There is no way for me to know if this will help at all, but I had some fun playing. All of this was on a 16 core 16GB desktop running Cygwin. Linux often performs better on some things because Cygwin is really sitting on top of the Windows runtime library.
As an extrapolation from my testing below:
Try -
Code:
sort --parallel 4 --batchsize=256
Since you have four cores - this will improve throughput. It means run four independent threads of sorting - one on each core, break the large file into 256 chunks, sort and then merge 256 chunks (NMERGE in the documentation).
Your data presents problems.
1. The key is the entire line. Using a tag sort (some kind of key compression on the first 64 bytes to turn the data into an integer or a set of 3 integers) speeds up comparison times by an order or magnitude. To make a numeric comparison on the first radix key (if you like the term) use
Code:
-k1n -k2n -k3n
. This will have sort use three 64 bit words, using 3bit translation ( 3 values * 64) for comparison which is three compare ops. versus having to compare 64 characters.
2. Locality. Line 1301010 may really need to be after to line 14 in the final output.
By getting NMERGE to reasonable value ( 300GB/256 = 1.17GB) This creates a first
pass scenario where locality is less of an issue since all of 1.17 will easily fit in memory.
3. sort uses disk intensively. make sure TMPDIR is assigned to a filesystem that is a separate disk with lots of CONTIGUOUS free space. i.e., Pretty much wiped empty.
An SSD will make a huge difference in performance.
I dummied up a file with a 27183337 lines == almost exactly 2GB. I ran it on Cygwin.
Box has 16 cores 16 GB memory.
Code:
$ ulimit -a
core file size (blocks, -c) unlimited
data seg size (kbytes, -d) unlimited
file size (blocks, -f) unlimited
open files (-n) 256
pipe size (512 bytes, -p) 8
stack size (kbytes, -s) 2025
cpu time (seconds, -t) unlimited
max user processes (-u) 256
virtual memory (kbytes, -v) unlimited
Run using 7200 rpm drive not on SSD
Code:
TMPDIR=/cygdrive/e/tmp # not the SSD drive
time sort --parallel 16 --batchsize=32 -o ./sorted bigfile
real 3m2.016s
user 4m24.744s
sys 3m1.015s
Notice the user + sys time adds up to more than elapsed time. Why? parallel.
Code:
TMPDIR=/cygdrive/f/tmp # this is SSD drive
time sort --parallel 16 --batchsize=32 -o ./sorted bigfile
real 1m18.223s
user 4m19.007s
sys 2m19.016s
This is about 40% of the wall time compared with a slow disk. sys is better due to shorter I/O request queues . And appears to me to be the limiting factor. The number of comparisons was the same as was a lot of the other overhead so user is about the same.
This User Gave Thanks to jim mcnamara For This Post:
I wrote a "locality of reference" sort once: mmap64() the file and start sorting by making 2 lists of one line, then a linked list of 2 lines sorted, twice, and then merge them, then again, then merge the two sets of 4, etc. A binary item counter tells you what is needed next. Since it works with lists of length 1 1 1 1 2 2 4 1 1 1 1 2 2 4 8 1 1 1 1 2 2 4 1 1 1 1 2 2 4 8 16 ..., it optimizes the levels of cache, ram, VM in use.m Short lists sort in L1 cache, really long lists sort in vm at disk speed, and in between, in between. The pretext is that the speed problem is bandwidth, not CPU speed.
There is no way for me to know if this will help at all, but I had some fun playing. All of this was on a 16 core 16GB desktop running Cygwin. Linux often performs better on some things because Cygwin is really sitting on top of the Windows runtime library.
... {snipped] ...
Very interesting.
I had completely missed the --parallel option in reading the man page. I use this machine for everything, so I'm not sure I'll always want to swamp the cores, but I'll surely use some. I think this will help just the first pass -- when the temp files are first created -- because I'm going to try really hard to tweak parameters to make the second pass the last one where there's a single output file.
I'm just starting, and my largest input file is 1.3 TB. Therefore, SSD drives are too expensive for me; the only way to use just the one I already own and is not cluttered with my stuff already would be to chop the sort into many segments -- and then the merge phase would have to be all on spinning drives anyway. Maybe in 10 years.
I may compress the records eventually, but I'm running tests now that show that user time is at most 25% of the total time. That limits the effectiveness. Plus, each run is a one-time thing, and I worry about the overhead of the compression since it has to be a separate process.
I've got a script that is testing combinations of batch-size, buffer-size, compress-program (with various levels of compressions) and I'll add --parallel to it. I've been curious about sort for some years, at this seems to be the moment when I really want to investigate.
---------- Post updated at 02:07 PM ---------- Previous update was at 11:54 AM ----------
Quote:
Originally Posted by DGPickett
I wrote a "locality of reference" sort once: mmap64() the file and start sorting by making 2 lists of one line, then a linked list of 2 lines sorted, twice, and then merge them, then again, then merge the two sets of 4, etc. A binary item counter tells you what is needed next. Since it works with lists of length 1 1 1 1 2 2 4 1 1 1 1 2 2 4 8 1 1 1 1 2 2 4 1 1 1 1 2 2 4 8 16 ..., it optimizes the levels of cache, ram, VM in use.m Short lists sort in L1 cache, really long lists sort in vm at disk speed, and in between, in between. The pretext is that the speed problem is bandwidth, not CPU speed.
An excellent idea for a cache-aware in-memory sort. I have never before seen this idea extended to VM like this. However, it won't work for my TB-scale files without TB's of swap space. I'm also not quite ready to rewrite GNU sort to use the idea.
Thinking of alternatives, I read somewhere that phase 1 of the existing sort used qsort on a full buffer, and spits it out as the result if it's small and as temporaries otherwise. I know qsort is a favorite because it's unusually good in it's average performance, and is in-place. I once wrote a sort that used a heapsort algorithm in phase 1 to produce longer temporaries -- averaging twice the size of the working storage, with guaranteed n-log-n performance. Too bad it wouldn't be a good general-purpose replacement -- it incurs memory fragmentation when using variable-length records. The qsort implementation doesn't because it cleans out the workspace on every buffer, and the heapsort method can't do that and still generate the long outputs.
---------- Post updated at 02:13 PM ---------- Previous update was at 02:07 PM ----------
I'm now running some tests with a "short" 14 GB file. Some extra eyes on the script would be appreciated, as I've already been caught in one error already in this thread.
Code:
#!/bin/bash
# find times for sorting as compared to copying time.
ulimit -Sn hard
export TMPDIR=/mnt/tmptmp
export DATA=s2740.posn
export DEST=/mnt/h2/tmp/junk
export TEMP=/mnt/tmptmp/junk
export LC_ALL=C
cd /mnt/h1/posn
echo prep
df -h .
df -h /mnt/tmptmp
df -h /mnt/h2/tmp
# do something to make the cache state consistent
rm -f $TEMP $DEST
bash -c 'cp $DATA $TEMP; cp $TEMP $DEST'
echo
echo -n first copy then second copy
rm -f $TEMP $DEST
time bash -c 'cp $DATA $TEMP'
time bash -c 'cp $TEMP $DEST'
echo
echo -n copy both
rm -f $TEMP $DEST
time bash -c 'cp $DATA $TEMP; cp $TEMP $DEST'
echo
echo -n sort with default params
rm -f $TEMP $DEST
time sort $DATA --output=$DEST
echo
echo -n sort with n=16, buffer=4g
rm -f $TEMP $DEST
time sort --buffer-size=4g --batch-size=16 $DATA --output=$DEST
echo
buf=1
while [ $buf -lt 16 ]
do
bat=10
while [ $bat -lt 101 ]
do
for par in 1 2 4
do
echo -n sort with parallel=$par buffer=${buf}g batch=$bat and gzip from none to 9
rm -f $TEMP $DEST
time sort --parallel=$par --buffer-size=${buf}g --batch-size=$bat $DATA --output=$DEST
export GZIP='-1'
rm -f $TEMP $DEST
time sort --parallel=$par --buffer-size=${buf}g --batch-size=$bat $DATA --output=$DEST
export GZIP='-2'
rm -f $TEMP $DEST
time sort --parallel=$par --buffer-size=${buf}g --batch-size=$bat $DATA --output=$DEST
export GZIP='-3'
rm -f $TEMP $DEST
time sort --parallel=$par --buffer-size=${buf}g --batch-size=$bat $DATA --output=$DEST
export GZIP='-4'
rm -f $TEMP $DEST
time sort --parallel=$par --buffer-size=${buf}g --batch-size=$bat $DATA --output=$DEST
export GZIP='-5'
rm -f $TEMP $DEST
time sort --parallel=$par --buffer-size=${buf}g --batch-size=$bat $DATA --output=$DEST
export GZIP='-6'
rm -f $TEMP $DEST
time sort --parallel=$par --buffer-size=${buf}g --batch-size=$bat $DATA --output=$DEST
export GZIP='-7'
rm -f $TEMP $DEST
time sort --parallel=$par --buffer-size=${buf}g --batch-size=$bat $DATA --output=$DEST
export GZIP='-8'
rm -f $TEMP $DEST
time sort --parallel=$par --buffer-size=${buf}g --batch-size=$bat $DATA --output=$DEST
export GZIP='-9'
rm -f $TEMP $DEST
time sort --parallel=$par --buffer-size=${buf}g --batch-size=$bat $DATA --output=$DEST
bat=$(( bat + 10 ))
echo
done
done
buf=$(( buf + 10 ))
done
---------- Post updated at 03:02 PM ---------- Previous update was at 02:13 PM ----------
Oops. I can already see I omitted the --compress-program parameter from the ones I wanted to use gzip. Consider it fixed.
Last edited by kogorman3; 11-09-2014 at 06:15 PM..
Reason: improve comment
There is no swap space use with mmap() unless you choose the more exotic options. The basic mmap() swaps in file pages directly to RAM, giving you a way to work huge files with all of RAM as a cache. If you overmap a file, you can write pages. Empty pages can exist, supporting sparse matrices. This is a very powerful door into application use of VM and full exploitation of RAM.
Sorting small into larger in steps allows the problem to periodically expand to the next level of storage: from the caches to the RAM to the disk. The only flaw is that it might be good to merge more than 2 at a time, so when you get to the disk size merges, there is just one or at least fewer. The merge could use some sort of comparison tournament tree to do the merge, sized to fit in L1 cache, where each replacement item needs to be fit into the tree before using the smallest from the tree. Note that each item will take a whole page of RAM, or two, and N-N+2 lines of L1 cache, depending on the cache and item sizes and page/line offsets. Trying to fill L1 cache is fraught with difficulty, as many items may demand the same cache bucket, so it pays to shoot a bit low, like 1/4 - 1/2 L1 cache size.
Since you have 300 GB of data, you are better off loading this into a database. Oracle would work well, especially if you used the Advanced Compression for OLTP with 11gR2 or possibly 12c. You can load it in a table with each row in the file as one row in the database and each character of the in the first half of the line as an individual CHAR(1). You could then create an index with the columns that you want to sort by and you would already have it sorted. You can also store the data as an index organised table. If this is not for profit you probably don't need an Oracle license. You just need to know how to install Oracle and create a database. You can also use mySQL to get nearly the same thing.
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11-09-2014
