking the Output

Leave the subscription running for one minute, then run the following:

docker logs flink-taskmanager -f --until=60s > stdout.log 2>stderr.log
cat stderr.log

After creating the subscription, the output on the console will be like the following:

Sensor(Tractor,19)
Sensor(Device,49)

Logger - Analyzing the Code

package org.fiware.cosmos.tutorial


import org.apache.flink.streaming.api.scala.{StreamExecutionEnvironment, _}
import org.apache.flink.streaming.api.windowing.time.Time
import org.fiware.cosmos.orion.flink.connector.{NGSILDSource}

object LoggerLD {

  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    // Create Orion Source. Receive notifications on port 9001
    val eventStream = env.addSource(new NGSILDSource(9001))

    // Process event stream
    val processedDataStream = eventStream
      .flatMap(event => event.entities)
      .map(entity => new Sensor(entity.`type`, 1))
      .keyBy("device")
      .timeWindow(Time.seconds(60))
      .sum(1)

    // print the results with a single thread, rather than in parallel
    processedDataStream.printToErr().setParallelism(1)
    env.execute("Socket Window NgsiLDEvent")
  }
}

case class Sensor(device: String, sum: Int)

The first lines of the program are aimed at importing the necessary dependencies, including the connector. The next step is to create an instance of the NGSILDSource using the class provided by the connector and to add it to the environment provided by Flink.

The NGSILDSource constructor accepts a port number (9001) as a parameter. This port is used to listen to the subscription notifications coming from the context broker and converted to a DataStream of NgsiEventLD objects. The definition of these objects can be found within the Orion-Flink Connector documentation.

The stream processing consists of five separate steps. The first step (flatMap()) is performed in order to put together the entity objects of all the NGSI-LD Events received in a period of time. Thereafter, the code iterates over them (with the map() operation) and extracts the desired attributes. In this case, we are interested in the entity type (Device or Tractor).

Within each iteration, we create a custom object with the properties we need: the sensor type and the increment of each notification. For this purpose, we can define a case class as shown:

case class Sensor(device: String, sum: Int)

Therefter, can group the created objects by the type of device (keyBy("device")) and perform operations such as timeWindow() and sum() on them.

After the processing, the results are output to the console:

processedDataStream.print().setParallelism(1)

Feedback Loop - Persisting Context Data

The second example turns on a water faucet when the soil humidity is too low and turns it back off it when the soil humidity it is back to normal levels. This way, the soil humidity is always kept at an adequate level.

The dataflow stream uses the NGSILDSource operator in order to receive notifications and filters the input to only respond to motion sensors and then uses the NGSILDSink to push processed context back to the Context Broker. You can find the source code of the example in org/fiware/cosmos/tutorial/FeedbackLD.scala.

Feedback Loop - Installing the JAR

Goto http://localhost:8081/#/job/running:

Select the running job (if any) and click on Cancel Job.

Thereafter, goto http://localhost:8081/#/submit:

Submit new job:

Filename: cosmos-examples-1.2.jar.
Entry Class: org.fiware.cosmos.tutorial.FeedbackLD.

Feedback Loop - Subscribing to context changes

A new subscription needs to be set up to run this example. The subscription is listening to changes of context on the soil humidity sensor.

3 Request:

curl -L -X POST 'http://localhost:1026/ngsi-ld/v1/subscriptions/' \
-H 'Content-Type: application/ld+json' \
-H 'NGSILD-Tenant: openiot' \
--data-raw '{
  "description": "Notify Flink of changes of Soil Humidity",
  "type": "Subscription",
  "entities": [{"type": "SoilSensor"}],
  "watchedAttributes": ["humidity"],
  "notification": {
    "attributes": ["humidity"],
    "format": "normalized",
    "endpoint": {
      "uri": "http://flink-taskmanager:9001",
      "accept": "application/json"
    }
  },
   "@context": "http://context/user-context.jsonld"
}'

If a subscription has been created, we can check to see if it is firing by making a GET request to the /ngsi-ld/v1/subscriptions/ endpoint.

4 Request:

curl -X GET \
'http://localhost:1026/ngsi-ld/v1/subscriptions/' \
-H 'NGSILD-Tenant: openiot'

Feedback Loop - Checking the Output

Go to http://localhost:3000/device/monitor

Raise the temperature in Farm001 and wait until the humidity value is below 35, then the water faucet will be automatically turned on to increase the soil humidity. When the humidity rises above 50, the water faucet will be turned off automatically as well.

Feedback Loop - Analyzing the Code

package org.fiware.cosmos.tutorial


import org.apache.flink.streaming.api.scala.{StreamExecutionEnvironment, _}
import org.fiware.cosmos.orion.flink.connector._

object FeedbackLD {
  final val CONTENT_TYPE = ContentType.JSON
  final val METHOD = HTTPMethod.PATCH
  final val CONTENT = "{\n  \"type\" : \"Property\",\n  \"value\" : \" \" \n}"
  final val HEADERS = Map(
    "NGSILD-Tenant" -> "openiot",
    "Link" -> "<http://context/user-context.jsonld>; rel=\"http://www.w3.org/ns/json-ld#context\"; type=\"application/ld+json\""
  )
  final val LOW_THRESHOLD = 35
  final val HIGH_THRESHOLD = 50

  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    // Create Orion Source. Receive notifications on port 9001
    val eventStream = env.addSource(new NGSILDSource(9001))
    // Process event stream
    val processedDataStream = eventStream.flatMap(event => event.entities)
      .filter(ent => ent.`type` == "SoilSensor")

    /* High humidity */

    val highHumidity = processedDataStream
      .filter(ent =>  (ent.attrs("humidity") != null) && (ent.attrs("humidity")("value").asInstanceOf[BigInt] > HIGH_THRESHOLD))
      .map(ent => (ent.id,ent.attrs("humidity")("value")))

    val highSinkStream= highHumidity.map(sensor => {
      OrionSinkObject(CONTENT,"http://orion:1026/ngsi-ld/v1/entities/urn:ngsi-ld:Device:water"+sensor._1.takeRight(3)+"/attrs/off",CONTENT_TYPE,METHOD,HEADERS)
    })

    highHumidity.map(sensor => "Sensor" + sensor._1 + " has detected a humidity level above " + HIGH_THRESHOLD + ". Turning off water faucet!").print()
    OrionSink.addSink( highSinkStream )


    /* Low humidity */
    val lowHumidity = processedDataStream
      .filter(ent => (ent.attrs("humidity") != null) && (ent.attrs("humidity")("value").asInstanceOf[BigInt] < LOW_THRESHOLD))
      .map(ent => (ent.id,ent.attrs("humidity")("value")))

    val lowSinkStream= lowHumidity.map(sensor => {
      OrionSinkObject(CONTENT,"http://orion:1026/ngsi-ld/v1/entities/urn:ngsi-ld:Device:water"+sensor._1.takeRight(3)+"/attrs/on",CONTENT_TYPE,METHOD,HEADERS)
    })

    lowHumidity.map(sensor => "Sensor" + sensor._1 + " has detected a humidity level below " + LOW_THRESHOLD + ". Turning on water faucet!").print()
    OrionSink.addSink( lowSinkStream )

    env.execute("Socket Window NgsiEvent")
  }

  case class Sensor(id: String)
}

As you can see, it is similar to the previous example. The main difference is that it writes the processed data back in the Context Broker through the OrionSink.

The arguments of the OrionSinkObject are:

Message: "{\n \"type\" : \"Property\",\n \"value\" : \" \" \n}".
URL: "http://orion:1026/ngsi-ld/v1/entities/urn:ngsi-ld:Device:water"+sensor._1.takeRight(3)+"/attrs/on" or "http://orion:1026/ngsi-ld/v1/entities/urn:ngsi-ld:Device:water"+sensor._1.takeRight(3)+"/attrs/off", depending on whether we are turning on or off the water faucet. TakeRight(3) gets the number of the sensor, for example '001'.
Content Type: ContentType.JSON.
HTTP Method: HTTPMethod.PATCH.
Headers: Map("NGSILD-Tenant" -> "openiot", "Link" -> "<http://context/user-context.jsonld>; rel=\"http://www.w3.org/ns/json-ld#context\"; type=\"application/ld+json\"" ). We add the headers we need in the HTTP Request.