Projects sharing features with Dora

litegraph.js is a JavaScript dataflow framework and visual node graph engine used to define programmable logic and data flow. It provides a node-based visual programming tool for designing complex logic through connected functional blocks. The library allows for the creation of hierarchical logic by nesting multiple nodes into recursive subgraphs. It also supports the development of custom node types with unique inputs and outputs, as well as custom widgets and live views that can hide the underlying graph structure to present a visual interface. The engine enables the execution of logic gra

Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis

ROS 2 is a distributed communication middleware for robot systems, built on a peer-to-peer Data Distribution Service (DDS) foundation. It provides a publish-subscribe messaging layer where typed data flows between decoupled components across a network, along with synchronous remote procedure calls and action-based task execution for long-running goals with periodic feedback. The framework includes an interface definition language that generates type-safe code for messages, services, and actions, ensuring cross-language compatibility, and supports fine-grained Quality of Service profiles that c

Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies across global equities, futures, and cryptocurrencies. It integrates an event-driven backtesting engine, a multi-market execution gateway for order routing, and a quantitative data pipeline for ingesting and storing multi-asset market data. The system features a Rust-accelerated financial library that utilizes Apache Arrow for high-performance technical indicator calculation and zero-copy data processing. It provides a containerized infrastructure model designed for orchestrati

BullMQ is a Redis-backed message queue library and background processor designed for distributed task queueing. It functions as a distributed queue manager and task scheduler, utilizing Redis to manage asynchronous job processing and persistence. The system distinguishes itself through its role as a job workflow orchestrator, enabling the definition of complex parent-child job dependencies and hierarchies for multi-step workflows. It provides sandboxed process execution to isolate heavy workloads and prevent event loop blocking, alongside distributed rate limiting to protect downstream servic

Fluent Bit is a cloud-native log shipper and unified telemetry collector designed as a resource-efficient data pipeline. It ingests logs, metrics, and traces from multiple sources, processing them in real-time before routing the data to external storage backends. The project functions as a real-time stream processor and OpenTelemetry log processor, capable of transforming and filtering data using SQL and conditional logic. It also acts as a distributed tracing agent that can sample traces to reduce data volume while preserving full request paths. The system provides reliable data delivery th

Nextflow is a dataflow workflow engine and distributed computing framework used to build and execute data-intensive pipelines. It serves as a scientific workflow language that allows users to define reproducible data processing sequences, supporting any scripting language through shebang declarations. The system functions as a containerized pipeline orchestrator, utilizing container technologies to ensure software dependencies remain consistent across different environments. It decouples workflow logic from the underlying infrastructure, enabling the same pipeline to run on local machines, cl

Quartz.NET is a job scheduler for .NET applications designed to schedule and execute programmatic tasks. It functions as a distributed task orchestrator and enterprise task orchestrator, capable of managing recurring jobs with concurrency limits and complex intervals. The system provides high availability through a clustered execution model that balances loads and provides fail-over redundancy across multiple server instances. It utilizes a relational database job store to persist job and trigger states, ensuring that scheduled tasks survive application restarts. The framework includes capab

Azkaban is a distributed workflow manager and DAG-based job orchestrator designed as an enterprise batch processor. It serves as a Java-based workflow engine that schedules and executes complex job sequences across a cluster of executor servers, with specific functionality for managing big data workloads on Hadoop clusters. The system distinguishes itself through a distributed executor model that coordinates state via a shared database to ensure high availability. It employs a plugin-based architecture that allows for custom job types and system functionality extensions, including the ability

Cycle.js is a functional reactive web framework and observable-driven UI library used for building web applications. It employs a reactive stream architecture that treats the document object model and side effects as continuous data streams, coordinating state and logic through a functional cycle. The framework distinguishes itself by isolating side effects from core logic using a system of drivers. This architecture maps external events to internal commands through a symmetric input-output pipeline, ensuring that the application remains a cycle of pure functions. The project provides capabi

Pathway is a high-performance data processing framework designed for building unified batch and streaming pipelines. It functions as an orchestrator for complex data transformations, utilizing a differential dataflow engine to process updates incrementally. By treating static datasets and continuous event streams with identical logic, the platform ensures exactly-once processing semantics and consistent results across diverse data sources. The framework distinguishes itself through its specialized support for real-time artificial intelligence and retrieval-augmented generation. It features in

Noria is a dynamic dataflow engine and materialized view database that transforms expensive read operations into fast keyed lookups. It operates as a stateful system that incrementally updates dependent views as underlying relational base tables change. The system can function as a MySQL protocol emulator, mimicking the binary protocol to allow applications to fetch precomputed data without changing client code. It can also be used as a Rust embedded database, integrating the server directly into a program via native bindings to perform dataflow operations without external coordination. The

This project is an open-source software development kit and framework for implementing the Matter smart home standard. It provides a universal IPv6-based application layer and a cluster-based data model to ensure interoperability between diverse smart home devices and controllers. The system is distinguished by its multi-transport network abstraction, which maps Bluetooth LE, Thread, and Wi-Fi implementations to a common layer. It includes specialized tooling for secure device commissioning via QR codes and NFC, as well as a comprehensive over-the-air firmware update system for distributing s

Arroyo is a high-performance stream processing platform built in Rust. It executes continuous SQL queries on streaming data with event-time semantics, enabling accurate windowed aggregations, joins, and stateful computations on unbounded event streams. The platform uses native Rust execution for high throughput and low latency, with periodic checkpointing for exactly-once fault tolerance and horizontal scaling across distributed workers. The system integrates deeply with Kafka for reading and writing topics with exactly-once delivery and supports change data capture (CDC) from MySQL and Postg

This project is a learning curriculum and programming guide for Apache Spark, providing a structured set of educational resources and practical code examples for mastering distributed data processing. It serves as a course for building scalable data workflows and big data engineering pipelines. The repository provides practical source code and project layouts that demonstrate how to connect external data stores, process streaming data, and organize code for distributed environments. It includes implementation examples for scaling machine learning algorithms across clusters to handle large tra

Storm is a distributed stream processing framework designed to execute unbounded computations across a cluster to process real-time data streams. It functions as a data pipeline orchestrator that allows users to define and deploy declarative data flow graphs connecting streaming sources to processing components. The system operates as a multi-tenant distributed compute engine that isolates workloads and limits resource usage across shared clusters using dedicated pools and access control. It is also a secure distributed processing engine that employs encrypted node communication and SSL-secur

Fluvio is a distributed event streaming platform and cloud-native streaming engine designed for collecting, persisting, and replicating real-time data streams across a distributed cluster. It functions as a real-time data pipeline for building stateful workflows that ingest, enrich, and export data between external sources and sinks. The platform is distinguished by its use of WebAssembly to execute compiled modules for in-line data transformations and filtering. This allows for the execution of custom business logic to reshape information in motion without requiring a restart of the cluster.

Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e

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

APScheduler is a Python task scheduler designed to execute functions at specific times or recurring intervals. It functions as an asynchronous background scheduler and distributed job dispatcher, allowing tasks to run concurrently with application lifecycles and web server request handling. The system distinguishes itself through a persistent job store that saves schedules and task states in external databases, ensuring continuity across process restarts. It separates task scheduling from execution by dispatching jobs to distributed workers in separate processes to prevent execution bottlenec

Oban is a distributed background job processing system and task scheduler that uses PostgreSQL for transactional job storage and reliable execution across multiple nodes. It serves as a PostgreSQL-backed background worker and job queue, coordinating task execution and concurrency through a relational database to ensure delivery guarantees. The system differentiates itself through a distributed workflow orchestrator capable of managing multi-step processing pipelines, dependent job sequencing, and shared context. It provides advanced orchestration tools including job batching, chunked processi

Perspective is a columnar data analytics library and streaming data visualization engine. It provides an interactive data grid component and notebook analytics widgets designed for processing high-volume data and rendering interactive charts and grids. The system utilizes a high-performance query engine to enable real-time data analysis and streaming dataset visualization. It supports the creation of customizable dashboards and reports that update automatically as new data arrives without requiring full dataset reloads. The project covers large-scale dataset analytics through a schema-driven

Apache Storm is a distributed stream processing framework and real-time data processing engine. It functions as a fault-tolerant distributed computing system designed to analyze data in motion across a cluster of machines for continuous stream computation. The system enables the creation of fault-tolerant data pipelines and scalable event processing by distributing workloads across a network of computing nodes. This architecture ensures low latency and high throughput for live data while allowing the system to recover automatically from individual node failures. The framework provides capabi

pysheeet is a technical reference library providing a curated collection of code snippets and implementation patterns for advanced Python development, system integration, and high-performance computing. It serves as a comprehensive guide for implementing low-level network programming, native C extensions, and asynchronous and concurrent programming. The project provides specialized frameworks for the development and deployment of large language models, including tools for distributed GPU inference and high-performance serving. It also includes detailed patterns for high-performance computing

Ignite is a distributed in-memory data grid and compute platform. It functions as a distributed SQL database and storage engine designed to store and process large datasets in RAM to minimize latency and increase calculation speed. The system is distinguished by a multi-tier storage engine that manages data placement across memory and disk to balance high-speed access with large capacity. It features a distributed compute grid that executes custom logic directly on the nodes where data resides to reduce network traffic. The platform provides a broad set of capabilities including ACID transac

BehaviorTree.CPP is a C++ library for designing task-switching logic and reactive behaviors using a hierarchical tree structure. It provides a framework for creating reactive logic systems that manage complex behaviors through an execution engine and a centralized data store for sharing state between nodes. The library decouples high-level behavioral logic from source code by using an XML parser to define tree morphology. It further extends its functionality through a dedicated visualizer for monitoring real-time execution states and a specialized bridge for integrating ROS2 actions and servi

GoCD is a continuous delivery server and build automation platform designed to orchestrate software delivery pipelines. It functions as a CD pipeline orchestrator that manages the automated execution of build, test, and deployment stages to move code from commit to production. The system utilizes an agent-based job execution model where remote agents pull work from a central server via polling. It employs a state-machine approach to pipeline orchestration, tracking the progression of software through stages and managing immutable build outputs via a central artifact repository to ensure consi