SharedHashFile

https://github.com/simonhf/sharedhashfile

by s1m0n • 12 years ago

3 10 12 years ago

github.com

Share Hash Tables Stored In Memory Mapped Files Between Arbitrary Processes & Threads

checker6593• 12 years ago

If this your project, good job dude. Reminds of of LMDB (symas.com/mdb/).

hyc_symas3• 12 years ago

Aside from the fact that both use mmap, there's little resemblance. LMDB does zero-copy reads and writes, this does not. LMDB is a persistent data store, this is not. B+trees are inherently very memory efficient, hash tables are not.

This is limited to the size of RAM, LMDB is not - LMDB is only limited to the size of the processor address space. This uses locks for reads, LMDB does not - LMDB reads will scale to an arbitrary number of CPUs, perfectly linearly, this will not.

Hash tables are great for small data sets, horrible for larger data.

s1m0n3• 12 years ago

Here's a link [1] which tests LMDB on a Rackspace server with 16 vCPUs. To make the test fairer to ShardHashFile then the LMDB data file is stored in /dev/shm so that the disk does not get in the way of the test. The test first inserts 70 million keys (tried but failed to insert 100 million keys; how to do that?) using 16 processes (one for each CPU), then it reads the 70 million keys again using the 16 processes, then it updates 2% of the keys while reading the other 98% of the keys again, again using the 16 processes.

Read performance without any writing does seem excellent at 6.x million reads per second across the 16 processes. However, insert speed is very slow at only 0.1 million inserts per second. But then LMBD does not claim to be fast at writing. Unfortunately the mix 2% update, 98% read brings the read performance down from 6.x million ops per second to only 0.7 million ops per second. So LMDB seems like an excellent solution if one hardly ever wants to insert.

I would also be very happy if anybody can find ways to optimize the test since I could not find a tutorial on programming with the LMDB API. For example, is it necessary to always use a txn when putting and getting? I also couldn't figure out how to insert 100 million keys because in order to make the map size big enough then mdb_env_set_mapsize() always complained when giving it super large values. Is this a limitation of LMDB or how else to increase the map size so that 100 million keys (or more) can be mapped? And as a side question: Inserting the keys is so slow: Is there a faster way to initially insert all the keys in order to speed up the performance test?

I was also surprised at how big the LMDB data.mdb file gets with 70 million keys & values. The keys & values are 4 byte + 4 byte, so 8 bytes each. However, the data.mdb file ended up as 1.8GB which works out to about 27.6 bytes per key,value pair... which does not seem that good compared to a hash table, or?

[1] https://gist.github.com/simonhf/9677776

s1m0n3• 12 years ago

FYI I experimented a little bit with the perf test and instead of using consecutive 32bit ints as keys then I used randomized 64bit ints as keys. I had to reduce the number of keys to 60 million otherwise it ran out of map space :-( Anyway, the resulting LMDB data file ended up as 1.9GB which makes an average of 34 bytes per key,value pair. Even bigger than before. I guess B+trees are only inherently more efficient than hash tables when a significant number of keys are very similar?

s1m0n3• 12 years ago

FYI I also tried changing the mix so that 10% of the ops were del/put and 90% of the ops were gets. Unfortunately the ops per second went down to between 0.1 and 0.2 million :-(

checker6593• 12 years ago

There's a reason why I said 'it reminds me of'. :)

(edit: Also, just realized that you're the guy who wrote lmdb. Nice meeting you good sir.)

iwasphone• 12 years ago

Ambitious undertaking, possibly transformational.

SharedHashFile

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