The author benchmarks MariaDB 10.3, MySQL 8.0, and PostgreSQL 12 using a real Turkish motorcycle classifieds app with many simple queries, finding MariaDB 5% faster on average and 7% faster at p95 with lower CPU usage, though MySQL handles tail latency better and memory differences stem from default performance_schema settings.
Based on 27 real projects across finance, healthcare, and government, this article compares JMeter, k6, Gatling, Locust, and Artillery across programmability, observability, scalability, and engineering integration, revealing why k6 and Locust excel in cloud-native DevOps while JMeter remains viable for Java-heavy teams.
The article examines why traditional JMeter‑based load testing falls short for microservices and presents a four‑layer open‑source stack—Gatling, k6, Prometheus/VictoriaMetrics/Grafana + Pyroscope, and Chaos Mesh/LitmusChaos—plus a 3C methodology and automated closed‑loop workflow to achieve observable, programmable, and resilient performance testing in cloud‑native environments.
Cloudflare migrated its blog to EmDash CMS as a production testbed, load-tested with k6, built a multi-layer caching architecture on Cloudflare Workers, rolled out via canary deployment, achieved flatter latency and fewer errors, and exposed blog content via MCP for AI agents.
The article deeply compares JMeter, Gatling, k6, and LoadRunner from an engineering perspective, showing how observability, script maintainability, DevOps integration, and cloud‑native support turn performance testing into a quantifiable driver for system optimization.
The article explains why single‑endpoint load tests miss critical bottlenecks in high‑traffic e‑commerce, live‑streaming and financial systems, and presents an open‑source, CI‑integrated stack—including SkyWalking, k6, OpenResty, Istio, Debezium, Kafka, Prometheus, and Grafana—plus pitfalls and emerging AI‑driven enhancements for reliable end‑to‑end performance testing.
The article outlines how 2026 database performance testing has shifted from simple TPS checks to a four‑layer, AI‑augmented, observable methodology that tackles multimodal queries, serverless scaling, and AI‑generated SQL, using tools like K6, eBPF, and ChaosMesh.
This article explains how to combine k6 load testing with DeepEval LLM evaluation to create two independent gates—one measuring latency and token flow, the other checking faithfulness and relevance—so that RAG applications can detect performance regressions and hallucinations before reaching production.
This article translates and expands Anton Putra's benchmark, detailing how to deploy Nginx and Envoy on AWS with Terraform and Ansible, run HTTP, HTTPS, and gRPC load tests using K6, measure CPU and latency with Prometheus, and compare the resulting throughput and stability of both proxies.
This article walks through a real-world investigation of memory leaks in a Rust-based frontend service, detailing common leak scenarios, profiling tools like tokio-console and jemalloc, load testing with k6, and the step-by-step analysis that uncovered regex misuse and cache bugs, ultimately stabilizing memory usage.
This article explores Goja, a pure‑Go JavaScript engine, detailing its features, integration with the K6 load‑testing tool, and practical code examples for embedding scripts, passing values, handling structs, invoking Go functions, error handling, and VM pooling to achieve high performance in Go applications.
A comprehensive benchmark compares multiple PHP runtimes—including Apache, Nginx, Nginx Unit, Roadrunner, FrankenPHP, and Swoole—under identical Docker‑based test conditions using k6 load testing, revealing significant performance differences and offering practical recommendations for Symfony applications.
In a series of local benchmarks on a 2.6 GHz six‑core Intel i7 machine, the author compares K6, Gatling, and FunTester under 10 k to 20 k QPS loads, detailing CPU, memory, and response‑time metrics, analyzing script languages, JVM settings, and offering optimization suggestions for FunTester.
This article benchmarks four load‑testing frameworks—JMeter, k6, Locust, and FunTester—across multiple concurrency levels, measuring CPU, memory, QPS and response time to reveal each tool’s strengths, weaknesses, and scalability limits.
