Database indexing is key to fast, scalable apps: use the right index (B-Tree for ranges, Hash for equality, Full-Text for text search, Clustered for primary keys), prioritize high-cardinality columns, analyze queries, avoid over-indexing, and maintain/rebuild routinely; balance read speed with write overhead, and apply in e-commerce, social media, and banking to boost responsiveness.
The article explains why relational databases and SQL are core skills for full-stack developers, covering tables, relationships, core SQL elements (SELECT, JOIN, DML/DQL), an example join query, and performance tips (indexes, limiting results, avoiding SELECT *, optimizing joins). A GreenMart use case shows insights from sales data, and suggested books guide deeper learning.
A slow-performing database can bring even the most robust applications to their knees, causing frustration for users and developers alike. Advanced indexing strategies like composite indexes, covering indexes, and index intersection, along with techniques like materialized views, query rewriting, and data partitioning, can unlock greater gains in query performance.
Common backend issues can bring an application to a halt, but with the right approaches, they can be identified and fixed. This guide covers three common issues: mysterious 500 errors, slow database queries, and unhandled errors in async code, providing solutions such as enabling debug mode, checking server logs, verifying database connections, analyzing query performance, optimizing database indexes, and wrapping async code in try-catch blocks.
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