[Pulp-dev] Memory consumption on RPM sync
Tatiana Tereshchenko
ttereshc at redhat.com
Wed Nov 27 11:39:05 UTC 2019
I forgot to mention that Advisories are not handled by `remove_duplicates`
utility.
Advisory model doesn't have `repo_key_fields` defined, so its processing
shouldn't go further than
https://github.com/pulp/pulpcore/blob/master/pulpcore/plugin/repo_version_utils.py#L33
.
Tanya
On Wed, Nov 27, 2019 at 12:28 PM 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
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>> https://www.redhat.com/mailman/listinfo/pulp-dev
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>
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