thehardwarestore.ai

Netdata is a real-time, distributed monitoring and observability platform built as a multi-language agent (C core, Go collectors, Rust sidecars, legacy Python plugins). It collects system, container, application, network, and cloud metrics at per-second resolution, stores them in a custom tiered time-series database (dbengine), and exposes rich query APIs (v1/v2/v3), streaming/replication, ML-based anomaly detection, and an extensive alerting framework. The agent can operate standalone, stream to centralization points, or connect to Netdata Cloud via the ACLK (Agent-Cloud Link) using MQTT and protobuf. It also includes a network topology discovery subsystem, netflow/IPFIX/sFlow ingestion, systemd-journal log management, and an OpenTelemetry-compatible OTLP ingestion path. Architecturally, the repository is a hybrid of a daemon (C) with a plugin system that spawns many independent processes (Go, Python, shell) for metric collection. The Go plugin framework enforces a consistent collector lifecycle (config validation, init, collect, cleanup) with embedded JSON schemas and chart templates. The Rust crates handle high-throughput streams like netflow and journal indexing. The codebase is professionally engineered with extensive test coverage, benchmarks, packaging for multiple Linux distros, Windows, and macOS, and a heavy investment in performance optimizations (arena allocators, lock-free data structures, compressed bitmaps). Despite its size, it exhibits strong modularity and reusable abstractions, though some legacy duplication (e.g., Python vs Go collectors) and a few vendored components remain.

Grafana Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus. It is designed to be cost-effective and easy to operate by indexing only log metadata (labels) rather than the log contents. The system consists of several loosely coupled services (distributor, ingester, querier, compactor, ruler) that can be deployed in a monolithic or microservices architecture. Logs are ingested via various protocols (HTTP, gRPC, Kafka) and integrations (fluent-bit, fluentd, logstash, Docker driver), stored in chunk format in object stores, and indexed by labels in a separate store. Queries are executed using LogQL, which allows filtering by label expressions and log content. The system includes a ruler for alerting and recording rules, a compactor for storage optimization, and a Kubernetes operator for deployment and management.

Grafana is an open-source observability platform for querying, visualizing, and alerting on metrics, logs, and traces. It is a full-stack application with a Go backend and React/TypeScript frontend, featuring a plugin system for datasources, panels, and apps. The architecture is a monolith with Kubernetes-style API extensions, supporting multiple database backends (MySQL, PostgreSQL, SQLite). It includes built-in alerting, dashboard management, provisioning, and RBAC. The codebase is large and well-maintained, with high-quality UI components and data abstractions, but has some technical debt from dual build systems and generated code.

VictoriaLogs is a high-performance, lightweight log management system designed for both small and large-scale deployments. It consists of multiple Go-based services for log ingestion (vlagent, vlinsert), storage (vlstorage), and querying (vlselect), complemented by a Preact/TypeScript web UI (vmui). The system uses a columnar storage approach with bloom filters, custom encoding, and a pipeline-based query engine (LogsQL) for efficient log processing. Key architectural patterns include composable data transformation pipes, extensible filter systems, mergeable statistical aggregations, and object pooling for performance optimization. The frontend features a modular component architecture with custom hooks for state management, contextual controls, and interactive visualizations.

TeslaMate is a self-hosted data logger for Tesla vehicles that collects telemetry via the Tesla API (both polling and streaming), stores data in PostgreSQL, and provides visualization through a Phoenix LiveView web interface and Grafana dashboards. It integrates with home automation systems via MQTT and supports importing historical vehicle data from CSV files (TeslaFi format). The application uses a supervision tree to manage background processes for data collection, MQTT publishing, import tasks, and update checking. Key features include geofencing for location-based cost tracking, encrypted storage for API tokens using Cloak, internationalization via Gettext, and custom Ecto expressions for geographic calculations. The architecture is monolithic with clear separation of concerns through Phoenix contexts (Log, Vehicles, Settings, Locations).

This repository builds and maintains the `grafana/otel-lgtm` Docker image, an all-in-one OpenTelemetry backend for development, demo, and testing. It bundles Grafana, Prometheus, Tempo, Loki, Pyroscope, and the OpenTelemetry Collector into a single container, providing a complete observability stack with a single `docker run` command. The image supports optional eBPF-based auto-instrumentation (OBI) for zero-code traces and RED metrics, and can export telemetry to external vendors via OTLP. The architecture is a multi-stage Docker build that downloads and verifies each component using checksums or cosign signatures. The container entrypoint (`run-all.sh`) starts all services in parallel, waits for health checks, and handles graceful shutdown. The OpenTelemetry Collector configuration is dynamically extended based on environment variables (debug exporter, external endpoints, custom headers). The repository also includes example applications in Java, Go, Python, .NET, and Node.js that demonstrate OpenTelemetry instrumentation, along with acceptance tests (OATS) that validate traces, metrics, logs, and profiles. CI/CD is comprehensive: automated builds, linting (flint, hadolint, shellcheck, etc.), container scanning with Trivy, CodeQL analysis, and weekly scheduled releases with immutable version tags. Container images are signed with cosign and attested via GitHub Artifact Attestations. The repository is actively maintained with Renovate for dependency updates and a well-defined release process.

This repository provides Docker Compose and Kubernetes configuration files for deploying a variety of self-hosted applications and system services. It serves as a template repository for deploying services like AdGuard Home, Bitwarden, MinIO, Traefik, and others, as well as system tools like Fail2ban and acme.sh for certificate management. The architecture is modular, with each application having its own directory containing deployment configurations (docker-compose.yml, Helm charts, Kubernetes manifests). There is no single unified application; instead, it offers reusable deployment patterns for individual services. Key entry points are the docker-compose.yml or Helm chart files within each application's directory. The repository relies on external dependencies like Docker, Kubernetes, and Helm for deployment, and the applications themselves depend on their respective upstream container images.

This repository is the official Helm chart distribution hub for Grafana's observability stack, containing over 20 charts for deploying Loki, Tempo, Mimir, Promtail, Grafana Agent, enterprise metrics/logs, and supporting components (rollout-operator, pdc-agent, snyk-exporter, etc.) on Kubernetes. The codebase is entirely Helm templates, values files, CRDs, and CI/CD scripts—no application source code. The architecture follows standard Helm patterns with per-component subdirectories, shared helpers, and extensive use of conditional logic for enterprise features, scaling, and storage backends. Many charts are deprecated or have their source maintained in separate product repositories (e.g., grafana/loki, grafana/tempo), with releases still published here. The repository shows signs of technical drift (old API versions, duplicated template logic, legacy dependencies) but remains actively maintained for the remaining non-deprecated charts with recent CI updates and release automation. It serves as both a legacy archive and an active distribution point.

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.

This repository is a comprehensive DevOps reference guide and cheatsheet collection, not a production application. It contains hundreds of markdown files covering topics such as Docker, Kubernetes, CI/CD tools (Jenkins, GitHub Actions, CircleCI), infrastructure provisioning (Terraform, Ansible, Puppet), cloud providers (AWS, Azure, GCP), networking, Linux, observability (Prometheus, Grafana), and interview preparation. The codebase includes a few illustrative examples (a Node.js Express app with Dockerfile, a bash system management script, Apache configuration snippets) but is overwhelmingly documentation. The content is organized by tool with consistent subdirectories (concepts, commands, tutorials), though many files are stubs or incomplete. The repository serves as a learning roadmap and quick reference, but suffers from outdated references (e.g., Node 10, Python 2), broken links, and inconsistent quality. It is not a runnable system and has no data pipelines or analytical models.