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Reflections on
Performance.

Browsing 3 articles tagged with Performance.

How to Use Rust and WebAssembly for Real-Time Data Processing in the Browser - Compile performance-critical Rust logic to WASM and run it at near-native speed inside any modern web browser — no server round-trip, no bottleneck
· 7 min read · Rust

How to Use Rust and WebAssembly for Real-Time Data Processing in the Browser

Compile performance-critical Rust logic to WASM and run it at near-native speed inside any modern web browser — no server round-trip, no bottleneck

WebAssembly finally makes it practical to run genuinely fast computation inside a browser tab. When you combine it with Rust — a language designed around zero-cost abstractions and memory safety — you get a pipeline that can process tens of thousands of records per frame without blocking the UI thread. This post walks through the full workflow: writing the Rust crate, compiling to WASM, and wiring it into a JavaScript frontend.

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Building High-Throughput Spatial Pipelines with Go Concurrency - Using goroutines, channels, and context cancellation to process millions of geospatial records in parallel
· 6 min read · Go

Building High-Throughput Spatial Pipelines with Go Concurrency

Using goroutines, channels, and context cancellation to process millions of geospatial records in parallel

Go's concurrency primitives are among the most ergonomic in any systems language. This post shows how to put goroutines and channels to work on a real problem: processing large spatial datasets at speed.

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Rust's Zero-Cost Abstractions for Geospatial Processing - How Rust's iterator model and trait system let you write elegant code that compiles to bare-metal performance
· 6 min read · Rust

Rust's Zero-Cost Abstractions for Geospatial Processing

How Rust's iterator model and trait system let you write elegant code that compiles to bare-metal performance

Zero-cost abstractions are Rust's central promise: you pay nothing at runtime for the high-level constructs you write at the source level. For geospatial data processing—where datasets routinely run to hundreds of millions of coordinate pairs—this matters enormously.

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