Just developing things , testing and deploying to server is not enough to ensure up-time SLA. Once things are deployed in production environment, in-spite of rigorous testing in staging environment, things may go wrong in production.

Some of the reasons for malfunctioning in production are:

  • Unexpected infra problem such as disk full.
  • Missed test case etc.

No code and human are 100% perfect

But since we have to meet our SLA, for a better user experience. We can reduce the blast radius after the code is deployed in the server, for any fault.

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Math for me when I was a student, was just scoped to the exam. In most of my learnings I didn't thought of applications in real life. References from which I was studying mostly didn't have any side note of its real life usage, also I personally didn't took efforts and search for it on internet.

As I started my career as a Full Stack Developer, I started encountering those concepts that I learned in my school/college. While developing we always get the most efficient solution towards a problem, which often requires these basic concepts to be clear. In my…

Instead of traditional REST call to server from app, we can use gRPC to fetch data from server. gRPC has many advantages over traditional REST call

In last tutorial we saw inter-service communication between Microservices. We’ll be using the same server but today we’ll try to communicate with flutter gRPC client. This will make it end to end i.e mobile to server request.

Steps for implementation.

  1. Generate proto files
  2. Flutter client implementation
  3. Test live

Step 1: Activate Proto plugin

Make sure you have a flutter environment setup done.

flutter pub global activate protoc_plugin

We now need…

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The Micro-Services architecture pattern is getting a lot of attention these days and it’s trending.

The Micro-Service architecture enables the rapid, frequent and reliable delivery of large, complex applications.

With great power comes great responsibility.

With this architecture we come across problems related to Inter-Service Communication.

To overcome, we use the following:

  • Message queue (Kafka, Redis, etc.)
  • RPC (gRPC, zerorpc, etc.)
  • And many more…

We’ll be trying out the 3rd solution- RPC.

With the need of RPC in Micro-Services architecture, gRPC was initially created by Google.

That’s all with introduction, let’s jump to the…

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Tired of waiting to build your docker image?

Here’s a solution → Base Docker Image.

Most organisations use docker for running their code in production / staging. They have multiple services with different codebases but similar environment and dependencies.

We can make a base docker image and have any other specific dependency on top of it, for other builds.

What do we achieve with this?

Improved speed of build.

Yes, we can save a lot of time in docker build.

There are other strategies too which we can reduce overall build time, but this blog is all about how can…

Here’s a tutorial on how we can have kafka running with our favourite Go lang.

Confluent is one of the most used kafka client/server.

We will be using confluent kafka server via docker, and will try 2 major client libraries in Golang for using kafka server.

Client libraries:

Let's get started with server setup via docker.

Here we’ll need to spin up following container.

  • Kafka

Below is a docker-compose file to get both running.

In the above docker-compose file, the environment of each container plays an important role. …

This blog is all about how to start mongo db in a docker environment.

Manage, create user and databases, as well as handling their access control.

Lets get started with mongo instance creation via Docker

Step 1:

Create a docker-compose.yml file

This tutorial is to create a library in golang, put it in bitbucket and use it across your organisation.

Note: This tutorial was for golang version 1.14

Step 1:

Create a Golang project, I use the following structure (yours might be different)

Pandas: Data Frame

Pandas is the most popular python library that is used for data analysis.

Data analysis is important in business to understand problems facing in an organisation, and to explore data in meaningful ways.

Steps in Data Analysis:

  • Data Collection
  • Data Cleaning
  • Data Analysis
  • Actions

We will discuss each one of it as we proceed.

The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional)

In this article we will learn some basics about Dataframe:

But before using data frame you might have a question about why only data frames ?

Karan Churi

Full stack dev.

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