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Performance

Mastering Database Connection Pooling in Node.js: MySQL, PostgreSQL, and MongoDB

·1636 words·8 mins
Backend Engineering Performance Database Node-Js Mysql PostgreSQL Mongodb Performance Scalability Architecture
Introduction # If you are building a high-throughput Node.js application in 2025, handling database connections inefficiently is the fastest way to kill your performance. Whether you are dealing with a monolithic REST API or a distributed microservice architecture, the database is almost always the bottleneck.

Mastering Async Rust: Under the Hood to Production Scale

·1972 words·10 mins
Advanced Rust System Architecture Performance Rust Async Tokio Concurrency Performance Software Architecture Best Practices
As we settle into 2025, Rust has firmly established itself not just as a systems language, but as the premier choice for high-performance network services. The days of “Are we async yet?” are long gone. Today, the question isn’t whether libraries exist, but whether we are using the asynchronous model correctly to squeeze every ounce of performance out of our hardware.

Mastering Go Concurrency: Advanced Worker Pools and Pipeline Patterns

·1958 words·10 mins
Go Advanced System Design Performance Golang Concurrency Worker-Pools Pipelines Performance Go-Routines System Design Best Practices
It is 2025, and the landscape of backend development has solidified around high-concurrency, low-latency requirements. While the hardware isn’t getting infinitely faster per core, it is getting “wider”—more cores, more threads. Go (Golang) remains the undisputed champion of this domain, thanks to its lightweight goroutines and the CSP (Communicating Sequential Processes) model.

Mastering Python String Optimization: The StringBuilder Equivalent

·1465 words·7 mins
Python Basics Performance Software Architecture Python-3-15 Performance Memory-Management String-Manipulation Best Practices Optimization
If you are coming to Python from languages like Java, C#, or Go, one of the first things you might search for is a StringBuilder class. You know the drill: strings are immutable, and concatenating them in a loop is a performance killer. You look through the Python standard library, expecting to find string.Builder, but it isn’t there.

Python Memory Deep Dive: Primitives vs. Objects Performance in 2025

·1543 words·8 mins
Python Internals Performance Software Engineering Python-3-14 Memory-Management Optimization Sys-Module Cpython Benchmarking Slots
In the landscape of 2025, Python remains the dominant language for data engineering, AI orchestration, and backend web services. With the maturation of the No-GIL CPython (introduced experimentally in 3.13 and stabilized in subsequent versions), threading performance has skyrocketed. However, one fundamental constraint remains: Memory.