thehardwarestore.ai

M3 is a distributed time-series database and metrics platform originally developed by Uber and now under the Linux Foundation. It provides a full-stack solution for collecting, aggregating, storing, and querying high-volume metric data at scale. The system comprises multiple services: m3dbnode (storage node with custom encoding and persistence), m3coordinator (routing and downsampling gateway), m3aggregator (clustered metrics aggregation with quantile estimation), and m3query (PromQL/Graphite query engine). These services communicate via Thrift, gRPC, and HTTP, and are coordinated through an etcd-based cluster management layer that handles placement, leader election, and configuration distribution. The architecture is highly performance-oriented, featuring extensive object pooling, zero-copy I/O, custom bit-level encoding (m3tsz), and an inverted index (m3ninx) backed by FST segments and Roaring Bitmaps for fast metadata search. The codebase is written in a custom Go-like transpiled language and includes generated code for mocks, protobuf, and Thrift bindings. It integrates deeply with Prometheus (remote write, scrape, and query) and Graphite protocols, and provides a React-based admin UI (m3ctl) for namespace and rule management. The repository also contains a comprehensive suite of unit, integration, and property-based tests, along with Docker-based development and CI environments.

Scaphandre is a Rust-based CLI tool for monitoring electric power and energy consumption of hosts and processes. It reads RAPL (Running Average Power Limit) energy counters from Linux sysfs or Windows MSR, computes per-process power attribution using CPU usage ratios, and exports metrics via multiple backends: Prometheus (HTTP scrape or push gateway), JSON, stdout, Riemann, Warp10, and QEMU for virtual machines. The architecture is modular, built on `Sensor` and `Exporter` traits, allowing easy extension. It is packaged for Docker, Kubernetes (Helm chart), Linux (RPM/DEB), and Windows (installer with custom driver). The project is production-ready, actively maintained, and backed by extensive documentation and CI/CD pipelines. It is intended for infrastructure sustainability monitoring and carbon footprint tracking.

This is a Prometheus exporter for NATS server monitoring. It polls NATS server HTTP monitoring endpoints (varz, connz, subz, routez, healthz, jsz, etc.) and exposes metrics in Prometheus format. The architecture is a standalone binary with an embeddable library API. Key entry points are the CLI (main.go) and the exporter.NewExporter function. Data flow: on each scrape, collectors make HTTP requests to NATS monitoring endpoints, parse JSON responses, and convert them to Prometheus gauge metrics. The exporter supports TLS, basic auth, custom metric prefixes, and multiple NATS servers (though one per exporter is recommended). It includes pre-built Grafana dashboards for both NATS core and JetStream metrics. The codebase is well-structured with separate packages for collectors, exporter, and test utilities. It uses reflection-based metric extraction for simple endpoints and custom collectors for complex ones like JetStream, connections, and gateways. The project is actively maintained with recent commits and modern dependencies.

Parca is a continuous profiling platform that collects, stores, queries, and visualizes pprof profiles in real time. The Go backend ingests profiles via gRPC (both pprof and OpenTelemetry OTLP), normalizes them into Apache Arrow columnar format, and stores them in either FrostDB (default) or ClickHouse. It provides a rich gRPC+JSON API for querying profiles (flamegraph, table, source view, callgraph, pprof download), scraping Prometheus-style targets, managing debug information for symbolization, and sharing profiles via links. The TypeScript/React frontend delivers an interactive profiling UI with flamegraphs, tables, source views, metrics graphs, sandwich views, and comparison mode, all backed by efficient Arrow-based querying. The codebase demonstrates strong engineering with reusable patterns (generic caching, signed request abstraction, Arrow compaction), but carries some drift risk from dual storage backends, generated protobuf code, and a complex UI state layer built with multiple state management strategies.

Updo is a production-grade CLI tool for multi-target website uptime and performance monitoring. It continuously checks HTTP endpoints from the command line or a TOML configuration file, displaying real-time results in an interactive TUI or simple text output. The core monitoring engine performs configurable HTTP requests (GET/POST/PUT, custom headers, assertion text, redirect handling, SSL verification) and captures detailed timing breakdowns (DNS, TCP, TTFB, download). It can also deploy and invoke AWS Lambda functions across 13 global regions for geographically distributed probing, aggregating results from multiple vantage points. The tool integrates with Prometheus via Remote Write for long-term metric storage and visualization, and supports desktop alerts via beeep plus webhook notifications to Slack, Discord, or custom endpoints with auto-detected formatting. Architecture is a monolith Go application built on cobra for CLI management, termui for the TUI, and AWS SDK v2 for Lambda operations. Key entry points are the `updo monitor` and `updo aws` subcommands. Data flows from config or CLI flags through a monitoring loop that calls the HTTP checker or Lambda invoker, accumulates stats (t-digest for percentiles, rolling uptime), and pushes structured logs or Prometheus time series.

SigNoz is a full-stack open-source observability platform that ingests OpenTelemetry data (traces, metrics, logs), stores it in ClickHouse, and provides a rich web UI for querying, dashboards, alerts, and infrastructure monitoring. The backend is written in Go with a modular architecture using factory/provider patterns, SQL migrations, and OpenFGA-based authorization. The frontend is a React/TypeScript SPA with Zustand state management, react-query, uPlot charts, and a comprehensive query builder supporting PromQL, ClickHouse SQL, and builder queries. The enterprise edition adds anomaly detection, RBAC, licensing, and usage metering. The codebase is actively maintained with extensive testing (unit, integration, e2e) and CI/CD, though some legacy API versions and deprecated clients indicate ongoing migration.

This is a full-stack SaaS application for cron job monitoring (cron-job.org). It consists of a React frontend (Material-UI, Redux, i18n), a PHP REST API, a C++ execution daemon named 'Chronos', a WAF validator, and a public status page SPA. The system uses Apache Thrift for inter-service RPC, MySQL for job definitions and user data, per-user SQLite databases for historical execution logs, and Redis for rate limiting. The Chronos daemon implements an efficient async HTTP executor using libcurl, libev, and c-ares, with Prometheus metrics exposure. The codebase is production-grade with features like MFA (TOTP/Yubico), API keys, subscription billing (Stripe/Paddle), status pages with custom domains, and comprehensive i18n. However, it shows signs of technical drift: the frontend uses the deprecated Material-UI v4, password hashing is weak (SHA1+MD5), and the Thrift auto-generated code is fragile. The architecture is well-documented and the core execution engine is highly optimized, but the overall codebase would benefit from modernization of the frontend and authentication layers.

This repository is the official Kubernetes performance testing suite, housing multiple tools for benchmarking, load testing, and monitoring Kubernetes clusters. The core tool is clusterloader2 (CL2), a powerful framework that deploys scalable workloads, measures key performance indicators (API responsiveness, pod startup latency, scheduling throughput), and validates SLOs. It uses a pluggable measurement system, tuning sets for load patterns, and a provider abstraction layer supporting various environments (GKE, EKS, AKS, Kind, Kubemark). Other tools include a metric comparison utility (benchmark), a historical performance dashboard (perfdash), a real-time SLO Prometheus exporter (slo-monitor), DNS and network performance benchmarks, and data ingestion pipelines (perflens). The codebase is predominantly Go with Python scripts for data processing and extensive Prometheus/Grafana configurations. While primarily an application, it incorporates data pipelines for metric ingestion and SLO analysis, making it a hybrid toolset for performance engineering.

Besticon is a Go-based application and library designed to discover and generate website icons (favicons, apple-touch-icons, etc.). It functions as a monolithic service that can be deployed as a web server, a CLI tool, or imported as a library. The core logic scans HTML for icon links, parses .ico files, extracts dominant colors from images, and generates letter-based placeholder icons when no icons are found. It exposes an HTTP API with endpoints for icon fetching and a web UI for demonstration. The architecture separates concerns into a core library (`besticon`), a command-line interface (`besticon/cmd.go`), and a web server (`iconserver`). The server integrates Prometheus metrics for observability and implements SSRF protection to prevent security vulnerabilities during external HTTP requests. The codebase utilizes Go's standard library for image processing and HTML parsing, with external dependencies for caching (`groupcache`) and metrics (`prometheus`). A notable engineering practice is the use of VCR (Video Cassette Recorder) cassettes for testing HTTP interactions, allowing deterministic testing without live network access. The project also employs the functional options pattern for configuration, ensuring a clean API for the library consumers. However, the `go.mod` file specifies a future Go version (1.26), which indicates a configuration anomaly that may hinder standard builds.

Bull Exporter is a Prometheus metrics exporter for Bull queues running on Redis. It provides an HTTP server (default port 9538) that exposes queue state metrics (waiting, active, delayed, failed, completed counts) and job duration summaries. It supports automatic queue discovery by scanning Redis keys, and can also be run in one-shot mode to print metrics to stdout. The application is built with Express, Bull, ioredis, and prom-client, and is packaged as a Docker image. The codebase is well-structured with TypeScript, strict compiler options, comprehensive unit tests using Jest and real Redis, and CI/CD pipelines (Travis CI and CircleCI). However, the project has been stale since October 2020, uses very outdated dependencies (Node.js 10, TypeScript 3.9, old prom-client), and has no recent maintenance activity. The code quality is solid for its time but suffers from technical drift.

This repository is a companion to the Korean book '컨테이너 인프라 환경 구축을 위한 쿠버네티스/도커' (Kubernetes/Docker for Container Infrastructure). It provides a collection of Vagrant-based lab environments, shell provisioning scripts, Kubernetes YAML manifests, Helm charts, and CI/CD examples (Jenkins) to help readers build and manage Kubernetes clusters on VirtualBox. The codebase includes single-master and multi-master (via Kubespray) cluster setups, along with demonstrations of core Kubernetes concepts (Pods, Services, Ingress, Storage, Monitoring). While the repository is actively maintained with yearly architecture documents, the actual scripts and configurations rely on outdated software versions (e.g., Kubernetes 1.18, Docker 18.06, CentOS 7) and external URLs that may become stale. The code is functional for educational purposes but is boilerplate-heavy and repetitive across chapters, limiting its reusability in production environments.