Best of Backend Development — May 2024
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LogRocket·2y
Building a customizable dashboard with Dashy
Learn how to create a customizable dashboard with Dashy, a free and open source self-hosted dashboard app developed with Vue and Node.js. Dashy allows users to aggregate web service links and widgets into one web page, offering features such as multi-language support, authentication, theme customization, and more. Use cases for Dashy include creating dashboards for home lab servers, managing bookmarks, and storing app and admin page URLs. The article also provides a practical guide on installing and using Dashy via Docker.
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Community Picks·2y
Mastering SQL: Comprehensive Guide To Structured Query Language
This post provides a comprehensive guide to SQL, covering topics such as basic queries, filtering data, sorting data, aggregate functions, grouping data, joins, subqueries, views, indexing, transactions, stored procedures, backup and recovery, and SQL dialects.
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CRUD App with Prisma ORM & Node.js
Learn how to create a CRUD App with Prisma ORM and Node.js using TypeScript and PostgreSQL. Prisma is a next-generation ORM tool that simplifies database management and access. Follow the steps to setup a Prisma project, create models, perform database migrations, and perform CRUD operations using Prisma Client.
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ByteByteGo·2y
100X Scaling: How Figma Scaled its Databases
Figma, a collaborative design platform, successfully scaled its databases to keep up with increasing demand. They implemented vertical partitioning and logical sharding to address scalability issues. Additionally, Figma explored alternative solutions but ultimately chose to build a horizontally sharded solution on top of their existing infrastructure. Their unique sharding implementation included the use of colocations, logical and physical sharding, a query engine, and a shadow application readiness framework. Figma aims to horizontally shard every table in their database for near-infinite scalability.
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The Scaling Journey of LinkedIn
LinkedIn faced challenges in scaling its platform as its user base and content grew. They adopted a distributed and partitioned graph system, built a search service using Lucene, and migrated to a service-oriented architecture. They also used caching and implemented data collection with Kafka. Additionally, LinkedIn developed tools like Rest.li for API development, introduced super blocks to manage multiple services, and implemented multi-data center support. Other advanced developments included real-time analytics with Pinot and managing authorization at scale.
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Medium·2y
FastAPI Best Practices: A Condensed Guide with Examples
FastAPI is a high-performance web framework for building APIs with Python. It is easy to learn and use, enforces type safety, supports asynchronous programming, and automatically generates API documentation. Following best practices when working with FastAPI is crucial for maintainability, scalability, security, performance, and community standards. It is recommended to set up a virtual environment and structure your project properly. Data validation, handling HTTP requests, and implementing exception handling, middleware, and CORS are important aspects of working with FastAPI.
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Javarevisited·2y
Top 10 Free Frontend and Backend Development Courses in 2024
Top 10 free frontend and backend development courses for beginners and intermediate developers in 2024. Learn web development with free online courses and tutorials. Includes HTML, CSS, JavaScript, React, Angular, Node.js, PHP, Spring, and more.
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ThePrimeTime·2yYour Next Backend Should Be Written In...
The post discusses the benefits of using Gleam for building backend APIs, focusing on its type safety, simplicity, and concurrency features like tasks and actors. It also demonstrates how to cache external API calls and manage long-running background processes. The author recommends trying out Gleam for future projects.
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TigerData (Creators of TimescaleDB)·2y
What We’re Excited About PostgreSQL 17
PostgreSQL 17 is an upcoming release with significant changes to modernize the database. It includes features like pg_createsubscriber, support for merge and split partitions, incremental file system backup, failover of logical slots, and reporting of optimizer memory usage in EXPLAIN command. Timescale has made several contributions to PostgreSQL 17. The release is aimed at enhancing performance, scalability, security, and compatibility.
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AI in Plain English·2y
Your guide to Vector Databases
This post provides a guide to understanding Vector Databases, including their definition, functionality, and applications. It also highlights the features of a good Vector Database and offers best practices for choosing the right one. The post includes a list of over 80 Vector Databases for AI projects.
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Custom methods in REST API endpoints
The post discusses the problem of mapping business operations to predefined HTTP methods and provides ways to implement custom methods in REST API endpoints. It explores the pros and cons of different solutions, including sub-resources, special 'actions' endpoints, query parameters, and the use of a colon punctuation mark. The post concludes with a reminder to use custom methods with caution to avoid turning the RESTful API into an RPC.
