Finagle VS Apache Thrift

Not sure about now but a few years back the company I worked for was heavily vested in Finagle [1] using Future pools. I'm sure virtual threads would only enhance this framework. Also, Spring and it's reactive webflux would probably benefit as well [2]. [1] https://twitter.github.io/finagle/ [2] https://docs.spring.io/spring-framework/reference/web/webflux/reactive-spring.html. - Source: Hacker News / 9 months ago

Don't really see how "enterprise scala" has anything to do with this, scala is meant to be parallelized , that's like it's whole thing with akka / actors / twitter's finagle (https://twitter.github.io/finagle/). Source: about 1 year ago

Bro it's their fucking project lolhttps://twitter.github.io/finagle/. Source: over 1 year ago

You can even see it mentioned in Finagle's project, which is what Twitter uses https://twitter.github.io/finagle/. Source: over 1 year ago

RPC generally means server side calls, probably this https://twitter.github.io/finagle/, and XHR is not RPC. - Source: Hacker News / over 1 year ago

While gRPC and Apache Thrift have served the microservice architecture well, CloudWeGo's advanced features and performance metrics set it apart as a promising open source solution for the future. - Source: dev.to / 2 months ago

Services in general communicate via Thrift (and in some cases HTTP). Source: about 1 year ago

Protocol Buffers is the most popular one, but there are many others such as Apache Thrift and my own Typical. Source: about 1 year ago

RPC is not strictly OO, but you can think of RPC calls like method calls. In general it will reflect your interface design and doesn't have to be top-down, although a good project usually will look that way. A good contrast to REST where you use POST/PUT/GET/DELETE pattern on resources where as a procedure call could be a lot more flexible and potentially lighter weight. Think of it like defining methods in code... Source: over 1 year ago

The information can be stored in a database or as files, serialized in a standard format and with a schema agreed with your Data Engineering team. Depending on your information and requirements, it can be as simple as CSV, XML or JSON, or Big Data formats such as Parquet, Avro, ORC, Arrow, or message serialization formats like Protocol Buffers, FlatBuffers, MessagePack, Thrift, or Cap'n Proto. - Source: dev.to / over 1 year ago