easyjson VS Juju

Compile time means you catch issues at... well compile time. It also means that the code is optimized. You can look at the performance different between encoding/json and easyjson for why you may desire that.

easyjson produced an empty file

There is https://github.com/mailru/easyjson out there if you are absolutely sure that serialization is the bottleneck. Otherwise I'd go for stdlib.

https://github.com/mailru/easyjson fast JSON (de)serializer which go generates code instead of using reflect at runtime.

If you know the schema of the JSON ahead of time and you need to parse the whole object, I would recommend https://github.com/mailru/easyjson as that will likely give you the fastest result. This works in almost all use cases, and easy to use features such as string interning can save you a lot of time on memory allocation if you parse a lot of JSON objects with identical values.

What to use Easyjson is about the top of the pack and it's straightforward. The downside of efficient tools is that they use code generation to create the code required to turn your structs into json to minimise allocations. This is a manual build step which is annoying. Interestingly json-iterator also uses reflection but it's significantly faster. I suspect black magic.

I'm looking for something similar to https://github.com/mailru/easyjson where one can generate a concrete JSON parser for some types.

Бібліотека easyjson теж для серіалізації працює через додатковий код замість використання рефлексії. Але після внесення в easyjson одної з оптимізацій, час від часу почали отримувати зламаний JSON, ось приклад тесту який покаже помилку.

Basically juju is used to deploy microservices, and other stuff too: https://juju.is/

Juju https://juju.is/

https://juju.is/

Each app is packaged in a charm which seems to be a yaml declaring inputs, dependencies and other meta data and optional python code that can respond to certain lifecycle hooks

https://discourse.charmhub.io/t/implementing-relations/1051

   name: my-node-app

I am currently working myself through setting up our new Research GPU-Cluster where we have "sort of" managed to deploy MAAS to manage all the servers more efficiently, and on top of MAAS then use Juju to deploy the further components of the cluster. The components here are

Kubernetes Custom Resources were created to allow the Kubernetes API to be extended for domain-specific logic, by defining new resource types and controllers. OSS frameworks like operator-sdk, kubebuilder and juju were created to simplify the creation of custom resources and their controllers. Tools built with these frameworks came to be known as Operators.

Depending on what you're trying to accomplish, Juju and MAAS, if not just automate with preseed for custom desktops.

At Canonical I work on two open-source projects written in Go: Juju, a large cloud-based application deployment tool, and Pebble, a small Linux service manager. Both include CLI clients and API-based server daemons. Juju in particular is a large distributed system.

The new Portainer charm allows users of Canonical’s Charmed Kubernetes distribution to automatically install and integrate Portainer Business as part of the Kubernetes cluster deployment process, using Juju, the Charmed Operator framework.