基准测试显示,cereal
库反序列化我的数据结构(详细信息如下)所需的时间是从驱动器中读取相同数据所需时间的100倍:
benchmarking Read
mean: 465.7050 us, lb 460.9873 us, ub 471.0938 us, ci 0.950
std dev: 25.79706 us, lb 22.19820 us, ub 30.81870 us, ci 0.950
found 4 outliers among 100 samples (4.0%)
4 (4.0%) high mild
variance introduced by outliers: 53.460%
variance is severely inflated by outliers
benchmarking Read + Decode
collecting 100 samples, 1 iterations each, in estimated 6.356502 s
mean: 68.85135 ms, lb 67.65992 ms, ub 70.05832 ms, ci 0.950
std dev: 6.134430 ms, lb 5.607914 ms, ub 6.755639 ms, ci 0.950
variance introduced by outliers: 74.863%
variance is severely inflated by outliers
在我的一个程序中分析此数据结构的典型反序列化用法也支持这一点,其中98%的时间用于反序列化数据,1%的时间是IO
加上核心算法:
COST CENTRE MODULE %time %alloc
getWord8 Data.Serialize.Get 30.5 40.4
unGet Data.Serialize.Get 29.5 17.9
getWord64be Data.Serialize.Get 14.0 10.7
getListOf Data.Serialize.Get 10.2 12.8
roll Data.Serialize 8.2 11.5
shiftl_w64 Data.Serialize.Get 3.4 2.9
decode Data.Serialize 2.9 3.1
main Main 1.3 0.6
我正在反序列化的数据结构是一个IntMap [Triplet Atom]
,组件类型的定义如下:
type Triplet a = (a, a, a)
data Point = Point {
_x :: {-# UNPACK #-} !Double ,
_y :: {-# UNPACK #-} !Double ,
_z :: {-# UNPACK #-} !Double }
data Atom = Atom {
_serial :: {-# UNPACK #-} !Int ,
_r :: {-# UNPACK #-} !Point ,
_n :: {-# UNPACK #-} !Word64 }
我使用cereal
提供的默认IntMap
、(,,)
和[]
实例,以及以下类型和自定义类型的实例:
instance Serialize Point where
put (Point x y z) = do
put x
put y
put z
get = Point <$> get <*> get <*> get
instance Serialize Atom where
put (Atom s r n) = do
put s
put r
put n
get = Atom <$> get <*> get <*> get
所以我的问题是:
IntMap
/[]
)以使反序列化更快?Atom
/Point
)以使反序列化更快?cereal
更快的替代方案,或者我是否应该将数据结构存储在C-land中以进行更快速的反序列化(即使用Haskell我正在反序列化的这些文件用于搜索引擎的子索引,因为目标计算机(消费级台式机)的内存无法容纳完整的索引,所以我将每个子索引存储在磁盘上,并对驻留在内存中的初始全局索引所指向的子索引进行read+decode。此外,我不关心序列化速度,因为搜索索引是最终用户的瓶颈,而且cereal
当前的序列化性能对于生成和更新索引是令人满意的。
编辑:
尝试了Don提出的使用节省空间的三元组的建议,速度提高了四倍:
benchmarking Read
mean: 468.9671 us, lb 464.2564 us, ub 473.8867 us, ci 0.950
std dev: 24.67863 us, lb 21.71392 us, ub 28.39479 us, ci 0.950
found 2 outliers among 100 samples (2.0%)
2 (2.0%) high mild
variance introduced by outliers: 50.474%
variance is severely inflated by outliers
benchmarking Read + Decode
mean: 15.04670 ms, lb 14.99097 ms, ub 15.10520 ms, ci 0.950
std dev: 292.7815 us, lb 278.8742 us, ub 308.1960 us, ci 0.950
variance introduced by outliers: 12.303%
variance is moderately inflated by outliers
但是,它仍然是瓶颈,占用的时间是IO的25倍。另外,有人能解释一下Don的建议为什么有效吗?这是否意味着如果我切换到列表以外的其他类型(比如数组)?它可能也会带来进步?
编辑#2:刚刚切换到最新的Haskell平台,并重新运行了谷类的分析。信息要详细得多,我提供了它的hpaste。
发布于 2012-06-07 20:00:03
好的。用建议的摘要来回答这个问题。对于数据的快速反序列化:
cereal
(严格字节串输出)或binary
(惰性字节串输出)https://stackoverflow.com/questions/10902405
复制相似问题