REST vs GraphQL

Aditya Pandey — Tue, 16 Jan 2024 05:50:47 +0000

Each has advantages and disadvantages, or strengths and weaknesses, in terms of API architecture, when it comes to REST and GraphQL.

Below is the strength and weakness for both REST and GraphQL.

REST

Works well when you need straightforward, consistent interfaces between different services or applications. - Uses standard HTTP methods like GET, POST, PUT, and DELETE for CRUD operations.
Implementing caching strategies is simple.
The drawback is that gathering relevant data from various endpoints might necessitate making several round trips.

GraphQL

Gives clients access to a single endpoint where they can query for the exact data they require.
In nested queries, clients define the precise fields they need, and the server returns optimised payloads with only those fields.
Allows subscriptions for in-the-moment notifications and mutations for changing data. Excellent at combining data from several sources and adapting quickly to changing front-end needs.
But if not adequately protected, it transfers complexity to the client side and opens the door to abusive inquiries. Compared to REST, caching strategies can be more intricate.

Conclusion -
The particular needs of the application and development team will determine which of REST and GraphQL is the better option. REST is more appropriate for applications where clear and unambiguous contracts are desired, whereas GraphQL is a better fit for complicated or dynamic frontend requirements.

If you have any questions, please feel free to ask.

Feel free to share any thoughts or opinions or questions, I will be happy to help you as I can, Thanks for reading!

If you are interested in web development, you can connect with me (https://www.linkedin.com/in/aditya-pandey-40990a1b8/).

What is Elastic Search ?

Aditya Pandey — Thu, 11 Jan 2024 06:14:16 +0000

Elasticsearch is a distributed, free and open search and analytics engine for all types of data, including textual, numerical, geo-spatial, structured, and unstructured. Elasticsearch is built on Apache Lucene and was first released in 2010 by Elasticsearch N.V. (now known as Elastic). Known for its simple REST APIs, distributed nature, speed, and scalability, Elasticsearch is the central component of the Elastic Stack, a set of free and open tools for data ingestion, enrichment, storage, analysis, and visualization. Commonly referred to as the ELK Stack (after Elasticsearch, Logstash, and Kibana), the Elastic Stack now includes a rich collection of lightweight shipping agents known as Beats for sending data to Elasticsearch.

Why Elastic Search ?

It is because users are forever searching weather it is an e-commerce site, social media handle or anything else.

Elasticsearch is fast. Because Elasticsearch is built on top of Lucene, it excels at full-text search. Elasticsearch is also a near real-time search platform, meaning the latency from the time a document is indexed until it becomes searchable is very short — typically one second. As a result, Elasticsearch is well suited for time-sensitive use cases such as security analytics and infrastructure monitoring.
Elasticsearch is distributed by nature. The documents stored in Elasticsearch are distributed across different containers known as shards, which are duplicated to provide redundant copies of the data in case of hardware failure. The distributed nature of Elasticsearch allows it to scale out to hundreds (or even thousands) of servers and handle petabytes of data.

Features of Elastic Search

Distributed
Open-source
NoSQL
JSON based
RESTful
Logs, Metrices

How does Elastic Search works ?

Raw data flows into Elasticsearch from a variety of sources, including logs, system metrics, and web applications. Data ingestion is the process by which this raw data is parsed, normalized, and enriched before it is indexed in Elasticsearch. Once indexed in Elasticsearch, users can run complex queries against their data and use aggregations to retrieve complex summaries of their data. From Kibana, users can create powerful visualizations of their data, share dashboards, and manage the Elastic Stack.

What programming languages does Elasticsearch support?

Java
JavaScript (Node.js)
Go
.NET (C#)
PHP
Perl
Python
Ruby