On Tuesday, 17 December 2013 at 19:09:49 UTC, H. S. Teoh wrote:
Another OT thread to pick your brains. :)
What's a good, efficient file structure for storing extremely
large
lookup tables? (Extremely large as in > 10 million entries,
with keys
and values roughly about 100 bytes each.) The structure must
support
efficient adding and lookup of entries, as these two operations
will be
very frequent.
I did some online research, and it seems that hashtables
perform poorly
on disk, because the usual hash functions cause random
scattering of
related data (which are likely to be access with higher temporal
locality), which incurs lots of disk seeks.
I thought about B-trees, but they have high overhead (and are a
pain to
implement), and also only exhibit good locality if table
entries are
accessed sequentially; the problem is I'm working with
high-dimensional
data and the order of accesses is unlikely to be sequential.
However,
they do exhibit good spatial locality in higher-dimensional
space (i.e.,
if entry X is accessed first, then the next entry Y is quite
likely to
be close to X in that space). Does anybody know of a good data
structure that can take advantage of this fact to minimize disk
accesses?
T
As a first attempt could you use a key-value database (like REDIS
if you have enough memory to fit everything in)? Or is that out
of the question.
Another question is can your queries be batched? If that is the
case and your data is bigger than your available memory, then try
Googling "Lars Arge Buffer Tree" which might work well. However,
if you thought implementing a B-tree was going to be painful,
that might not appeal to you. If you don't want to implement
that yourself you could look at TPIE:
http://www.madalgo.au.dk/tpie/
Although it is in C++.
If I had to design something quick on the spot, my first guess
would be to use a grid on the first two dimensions and then bin
the 'points' or keys within each grid square and build a simpler
structure on those. This won't work so well though for really
high dimension data or if the 'points' are randomly distributed.
Also, what exactly do you mean by "in that space" when you say:
"if entry X is accessed first, then the next entry Y is quite
likely to be close to X in that space".
Do you mean that the value of Y in the next dimension is
numerically close (or expected to be) to X?
Cheers,
Craig
