Explanation of Git design principles through Git internals

In this blog we’ll explore the internals of how Git works. Having some behind-the-scenes working knowledge of Git will help you understand why Git is so much faster than traditional version control systems. Git also helps you recover data from unexpected crash/ delete scenarios.

For a developer, it is quite useful to understand the design principles of Git and see how it manages both speed of access (traversing to previous commits) and small disk space for repository.

iOS Build Management using Custom Build Scheme

Introduction

One of the best practices in iOS development is to be able to manage multiple environments during development of a project. Many a time we might have to jump between DEV, QA, STAGE, and Production environments. As the owner of a product, clients request to have both development version of the app and production version of the app i.e. App store released version of the app on the same device.

If you have ever faced or might face this situation, then you need a custom build scheme.

Performance Testing for Serverless Architecture (AWS)

Introduction

This document provides a brief description of how JMETER can be used with a server-less architecture like AWS. This can be used to evaluate the read/write capacity of the underlying DB, to benchmark the load an application can survive with.

What are AWS Lambdas?

The code written is deployed to AWS lamba over the cloud with one or more lambda functions. A compute service runs the code on our behalf.

AWS Lambda Automated Deployment

Objective

This blog details the steps to build, deploy and maintain different versions of Lambda functions across various product stages such as development, testing, staging and production on multiple AWS accounts.

Creating time-based index in Elasticsearch using NEST (.NET clients for Elasticsearch)

Introduction

One of the most common use cases in Elasticsearch is to create time-based indexes for logs. In this blog, we will see how to create time-based index on run time using NEST (.NET clients for Elastic search).

When it comes to logging, we usually create a log file everyday to isolate the logs and get only the ones relevant for analysis, when required. If we store the logs in a relational database, we commonly have one table. With time, the entries on this table grow and to check the number of records on table, we usually delete the old records from the table at specific interval.