What is Go?
Who uses Go?
Why developers like Go?
Here are some stack decisions, common use cases and reviews by members of with Go in their tech stack.
Working on a project recently, wanted an easy modern frontend to work with, decoupled from our backend. To get things going quickly, decided to go with React, Redux.js, redux-saga, Bootstrap.
On the backend side, Go is a personal favourite, and wanted to minimize server overheads so went with a #serverless architecture leveraging AWS Lambda, AWS CloudFormation, Amazon DynamoDB, etc.
For IDE/tooling I tend to stick to the #JetBrains tools: WebStorm / Goland.
Obviously using Git, with GitLab private repo's for managing code/issues/etc.
#te Go lkj
Kafka #hoho hajhgjh
I chose Sqreen because it provides an out-of-the-box Security as a Service solution to protect my customer data. I get full visibility over my application security in real-time and I reduce my risk against the most common threats. My customers are happy and I don't need to spend any engineering resources or time on this. We're only alerted when our attention is required and the data that is provided helps engineering teams easily remediate vulnerabilities. The platform grows with us and will allow us to have all the right tools in place when our first security engineer joins the company. Advanced security protections against business logic threats can then be implemented.
Installation was super easy on my Node.js and Ruby apps. But Sqreen also supports Python , Java , PHP and soon Go .
It integrates well with the tools I'm using every day Slack , PagerDuty and more.
Go is used for the server side code that harvests data, generates reports, and hosts the API for interacting with Social Harvest. It is the major piece of technology used to make Social Harvest possible. Go
Here are some stack decisions, common use cases and reviews by companies and developers who chose Go in their tech stack.
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.
After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...
We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. Behind the scenes the Config API is built with Go , GRPC and Envoy.
At Segment, we build new services in Go by default. The language is simple so new team members quickly ramp up on a codebase. The tool chain is fast so developers get immediate feedback when they break code, tests or integrations with other systems. The runtime is fast so it performs great at scale.
For the newest round of APIs we adopted the GRPC service #framework.
The Protocol Buffer service definition language makes it easy to design type-safe and consistent APIs, thanks to ecosystem tools like the Google API Design Guide for API standards,
uber/prototool for formatting and linting .protos and
lyft/protoc-gen-validate for defining field validations, and
grpc-gateway for defining REST mapping.
With a well designed .proto, its easy to generate a Go server interface and a TypeScript client, providing type-safe RPC between languages.
For the API gateway and RPC we adopted the Envoy service proxy.
segmentapis.com endpoint is an Envoy front proxy that rate-limits and authenticates every request. It then transcodes a #REST / #JSON request to an upstream GRPC request. The upstream GRPC servers are running an Envoy sidecar configured for Datadog stats.
The result is API #security , #reliability and consistent #observability through Envoy configuration, not code.
We experimented with Swagger service definitions, but the spec is sprawling and the generated clients and server stubs leave a lot to be desired. GRPC and .proto and the Go implementation feels better designed and implemented. Thanks to the GRPC tooling and ecosystem you can generate Swagger from .protos, but it’s effectively impossible to go the other way.
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
We are hardcore Kubernetes users and contributors. We loved the automation it provides. However, as our team grew and added more clusters and microservices, capacity and resources management becomes a massive pain to us. We started suffering from a lot of outages and unexpected behavior as we promote our code from dev to production environments. Luckily we were working on our AI-powered tools to understand different dependencies, predict usage, and calculate the right resources and configurations that should be applied to our infrastructure and microservices. We dogfooded our agent (http://github.com/magalixcorp/magalix-agent) and were able to stabilize as the #autopilot continuously recovered any miscalculations we made or because of unexpected changes in workloads. We are open sourcing our agent in a few days. Check it out and let us know what you think! We run workloads on Microsoft Azure Google Kubernetes Engine and Amazon EC2 and we're all about Go and Python!
In our company we have think a lot about languages that we're willing to use, there we have considering Java, Python and C++ . All of there languages are old and well developed at fact but that's not ideology of araclx. We've choose a edge technologies such as Node.js , Rust , Kotlin and Go as our programming languages which is some kind of fun. Node.js is one of biggest trends of 2019, same for Go. We want to grow in our company with growth of languages we have choose, and probably when we would choose Java that would be almost impossible because larger languages move on today's market slower, and cannot have big changes.