1初始化
2读写流程
3总结
go开发缓存场景一般使用map或者缓存框架,为了线程安全会使用sync.Map或线程安全的缓存框架。
缓存场景中如果数据量大于百万级别,需要特别考虑数据类型对于gc的影响(注意string类型底层是指针+Len+Cap,因此也算是指针类型),如果缓存key和value都是非指针类型的话就无需多虑了。但实际应用场景中,key和value是(包含)指针类型数据是很常见的,因此使用缓存框架需要特别注意其对gc影响,从是否对GC影响角度来看缓存框架大致分为2类:
零GC开销:比如freecache或bigcache这种,底层基于ringbuf,减小指针个数;
有GC开销:直接基于Map来实现的缓存框架。
对于map而言,gc时会扫描所有key/value键值对,如果其都是基本类型,那么gc便不会再扫描。下面以freecache为例分析下其实现原理,代码示例如下:
func main() {
cacheSize := 100 * 1024 * 1024
cache := freecache.NewCache(cacheSize)
for i := 0; i < N; i++ {
str := strconv.Itoa(i)
_ = cache.Set([]byte(str), []byte(str), 1)
}
now := time.Now()
runtime.GC()
fmt.Printf("freecache, GC took: %s\n", time.Since(now))
_, _ = cache.Get([]byte("aa"))
now = time.Now()
for i := 0; i < N; i++ {
str := strconv.Itoa(i)
_, _ = cache.Get([]byte(str))
}
fmt.Printf("freecache, Get took: %s\n\n", time.Since(now))
}
1 初始化
freecache.NewCache会初始化本地缓存,size表示存储空间大小,freecache会初始化256个segment,每个segment是独立的存储单元,freecache加锁维度也是基于segment的,每个segment有一个ringbuf,初始大小为size/256。freecache号称零GC的来源就是其指针是固定的,只有512个,每个segment有2个,分别是rb和slotData(注意切片为指针类型)。
type segment struct {
rb RingBuf // ring buffer that stores data
segId int
_ uint32 // 占位
missCount int64
hitCount int64
entryCount int64
totalCount int64 // number of entries in ring buffer, including deleted entries.
totalTime int64 // used to calculate least recent used entry.
timer Timer // Timer giving current time
totalEvacuate int64 // used for debug
totalExpired int64 // used for debug
overwrites int64 // used for debug
touched int64 // used for debug
vacuumLen int64 // up to vacuumLen, new data can be written without overwriting old data.
slotLens [256]int32 // The actual length for every slot.
slotCap int32 // max number of entry pointers a slot can hold.
slotsData []entryPtr // 索引指针
}
func NewCacheCustomTimer(size int, timer Timer) (cache *Cache) {
cache = new(Cache)
for i := 0; i < segmentCount; i++ {
cache.segments[i] = newSegment(size/segmentCount, i, timer)
}
}
func newSegment(bufSize int, segId int, timer Timer) (seg segment) {
seg.rb = NewRingBuf(bufSize, 0)
seg.segId = segId
seg.timer = timer
seg.vacuumLen = int64(bufSize)
seg.slotCap = 1
seg.slotsData = make([]entryPtr, 256*seg.slotCap) // 每个slotData初始化256个单位大小
}
2 读写流程
freecache的key和value都是[]byte数组,使用时需要自行序列化和反序列化,如果缓存复杂对象不可忽略其序列化和反序列化带来的影响,首先看下Set流程:
_ = cache.Set([]byte(str), []byte(str), 1)
Set流程首先对key进行hash,hashVal类型uint64,其低8位segID对应segment数组,低8-15位表示slotId对应slotsData下标,高16位表示slotsData下标对应的[]entryPtr某个数据,这里需要查找操作。注意[]entryPtr数组大小为slotCap(初始为1),当扩容时会slotCap倍增。
每个segment对应一个lock(sync.Mutex),因此其能够支持较大并发量,而不像sync.Map只有一个锁。
func (cache *Cache) Set(key, value []byte, expireSeconds int) (err error) {
hashVal := hashFunc(key)
segID := hashVal & segmentAndOpVal // 低8位
cache.locks[segID].Lock() // 加锁
err = cache.segments[segID].set(key, value, hashVal, expireSeconds)
cache.locks[segID].Unlock()
}
func (seg *segment) set(key, value []byte, hashVal uint64, expireSeconds int) (err error) {
slotId := uint8(hashVal >> 8)
hash16 := uint16(hashVal >> 16)
slot := seg.getSlot(slotId)
idx, match := seg.lookup(slot, hash16, key)
var hdrBuf [ENTRY_HDR_SIZE]byte
hdr := (*entryHdr)(unsafe.Pointer(&hdrBuf[0]))
if match { // 有数据更新操作
matchedPtr := &slot[idx]
seg.rb.ReadAt(hdrBuf[:], matchedPtr.offset)
hdr.slotId = slotId
hdr.hash16 = hash16
hdr.keyLen = uint16(len(key))
originAccessTime := hdr.accessTime
hdr.accessTime = now
hdr.expireAt = expireAt
hdr.valLen = uint32(len(value))
if hdr.valCap >= hdr.valLen {
// 已存在数据value空间能存下此次value大小
atomic.AddInt64(&seg.totalTime, int64(hdr.accessTime)-int64(originAccessTime))
seg.rb.WriteAt(hdrBuf[:], matchedPtr.offset)
seg.rb.WriteAt(value, matchedPtr.offset+ENTRY_HDR_SIZE+int64(hdr.keyLen))
atomic.AddInt64(&seg.overwrites, 1)
return
}
// 删除对应entryPtr,涉及到slotsData内存copy,ringbug中只是标记删除
seg.delEntryPtr(slotId, slot, idx)
match = false
// increase capacity and limit entry len.
for hdr.valCap < hdr.valLen {
hdr.valCap *= 2
}
if hdr.valCap > uint32(maxKeyValLen-len(key)) {
hdr.valCap = uint32(maxKeyValLen - len(key))
}
} else { // 无数据
hdr.slotId = slotId
hdr.hash16 = hash16
hdr.keyLen = uint16(len(key))
hdr.accessTime = now
hdr.expireAt = expireAt
hdr.valLen = uint32(len(value))
hdr.valCap = uint32(len(value))
if hdr.valCap == 0 { // avoid infinite loop when increasing capacity.
hdr.valCap = 1
}
}
// 数据实际长度为 ENTRY_HDR_SIZE=24 + key和value的长度
entryLen := ENTRY_HDR_SIZE + int64(len(key)) + int64(hdr.valCap)
slotModified := seg.evacuate(entryLen, slotId, now)
if slotModified {
// the slot has been modified during evacuation, we need to looked up for the 'idx' again.
// otherwise there would be index out of bound error.
slot = seg.getSlot(slotId)
idx, match = seg.lookup(slot, hash16, key)
// assert(match == false)
}
newOff := seg.rb.End()
seg.insertEntryPtr(slotId, hash16, newOff, idx, hdr.keyLen)
seg.rb.Write(hdrBuf[:])
seg.rb.Write(key)
seg.rb.Write(value)
seg.rb.Skip(int64(hdr.valCap - hdr.valLen))
atomic.AddInt64(&seg.totalTime, int64(now))
atomic.AddInt64(&seg.totalCount, 1)
seg.vacuumLen -= entryLen
return
}
seg.evacuate会评估ringbuf是否有足够空间存储key/value,如果空间不够,其会从空闲空间尾部后一位(也就是待淘汰数据的开始位置)开始扫描(oldOff := seg.rb.End() + seg.vacuumLen - seg.rb.Size()),如果对应数据已被逻辑deleted或者已过期,那么该块内存可以直接回收,如果不满足回收条件,则将entry从环头调换到环尾,再更新entry的索引,如果这样循环5次还是不行,那么需要将当前oldHdrBuf回收以满足内存需要。
执行完seg.evacuate所需空间肯定是能满足的,然后就是写入索引和数据了,insertEntryPtr就是写入索引操作,当[]entryPtr中元素个数大于seg.slotCap(初始1)时,需要扩容操作,对应方法见seg.expand,这里不再赘述。
写入ringbuf就是执行rb.Write即可。
func (seg *segment) evacuate(entryLen int64, slotId uint8, now uint32) (slotModified bool) {
var oldHdrBuf [ENTRY_HDR_SIZE]byte
consecutiveEvacuate := 0
for seg.vacuumLen < entryLen {
oldOff := seg.rb.End() + seg.vacuumLen - seg.rb.Size()
seg.rb.ReadAt(oldHdrBuf[:], oldOff)
oldHdr := (*entryHdr)(unsafe.Pointer(&oldHdrBuf[0]))
oldEntryLen := ENTRY_HDR_SIZE + int64(oldHdr.keyLen) + int64(oldHdr.valCap)
if oldHdr.deleted { // 已删除
consecutiveEvacuate = 0
atomic.AddInt64(&seg.totalTime, -int64(oldHdr.accessTime))
atomic.AddInt64(&seg.totalCount, -1)
seg.vacuumLen += oldEntryLen
continue
}
expired := oldHdr.expireAt != 0 && oldHdr.expireAt < now
leastRecentUsed := int64(oldHdr.accessTime)*atomic.LoadInt64(&seg.totalCount) <= atomic.LoadInt64(&seg.totalTime)
if expired || leastRecentUsed || consecutiveEvacuate > 5 {
// 可以回收
seg.delEntryPtrByOffset(oldHdr.slotId, oldHdr.hash16, oldOff)
if oldHdr.slotId == slotId {
slotModified = true
}
consecutiveEvacuate = 0
atomic.AddInt64(&seg.totalTime, -int64(oldHdr.accessTime))
atomic.AddInt64(&seg.totalCount, -1)
seg.vacuumLen += oldEntryLen
if expired {
atomic.AddInt64(&seg.totalExpired, 1)
} else {
atomic.AddInt64(&seg.totalEvacuate, 1)
}
} else {
// evacuate an old entry that has been accessed recently for better cache hit rate.
newOff := seg.rb.Evacuate(oldOff, int(oldEntryLen))
seg.updateEntryPtr(oldHdr.slotId, oldHdr.hash16, oldOff, newOff)
consecutiveEvacuate++
atomic.AddInt64(&seg.totalEvacuate, 1)
}
}
}
freecache的Get流程相对来说简单点,通过hash找到对应segment,通过slotId找到对应索引slot,然后通过二分+遍历寻找数据,如果找不到直接返回ErrNotFound,否则更新一些time指标。Get流程还会更新缓存命中率相关指标。
func (cache *Cache) Get(key []byte) (value []byte, err error) {
hashVal := hashFunc(key)
segID := hashVal & segmentAndOpVal
cache.locks[segID].Lock()
value, _, err = cache.segments[segID].get(key, nil, hashVal, false)
cache.locks[segID].Unlock()
return
}
func (seg *segment) get(key, buf []byte, hashVal uint64, peek bool) (value []byte, expireAt uint32, err error) {
hdr, ptr, err := seg.locate(key, hashVal, peek) // hash+定位查找
if err != nil {
return
}
expireAt = hdr.expireAt
if cap(buf) >= int(hdr.valLen) {
value = buf[:hdr.valLen]
} else {
value = make([]byte, hdr.valLen)
}
seg.rb.ReadAt(value, ptr.offset+ENTRY_HDR_SIZE+int64(hdr.keyLen))
}
定位到数据之后,读取ringbuf即可,注意一般来说读取到的value是新创建的内存空间,因此涉及到[]byte数据的复制操作。
3 总结从常见的几个缓存框架压测性能来看,Set性能差异较大但还不是数量级别的差距,Get性能差异不大,因此对于绝大多数场景来说不用太关注Set/Get性能,重点应该看功能是否满足业务需求和gc影响,性能压测比较见:https://golang2.eddycjy.com/posts/ch5/04-performance/
缓存有一个特殊的场景,那就是将数据全部缓存在内存,涉及到更新时都是全量更新(替换),该场景下使用freecache,如果size未设置好可能导致部分数据被淘汰,是不符合预期的,这个一定要注意。为了使用freecache避免该问题,需要将size设置"足够大",但也要注意其内存空间占用。
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