Archive for March, 2016

Solving problems of scale using Kafka

At 7digital I was in a team which was tasked with solving a problem created by taking on a large client capable of pushing the 7digital API to its limits.   The client had many users and expected their numbers to exponentially increase. Whilst 7digital’s streaming infrastructure could scale very well, the requirement was that client wanted to send back the logs of the streams back to 7digital via the API. This log data would be proportional to the number of users. 7digital had no facility for logging said data being sent from a client, so this would be a new problem to solve.

We needed to build an Web API which exposed an endpoint for a 7digital client to send large amounts of JSON formatted data, and to generate periodic reports based on such data. The expected volume of data was thought to be much higher than what the infrastructure in the London data centre was capable of supporting. It was very slow, costly and difficult to scale up the London data centre to meet the traffic requirements. It was deemed that building the API in AWS and transporting the data back to the data centre asynchronously would be the best approach.

Kafka was to be used to decouple the AWS hosted web service accepting incoming data from the London database storing it. Kafka was used as a message bus to transfer the data from an AWS region back to the London data centre. It was already operational with the 7digital platform at this time for non real time reporting purposes.

Since there was no need to use the London data centre, there was no advantage in writing another application in C# that could be hosted on the existing Windows webservers running IIS. Given the much faster boot times of Linux EC2 instances and the greater ease of using Docker in Linux, we elected to write the web application in Python. We could use Docker to speed up development.

The application used the Flask web framework. This was deployed in an AWS ECS cluster, along with a nGinx container to proxy requests to the python API container and an DataDog container which was used for monitoring the application

The API was very simple, once the inbound POST request was validated, the application would write the JSON to a topic on a kafka cluster. This topic was later consumed and written into a relational database so reports could be generated from it. The decoupling of the POST requests from the process that that writes to the database meant we could avoid locking the database, by consuming the data from the topic at a rate that was sustainable for the database.

Since reports were only generated once a day, covering the data received during the previous day, there could be a backlog of data on the kafka topic; there was no requirement for Near Real time Data.

In line with the usual techniques of software development at 7digital, we used tests to drive the design of this feature and were able to achieve continuous delivery. By creating the build pipeline early on, we could build the product in small increments and deploy them frequently.

We had the ability to run a makefile on a developer machine which built a docker container running the Python web app. We could then use the Python test frame unittest to run unit, integration and smoke tests for our application.  The integration tests were testing if the app could write to Kafka topics and the smoke tests were end to end tests, which ran after a deploy to UAT or Production to verify a successful deployment.

We successfully completed the project and the web application worked very well with the inbound traffic from the client. Since it was hosted in an EC2 cluster we could scale up both the number and the resources of the instances running our application. The database was able to cope with the import of the voluminous user data too. It served as a good example of how to develop an scalable web API which communicated with a database located in a datacentre. It was 7digital’s first application capable of doing so and remains in use today.


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