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Source channel @githubtrending · Post #15049 · Aug 12

#java#distributed_systems#durable_execution#grpc#java#javascript#microservice_orchestration#orchestration_engine#orchestrator#reactjs#spring_boot#workflow_automation#workflow_engine#workflow_management#workflows Conductor is an open-source tool that helps you manage and automate complex workflows involving many microservices and systems. It makes your workflows flexible, reliable, and scalable by handling retries, errors, and monitoring automatically. You can define workflows as code in JSON, use various task types, and manage workflows dynamically without tightly coupling services. It offers an easy-to-use web interface and supports multiple databases like Redis and MySQL. This helps you build, run, and monitor workflows efficiently, saving time and reducing errors in managing distributed applications. It also has SDKs for Java, Python, JavaScript, Go, and C# to integrate easily with your projects. https://github.com/conductor-oss/conductor

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djangoproject

@djangoproject · Post #118 · 08/08/2016, 11:44 AM

https://docs.python.org/3/library/multiprocessing.html multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows. The #multiprocessing module also introduces #APIs which do not have analogs in the #threading#module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data #parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,