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Lock granularity optimization skills for golang function concurrent cache
Lock granularity optimization skills for golang function concurrent cache
May 05, 2024 am 08:45 AM
Lock granularity tips for optimizing Go concurrent cache performance: Global lock: Simple implementation, if the lock granularity is too large, unnecessary competition will occur. Key-level locking: The lock granularity is refined to each key, but it will introduce a large number of locks and increase overhead. Shard lock: Divide the cache into multiple shards, each shard has a separate lock, to achieve a balance between concurrency and lock contention.
Lock granularity optimization techniques for Go function concurrent cache
In Go concurrent programming, cache is usually used to improve application performance . However, if the lock granularity of the cache is too large, it may cause unnecessary contention and affect concurrency. This article will explore how to improve the performance of Go concurrent cache by optimizing lock granularity.
Lock granularity
Lock granularity refers to the data range protected by a lock. In caching scenarios, there is usually a global lock protecting the entire cache, or a separate lock for each key in the cache.
Global lock
Global lock provides a simple implementation, but the lock granularity is too large. When multiple coroutines access different keys at the same time, competition will also occur.
Key-level lock
Key-level lock reduces the lock granularity to each key, allowing multiple coroutines to access different keys concurrently. But this will introduce a lot of locks, increase memory overhead and contention.
Shard lock
Shard lock divides the cache into multiple shards, each shard has a separate lock. This provides a compromise between global and key-level locks, reducing lock contention while maintaining some concurrency.
Practical case
Consider the following simple cache implementation using global locks:
type Cache struct {
m map[string]interface{}
mu sync.Mutex
}
func (c *Cache) Get(key string) (interface{}, bool) {
c.mu.Lock()
defer c.mu.Unlock()
return c.m[key], true
}Using shard locks, we can optimize the lock granularity:
type Cache struct {
shards []*sync.Mutex
data []map[string]interface{}
}
func NewCache(numShards int) *Cache {
shards := make([]*sync.Mutex, numShards)
data := make([]map[string]interface{}, numShards)
for i := 0; i < numShards; i++ {
shards[i] = &sync.Mutex{}
data[i] = make(map[string]interface{})
}
return &Cache{
shards: shards,
data: data,
}
}
func (c *Cache) Get(key string) (interface{}, bool) {
shardIndex := hash(key) % len(c.shards)
c.shards[shardIndex].Lock()
defer c.shards[shardIndex].Unlock()
return c.data[shardIndex][key], true
}By dividing the cache into multiple shards, we reduce contention for each lock, thereby improving concurrency.
Selecting the appropriate lock granularity based on the application's load patterns and access patterns is critical to optimizing Go concurrent cache.
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