Why GraphQL? 8 Reasons After Reading 13 GraphQL Case studies

GraphQL is a query language built by Facebook. It is an alternative to building REST APIs. In the last few years it has become popular and there are many big organisations like Facebook, Github, NewYork Times, Shopify, Walmart Labs, and many others that are using it to build their web applications.

GraphQL is a query language and a runtime system. Clients form requests (called queries and mutations) by using the GraphQL query language, and the GraphQL server executes the request and returns the data in a response. Unlike REST APIs, which expose a different endpoint for each resource object, a GraphQL API makes all data available at a single endpoint. A client specifies exactly the data that it wants, and the server responds with only that data.

For last few months I am thinking to spend time learning about GraphQL but before I get deeper into GraphQL I wanted to understand why many organisations are adopting it. So, I spent a day reading GraphQL case studies mentioned on the GraphQL website.

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Using Redis Streams To Implement Near Cache Invalidation

Early last year I was working on an application that kept data in a near cache. I will not delve into reasons why we used near cache other than that we had strict performance requirements that required data closer to the compute.

Near cache for those who have not heard this term is a smaller local(in the same process as your application) cache that stores most recently used or most frequently accessed data. So, if you are running a Java application near cache could be as simple as a ConcurrentHashMap or you can use libraries like Cache2k or Caffeine to implement a near cache.

You can read the complete post on Xebia Engineering Medium publication.

LIL #3: Lessons I Learnt This Week

Welcome to the third post of lessons I learnt(LIL) series. I had a good week and I am content with what I achieved. Telling yourself repeatedly that life is not a race and you can take your time and be at peace with yourself is a powerful feeling. There were two thoughts that repeatedly came to my mind this week that I wanted to share with you this week.

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The 5 Minute Introduction to DuckDB: The SQLite for Analytics

A couple of weeks back I learnt about DuckDB while going over DB Weekly newsletter. It immediately caught my attention as I was able to quickly understand why need for such a database exist. Most developers are used to working with an embedded file based relational database in their local development environment. Most popular choice among embeddable RDBMS is SQLite. Developers use embeddable databases because there is no set up required and they can get started quickly in a couple of minutes. This enables quick prototyping and developers can quickly iterate on business features.

DuckDB is similar to SQLite in the sense it is also designed to be used as an embeddable database. Developers can easily include it as a library in their code and start using it. Later in this post, I will cover how we can use DuckDB with Python.

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Problem Details for HTTP APIs with Spring Boot

If you are building REST APIs you will understand that HTTP status codes at times are not sufficient to convey enough information about an error. There is a specification RFC 7807 that defines how you can return error information back to the client. The way it works is by identifying a specific type of problem with a URI.

To make it more clear let’s suppose you are building a todo list application where you want to build API to move items from one list to other. If the target list has a cap on maximum items it can hold and you try to move an item from source list to target list the list then you should get an exception.

If all you have is HTTP status code then you can just return HTTP status code 403 forbidden. However, it does not give the API client enough information about why the request was forbidden. Different developers have solved this problem differently by building their application specific error objects. Problem Details for HTTP APIs specification solves this problem by defining a standard format in which API error response should be returned.

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LIL #2: Lessons I Learnt This Week

Welcome to the second post of lessons I learnt(LIL) series. This week I went from being stressed, to meh, to content, and finally to the state of happiness. It is immensely powerful to know your feelings so that you can take corrective actions if required. Each week give us an opportunity to correct ourselves, learn from our mistakes, and brings a ray of hope that we can do better.

I find hope in the darkest of days, and focus in the brightest. I do not judge the universe. – Dalai Lama

This week I only have one lesson to share with you. I thought multiple times about in the last one week and I think if we can master it we can build great teams.

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The 5 minute introduction to Log-Based Change Data Capture with Debezium

Few years back I was working on an application where I had to pull data from an event table(populated using database triggers) and update my application specific data stores . This is a common problem that most software web developers need to solve. At that time I was not aware that this problem has a name. Sometime later I learnt that this is called Change Data Capture (CDC). As per wikipedia article on change data capture,

In databases, change data capture (CDC) is a set of software design patterns used to determine (and track) the data that has changed so that action can be taken using the changed data.

The key benefit of CDC is that you can identify the changed data in your source database which you can then incrementally apply to your target system. In absence of CDC, we are left with doing bulk loading of the data which is both time consuming and costly.

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