[Pulp-dev] Memory consumption on RPM sync
Daniel Alley
dalley at redhat.com
Mon Dec 2 20:06:05 UTC 2019
sorted() creates a new, duplicate list instead of sorting in place like
.sort(). Make sure it's not crashing because of duplicating that list vs.
processing a particular troublesome package first.
Speaking of which, do you know which package that is?
On Mon, Dec 2, 2019 at 2:49 PM Fabricio Aguiar <fabricio.aguiar at redhat.com>
wrote:
> Today I started to notice that every time the process went out of memory
> it was around 24000 packages saved,
> so I started to look at what could cause it.
> At first, I thought the problem was ProgressReport being incremented and
> saved in each iteration,
> so I changed it, and I had some considerable improvement in memory
> consumption, but the process still crashing.
>
> Then I looked at the logs, I saw that even after crashing it still logging
> a very huge text, so I did it:
>
> packages_values = sorted(
> packages.values(),
> key=lambda p: len(str(p.changelogs)),
> reverse=True
> )for pkg in packages_values:
> package = Package(**Package.createrepo_to_dict(pkg))
>
>
> I sorted the packages by the size of its changelog, and then I saw it crashing right on the beginning of the sync:
>
>
> In [2]: len(Package.objects.last().changelogs)
> Out[2]: 1071
>
> In [3]: len(str(Package.objects.last().changelogs))
> Out[3]: 8232715
>
>
> Best regards,
> Fabricio Aguiar
> Software Engineer, Pulp Project
> Red Hat Brazil - Latam <https://www.redhat.com/>
> +55 11 999652368
>
>
> On Wed, Nov 27, 2019 at 10:13 AM Fabricio Aguiar <
> fabricio.aguiar at redhat.com> wrote:
>
>> Thank you for these numbers,
>> I put it on my scrum status, but it worth to share it here also:
>> -- got some really nice feedback from dalley and dkliban
>> -- performance problem seems to be related with query issues
>> -- started to dig into queries
>> -- find out process crashes during ContentSaver
>> -- when using rpdb noticed a decrease in the use of memory
>> -- replaced rpdb with timer.sleep(5) and noticed a slight decrease in
>> the use of memory
>> -- my theory is: with a huge amount of content, and with stages API
>> going so fast, it cannot release memory fast enough so cache is accumulating
>> -- started to read about django cache
>> -- found this [1], tomorrow I will try to replace with [2]
>>
>> [1]
>> https://docs.djangoproject.com/en/2.2/topics/cache/#local-memory-caching
>> [2] https://docs.djangoproject.com/en/2.2/topics/cache/#memcached
>>
>> So taking these numbers, for ContentSaver what matter is not a specific
>> type, then:
>>
>> CentOS7 - 10 000 RPMs, 0 Advisories = 10 000 contents
>> EPEL7 - 13 500 RPMs, 4400 Advisories = 17 900 contents
>> RHEL7 - 26 000 RPMs, 3900 Advisories = 29 900 contents
>>
>> I have found this really nice article, and I believe our problem is
>> pretty similar:
>>
>> https://medium.com/@chakrabortypritish/django-optimization-how-to-avoid-memory-mishaps-1e55b2aa947c
>>
>>
>> Best regards,
>> Fabricio Aguiar
>> Software Engineer, Pulp Project
>> Red Hat Brazil - Latam <https://www.redhat.com/>
>> +55 11 999652368
>>
>>
>> On Wed, Nov 27, 2019 at 8:28 AM Tatiana Tereshchenko <ttereshc at redhat.com>
>> wrote:
>>
>>> You know that I'd be the first one to suspect Advisories (aka Errata) :)
>>> For the sake of fairness to Advisories, here are some rough numbers (at
>>> least for the repos which we are using):
>>>
>>> CentOS7 - 10 000 RPMs, 0 Advisories
>>> EPEL7 - 13 500 RPMs, 4400 Advisories
>>> RHEL7 - 26 000 RPMs, 3900 Advisories
>>>
>>> If the RHEL7 repo causes the problem and EPEL7 does not, then it seems
>>> like the main impact is from the number of RPMs. Maybe focusing on what
>>> Daniel pointed out is a good start?
>>>
>>> If we profile CentOS and EPEL, we'll get slightly better idea on
>>> the impact of Advisories but still EPEL7 has 30% more RPMs. It's hard to
>>> compare.
>>>
>>> Tanya
>>>
>>>
>>>
>>> On Tue, Nov 26, 2019 at 9:25 PM Dana Walker <dawalker at redhat.com> wrote:
>>>
>>>> Out of curiosity, do we have any data on performance/memory usage from
>>>> before some of the major features were recently merged? I'm curious as to
>>>> whether comps added significantly to this given how many relations are
>>>> handled since you mentioned the deduplication step being a key point.
>>>>
>>>> Dana Walker
>>>>
>>>> She / Her / Hers
>>>>
>>>> Software Engineer, Pulp Project
>>>>
>>>> Red Hat <https://www.redhat.com>
>>>>
>>>> dawalker at redhat.com
>>>> <https://www.redhat.com>
>>>>
>>>>
>>>>
>>>> On Tue, Nov 26, 2019 at 3:07 PM Daniel Alley <dalley at redhat.com> wrote:
>>>>
>>>>> JSON decoding was also a significant slice of time, so, possibly the
>>>>> problem is how JSON blobs behave with these big queries. And of course
>>>>> Erratas have tons of those.
>>>>>
>>>>> On Tue, Nov 26, 2019 at 1:50 PM Fabricio Aguiar <
>>>>> fabricio.aguiar at redhat.com> wrote:
>>>>>
>>>>>> Thanks Dennis and Daniel!
>>>>>>
>>>>>> those are really good points Daniel!
>>>>>>
>>>>>> I noticed sync and re-sync behaves almost the same for a period of
>>>>>> time:
>>>>>> [image: sync_and_resync.jpg]
>>>>>>
>>>>>> so I was guessing the problem would be when starting to save the
>>>>>> contents.
>>>>>> Combining Daniel and Dennis hints, probably the problem is a very
>>>>>> long query with many Erratas!
>>>>>>
>>>>>> Best regards,
>>>>>> Fabricio Aguiar
>>>>>> Software Engineer, Pulp Project
>>>>>> Red Hat Brazil - Latam <https://www.redhat.com/>
>>>>>> +55 11 999652368
>>>>>>
>>>>>>
>>>>>> On Tue, Nov 26, 2019 at 3:26 PM Daniel Alley <dalley at redhat.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Fabricio, this is great work!
>>>>>>>
>>>>>>> One thing that stands out is that a very large amount of time is
>>>>>>> being spent in remove_duplicates(), 65% of the total runtime of the sync.
>>>>>>>
>>>>>>> - 13% of the total runtime spent on this inside cast()
>>>>>>> https://github.com/pulp/pulpcore/blob/master/pulpcore/plugin/repo_version_utils.py#L31
>>>>>>> - 31% of the total runtime spent in these lines
>>>>>>> https://github.com/pulp/pulpcore/blob/master/pulpcore/plugin/repo_version_utils.py#L37-L38
>>>>>>> - 20% of the total runtime spent on this line
>>>>>>> https://github.com/pulp/pulpcore/blob/master/pulpcore/plugin/repo_version_utils.py#L42
>>>>>>>
>>>>>>> There's a couple of suggestions on how to improve this code written
>>>>>>> up here https://pulp.plan.io/issues/5701
>>>>>>>
>>>>>>> The memory usage seems to be coming from the Django ORM backend. My
>>>>>>> guess is that lines 37 & 38 (linked above) are generating some extremely
>>>>>>> long SQL, because it's basically doing OR with a vast number of individual
>>>>>>> queries. And I think that is also not great for performance.
>>>>>>> https://www.cybertec-postgresql.com/en/avoid-or-for-better-performance/
>>>>>>>
>>>>>>> Another hint that that might be the case is that the memory usage
>>>>>>> grows and grows and then collapses all at once. That would make sense if
>>>>>>> the SQL compiler's data structure is growing and growing and then being
>>>>>>> destroyed after the query executes.
>>>>>>>
>>>>>>> I would imagine that if we fix our queries, we'll both fix the
>>>>>>> memory consumption and improve performance at the same time.
>>>>>>>
>>>>>>> On Tue, Nov 26, 2019 at 11:57 AM Fabricio Aguiar <
>>>>>>> fabricio.aguiar at redhat.com> wrote:
>>>>>>>
>>>>>>>> Hi everyone,
>>>>>>>>
>>>>>>>> I've been investigating memory consumption for syncing on RPM
>>>>>>>> plugin - issue 5688 <https://pulp.plan.io/issues/5688>, and I want
>>>>>>>> to share what I've found so far.
>>>>>>>>
>>>>>>>> First, it seems that a new process is created when we started to
>>>>>>>> sync, and this new process is responsible for the large amount of memory
>>>>>>>> consumption observed.
>>>>>>>>
>>>>>>>> As I shared on some notes here: https://pulp.plan.io/issues/5688
>>>>>>>> The problem seems to affect only RHEL, I did not see a large
>>>>>>>> consumption of memory for the following repos:
>>>>>>>> - CentOS 6: http://mirror.centos.org/centos-6/6.10/os/x86_64/
>>>>>>>> - CentOS 7: http://mirror.linux.duke.edu/pub/centos/7/os/x86_64/
>>>>>>>> - CentOS 8 AppStrem:
>>>>>>>> http://mirror.linux.duke.edu/pub/centos/8/AppStream/x86_64/os/
>>>>>>>> - CentOS 8 BaseOS:
>>>>>>>> http://mirror.linux.duke.edu/pub/centos/8/BaseOS/x86_64/os/
>>>>>>>> - EPEL 7: https://dl.fedoraproject.org/pub/epel/7/x86_64/
>>>>>>>>
>>>>>>>> As stated on the issue, with RHEL 7 we can observe the problem, for
>>>>>>>> my tests, I used the following repo:
>>>>>>>> http://cdn.stage.redhat.com/content/dist/rhel/server/7/7Server/x86_64/os/
>>>>>>>>
>>>>>>>> For my tests, I used pulp3-source-fedora30 vagrant box, initially
>>>>>>>> set with 4GB, then I increased to 16GB, and it was not enough. I only was
>>>>>>>> able to sync RHEL 7 when I increased the memory up to 23GB.
>>>>>>>>
>>>>>>>> Utilizing py-spy I got those SVG profiles attached, you can find
>>>>>>>> them hosted here:
>>>>>>>> Sync - https://sendeyo.com/up/d/90a8ae4c8f
>>>>>>>> Re-sync - https://sendeyo.com/up/d/4c855bcce3
>>>>>>>>
>>>>>>>> With the following branch, I was able to take some tracemalloc
>>>>>>>> <https://docs.python.org/3/library/tracemalloc.html> snapshots
>>>>>>>> (every 30 seconds):
>>>>>>>>
>>>>>>>> https://github.com/pulp/pulp_rpm/commit/f3f079010cfe81b7f5cf3ef94c2402b1ccf7d90c
>>>>>>>>
>>>>>>>> *Tracemalloc for Sync at peak of memory consumption:*
>>>>>>>>
>>>>>>>> #1: sql/compiler.py:1512: 7825340.4 KiB
>>>>>>>> #2: tasks/synchronizing.py:154: 4062651.9 KiB
>>>>>>>> #3: stages/declarative_version.py:149: 4062601.9 KiB
>>>>>>>> #4: models/repository.py:631: 4062130.0 KiB
>>>>>>>> #5: models/repository.py:96: 4062127.7 KiB
>>>>>>>> #6: rq/job.py:611: 4060459.2 KiB
>>>>>>>> #7: rq/job.py:605: 4058273.8 KiB
>>>>>>>> #8: rq/worker.py:822: 4053883.3 KiB
>>>>>>>> #9: tasking/worker.py:100: 4053875.9 KiB
>>>>>>>> #10: rq/worker.py:684: 4053849.9 KiB
>>>>>>>> #11: rq/worker.py:610: 4051645.7 KiB
>>>>>>>> #12: plugin/repo_version_utils.py:31: 4028192.9 KiB
>>>>>>>> #13: models/base.py:124: 4028191.2 KiB
>>>>>>>> #14: fields/related_descriptors.py:401: 4028165.2 KiB
>>>>>>>> #15: models/query.py:1242: 4021212.9 KiB
>>>>>>>> #16: models/query.py:402: 3995284.5 KiB
>>>>>>>> #17: models/query.py:256: 3995284.5 KiB
>>>>>>>> #18: models/query.py:55: 3952718.2 KiB
>>>>>>>> #19: sql/compiler.py:1133: 3919417.7 KiB
>>>>>>>> #20: db/utils.py:96: 3912674.4 KiB
>>>>>>>> #21: psycopg2/_json.py:166: 3837534.7 KiB
>>>>>>>> #22: json/decoder.py:337: 3837533.2 KiB
>>>>>>>> obj, end = self.raw_decode(s, idx=_w(s, 0).end())
>>>>>>>> #23: json/__init__.py:348: 3837533.2 KiB
>>>>>>>> return _default_decoder.decode(s)
>>>>>>>> #24: json/decoder.py:353: 3837532.7 KiB
>>>>>>>> obj, end = self.scan_once(s, idx)
>>>>>>>> #25: rq/worker.py:670: 211891.5 KiB
>>>>>>>> #26: tasking/worker.py:69: 198267.0 KiB
>>>>>>>> #27: rq/worker.py:477: 191261.5 KiB
>>>>>>>> #28: cli/cli.py:252: 186110.9 KiB
>>>>>>>> #29: click/core.py:555: 185957.8 KiB
>>>>>>>> #30: cli/cli.py:78: 104822.7 KiB
>>>>>>>> #31: models/query.py:73: 48350.9 KiB
>>>>>>>> #32: models/base.py:513: 48350.9 KiB
>>>>>>>> #33: sql/compiler.py:405: 38126.1 KiB
>>>>>>>> #34: sql/compiler.py:1087: 33355.6 KiB
>>>>>>>> #35: click/core.py:956: 29580.3 KiB
>>>>>>>> #36: click/core.py:1137: 29498.3 KiB
>>>>>>>> #37: models/base.py:430: 29340.9 KiB
>>>>>>>> #38: models/query.py:274: 25946.8 KiB
>>>>>>>> #39: plugin/repo_version_utils.py:30: 25850.4 KiB
>>>>>>>> #40: models/query.py:892: 24105.6 KiB
>>>>>>>> #41: models/query.py:910: 24089.7 KiB
>>>>>>>> #42: models/query.py:399: 24045.0 KiB
>>>>>>>> #43: sql/query.py:1290: 17343.9 KiB
>>>>>>>> #44: sql/where.py:81: 15629.7 KiB
>>>>>>>> #45: sql/compiler.py:489: 15581.6 KiB
>>>>>>>> #46: models/lookups.py:162: 15527.0 KiB
>>>>>>>> #47: psycopg2/extras.py:678: 15027.3 KiB
>>>>>>>> #48: sql/query.py:1323: 13486.4 KiB
>>>>>>>> #49: sql/query.py:796: 13486.3 KiB
>>>>>>>> #50: sql/compiler.py:474: 11291.3 KiB
>>>>>>>> #51: sql/compiler.py:54: 11282.7 KiB
>>>>>>>> #52: sql/compiler.py:45: 11282.7 KiB
>>>>>>>> #53: click/core.py:717: 10190.6 KiB
>>>>>>>> #54: models/query.py:72: 9967.2 KiB
>>>>>>>> #55: models/query.py:1219: 8855.6 KiB
>>>>>>>> #56: models/query.py:1231: 8855.6 KiB
>>>>>>>> #57: models/query.py:1072: 8840.0 KiB
>>>>>>>> #58: models/query.py:401: 8840.0 KiB
>>>>>>>> #59: models/lookups.py:153: 8779.2 KiB
>>>>>>>> #60: models/lookups.py:79: 8773.0 KiB
>>>>>>>> #61: models/expressions.py:332: 8769.2 KiB
>>>>>>>> #62: models/expressions.py:238: 8769.2 KiB
>>>>>>>> #63: plugin/repo_version_utils.py:37: 7602.2 KiB
>>>>>>>> #64: models/base.py:408: 7378.9 KiB
>>>>>>>> #65: click/core.py:764: 7371.2 KiB
>>>>>>>> #66: sql/compiler.py:254: 6853.7 KiB
>>>>>>>> #67: models/expressions.py:737: 6851.8 KiB
>>>>>>>> #68: models/lookups.py:159: 6745.8 KiB
>>>>>>>> #69: sql/query.py:1293: 6744.2 KiB
>>>>>>>> #70: sql/query.py:2258: 6743.6 KiB
>>>>>>>> #71: sql/query.py:763: 6743.6 KiB
>>>>>>>> #72: sql/query.py:2259: 6742.7 KiB
>>>>>>>> #73: sql/compiler.py:1054: 6696.5 KiB
>>>>>>>> #74: utils/deconstruct.py:17: 6261.2 KiB
>>>>>>>> #75: fields/related.py:986: 6246.5 KiB
>>>>>>>> #76: fields/__init__.py:381: 6245.8 KiB
>>>>>>>> #77: models/base.py:411: 5990.2 KiB
>>>>>>>> #78: python3.7/uuid.py:204: 5876.0 KiB
>>>>>>>> self.__dict__['int'] = int
>>>>>>>> #79: sql/compiler.py:472: 5784.0 KiB
>>>>>>>> #80: sql/compiler.py:1019: 5023.7 KiB
>>>>>>>> #81: sql/compiler.py:1053: 4916.2 KiB
>>>>>>>> #82: sql/query.py:350: 4490.9 KiB
>>>>>>>> #83: sql/query.py:309: 4438.3 KiB
>>>>>>>> #84: sql/compiler.py:219: 4406.5 KiB
>>>>>>>> #85: sql/compiler.py:666: 4401.8 KiB
>>>>>>>> #86: python3.7/copy.py:96: 4399.3 KiB
>>>>>>>> rv = reductor(4)
>>>>>>>> #87: models/expressions.py:159: 4398.6 KiB
>>>>>>>> #88: python3.7/copyreg.py:88: 4372.9 KiB
>>>>>>>> return cls.__new__(cls, *args)
>>>>>>>> #89: python3.7/copy.py:274: 4372.9 KiB
>>>>>>>> y = func(*args)
>>>>>>>> #90: python3.7/copy.py:106: 4372.8 KiB
>>>>>>>> return _reconstruct(x, None, *rv)
>>>>>>>> #91: models/query_utils.py:82: 4223.2 KiB
>>>>>>>> #92: bin/rq:10: 4115.9 KiB
>>>>>>>> #93: python3.7/uuid.py:161: 4025.9 KiB
>>>>>>>> int = int_(hex, 16)
>>>>>>>> #94: sql/query.py:1318: 3846.5 KiB
>>>>>>>> #95: models/expressions.py:749: 3362.8 KiB
>>>>>>>> #96: fields/__init__.py:709: 3340.3 KiB
>>>>>>>> #97: utils/tree.py:108: 3008.9 KiB
>>>>>>>> #98: models/query_utils.py:72: 2534.5 KiB
>>>>>>>> #99: models/query_utils.py:85: 2534.2 KiB
>>>>>>>> #100: models/base.py:503: 2214.3 KiB
>>>>>>>> #101: models/query.py:199: 2190.8 KiB
>>>>>>>> #102: models/query.py:190: 2178.6 KiB
>>>>>>>> #103: models/query_utils.py:74: 2164.6 KiB
>>>>>>>> #104: utils/tree.py:23: 2115.5 KiB
>>>>>>>> #105: models/query_utils.py:59: 2113.1 KiB
>>>>>>>> #106: sql/query.py:1251: 1895.8 KiB
>>>>>>>> #107: sql/query.py:1116: 1895.3 KiB
>>>>>>>> #108: models/lookups.py:19: 1892.1 KiB
>>>>>>>> #109: sql/query.py:68: 1882.4 KiB
>>>>>>>> #110: sql/query.py:1249: 1881.4 KiB
>>>>>>>> #111: fields/related.py:974: 1679.4 KiB
>>>>>>>> #112: sql/compiler.py:1018: 1672.8 KiB
>>>>>>>> #113: base/operations.py:564: 1672.8 KiB
>>>>>>>> #114: models/expressions.py:747: 1660.9 KiB
>>>>>>>> #115: models/query.py:1893: 1590.1 KiB
>>>>>>>> #116: models/query.py:63: 1590.1 KiB
>>>>>>>> #117: models/query.py:62: 1554.4 KiB
>>>>>>>> #118: models/query.py:61: 1554.4 KiB
>>>>>>>> 962 other: 9.8 MiB
>>>>>>>> Total allocated size: 99186.9 MiB
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> *Tracemalloc for Re-sync at peak of memory consumption:*
>>>>>>>>
>>>>>>>> #1: sql/compiler.py:1512: 1813416.3 KiB
>>>>>>>> #2: asyncio/events.py:88: 1159219.2 KiB
>>>>>>>> self._context.run(self._callback, *self._args)
>>>>>>>> #3: stages/api.py:43: 1159151.2 KiB
>>>>>>>> #4: asyncio/base_events.py:1775: 1158553.1 KiB
>>>>>>>> if bool(enabled) == bool(self._coroutine_origin_tracking_enabled):
>>>>>>>> #5: asyncio/base_events.py:539: 1157867.4 KiB
>>>>>>>> self._thread_id = None
>>>>>>>> #6: asyncio/base_events.py:571: 1156528.6 KiB
>>>>>>>> # local task.
>>>>>>>> #7: stages/declarative_version.py:149: 1152654.5 KiB
>>>>>>>> #8: tasks/synchronizing.py:154: 1151586.8 KiB
>>>>>>>> #9: rq/job.py:611: 1147795.3 KiB
>>>>>>>> #10: rq/job.py:605: 1147534.7 KiB
>>>>>>>> #11: rq/worker.py:822: 1141556.0 KiB
>>>>>>>> #12: tasking/worker.py:100: 1139872.3 KiB
>>>>>>>> #13: rq/worker.py:684: 1137457.4 KiB
>>>>>>>> #14: rq/worker.py:610: 1136707.1 KiB
>>>>>>>> #15: models/query.py:274: 940312.1 KiB
>>>>>>>> #16: models/query.py:1242: 921581.8 KiB
>>>>>>>> #17: models/query.py:55: 908503.7 KiB
>>>>>>>> #18: sql/compiler.py:1133: 906747.3 KiB
>>>>>>>> #19: db/utils.py:96: 906707.8 KiB
>>>>>>>> #20: stages/content_stages.py:48: 899314.9 KiB
>>>>>>>> #21: psycopg2/_json.py:166: 872466.2 KiB
>>>>>>>> #22: json/decoder.py:337: 871817.4 KiB
>>>>>>>> obj, end = self.raw_decode(s, idx=_w(s, 0).end())
>>>>>>>> #23: json/__init__.py:348: 871817.4 KiB
>>>>>>>> return _default_decoder.decode(s)
>>>>>>>> #24: json/decoder.py:353: 871816.8 KiB
>>>>>>>> obj, end = self.scan_once(s, idx)
>>>>>>>> #25: click/core.py:555: 448794.2 KiB
>>>>>>>> #26: rq/worker.py:670: 259351.8 KiB
>>>>>>>> #27: tasking/worker.py:69: 257743.4 KiB
>>>>>>>> #28: rq/worker.py:477: 249243.3 KiB
>>>>>>>> #29: cli/cli.py:252: 244023.2 KiB
>>>>>>>> #30: cli/cli.py:78: 226347.5 KiB
>>>>>>>> #31: click/core.py:956: 220086.5 KiB
>>>>>>>> #32: click/core.py:1137: 213146.8 KiB
>>>>>>>> #33: click/core.py:717: 188052.0 KiB
>>>>>>>> #34: tasks/synchronizing.py:581: 177471.0 KiB
>>>>>>>> #35: models/package.py:232: 138350.9 KiB
>>>>>>>> #36: stages/artifact_stages.py:219: 54726.5 KiB
>>>>>>>> #37: stages/artifact_stages.py:251: 54723.6 KiB
>>>>>>>> #38: models/query.py:1625: 47373.9 KiB
>>>>>>>> #39: models/query.py:1738: 46489.3 KiB
>>>>>>>> #40: fields/related_descriptors.py:627: 42352.2 KiB
>>>>>>>> #41: models/package.py:238: 24673.4 KiB
>>>>>>>> #42: models/query.py:1591: 24325.2 KiB
>>>>>>>> #43: models/query.py:1244: 18744.3 KiB
>>>>>>>> #44: models/query.py:771: 18744.3 KiB
>>>>>>>> #45: models/query.py:73: 12827.6 KiB
>>>>>>>> #46: models/base.py:513: 12827.6 KiB
>>>>>>>> #47: sql/compiler.py:405: 11912.8 KiB
>>>>>>>> #48: psycopg2/extras.py:678: 11666.5 KiB
>>>>>>>> #49: models/package.py:245: 10837.2 KiB
>>>>>>>> #50: sql/compiler.py:1087: 7407.8 KiB
>>>>>>>> #51: sql/where.py:81: 6915.5 KiB
>>>>>>>> #52: models/base.py:741: 6705.2 KiB
>>>>>>>> #53: models/base.py:779: 6697.1 KiB
>>>>>>>> #54: models/progress.py:144: 6607.2 KiB
>>>>>>>> #55: click/core.py:764: 6603.1 KiB
>>>>>>>> #56: models/progress.py:191: 6601.5 KiB
>>>>>>>> #57: tasks/synchronizing.py:609: 6574.8 KiB
>>>>>>>> #58: python3.7/uuid.py:204: 6532.6 KiB
>>>>>>>> self.__dict__['int'] = int
>>>>>>>> #59: models/base.py:851: 6485.3 KiB
>>>>>>>> #60: models/base.py:411: 6366.6 KiB
>>>>>>>> #61: models/base.py:900: 6178.2 KiB
>>>>>>>> #62: models/query.py:760: 5612.1 KiB
>>>>>>>> #63: sql/compiler.py:1429: 5528.5 KiB
>>>>>>>> #64: models/base.py:408: 5042.7 KiB
>>>>>>>> #65: tasks/synchronizing.py:575: 4724.0 KiB
>>>>>>>> #66: createrepo_c/__init__.py:312: 4722.6 KiB
>>>>>>>> #67: tasks/synchronizing.py:285: 4722.6 KiB
>>>>>>>> #68: models/lookups.py:162: 4583.4 KiB
>>>>>>>> #69: tasks/synchronizing.py:582: 4236.2 KiB
>>>>>>>> #70: tasks/synchronizing.py:256: 3655.1 KiB
>>>>>>>> #71: sql/query.py:1312: 3358.8 KiB
>>>>>>>> #72: sql/compiler.py:1417: 3312.9 KiB
>>>>>>>> #73: models/query.py:892: 3121.7 KiB
>>>>>>>> #74: models/lookups.py:153: 2934.4 KiB
>>>>>>>> #75: models/lookups.py:79: 2931.5 KiB
>>>>>>>> #76: models/expressions.py:332: 2918.8 KiB
>>>>>>>> #77: models/expressions.py:238: 2918.5 KiB
>>>>>>>> #78: fields/related_descriptors.py:219: 2766.3 KiB
>>>>>>>> #79: fields/related_descriptors.py:629: 2765.8 KiB
>>>>>>>> #80: models/query.py:910: 2575.7 KiB
>>>>>>>> #81: sql/query.py:1290: 2513.6 KiB
>>>>>>>> #82: sql/query.py:1318: 2257.2 KiB
>>>>>>>> #83: stages/association_stages.py:36: 2219.3 KiB
>>>>>>>> #84: sql/compiler.py:1452: 2214.1 KiB
>>>>>>>> #85: sql/compiler.py:1370: 2214.1 KiB
>>>>>>>> #86: python3.7/uuid.py:161: 2179.2 KiB
>>>>>>>> int = int_(hex, 16)
>>>>>>>> #87: models/base.py:503: 2082.6 KiB
>>>>>>>> #88: models/manager.py:82: 2008.6 KiB
>>>>>>>> #89: tasks/synchronizing.py:585: 1803.4 KiB
>>>>>>>> #90: fields/related_descriptors.py:527: 1767.5 KiB
>>>>>>>> #91: python3.7/copy.py:96: 1754.9 KiB
>>>>>>>> rv = reductor(4)
>>>>>>>> #92: urllib/parse.py:562: 1725.2 KiB
>>>>>>>> resolved_path.append('')
>>>>>>>> #93: urllib/parse.py:489: 1725.2 KiB
>>>>>>>> if url and url[:1] != '/': url = '/' + url
>>>>>>>> #94: urllib/parse.py:475: 1725.2 KiB
>>>>>>>> _coerce_args(*components))
>>>>>>>> #95: models/lookups.py:159: 1647.3 KiB
>>>>>>>> #96: models/expressions.py:159: 1625.9 KiB
>>>>>>>> #97: tasks/synchronizing.py:512: 1619.8 KiB
>>>>>>>> #98: models/query.py:1652: 1619.0 KiB
>>>>>>>> #99: fields/__init__.py:801: 1604.3 KiB
>>>>>>>> #100: models/base.py:473: 1545.2 KiB
>>>>>>>> #101: python3.7/uuid.py:759: 1488.4 KiB
>>>>>>>> def uuid4():
>>>>>>>> #102: models/package.py:248: 1455.4 KiB
>>>>>>>> #103: sql/compiler.py:489: 1332.9 KiB
>>>>>>>> #104: python3.7/copy.py:106: 1293.2 KiB
>>>>>>>> return _reconstruct(x, None, *rv)
>>>>>>>> #105: python3.7/copyreg.py:88: 1279.1 KiB
>>>>>>>> return cls.__new__(cls, *args)
>>>>>>>> #106: python3.7/copy.py:274: 1279.1 KiB
>>>>>>>> y = func(*args)
>>>>>>>> #107: sql/query.py:350: 1119.1 KiB
>>>>>>>> #108: models/query.py:1231: 1057.4 KiB
>>>>>>>> #109: models/query.py:1219: 1057.4 KiB
>>>>>>>> 1019 other: 34.8 MiB
>>>>>>>> Total allocated size: 28304.4 MiB
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> The snapshots can be found here:
>>>>>>>>
>>>>>>>> memory_data
>>>>>>>> <https://drive.google.com/drive/folders/1mdJtdGDvbwq_jLc5m_T8VBjjb-bDRDmS>
>>>>>>>>
>>>>>>>> *Memory consumption over time:*
>>>>>>>> [image: sync.png]
>>>>>>>>
>>>>>>>> [image: resync.png]
>>>>>>>>
>>>>>>>> [image: sync_and_resync.png]
>>>>>>>>
>>>>>>>> Best regards,
>>>>>>>> Fabricio Aguiar
>>>>>>>> Software Engineer, Pulp Project
>>>>>>>> Red Hat Brazil - Latam <https://www.redhat.com/>
>>>>>>>> +55 11 999652368
>>>>>>>> _______________________________________________
>>>>>>>> Pulp-dev mailing list
>>>>>>>> Pulp-dev at redhat.com
>>>>>>>> https://www.redhat.com/mailman/listinfo/pulp-dev
>>>>>>>>
>>>>>>> _______________________________________________
>>>>> Pulp-dev mailing list
>>>>> Pulp-dev at redhat.com
>>>>> https://www.redhat.com/mailman/listinfo/pulp-dev
>>>>>
>>>> _______________________________________________
>>>> Pulp-dev mailing list
>>>> Pulp-dev at redhat.com
>>>> https://www.redhat.com/mailman/listinfo/pulp-dev
>>>>
>>> _______________________________________________
> Pulp-dev mailing list
> Pulp-dev at redhat.com
> https://www.redhat.com/mailman/listinfo/pulp-dev
>
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