In this article I’m exploring the process of implementing a Fan-Out/ Fan-In pipeline in Go and Rust. Fan-Out is a term to describe the process of starting multiple workers to handle input from the pipeline, and Fan-In is a term to describe the process of combining multiple results into one channel. I’m assuming that you have some familiarity with both Go and Rust languages. I’m new to Rust also, therefore it’s fair to say that this is about concurrency in Go & Rust through the eyes of a GO developer.
This is the continuation of the previous post. I would strongly recommend reading Part 1 first then continue here as the description of the program, the intent and the current state are relevant as we move forward. Iter 3 - Remove Contention Iter 3 code on Github Reading line by line from the stream and sending those over to the goroutines to be processed works, but may not be the most efficient way to process a large file.
In this blog post I’ll iterate over a simple script and refactor it each time to improve its performance. Before diving into the details I would quote a couple of well known individuals from the GO Community. “You want to write code that is optimized for correctness. Don’t make coding decisions based on what you think might perform better. You must benchmark or profile to know if code is not fast enough.
TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks and it is used for machine learning applications such as neural networks. Tensorflow has grown to become one of the widely adopted ML platforms in the world. TensorFlow offers multiple levels of abstraction so you can choose the right one for your needs. You can build and train models by using the high-level Keras API, which makes getting started with TensorFlow and machine learning easy.
gRPC describes itself as: gRPC is a modern open source high performance RPC framework that can run in any environment. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking and authentication. It is also applicable in last mile of distributed computing to connect devices, mobile applications and browsers to backend services. This is Part 3 of a series of three articles describing gRPC functionality.
Let’s assume that the dev teams have been asked to extend the services and there are multiple teams working on it. Often, the teams prefer a specific programming language for a certain job. Luckily, gRPC is supported by a broad range of languages. We are going to extend the service to communicate to a storage service, but this time the service will be written in Python. This simple service can be written in whatever language of choice but in order to showcase the gRPC functionality in Python we could say that it is a project specification.
As we are moving more towards distributed systems and micro-services, the way the services are communicating between each other becomes increasingly important. As you can imagine, the request-response delay is multiplied by the number micro-services running in the backend. Until recently, the industry standard was JSON over REST, which is a software architectural style that defines a set of recommendations for designing loosely coupled applications that use the HTTP protocol for data transmission.