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High Performance
Systems in Go
Derek Collison
April 24, 2014
GopherCon
About
Architected/Built TIBCO Rendezvous and EMS Messaging Systems

Designed and Built CloudFoundry at VMware

Co-founded AJAX APIs group at Google

Distributed Systems

Founder of Apcera, Inc. in San Francisco, CA

@derekcollison

derek@apcera.com
Derek Collison
Why Go?
• Simple Compiled Language
• Good Standard Library
• Concurrency
• Synchronous Programming Model
• Garbage Collection
• STACKS!
Why Go?
• Not C/C++
• Not Java (or any JVM based language)
• Not Ruby/Python/Node.js
High Performance Systems in Go - GopherCon 2014
What about
High Performance?
NATS
High Performance Systems in Go - GopherCon 2014
NATS Messaging 101
• Subject-Based
• Publish-Subscribe
• Distributing Queueing
• TCP/IP Overlay
• Clustered Servers
• Multiple Clients (Go, Node.js, Java, Ruby)
High Performance Systems in Go - GopherCon 2014
High Performance Systems in Go - GopherCon 2014
NATS
• Originally written to support CloudFoundry
• In use by CloudFoundry, Baidu, Apcera and others
• Written first in Ruby -> 150k msgs/sec
• Rewritten at Apcera in Go (Client and Server)
• First pass -> 500k msgs/sec
• Current Performance -> 5-6m msgs/sec
Tuning NATS (gnatsd)
or
how to get from 500k to 6m
High Performance Systems in Go - GopherCon 2014
Target Areas
• Shuffling Data
• Protocol Parsing
• Subject/Routing
Target Areas
• Shuffling Data
• Protocol Parsing!
• Subject/Routing
Protocol Parsing
• NATS is a text based protocol
• PUB foo.bar 2rnokrn	
• SUB foo.> 2rn	
• Ruby version based on RegEx
• First Go version was port of RegEx
• Current is zero allocation byte parser
High Performance Systems in Go - GopherCon 2014
High Performance Systems in Go - GopherCon 2014
High Performance Systems in Go - GopherCon 2014
Some Tidbits
• Early on, defer was costly
• Text based proto needs conversion from ascii to int
• This was also slow due to allocations in
strconv.ParseInt
defer
defer Results
golang1.3 looks promising
parseSize
parseSize
vs
strconv.ParseInt
Target Areas
• Shuffling Data
• Protocol Parsing
• Subject/Routing
Subject Router
• Matches subjects to subscribers
• Utilizes a trie of nodes and hashmaps
• Has a frontend dynamic eviction cache
• Uses []byte as keys (Go’s builtin does not)
Subject Router
• Tried to avoid []byte -> string conversions
• Go’s builtin hashmap was slow pre 1.0
• Built using hashing algorithms on []byte
• Built on hashmaps with []byte keys
Hashing Algorithms
Hashing Algorithms
Jesteress
HashMap Comparisons
Some Lessons Learned
• Use go tool pprof (linux)
• Avoid short lived objects on the heap
• Use the stack or make long lived objects
• Benchmark standard library builtins (strconv)
• Benchmark builtins (defer, hashmap)
• Don’t use channels in performance critical path
Big Lesson Learned?
Go is a good choice
for performance based
systems
Go is getting better
faster than the others
Thanks
Resources
• https://github.com/apcera/gnatsd
• https://github.com/apcera/nats
• https://github.com/derekcollison/nats

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