Ways to reduce the api response time.

Listing down some of the ways, through which we can cache the api response and make the api faster, and offload the load from the server.

  • CDN server caching (Akamai)
  • Varnish (using redis or memcache)
  • Using Redis or memcache or in-memory database to store main db result

Pre-requisites are :-

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Written on July 13, 2016

Why Nodejs ???

Listing down reasons for anyday choosing Nodejs over any other programming language.

  • Its lightening Fast
  • Single Event loop
  • Package Manager (NPM)
  • Async behavior (Additional speed boast)
  • At last its javascript (Will help in Mongo shell too)
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Written on July 1, 2016

Tips for On Premises to AWS Migration

In this blog, I will be sharing some things which we learnt while migrating our app from on-Premises to AWS. These are not in any specific order, each point is a unique learning in itself.

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Written on June 29, 2016

Running a web app on production boxes

In this blog, I will be explaining the architecture for a production level app, including the app servers (loopback node application), web servers (nginx), mongo (replica set), redis (M-S replica), ELB (if on AWS) along with the security groups, and data communications among them.

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Written on June 21, 2016

How Celery interacts with Redis

There are 2 python modules {celery} and {celery-beat}, which we can be used to execute the asynchronous tasks, and to run the schedule tasks. Pre-requisites are:- A very basic knowledge of

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Written on June 17, 2016

Best project practices

Listing down some good practises that we have used in our project.

  • TEST CASES
  • Automated Deployment
  • Monitoring tool
  • Automate IT infrastructure
  • Peer code review
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Written on May 20, 2016

Password less SSH Authentication

In this blog I will be stepwise explaining the flow of password less SSH authentication using the pem file. Pre-requisites are a basic knowledge of servers and SSH.

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Written on April 29, 2016

Mongo Performance Tunning

Let me first explain the conditions in which we were, using mongodb, with around 60GB data, around 50 million records in 10 collections with an average cpm of 20K and a target to achieve average query response time of 10ms.

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Written on April 13, 2016