Use mmap.PAGESIZE constant as value for first read #505
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xavfernandez
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With 60000 small files, switching (with a stable timing on multiple runs) to With a strong speedup on second run thanks to the OS filesystem cache. I admit that such numbers depend heavily on:
Therefore I don't think there is an always perfect |
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It's not good practice to poke around internal constants of code. If there's a better value we should change it. |
I agree it is not ideal. A small I thought about adding a Would this make more sense ? |
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Thinking a bit more, I'm not sure that this should make a difference at all. If the file is sparse then they'll be handled via the zero page, which doesn't use file cache. Do you have benchmarks? |
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Well my benchmark is kinda primitive. I create my 60k files by launching 60k times this python program: and I time with a simplified version of what the library performs: Before each run, I wipe the file cache with With a bunch of free memory (~ 26 GB) and a read size of 65535 bytes: With a bunch of free memory (~ 26 GB) and a read size of 4096 bytes: Now with ~ 2 GB of free memory and a read size of 65535 bytes: And finally with ~ 2 GB of free memory and a read size of 4096 bytes: And sure enough, if I constraint the system even more to have less than a 500 MB of free memory, the performance is bad even with 4096: And in the same way, even with 25 GB of available memory, if I push the read size to 650 350 kbytes: |
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Thanks, looks like we should change to value to 4k.
…On Thu 30 Jan 2020, 16:49 Xavier Fernandez, ***@***.***> wrote:
Well my benchmark is kinda primitive.
I create my 60k files by launching 60k times this python program:
import os
os.environ['prometheus_multiproc_dir'] = 'tmp'
import random
import prometheus_client
METRIC = prometheus_client.Summary(
'some_duration_seconds',
'time spent somewhere',
labelnames=('label1', 'second_label'),
namespace='profile',
subsystem='lot_of_files',
)
METRIC.labels(random.choice(('success', 'error')), f'test{random.randint(0, 10)}').observe(random.random())
METRIC.labels(random.choice(('success', 'error')), f'test{random.randint(0, 10)}').observe(random.random())
METRIC.labels(random.choice(('success', 'error')), f'test{random.randint(0, 10)}').observe(random.random())
and I time with a simplified version of what the library performs:
# This is read_simple.py
import os
import sys
import time
BLOCK_SIZE = int(sys.argv[1])
print('BLOCK_SIZE', BLOCK_SIZE)
start = time.perf_counter()
for filename in os.listdir('tmp'):
if filename.endswith('.db'):
with open(os.path.join('tmp', filename), 'rb') as infp:
data = infp.read(BLOCK_SIZE)
print(time.perf_counter() - start)
Before each run, I wipe the file cache with echo 1 >
/proc/sys/vm/drop_caches).
------------------------------
With a bunch of free memory (~ 26 GB) and a read size of 65535 bytes:
$ ls tmp/ | wc -l
60000
$ free
total used free shared buff/cache available
Mem: 32537864 5188908 26183948 668028 1165008 26234928
Swap: 0 0 0
$ python read_simple.py 65535
BLOCK_SIZE 65535
12.427678888998344
0.9561608399963006
0.8500389300024835
0.8534953799971845
0.8507285489904461
0.8569888329948299
0.8777932550001424
0.8816033009934472
0.8621571760013467
0.8545428220095346
With a bunch of free memory (~ 26 GB) and a read size of 4096 bytes:
(prometheus_profile) ***@***.*** prometheus_profile]$ free
total used free shared buff/cache available
Mem: 32537864 5148264 26250408 645920 1139192 26298164
Swap: 0 0 0
(prometheus_profile) ***@***.*** prometheus_profile]$ python read_simple.py 4096
BLOCK_SIZE 4096
10.32166458798747
0.4410311039973749
0.39926853599899914
0.4384756700019352
0.4004494149994571
0.40352619100303855
0.3965474760043435
0.3989230669976678
0.39654788600455504
0.4002341330051422
Now with ~ 2 GB of free memory and a read size of 65535 bytes:
$ free
total used free shared buff/cache available
Mem: 32537864 29285200 2293668 663724 958996 2171332
Swap: 0 0 0
$ python read_simple.py 65535
BLOCK_SIZE 65535
12.443866290006554
11.633168715998181
12.085821863001911
11.749885090990574
12.8851961879991
12.385939428990241
11.35605888698774
11.659072276990628
11.294882508998853
11.368941648004693
And finally with ~ 2 GB of free memory and a read size of 4096 bytes:
$ free
total used free shared buff/cache available
Mem: 32537864 29260832 2405596 646916 871436 2247648
Swap: 0 0 0
$ python read_simple.py 4096
BLOCK_SIZE 4096
12.362105008010985
0.40657658501004335
0.3870056059968192
0.4089814879989717
0.381813013998908
0.3841210550017422
0.38265788700664416
0.38066420500399545
0.3836389770003734
0.3772799519938417
And sure enough, if I constraint the system even more to have less than a
500 MB of free memory, the performance is bad even with 4096:
(prometheus_profile) ***@***.*** prometheus_profile]$ free
total used free shared buff/cache available
Mem: 32537864 31400812 335304 621992 801748 155100
Swap: 0 0 0
(prometheus_profile) ***@***.*** prometheus_profile]$ python read_simple.py 4096
BLOCK_SIZE 4096
11.380508398986422
10.212263110995991
12.595423141989158
11.597996773009072
9.844564571001683
11.246935460992972
11.166533361989423
10.640836154998397
10.942926752992207
11.625870076008141
And in the same way, even with 25 GB of available memory, if I push the
read size to 650 350 kbytes:
$ free
total used free shared buff/cache available
Mem: 32537864 5259756 26470680 632860 807428 26287820
Swap: 0 0 0
$ python read_simple.py 655350
BLOCK_SIZE 655350
22.036876083991956
20.698863850993803
23.11240423300478
21.609464114997536
21.656152483003098
22.610070716997143
22.320307180998498
24.78865629399661
23.689863965002587
20.960628273998736
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But why 4k and not 1k or 8k ? Depending on the number of files, the number of bytes used and the available memory, surely the optimum value might differ ? |
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4k is the page size, so Iower than that won't help. The performance numbers for 4k are acceptable, so I don't see a need to consider 8k - unless you've more data. |
This size can have a real impact on performance when a lot of files need to be read. The performance relies heavily on the file system cache so reading as little pages as possible helps a lot on memory constrained systems. Signed-off-by: Xavier Fernandez <xavier.fernandez@polyconseil.fr>
I tried to generate less but bigger files with: The resulting files ended up using ~ 58kB. And I updated the (which means 4096 will require a second read but 65535 won't). With very little RAM, the performance is slightly better with 65535 but not dramatically so: And with lot of RAM, as expected, there is little impact: So 4096 seems like a good compromise 👍 I updated the PR to use |
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Yip, that seems reasonable. Thanks! |
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Thanks for merging :) |
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I don't think there's anything pressing for release currently, I'll probably wait until the OpenMetrics stuff is a bit more finalised. |
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This size can have a real impact on performance when a lot of files need
to be read.
The performance relies heavily on the file system cache and having the
possibility to adapt _INITIAL_READ_SIZE can help on memory constrained
systems.