Package go-diameter is an implementation of the Diameter Base Protocol RFC 6733 and a stack for the Go programming language.
The current implementation is solid and works fine for general purpose clients and servers. It can send and receive messages efficiently as well as build and parse AVPs based on dictionaries.
See the API documentation at http://godoc.org/github.com/fiorix/go-diameter
- Comprehensive XML dictionary format
- Embedded dictionaries (base protocol and credit control RFC 4006)
- Human readable AVP representation (for debugging)
- TLS, IPv4 and IPv6 support for both clients and servers
- Stack based on net/http for simplicity
- Ships with sample client, server, snoop agent and benchmark tool
- State machines for CER/CEA and DWR/DWA for clients and servers
go-diameter requires at least Go 1.4.
Make sure Go is installed, and both GOPATH and GOROOT are set.
Install:
go get github.com/fiorix/go-diameter/diam
Check out the examples:
cd $GOPATH/src/github.com/fiorix/go-diameter/examples
See the test cases for more specific examples.
Clients and servers written with the go-diameter package can be quite performant if done well. Besides Go benchmarks, the package ships with a simple benchmark tool to help testing servers and identifying bottlenecks.
In the examples directory, the server has a pprof (http server) that
allows the go pprof tool to profile the server in real time. The client
can perform benchmarks using the -bench command line flag.
For better performance, avoid logging diameter messages. Although logging
is very useful for debugging purposes, it kills performance due to a number
of conversions to make messages look pretty. If you run benchmarks on the
example server, make sure to use the -s (silent) command line switch.
TLS degrades performance a bit, as well as reflection (Unmarshal). Those are important trade offs you might have to consider.
Besides this, the source code (and sub-packages) have function benchmarks that can help you understand what's fast and isn't. You will see that parsing messages is much slower than writing them, for example. This is because in order to parse messages it makes numerous dictionary lookups for AVP types, to be able to decode them. Encoding messages require less lookups and is generally simpler, thus faster.