Self-Hosted Application Performance Monitoring

This project is an application performance monitoring tool and JVM metrics library designed to measure workload behavior and export performance data to external monitoring databases. It serves as an instrumentation toolkit for tracking resource usage and internal runtime behavior within a Java execution environment. The system focuses on application performance measurement and JVM application monitoring, specifically tracking system health and runtime resource analysis to identify bottlenecks and stability issues. It provides a mechanism for external metrics export, sending captured data to third-party tools for long-term analysis and visualization.

SigNoz is a full-stack observability platform designed to collect, store, and visualize metrics, logs, and distributed traces in a unified environment. It leverages OpenTelemetry-based data collection to ingest telemetry from diverse sources using vendor-neutral protocols, ensuring interoperability across complex microservices architectures. The platform utilizes a high-performance columnar storage engine to enable rapid aggregation and filtering, providing a centralized backend for monitoring application health and performance. What distinguishes the platform is its focus on automated instrumentation and semantic correlation. It allows users to capture telemetry data across various programming languages and frameworks without manual code changes, often requiring only simple environment variable updates. Once ingested, the system automatically links logs, metrics, and traces through shared identifiers, enabling seamless navigation between different telemetry types during root cause analysis. The frontend further supports this by using virtualized rendering to efficiently display complex distributed traces containing millions of spans. The platform provides a comprehensive suite of tools for infrastructure monitoring, application performance tracking, and log management. Users can define complex alert conditions and manage monitoring configurations as version-controlled resources, ensuring consistency across deployment environments. Additionally, the system includes specialized support for monitoring large language model applications and provides visual query pipelines that translate user-defined filters into optimized database queries for real-time dashboard generation. The entire observability stack can be deployed using container orchestration tools, with built-in utilities for verifying service status and managing data retention.

DoraemonKit is a mobile frontend development toolset designed to optimize the lifecycle of web and hybrid mobile applications. It functions as a comprehensive suite of productivity tools, providing specialized utilities for mobile UI inspection, web view debugging, and on-device performance monitoring. The toolset distinguishes itself through several targeted simulation and interception capabilities. It includes a network traffic interceptor for mocking API responses without modifying source code, a device state simulator for overriding GPS coordinates, and a mobile web debugging bridge that allows for JavaScript execution and local storage inspection within embedded containers. The project further covers application sandbox management for manipulating internal files, and diagnostic monitoring to track CPU usage, memory consumption, and frame rates via real-time waveform charts. It also provides the ability to extract visual properties and layout coordinates from native on-screen elements.

This project is a comprehensive software observability suite and application performance monitoring platform designed to track runtime errors, performance bottlenecks, and system health. It functions as a centralized diagnostic service that aggregates and categorizes exceptions, providing the infrastructure necessary to visualize complex execution paths across distributed systems and microservices. The platform distinguishes itself through a high-throughput distributed event ingestion pipeline and a columnar storage analytics engine that enables rapid aggregation of large-scale performance metrics. It utilizes runtime-level instrumentation hooks to capture execution data directly from the host environment and employs symbolication-based stack trace resolution to map minified code or raw memory addresses back to original source files. Furthermore, the system includes specialized capabilities for monitoring the operational performance of AI agents and ensuring sensitive data compliance through schema-driven scrubbing of incoming event payloads. Beyond core error tracking and tracing, the platform supports a wide range of programming languages and frameworks, allowing for consistent visibility across diverse software architectures. It integrates with external services to automate incident response workflows and provides a command-line interface for managing releases, debug symbols, and project configurations. The system also features a modular, plugin-based architecture that facilitates connectivity with third-party tools for issue tracking and alerting.

Dropwizard is a Java web application stack and REST framework used to build production-ready web services. It integrates an embedded HTTP server with operational tools to provide a complete environment for developing network interfaces that exchange JSON data. The project provides a set of core technology stacks for relational data mapping, schema migration, and operational performance monitoring. It includes a relational database migration tool to track schema updates and execute versioned scripts before an application starts. The framework covers a broad capability surface including request data validation, JSON serialization, and object-relational mapping. It also features application performance monitoring to track real-time system metrics and stability in production environments.

Netdata is a distributed observability platform designed for real-time infrastructure monitoring and performance tracking. It functions as a high-frequency agent that collects system, container, and application metrics with per-second precision, providing both local visualization and centralized aggregation across complex, multi-cloud environments. The platform distinguishes itself through edge-based intelligence, utilizing local machine learning models to automatically detect performance anomalies without requiring manual configuration or external query engines. Its architecture prioritizes local-first data persistence and secure metadata-only synchronization, ensuring that granular observability data remains on the host while essential system information is routed to a cloud-connected management plane. This hierarchical approach allows for horizontal scaling through parent-child node relationships, enabling unified monitoring and alerting across distributed infrastructure. Beyond core collection and analysis, the system supports automated troubleshooting through natural language querying and intelligent metric correlation. It features a modular data acquisition engine that employs thread-per-core execution for low-latency performance, alongside isolated external processes for heterogeneous application support. The platform includes automated service discovery, diverse deployment options, and built-in diagnostic utilities to maintain visibility and connectivity across large-scale clusters. Installation is supported through various methods including package managers, automated scripts, source compilation, and containerized orchestration.

This project is a comprehensive server-side web framework designed for building scalable web applications and services. It provides a structured, component-based architecture that integrates a dependency injection container to manage service lifecycles and promote loose coupling across the software stack. The framework enables the creation of interactive client-side interfaces through a component-based model that synchronizes state directly with the browser. The platform distinguishes itself through a highly configurable middleware-based request pipeline and an attribute-based routing engine that maps web requests to application logic using metadata decorators. It supports high-performance service development through contract-first serialization and a runtime environment optimized for distributed systems. Additionally, the framework includes a persistent connection layer for real-time, bidirectional communication, allowing servers to push live data updates to clients without manual polling. Beyond these core capabilities, the framework offers tools for organizing complex business logic into maintainable layers and generating dynamic content through a compiled template engine. It provides integrated security features for authentication and authorization, alongside diagnostic utilities for monitoring performance and managing memory usage. The project is documented to support various architectural patterns, including page-based development and structured service-oriented designs.

Prometheus is a comprehensive monitoring and alerting platform designed to track infrastructure health and application performance. It functions as a time series database that ingests, indexes, and queries high-frequency numerical data points. By utilizing a pull-based model, the system periodically collects multi-dimensional metrics from monitored targets, storing them in an optimized block storage format that supports high-throughput ingestion and efficient historical analysis. The platform distinguishes itself through a specialized query engine that enables real-time analysis of performance data using a dedicated functional language. It maintains operational visibility in dynamic environments by integrating with infrastructure APIs for service discovery, allowing it to adapt automatically to changing topologies. To support diverse architectures, it includes mechanisms for buffering metrics from short-lived batch jobs and streaming data to external long-term storage systems via standardized protocols. Beyond core data collection, the system provides integrated alerting capabilities that continuously evaluate logical expressions against incoming data streams. It manages the full lifecycle of incident notifications by applying grouping, inhibition, and silence rules to reduce operational noise. The ecosystem also supports broad observability through service availability probing, legacy metric translation, and the instrumentation of application-level performance data. The software is available as pre-compiled binaries or container images, and it can be managed through standard infrastructure automation tools.

TodoApp is a task management web application designed for organizing and tracking pending items. It consists of a web-based interface and a REST API backend that handles business logic and data requests. The system includes an OAuth 2.0 identity provider for user authentication via passwords and external social providers, as well as an API gateway proxy that routes traffic from the frontend to the backend to prevent cross-origin resource sharing issues. Operational capabilities cover system observability through OpenTelemetry for collecting logs and metrics, request rate limiting to maintain service stability, and persistent data storage using SQLite. User identity is maintained through cookie-based session management.

Glances is a cross-platform system monitoring tool designed to track real-time resource usage and hardware health metrics across diverse computing environments. It functions as a command-line utility that provides a unified view of system performance, identifying bottlenecks and maintaining infrastructure stability through a consistent abstraction layer that translates kernel calls into actionable data. The project distinguishes itself through its distributed capabilities, offering a web-based interface that enables remote access to live performance metrics from any device without requiring direct terminal access. It also operates as a telemetry data exporter, utilizing an export-driven pipeline to stream collected statistics to external databases and monitoring tools for long-term historical analysis. The system supports a modular architecture that allows for extensible data collection through independent scripts. It facilitates remote monitoring by maintaining persistent network connections between lightweight data providers and centralized management interfaces.

The Datadog Agent is an infrastructure monitoring agent and host telemetry collector. It functions as a background process that gathers system metrics and application health data to send to a centralized monitoring platform. The project operates as a plugin-based metric collector, using a modular system of independent check scripts to gather data from various third-party services and applications. It serves as a remote telemetry transmitter, providing a pipeline to stream infrastructure and system information to a remote analysis and alerting backend. Its capabilities cover application performance monitoring, host resource tracking, and infrastructure performance monitoring. The agent collects low-level system telemetry from the operating system kernel and filesystem while aggregating application-level performance data to identify service degradation.

Grafana is an observability data platform designed to aggregate metrics, logs, and traces from diverse sources into a unified environment. It functions as a centralized interface for visualizing complex telemetry data, transforming raw streams into interactive dashboards that support real-time system health tracking and performance monitoring. The platform distinguishes itself through a plugin-based modular architecture that integrates disparate databases, cloud services, and monitoring tools via a standardized data abstraction layer. This framework allows for the dynamic loading of external components to support varied data sources and visualization types without requiring modifications to the core codebase. Additionally, the system incorporates a rule-based alerting engine that evaluates incoming data streams against defined thresholds to trigger automated notifications for incident response. Beyond its core visualization and alerting capabilities, the platform provides tools for infrastructure performance monitoring and operational data analysis. It utilizes a declarative, component-driven interface to manage dashboard states and a compiled backend to process high-throughput queries and API requests. The system maintains configuration persistence and state consistency across distributed instances through a centralized metadata storage layer.

Cat is a distributed application performance monitoring tool and tracing framework designed to track transactions, latency, and health across distributed services. It functions as a Kubernetes-native monitoring stack that utilizes multi-language monitoring clients and a real-time alerting system to maintain system visibility. The system provides monitoring clients for Java, Go, Python, Node.js, and C++ to collect performance metrics and trace data. It distinguishes itself by sampling request flows to record call chains and identify bottlenecks, while using a monitoring engine to trigger immediate notifications when performance indicators breach defined thresholds. The observability surface includes distributed trace analysis, application error logging, and web endpoint monitoring. It aggregates performance metrics and transaction data to generate statistical health reports and identify problematic requests through metadata capture and transaction tracking. The project is packaged for containerized deployment and supports automated installation via Helm charts.

Uptime Kuma is a self-hosted monitoring platform designed to track the availability and performance of network services and websites. It functions as a centralized dashboard that executes asynchronous health checks on a scheduled interval, providing real-time visibility into infrastructure health and service uptime. The platform distinguishes itself through a dedicated notification engine that dispatches alerts across multiple third-party messaging services, alongside a public status page generator that allows users to communicate service health and historical metrics via custom domains. Its architecture utilizes a reactive, single-page interface that maintains persistent bidirectional connections with the server to push live status updates without requiring manual page refreshes. The system is built for flexible deployment, supporting containerized environments, native package installations, and bare-metal execution. It manages monitoring configurations and historical data using a local, file-based relational database, while a decoupled abstraction layer ensures that alert delivery logic remains independent of the core monitoring engine.

AngularFire is a set of tools for connecting applications to Firebase services. It provides a library of client-side interfaces for managing authentication, object storage, NoSQL databases, and serverless functions. The project utilizes observables and dependency injection to integrate cloud services into the application hierarchy. It features a reactive interface for streaming real-time data, managing document-based databases, and tracking authentication state as a continuous stream of tokens. The platform covers a broad range of cloud capabilities, including identity verification, binary file management, and push notification delivery. It also includes tools for application performance monitoring, user behavior analytics, and the deployment of web applications and serverless functions. The library includes support for local service emulators to enable offline development and testing.

Spring Boot is an opinionated application framework designed to streamline the creation of production-ready services. It functions as a comprehensive development platform that utilizes a centralized dependency injection container to manage object lifecycles and wiring. By employing convention-over-configuration, the framework automates the instantiation of components based on the presence of specific libraries and configuration properties, significantly reducing the need for manual setup. The framework distinguishes itself by bundling the application and its web server into a single, self-contained executable archive. This approach eliminates the requirement for external application server deployments, allowing services to run as standalone artifacts. To support operational needs, it includes a production readiness suite that provides standardized endpoints for monitoring application state, performance metrics, and health checks, alongside a centralized system for managing compatible library versions. Beyond its core execution model, the project provides tools for externalizing configuration, mapping environment variables and property files into type-safe objects for consistent behavior across environments. It integrates security protocols for authentication and authorization, facilitating the development of scalable backend systems optimized for containerized and distributed infrastructure.

Boto3 is the AWS SDK for Python, providing a programmatic interface for managing and automating AWS cloud infrastructure and services. It serves as a cloud management API client and resource manager for provisioning, configuring, and scaling virtual servers, databases, and storage. The library enables the implementation of infrastructure-as-code through declarative templates and scripts, allowing for the deployment of identical resource stacks across multiple accounts and geographic regions. It also provides a framework for coordinating distributed workflows, serverless functions, and containerized applications within the cloud ecosystem. The toolkit covers a broad range of operational capabilities, including generative AI orchestration, identity and access control, and detailed cloud resource monitoring. It further extends to data lifecycle management, including automated backups and migrations, as well as comprehensive billing and cost optimization tools.

Loki is a horizontally scalable, highly available log aggregation engine designed to store and query massive volumes of unstructured log data. It functions as a distributed observability platform that correlates logs, metrics, and traces to provide comprehensive visibility into the health and performance of complex infrastructure. The system distinguishes itself through a distributed query execution model that processes large datasets in parallel across cluster nodes. It utilizes label-based stream indexing and a distributed index to map log data to specific chunks, enabling rapid retrieval without scanning entire datasets. Data is compressed into immutable chunks and stored in object storage, while a gossip-based protocol manages cluster membership to ensure high availability. The platform also supports multi-tenancy, allowing for isolated data storage across different teams or services. Beyond core log management, the platform provides a query-driven processor that uses a functional language to transform raw system events into structured insights. It integrates with the broader observability ecosystem to support incident response workflows, allowing users to search and visualize telemetry data to identify and resolve technical issues.

The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane. The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It employs a language-agnostic intermediate representation to synthesize these definitions into platform-specific configurations, while supporting aspect-oriented policy injection to apply security and compliance rules across infrastructure definitions during the synthesis phase. Beyond core provisioning, the project provides a modular component registry for distributing and reusing pre-configured infrastructure building blocks. It supports multi-account orchestration, allowing for the deployment of consistent resource sets across different regions and accounts from a single template, and includes capabilities for detecting infrastructure drift to ensure deployed environments remain aligned with their defined state. The project is distributed as a software development kit, providing programmatic interfaces to manage the full lifecycle of cloud resources and integrate infrastructure definitions directly into application codebases.

1Panel is a centralized server management and container orchestration platform designed to simplify the administration of Linux-based infrastructure. It provides a unified web interface for managing containerized workloads, automating system maintenance, and configuring server resources. By acting as a comprehensive control plane, the platform streamlines the deployment of applications, databases, and web services while offering granular control over host system internals and security settings. What distinguishes this platform is its integrated support for private artificial intelligence infrastructure. It functions as an AI infrastructure manager, allowing users to host, configure, and deploy local machine learning models and multi-agent workflows directly on their private servers. This capability is complemented by a programmable reverse proxy that handles web traffic routing, load balancing, and SSL termination, providing a high-performance layer for managing incoming requests and security filtering. The platform covers a broad range of administrative tasks, including automated data backups, system updates, and the deployment of curated open-source software through a centralized marketplace. It supports declarative service configuration and event-driven scheduling to maintain operational reliability across diverse hosting environments. Users can manage these operations through a command-driven environment that integrates natural language processing for system maintenance and incident response. The software can be installed on a Linux server using a single command script to initialize the management dashboard and begin infrastructure operations immediately.