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Streaming event pipeline around Apache Kafka and its ecosystem, simulating Real-time Data Streaming

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Optimizing Public Transportation

In this project, we will construct a streaming event pipeline around Apache Kafka and its ecosystem. Using public data from the Chicago Transit Authority, we will construct an event pipeline around Kafka that allows us to simulate and display the status of train lines in real time.

When the project is complete, you will be able to monitor a website to watch trains move from station to station. So a sample static view of the website page you create might look like this:

Sample Website Page View

Prerequisites

To complete your project, the following are required:

  • Docker
  • Python 3.7
  • Kafka ecosystem (Kafka Python Client, Kafka Connect, Schema Registry, Kafka REST Proxy, ksqlDB, Zookeeper, Kafka Connect UI, Kafka Topics UI, Schema Registry UI)
  • Faust
  • PostgreSQL
  • A minimum of 16gb+ RAM and a 4-core CPU on your computer to execute the simulation

Project Steps

The Chicago Transit Authority (CTA) has asked us to develop a dashboard displaying system status for its commuters. We have decided to use Kafka and ecosystem tools like REST Proxy and Kafka Connect to accomplish this task.

Our architecture will look like so:

Project Architecture

Step 1: Create Kafka Producers

The first step in our plan is to configure the train stations to emit some of the events that we need. The CTA has placed a sensor on each side of every train station that can be programmed to take an action whenever a train arrives at the station.

Step 2: Configure Kafka REST Proxy Producer

Our partners at the CTA have asked that we also send weather readings into Kafka from their weather hardware. Unfortunately, this hardware is old and we cannot use the Python Client Library due to hardware restrictions. Instead, we are going to use HTTP REST to send the data to Kafka from the hardware using Kafka's REST Proxy.

Step 3: Configure Kafka Connect

Finally, we need to extract station information from our PostgreSQL database into Kafka. We've decided to use the Kafka JDBC Source Connector.

  • You can run this file directly to test your connector, rather than running the entire simulation.
  • Make sure to use the Landoop Kafka Connect UI and Landoop Kafka Topics UI to check the status and output of the Connector
  • To delete a misconfigured connector: CURL -X DELETE localhost:8083/connectors/stations

Step 4: Configure the Faust Stream Processor

We will leverage Faust Stream Processing to transform the raw Stations table that we ingested from Kafka Connect. The raw format from the database has more data than we need, and the line color information is not conveniently configured. To remediate this, we're going to ingest data from our Kafka Connect topic, and transform the data.

Step 5: Configure the KSQL Table

Next, we will use KSQL to aggregate turnstile data for each of our stations. Recall that when we produced turnstile data, we simply emitted an event, not a count. What would make this data more useful would be to summarize it by station so that downstream applications always have an up-to-date count.

  • The KSQL CLI is the best place to build your queries. Try ksql in your workspace to enter the CLI.
  • You can run this file on its own simply by running python ksql.py
  • Made a mistake in table creation? DROP TABLE <your_table>. If the CLI asks you to terminate a running query, you can TERMINATE <query_name>

Step 6: Create Kafka Consumers

With all of the data in Kafka, our final task is to consume the data in the web server that is going to serve the transit status pages to our commuters.

Running and Testing

To run the simulation, you must first start up the Kafka ecosystem on your machine utilizing Docker-Compose:

%> docker-compose up

Docker-Compose will take 3-5 minutes to start, depending on your hardware. Please be patient and wait for the Docker-Compose logs to slow down or stop before beginning the simulation.

Once Docker-Compose is ready, the following services will be available on your local machine:

Services Specs

Note that to access these services from your own machine, you will always use the Host URL column.

When configuring services that run within Docker-Compose, like Kafka Connect, you must use the Docker URL. When you configure the JDBC Source Kafka Connector, for example, you will want to use the value from the Docker URL column.

Running the Simulation

There are mainly two pieces to the simulation, the producer and consumer. As you develop each piece of the code, it is recommended that you only run one piece of the project at a time.

However, when you are ready to verify the end-to-end system, it is critical that you open a terminal window for each piece and run them at the same time.

To run the producer:

cd producers
virtualenv venv
. venv/bin/activate
pip install -r requirements.txt
python simulation.py

Once the simulation is running, you may hit Ctrl+C at any time to exit.

To run the Faust Stream Processing Application:

cd consumers
virtualenv venv
. venv/bin/activate
pip install -r requirements.txt
faust -A faust_stream worker -l info

To run the KSQL Creation Script:

cd consumers
virtualenv venv
. venv/bin/activate
pip install -r requirements.txt
python ksql.py

To run the consumer:

Note: Do not run the consumer until you have reached Step 6!

cd consumers
virtualenv venv
. venv/bin/activate
pip install -r requirements.txt
python server.py

Once the server is running, you may hit Ctrl+C at any time to exit.

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