Z-axis scaling

Last year I was building an application that had to process million of records. The processing of each record was independent but complicated. To process all the records it was taking more time than the response time we had to meet as per SLA. We employed near cache and were processing all the data in memory. This made memory utilisation of the app high.

At that time I employed the strategy to shard the data so that each application instance can process a subset of the data. This helped us improve cache utilisation and reduce memory and I/O usage of the application. These days each time scalability is mentioned Microservices is thrown as the solution. We as software developers need to keep in mind three dimensions of scalability so that we can choose the best possible strategy for the problem. These were first mentioned in the book The Art of Scalability. I read this book in 2012 when I was working with OpenShift platform as a service.

As per the book The Art of Scalability there are three dimensions of scalability as shown below

scalecube

X-axis scaling

This is the traditional way of scaling monolithic applications. We run multiple copies of an application behind a load balancer.

Y-axis scaling

In this model, we break the application vertically into multiple independent services. This is what Microservices architecture allows us to achieve.

Z-axis scaling

In Z-axis scaling each server is responsible for processing subset of the data. This is the solution that I applied for my problem.

In case you want to read more about Scale Cube I suggest you read this post.

Building a Sentiment Analysis Python Microservice with Flair and Flask

Flair delivers state-of-the-art performance in solving NLP problems such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and text classification. It’s an NLP framework built on top of PyTorch.

In this post, I will cover how to build sentiment analysis Microservice with flair and flask framework.

Continue reading “Building a Sentiment Analysis Python Microservice with Flair and Flask”

How Docker uses cgroups to set resource limits?

Today, I was interested to know how does Docker uses cgroups to set resource limits. In this short post, I will share with you what I learnt.

I will assume that you have a machine on which Docker is installed.

Docker allows you to pass resource limits using the command-line options. Let’s assume that you want to limit the IO read rate to 1mb per second for a container. You can start a new container with the device-read-bps option as shown below

$ docker run -it --device-read-bps /dev/sda:1mb centos

In the above command, we are instantiating a new centos container. We specified device-read-bps option to limit the read rate to 1mb per second for /dev/sda device.

Continue reading “How Docker uses cgroups to set resource limits?”