Projects sharing features with Tempo

Perfetto is a platform for system-level performance tracing and analysis on Linux and Android. It combines a high-throughput trace recorder, a SQL-based query engine, and a browser-based visualizer into a single toolchain. The platform covers CPU scheduling and call-stack profiling, native and Java heap memory allocation tracking, GPU and graphics events, and system-wide counters such as CPU frequency and power consumption. The architecture decouples trace recording from offline analysis, using a compact protobuf format for event encoding and columnar storage for efficient SQL queries. The we

Coroot is an observability platform and Kubernetes performance monitor that utilizes eBPF to automatically collect metrics, logs, and traces without requiring manual code instrumentation. It functions as an OpenTelemetry trace analyzer and an LLM observability gateway, exposing system health data to large language models through the Model Context Protocol. The platform differentiates itself by combining automated root cause analysis and AI-driven diagnostics to investigate performance regressions. It also includes a cloud cost monitoring tool that attributes infrastructure spending to specifi

OpenObserve is a unified observability data platform designed to ingest, store, and analyze logs, metrics, and traces. It functions as a cloud-native monitoring tool that centralizes telemetry from diverse sources, including standard collectors and cloud service providers, into a single, scalable system. By utilizing a columnar storage engine backed by object storage, the platform enables efficient long-term data retention and high-performance analytical querying. The platform distinguishes itself through deep integration with artificial intelligence, allowing users to query data using natura

HyperDX is an OpenTelemetry observability platform that provides centralized log management, distributed tracing, and a self-hosted monitoring stack. It functions as a unified system for collecting, indexing, and visualizing logs, metrics, and traces from cloud and container environments. The platform distinguishes itself with specialized tooling for large language model monitoring and session replay, allowing user interactions in the browser to be linked to backend telemetry. It employs schema-less JSON parsing to index structured logs dynamically and uses source maps to resolve minified sta

GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without

Cortex is an open-source, horizontally scalable metrics platform that ingests, stores, and queries Prometheus-compatible time-series data with multi-tenant isolation. It accepts metrics via Prometheus remote write and OpenTelemetry, executes PromQL queries against both recent and historical data, and provides a Prometheus-compatible alerting and recording rule engine with an integrated Alertmanager. The system is built as a set of independently scalable microservices that use hash-ring-based sharding, gossip-based cluster membership, and tenant-aware object storage to distribute workloads acro

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 performanc

Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing AI agents built with computational graphs. It provides a containerized runtime environment that handles agent execution, state persistence, and the versioning of AI assistants. The platform distinguishes itself through deep integration with the Model Context Protocol, allowing agents to function as servers that expose tools and capabilities to external clients. It features a sophisticated observability suite for capturing execution traces, performing LLM-based evaluations agai

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

TUnit is a comprehensive C# testing framework, mocking library, and fluent assertion tool. It utilizes source generation for test discovery and mock creation, ensuring compatibility with Native AOT and IL trimming by eliminating the need for runtime reflection and proxies. The framework provides specialized capabilities for integration testing, including the management of distributed application lifecycles, isolated database schemas, and the correlation of telemetry and logs across process boundaries via OTLP. It also includes an HTTP testing utility to intercept network exchanges and mock AP

FoundationDB is an ACID-compliant distributed transactional key-value store. It functions as a scalable database engine that ensures strict serializability and data consistency across a cluster of servers using a shared-nothing architecture. The system is distinguished by its multi-region replication capabilities, allowing data to be synchronized across different datacenters for high availability and disaster recovery. It utilizes optimistic concurrency control to manage distributed transactions and employs a majority-based coordination system to maintain cluster state. The platform provides

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 instru

OpenSearch is a distributed search and analytics engine designed for indexing, searching, and analyzing massive volumes of structured and unstructured data in real time. It functions as a comprehensive platform that integrates enterprise-grade search capabilities, a vector database for high-dimensional similarity lookups, and a unified observability suite for monitoring logs, metrics, and traces across complex distributed environments. The platform distinguishes itself through its support for agentic workflow automation, allowing users to orchestrate multi-agent tasks and integrate foundation

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

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 immed

Highlight is a full-stack observability platform and monitoring system that aggregates logs, errors, and distributed traces to provide a unified view of application health. It functions as a distributed tracing system, an error monitoring service, and a session replay tool. The platform is available as a dockerized monitoring stack for self-hosted deployments on Linux. It distinguishes itself by combining backend observability with a visual recording system that captures document object model changes and network requests to replay user interactions. The system covers several core capability

Pinpoint is a distributed application performance management tool designed to trace requests and monitor metrics across large-scale distributed architectures. It functions as a request tracer, topology mapper, and JVM application monitor, providing a backend capable of collecting and visualizing trace data from OpenTelemetry compatible sources. The system distinguishes itself through a combination of bytecode-based instrumentation via a Java agent and topology-based visualization that renders live maps of service interconnections. It captures execution flow across asynchronous boundaries, suc

This project is a comprehensive educational resource and curriculum focused on site reliability engineering, distributed systems, and infrastructure operations. It provides technical guides, a systems engineering course, and instructional manuals designed to teach the principles of managing large-scale computing environments. The curriculum covers high-level architectural design for scalability and resilience, including fault-tolerant infrastructure, high-availability patterns, and microservices decomposition. It emphasizes the practical application of site reliability engineering through the

Databend is a cloud-native data warehouse and OLAP database designed for large-scale analytics. It functions as a SQL-compliant engine and serverless analytics platform that separates compute from storage to allow for independent scaling. The system integrates vector database capabilities, indexing high-dimensional embeddings to enable semantic, hybrid, and full-text searches across massive datasets. It further distinguishes itself through serverless compute management that automatically scales resources based on demand and shuts them down during idle periods. The platform covers a broad set

Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

InfluxDB is a high-performance time-series database designed for collecting, storing, and querying time-stamped metrics and event data. It functions as a columnar time-series store and a real-time analytics engine, providing a network-accessible interface for retrieving and analyzing temporal records. The system utilizes a specialized columnar storage format to support high ingestion rates and efficient data retrieval. It incorporates a programmable runtime for executing custom plugins and triggers, including integration for processing and transforming incoming data streams. The platform cov

Apache Druid is a real-time analytics database and distributed columnar time-series store designed for sub-second analytical queries. It functions as a data platform featuring a distributed SQL query engine and a real-time data ingestion system for moving historical and streaming data from external sources. The system is distinguished by its ability to provide low-latency analytics under high concurrency to power operational dashboards. It implements a Kerberos-secured environment for user authentication and employs a shared-nothing cluster architecture to enable horizontal scaling. The plat

This project is a comprehensive technical guide and diagnostic manual for analyzing memory, performance, and asynchronous behavior within Node.js applications. It provides detailed methods for asynchronous tracing, memory diagnostics, and performance analysis to resolve runtime errors and execution bottlenecks. The resource distinguishes itself by covering advanced diagnostic workflows, including the use of flame graphs for CPU profiling, the capture and comparison of heap snapshots for memory leak detection, and the mapping of asynchronous call stacks. It also provides technical guidance on

This project is a collection of reference implementations and demonstration projects covering computer vision, DevOps automation, distributed systems, and Java-based microservices. It provides a programming reference library and practical examples for building server-side applications, containerizing services, and orchestrating clusters. The repository features a comprehensive toolset for DevOps automation, including scripts and playbooks for CI/CD pipelines and automated cluster installation. It includes a computer vision demo project for image object detection and facial analysis, as well a

This project provides a comprehensive architectural blueprint and implementation set for building a platform-as-a-service on Kubernetes. It serves as a technical resource for deploying container orchestration environments, managing the full software development lifecycle, and integrating a complete DevOps toolchain. The implementation emphasizes automated software delivery through the integration of build and delivery pipelines, private container registries, and distributed configuration systems. It enables the decoupling of application settings from images via a centralized configuration man

DeepOps is a full-stack observability platform and application performance monitoring tool. It serves as a distributed service observability suite designed to track response times, resource usage, and service health across diverse infrastructure layers. The platform functions as a cross-stack telemetry aggregator, unifying metrics and logs into a single data stream. It incorporates a heuristic anomaly detection system that analyzes performance baselines to identify statistical outliers and predict operational failures. The system covers a broad range of monitoring capabilities, including rea

Meshery is a service mesh management plane and cloud native infrastructure orchestrator. It provides a visual design-as-code environment for modeling microservices and infrastructure components through declarative blueprints, functioning as a centralized platform for designing, deploying, and managing service mesh infrastructure. The platform is distinguished by its ability to translate visual designs into active deployments and its use of gRPC-based adapters to integrate with diverse infrastructure providers. It features a multi-tenant architecture that manages shared workspaces and role-bas