Websockets-Vertx-Kafka-Flink

A simple request response cycle using Websockets, Eclipse Vert-x server, Apache Kafka, Apache Flink.

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• An incoming request gets routed to a non blocking Vertx server which then writes the request to a specific Kafka topic.
• A Flink consumer implemented as another side project consumes the messages from the given request topic
• (Optional) Flink job hits a Rest API hosted on a Spring boot server. You can use Jax-Rs or even hardcode the response
• Flink writes the API result to another topic. Every message has a unique sender id. Flink sends the response with the same
• Finally the Vertx Kafka consumer listens for responses from the response topic and sends an event to a websocket handler
• Websocket consumer for a specific id writes the response to its own socket thus completing the entire async request cycle

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Prerequisites

• Java 1.8
• Apache Kafka 2.0.0
• Apache Zookeeper 3.4.8
• Eclipse Vertx 3.5.3
• Apache Flink 1.6.0

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Setting up Apache Kafka

  # Start Zookeeper instance 
  $ zookeeper-server-start.bat ..\..\config\zookeeper.properties
  
  # Start Kafka server
  $ kafka-server-start.bat ..\..\config\server.properties
  
  # Create a request topic
  $ kafka-topics.bat --create --zookeeper localhost:2181 --replication-factor 1 --partitions 3 --topic flink-demo

  # Create a response queue
  $ kafka-topics.bat --create --zookeeper localhost:2181 --replication-factor 1 --partitions 3 --topic flink-demo-resp
  
  # Verify the consumer of request queue flink-demo
  $ kafka-console-consumer.bat --bootstrap-server localhost:9092 --from-beginning --topic flink-demo

  # Verify the consumer of response queue flink-demo-resp
  $ kafka-console-consumer.bat --bootstrap-server localhost:9092 --from-beginning --topic flink-demo-resp
  

Make sure following is appended to config\server.properties

port = 9092
advertised.host.name = localhost 

Note: Replace .bat files with .sh files when working in a UNIX environment.

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What you do in the Flink Job depends on the use case. Options are

• Make async rest API call
• Interact with a database using an async clients
• Return a mock response

Caveats

• Here, we are making a request using the AsyncHTTP Client to an endpoint hosted on a Spring Boot Server
• The rest API Server is listening on port 9004
• You are free to experiment in this department.
• If you choose to continue using the Rest API given in this project, make sure you have an endpoint implementation.

Setting up the project

• Run the kafka-flink connector project that waits for incoming data stream from kafka queue "flink_resp"
• Run the ws-vertx project that invokes an event on the event bus which writes a sample API request to the topic.
• Verify that the message is written correctly on the topic "flink-demo"
• Flink Kafka connector consumes the message, serializes it, transforms the data stream into a response stream
• Flink job now writes the response back to the response topic "flink-demo-resp"

Testing the web socket flow

• Incuded within the vertx flow is a client socket verticle that emulates a single web socket request
• It is fired as soon as the server verticle is deployed. [Optional] Look for the following

  # Uncomment the below line for local UI testing: It creates a websocket request to the given server
 //vertx.deployVerticle(new ClientSocketRequestVerticle());

• You can however choose to send websocket requests from a client manually. Use the following

   # Use the following websocket URL
   ws://127.0.0.1:9443/wsapi/register

   # Once the socket opens, begin sending messages in the correct format
   {
	    "email": "your email",
	    "password": "your password ",
	    "registerAsAdmin": true
   }

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Websockets

• Websocket for communication between app & backend
• Async messages, non-blocking communication layer
• Full duplex communication channels over single TCP

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Vert-x

• A toolkit ecosystem, to build reactive application on JVM
• Vert-x library helps implement non-blocking asynchronous event bus implementation.
• Helps manage Websocket queue

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Kafka

• Distributed streaming platform.
• Kafka provides a fully integrated Streams API to allow an application to act as a stream processor, consuming an input stream from one or more topics and producing an output stream to one or more output topics, effectively transforming the input streams to output streams.
• Handles out-of-order data.

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Flink

• Open-source platform for distributed stream and batch data processing.
• Provides data distribution, communication, and fault tolerance for distributed computations over data streams.
• Builds batch processing on top of the streaming engine, overlaying native iteration support, managed memory, and program optimization.