[Pulp-dev] Memory consumption on RPM sync
Fabricio Aguiar
fabricio.aguiar at redhat.com
Mon Dec 9 15:22:54 UTC 2019
Hi everyone,
I would like to bring all PRs involved with boosting memory performance and
share some findings.
*PRs:*
- https://github.com/pulp/pulpcore/pull/440
- https://github.com/pulp/pulpcore/pull/441
- https://github.com/pulp/pulp_rpm/pull/1549
- https://github.com/pulp/pulp_rpm/pull/1550
- https://github.com/pulp/pulp_rpm/pull/1551
- https://github.com/pulp/pulp_rpm/pull/1552
*Hints:*
- avoid to save inside a loop, try to bulk_create [1]
- if it is necessary to save inside a loop, try to make it in batches (by
time: every 30 seconds or minute, by steps: saving when reaching 100
iterations, ...)
- be careful with properties and methods, are they doing some query? How
big/long this query would be?
- be careful with containers, are you really going to use the stored
objects?
- make sure all the objects you bring into memory will be used, if not,
prefer to use iterator in your queries [2]
[1] https://docs.djangoproject.com/en/2.2/ref/models/querysets/#bulk-create
[2] https://docs.djangoproject.com/en/2.2/ref/models/querysets/#iterator
*Result:*
===================================== test session starts
=====================================
platform linux -- Python 3.7.3, pytest-5.3.1, py-1.8.0, pluggy-0.13.1 --
/usr/local/lib/pulp/bin/python3
cachedir: .pytest_cache
rootdir: /home/vagrant/devel/pulp_rpm
collected 1 item
pulp_rpm/tests/performance/test_publish.py::PublishTestCase::test_rhel7
http://cdn.stage.redhat.com/content/dist/rhel/server/7/7Server/x86_64/os/
-> Sync => Waiting time (s): 0.094807 | Service time (s): 614.355159
-> Publish => Waiting time (s): 0.087938 | Service time (s): 501.340294
PASSED
=================================== slowest test durations
====================================
1123.81s call
pulp_rpm/tests/performance/test_publish.py::PublishTestCase::test_rhel7
0.41s setup
pulp_rpm/tests/performance/test_publish.py::PublishTestCase::test_rhel7
0.00s teardown
pulp_rpm/tests/performance/test_publish.py::PublishTestCase::test_rhel7
=============================== 1 passed in 1124.45s (0:18:44)
================================
The test above was run in a vagrant box with *8GB*
Best regards,
Fabricio Aguiar
Software Engineer, Pulp Project
Red Hat Brazil - Latam <https://www.redhat.com/>
+55 11 999652368
On Sat, Dec 7, 2019 at 2:27 PM Daniel Alley <dalley at redhat.com> wrote:
> Some results for new remove_duplicates: when isolating that one change
> (including all the other performance improvements) it looks like
> remove_duplicates went from 40-70% of the total sync runtime to about 10%.
> And I think it was also running (unnecessarily) on the initial sync, which
> explains why initial sync time dropped by 70-80% while resync only dropped
> by about half.
>
> On Sat, Dec 7, 2019 at 11:29 AM Daniel Alley <dalley at redhat.com> wrote:
>
>> I've been looking into RPM plugin performance a little bit as well due to
>> the remove_duplicates issues that Fabricio was able to confirm, and I think
>> I've found more low-hanging fruit that is really easy to fix. So, I did.
>>
>> Attached are some cProfile graphs of a CentOS 7 (
>> http://mirror.linux.duke.edu/pub/centos/7/os/x86_64/) sync and resync is
>> it is currently. An initial sync of CentOS 7 takes about 6 minutes on my
>> machine. A resync takes about 1 minute 45 seconds.
>>
>> Observations:
>>
>> - Progress Reporting is *really* expensive, because we're
>> incrementing the progress report one-by-one for each content unit (that is,
>> thousands and thousands of extra, separate database updates). It actually
>> takes much longer report the progress as it does to do the parsing itself
>> -- 30% of the total runtime of both syncs and resyncs. I'm working on a PR
>> to address this by cutting way down on the granularity of the reporting.
>> - gettext inside of plugin/stages/api.py::Stage.put() is taking up
>> 13% of the runtime, because it is doing a "log.debug(_('%(name)s - put:
>> %(content)s'), {'name': self, 'content': item})" for all of the content
>> going into the pipeline. I don't think it makes sense to do this even if
>> it didn't have a performance cost, so I'm going to remove it.
>>
>> With these two changes and my remove_duplicates changes, I managed to cut
>> the resync time down to 55 seconds and the initial sync time down to 1
>> minute 27 seconds.
>>
>> I expect that remove_duplicates will still be a performance bottleneck
>> even after my changes, but I don't have any numbers on that yet. Remove
>> duplicates only triggers when the metadata changes (new content added *and*
>> some exists already) or when you manually add content, so I'm going to wait
>> for CentOS metadata to change to capture the new results.
>>
>>
>> On Tue, Dec 3, 2019 at 1:36 PM Fabricio Aguiar <
>> fabricio.aguiar at redhat.com> wrote:
>>
>>> After many attempts, today I was able to sync using a vagrant box with
>>> 8GB of memory, and it took 9011.04181 seconds(more than 2 hours) to sync
>>>
>>> I opened 2 WIP PRs:
>>> https://github.com/pulp/pulpcore/pull/432
>>> https://github.com/pulp/pulp_rpm/pull/1547
>>>
>>> --- These are very WIP PRs, it was just some tweaks I tried, to see how
>>> sync would behave ---
>>>
>>> Attempts:
>>> 1 - Replace SQL OR clauses with IN clauses [a] - slight improvement on
>>> memory performance;
>>> 2 - Sometimes ProgressReports were in the middle of a loop, doing 50
>>> updates queries in a row - slight improvement on memory performance;
>>> 3 - UpdateCollection was being saved on each iteration of a loop,
>>> changed to bulk creation - good improvement on memory performance;
>>> 4 - Introduced batch size on bulk creation - good improvement on memory
>>> performance;
>>>
>>> Guesses:
>>> - As I said yesterday when it was around 24000 packages saved, process
>>> crashed out of memory,
>>> -- I started to look at what could cause it, and when I sorted packages
>>> by changelog size, it crashed on less than 3000 packages saved. Changed
>>> changelogs to empty list - somewhat improvement on memory performance.
>>> - Memory consumption was always increasing when it was close to
>>> finishing the sync
>>> -- it seemed it never released the packages objects, as packages saved
>>> increased, memory consumption increased - Commented comps (RHEL 7 doesn't
>>> have modules) - somewhat improvement on memory performance.
>>>
>>> Changed changelogs to empty list + Commented comps = Finally able to
>>> sync!
>>>
>>> My final guesses:
>>> - Models related to Comps also have huge JSONFields
>>> - Memory is not released after saving packages due to relations,
>>> Comps/Modules points to lots of packages objects. When saving
>>> PackageGroups, I could see memory being released.
>>>
>>> Log Analysis:
>>> After being able to sync with 8GB I started to look the logs to confirm
>>> Daniel's suspicion - #5701 <https://pulp.plan.io/issues/5701>
>>> removing duplicates is consuming a lot of memory also. Before removing
>>> duplicates, the peak of memory consumption was below 60%.
>>> During remove duplicates, the peak was above 80%
>>>
>>>
>>> [a]
>>> https://www.cybertec-postgresql.com/en/avoid-or-for-better-performance/
>>>
>>> Thanks @Daniel Alley <dalley at redhat.com>, @Dennis Kliban
>>> <dkliban at redhat.com>, @Tatiana Tereshchenko <ttereshc at redhat.com> and @Justin
>>> Sherrill <jsherril at redhat.com> for all of your suggestions!
>>>
>>> Best regards,
>>> Fabricio Aguiar
>>> Software Engineer, Pulp Project
>>> Red Hat Brazil - Latam <https://www.redhat.com/>
>>> +55 11 999652368
>>>
>>>
>>> On Mon, Dec 2, 2019 at 7:16 PM Fabricio Aguiar <
>>> fabricio.aguiar at redhat.com> wrote:
>>>
>>>> As Tanya suggested I tried to sync without changelogs, even so, I still
>>>> having memory problems =/
>>>>
>>>> Best regards,
>>>> Fabricio Aguiar
>>>> Software Engineer, Pulp Project
>>>> Red Hat Brazil - Latam <https://www.redhat.com/>
>>>> +55 11 999652368
>>>>
>>>>
>>>> On Mon, Dec 2, 2019 at 5:13 PM Fabricio Aguiar <
>>>> fabricio.aguiar at redhat.com> wrote:
>>>>
>>>>> from what I see the problem is not a package itself, it is the set of
>>>>> packages with huge changelogs.
>>>>> Observing the logs, I guess postgres take some time to save a package
>>>>> with very large changelog,
>>>>> so it seems like it creates a kind of queue for executing each query,
>>>>> ad I think that is the point when the memory consumption raises.
>>>>> So as huge as changelog is, more time it will take to save, more time
>>>>> equal more queries waiting to be executed leading to high consumption of
>>>>> memory (My guess due the logs, I don't know how django communicates with
>>>>> postgres).
>>>>>
>>>>> Best regards,
>>>>> Fabricio Aguiar
>>>>> Software Engineer, Pulp Project
>>>>> Red Hat Brazil - Latam <https://www.redhat.com/>
>>>>> +55 11 999652368
>>>>>
>>>>>
>>>>> On Mon, Dec 2, 2019 at 5:06 PM Daniel Alley <dalley at redhat.com> wrote:
>>>>>
>>>>>> 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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