-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf
Of Magnus Thor Torfason
Sent: Friday, November 01, 2013 8:23 AM
To: r-help at r-project.org
Subject: Re: [R] Inserting 17M entries into env took 18h, inserting 34M entries taking 5+
days
Sure,
I was attempting to be concise and boiling it down to what I saw as the
root issue, but you are right, I could have taken it a step further. So
here goes.
I have a set of around around 20M string pairs. A given string (say, A)
can either be equivalent to another string (B) or not. If A and B occur
together in the same pair, they are equivalent. But equivalence is
transitive, so if A and B occur together in one pair, and A and C occur
together in another pair, then A and C are also equivalent. I need a way
to quickly determine if any two strings from my data set are equivalent
or not.
The way I do this currently is to designate the smallest
(alphabetically) string in each known equivalence set as the "main"
entry. For each pair, I therefore insert two entries into the hash
table, both pointing at the mail value. So assuming the input data:
A,B
B,C
D,E
I would then have:
A->A
B->A
C->B
D->D
E->D
Except that I also follow each chain until I reach the end (key==value),
and insert pointers to the "main" value for every value I find along the
way. After doing that, I end up with:
A->A
B->A
C->A
D->D
E->D
And I can very quickly check equivalence, either by comparing the hash
of two strings, or simply by transforming each string into its hash, and
then I can use simple comparison from then on. The code for generating
the final hash table is as follows:
h : Empty hash table created with hash.new()
d : Input data
hash.deep.get : Function that iterates through the hash table until it
finds a key whose value is equal to itself (until hash.get(X)==X), then
returns all the values in a vector
h = hash.new()
for ( i in 1:nrow(d) )
{
deep.a = hash.deep.get(h, d$a[i])
deep.b = hash.deep.get(h, d$b[i])
equivalents = sort(unique(c(deep.a,deep.b)))
equiv.id = min(equivalents)
for ( equivalent in equivalents )
{
hash.put(h, equivalent, equiv.id)
}
}
I would so much appreciate if there was a simpler and faster way to do
this. Keeping my fingers crossed that one of the R-help geniuses who
sees this is sufficiently interested to crack the problem
Best,
Magnus
On 11/1/2013 1:49 PM, jim holtman wrote:
It would be nice if you followed the posting guidelines and at least
showed the script that was creating your entries now so that we
understand the problem you are trying to solve. A bit more
explanation of why you want this would be useful. This gets to the
second part of my tag line: Tell me what you want to do, not how you
want to do it. There may be other solutions to your problem.
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
On Fri, Nov 1, 2013 at 9:32 AM, Magnus Thor Torfason
<zulutime.net at gmail.com> wrote:
Pretty much what the subject says:
I used an env as the basis for a Hashtable in R, based on information that
this is in fact the way environments are implemented under the hood.
I've been experimenting with doubling the number of entries, and so far it
has seemed to be scaling more or less linearly, as expected.
But as I went from 17 million entries to 34 million entries, the completion
time has gone from 18 hours, to 5 days and counting.
The keys and values are in all cases strings of equal length.
One might suspect that the slow-down might have to do with the memory being
swapped to disk, but from what I know about my computing environment, that
should not be the case.
So my first question:
Is anyone familiar with anything in the implementation of environments that
would limit their use or slow them down (faster than O(nlog(n)) as the
number of entries is increased?
And my second question:
I realize that this is not strictly what R environments were designed for,
but this is what my algorithm requires: I must go through these millions of
entries, storing them in the hash table and sometimes retrieving them along
the way, in a more or less random manner, which is contingent on the data I
am encountering, and on the contents of the hash table at each moment.
Does anyone have a good recommendation for alternatives to implement huge,
fast, table-like structures in R?
Best,
Magnus