Concurrent data structures for Go. Aims to provide more scalable alternatives for some of the data structures from the standard sync package, but not only.
Covered with tests following the approach described here.
Benchmark results may be found here. I'd like to thank @felixge who kindly ran the benchmarks on a beefy multicore machine.
Also, a non-scientific, unfair benchmark comparing Java's j.u.c.ConcurrentHashMap and xsync.MapOf is available here.
The latest xsync major version is v3, so /v3 suffix should be used when importing the library:
import (
"github.com/puzpuzpuz/xsync/v3"
)Note for pre-v3 users: v1 and v2 support is discontinued, so please upgrade to v3. While the API has some breaking changes, the migration should be trivial.
A Counter is a striped int64 counter inspired by the j.u.c.a.LongAdder class from the Java standard library.
c := xsync.NewCounter()
// increment and decrement the counter
c.Inc()
c.Dec()
// read the current value
v := c.Value()Works better in comparison with a single atomically updated int64 counter in high contention scenarios.
A Map is like a concurrent hash table-based map. It follows the interface of sync.Map with a number of valuable extensions like Compute or Size.
m := xsync.NewMap()
m.Store("foo", "bar")
v, ok := m.Load("foo")
s := m.Size()Map uses a modified version of Cache-Line Hash Table (CLHT) data structure: https://github.com/LPD-EPFL/CLHT
CLHT is built around the idea of organizing the hash table in cache-line-sized buckets, so that on all modern CPUs update operations complete with minimal cache-line transfer. Also, Get operations are obstruction-free and involve no writes to shared memory, hence no mutexes or any other sort of locks. Due to this design, in all considered scenarios Map outperforms sync.Map.
One important difference with sync.Map is that only string keys are supported. That's because Golang standard library does not expose the built-in hash functions for interface{} values.
MapOf[K, V] is an implementation with parametrized key and value types. While it's still a CLHT-inspired hash map, MapOf's design is quite different from Map. As a result, less GC pressure and fewer atomic operations on reads.
m := xsync.NewMapOf[string, string]()
m.Store("foo", "bar")
v, ok := m.Load("foo")Apart from CLHT, MapOf borrows ideas from Java's j.u.c.ConcurrentHashMap (immutable K/V pair structs instead of atomic snapshots) and C++'s absl::flat_hash_map (meta memory and SWAR-based lookups). It also has more dense memory layout when compared with Map. Long story short, MapOf should be preferred over Map when possible.
An important difference with Map is that MapOf supports arbitrary comparable key types:
type Point struct {
x int32
y int32
}
m := NewMapOf[Point, int]()
m.Store(Point{42, 42}, 42)
v, ok := m.Load(point{42, 42})Both maps use the built-in Golang's hash function which has DDOS protection. This means that each map instance gets its own seed number and the hash function uses that seed for hash code calculation. However, for smaller keys this hash function has some overhead. So, if you don't need DDOS protection, you may provide a custom hash function when creating a MapOf. For instance, Murmur3 finalizer does a decent job when it comes to integers:
m := NewMapOfWithHasher[int, int](func(i int, _ uint64) uint64 {
h := uint64(i)
h = (h ^ (h >> 33)) * 0xff51afd7ed558ccd
h = (h ^ (h >> 33)) * 0xc4ceb9fe1a85ec53
return h ^ (h >> 33)
})When benchmarking concurrent maps, make sure to configure all of the competitors with the same hash function or, at least, take hash function performance into the consideration.
A MPMCQueue is a bounded multi-producer multi-consumer concurrent queue.
q := xsync.NewMPMCQueue(1024)
// producer inserts an item into the queue
q.Enqueue("foo")
// optimistic insertion attempt; doesn't block
inserted := q.TryEnqueue("bar")
// consumer obtains an item from the queue
item := q.Dequeue() // interface{} pointing to a string
// optimistic obtain attempt; doesn't block
item, ok := q.TryDequeue()MPMCQueueOf[I] is an implementation with parametrized item type. It is available for Go 1.19 or later.
q := xsync.NewMPMCQueueOf[string](1024)
q.Enqueue("foo")
item := q.Dequeue() // stringThe queue is based on the algorithm from the MPMCQueue C++ library which in its turn references D.Vyukov's MPMC queue. According to the following classification, the queue is array-based, fails on overflow, provides causal FIFO, has blocking producers and consumers.
The idea of the algorithm is to allow parallelism for concurrent producers and consumers by introducing the notion of tickets, i.e. values of two counters, one per producers/consumers. An atomic increment of one of those counters is the only noticeable contention point in queue operations. The rest of the operation avoids contention on writes thanks to the turn-based read/write access for each of the queue items.
In essence, MPMCQueue is a specialized queue for scenarios where there are multiple concurrent producers and consumers of a single queue running on a large multicore machine.
To get the optimal performance, you may want to set the queue size to be large enough, say, an order of magnitude greater than the number of producers/consumers, to allow producers and consumers to progress with their queue operations in parallel most of the time.
A RBMutex is a reader-biased reader/writer mutual exclusion lock. The lock can be held by many readers or a single writer.
mu := xsync.NewRBMutex()
// reader lock calls return a token
t := mu.RLock()
// the token must be later used to unlock the mutex
mu.RUnlock(t)
// writer locks are the same as in sync.RWMutex
mu.Lock()
mu.Unlock()RBMutex is based on a modified version of BRAVO (Biased Locking for Reader-Writer Locks) algorithm: https://arxiv.org/pdf/1810.01553.pdf
The idea of the algorithm is to build on top of an existing reader-writer mutex and introduce a fast path for readers. On the fast path, reader lock attempts are sharded over an internal array based on the reader identity (a token in the case of Golang). This means that readers do not contend over a single atomic counter like it's done in, say, sync.RWMutex allowing for better scalability in terms of cores.
Hence, by the design RBMutex is a specialized mutex for scenarios, such as caches, where the vast majority of locks are acquired by readers and write lock acquire attempts are infrequent. In such scenarios, RBMutex should perform better than the sync.RWMutex on large multicore machines.
RBMutex extends sync.RWMutex internally and uses it as the "reader bias disabled" fallback, so the same semantics apply. The only noticeable difference is in the reader tokens returned from the RLock/RUnlock methods.
Apart from blocking methods, RBMutex also has methods for optimistic locking:
mu := xsync.NewRBMutex()
if locked, t := mu.TryRLock(); locked {
// critical reader section...
mu.RUnlock(t)
}
if mu.TryLock() {
// critical writer section...
mu.Unlock()
}Licensed under MIT.
