<div dir="ltr">Thank you for these numbers,<div>I put it on my scrum status, but it worth to share it here also:<div> -- got some really nice feedback from dalley and dkliban</div><div> -- performance problem seems to be related with query issues</div><div> -- started to dig into queries</div><div> -- find out process crashes during ContentSaver</div><div> -- when using rpdb noticed a decrease in the use of memory</div><div> -- replaced rpdb with timer.sleep(5) and noticed a slight decrease in the use of memory</div><div> -- 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</div><div> -- started to read about django cache</div><div> -- found this [1], tomorrow I will try to replace with [2]</div><div><br></div><div>[1] <a href="https://docs.djangoproject.com/en/2.2/topics/cache/#local-memory-caching" target="_blank">https://docs.djangoproject.com/en/2.2/topics/cache/#local-memory-caching</a></div><div>[2] <a href="https://docs.djangoproject.com/en/2.2/topics/cache/#memcached" target="_blank">https://docs.djangoproject.com/en/2.2/topics/cache/#memcached</a></div><div><br></div><div>So taking these numbers, for ContentSaver what matter is not a specific type, then:</div><div><br></div><div><div>CentOS7 - 10 000 RPMs, 0 Advisories = 10 000 contents</div><div>EPEL7 - 13 500 RPMs, 4400 Advisories = 17 900 contents</div><div>RHEL7 - 26 000 RPMs, 3900 Advisories = 29 900 contents</div><br></div><div>I have found this really nice article, and I believe our problem is pretty similar:<br><a href="https://medium.com/@chakrabortypritish/django-optimization-how-to-avoid-memory-mishaps-1e55b2aa947c">https://medium.com/@chakrabortypritish/django-optimization-how-to-avoid-memory-mishaps-1e55b2aa947c</a><br></div><br class="gmail-Apple-interchange-newline"><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div dir="ltr"><br>Best regards,</div><div dir="ltr"><span style="color:rgb(0,0,0);font-family:RedHatText,sans-serif;font-size:14px;font-weight:700;text-transform:capitalize">Fabricio</span><span style="color:rgb(0,0,0);font-family:RedHatText,sans-serif;font-size:14px;font-weight:700;text-transform:capitalize"> </span><span style="color:rgb(0,0,0);font-family:RedHatText,sans-serif;font-size:14px;font-weight:700;text-transform:capitalize">Aguiar</span><div>Software Engineer, Pulp Project</div><div><a href="https://www.redhat.com/" style="color:rgb(0,136,206);font-family:RedHatText,sans-serif;font-size:12px;margin:0px" target="_blank">Red Hat Brazil - Latam</a><br></div><div>+55 11 999652368</div><div><img src="https://marketing-outfit-prod-images.s3-us-west-2.amazonaws.com/f5445ae0c9ddafd5b2f1836854d7416a/Logo-RedHat-Email.png" width="96" height="22"></div></div></div></div></div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Nov 27, 2019 at 8:28 AM Tatiana Tereshchenko <<a href="mailto:ttereshc@redhat.com">ttereshc@redhat.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr">You know that I'd be the first one to suspect Advisories (aka Errata) :)<div>For the sake of fairness to Advisories, here are some rough numbers (at least for the repos which we are using):</div><div><br></div><div>CentOS7 - 10 000 RPMs, 0 Advisories</div><div>EPEL7 - 13 500 RPMs, 4400 Advisories</div><div>RHEL7 - 26 000 RPMs, 3900 Advisories</div><div><br></div><div>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?</div><div><br></div><div>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.</div><div><br></div><div>Tanya</div><div><br></div><div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Nov 26, 2019 at 9:25 PM Dana Walker <<a href="mailto:dawalker@redhat.com" target="_blank">dawalker@redhat.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div>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.</div><div><br></div><div><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div>
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<td width="100px"><a href="https://www.redhat.com" target="_blank"> <img src="https://marketing-outfit-prod-images.s3-us-west-2.amazonaws.com/f5445ae0c9ddafd5b2f1836854d7416a/Logo-RedHat-Email.png" width="90" height="auto"></a> </td>
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</div></div></div></div></div></div></div></div></div></div></div></div></div></div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Nov 26, 2019 at 3:07 PM Daniel Alley <<a href="mailto:dalley@redhat.com" target="_blank">dalley@redhat.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">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.<br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Nov 26, 2019 at 1:50 PM Fabricio Aguiar <<a href="mailto:fabricio.aguiar@redhat.com" target="_blank">fabricio.aguiar@redhat.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Thanks Dennis and Daniel!<br><br><div>those are really good points Daniel!<br><br>I noticed sync and re-sync behaves almost the same for a period of time:<br><div><img src="cid:ii_k3g7mfje4" alt="sync_and_resync.jpg" width="562" height="205"><br></div><div><br></div><div>so I was guessing the problem would be when starting to save the contents.</div><div>Combining Daniel and Dennis hints, probably the problem is a very long query with many Erratas!</div><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><br>Best regards,</div><div dir="ltr"><span style="color:rgb(0,0,0);font-family:RedHatText,sans-serif;font-size:14px;font-weight:700;text-transform:capitalize">Fabricio</span><span style="color:rgb(0,0,0);font-family:RedHatText,sans-serif;font-size:14px;font-weight:700;text-transform:capitalize"> </span><span style="color:rgb(0,0,0);font-family:RedHatText,sans-serif;font-size:14px;font-weight:700;text-transform:capitalize">Aguiar</span><div>Software Engineer, Pulp Project</div><div><a href="https://www.redhat.com/" style="color:rgb(0,136,206);font-family:RedHatText,sans-serif;font-size:12px;margin:0px" target="_blank">Red Hat Brazil - Latam</a><br></div><div>+55 11 999652368</div><div><img src="https://marketing-outfit-prod-images.s3-us-west-2.amazonaws.com/f5445ae0c9ddafd5b2f1836854d7416a/Logo-RedHat-Email.png" width="96" height="22"></div></div></div></div></div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Nov 26, 2019 at 3:26 PM Daniel Alley <<a href="mailto:dalley@redhat.com" target="_blank">dalley@redhat.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div>Fabricio, this is great work!</div><div><br></div><div>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.<br></div><ul><li>13% of the total runtime spent on this inside cast() <a href="https://github.com/pulp/pulpcore/blob/master/pulpcore/plugin/repo_version_utils.py#L31" target="_blank">https://github.com/pulp/pulpcore/blob/master/pulpcore/plugin/repo_version_utils.py#L31</a></li><li>31% of the total runtime spent in these lines <a href="https://github.com/pulp/pulpcore/blob/master/pulpcore/plugin/repo_version_utils.py#L37-L38" target="_blank">https://github.com/pulp/pulpcore/blob/master/pulpcore/plugin/repo_version_utils.py#L37-L38</a></li><li>20% of the total runtime spent on this line <a href="https://github.com/pulp/pulpcore/blob/master/pulpcore/plugin/repo_version_utils.py#L42" target="_blank">https://github.com/pulp/pulpcore/blob/master/pulpcore/plugin/repo_version_utils.py#L42</a></li></ul><div>There's a couple of suggestions on how to improve this code written up here <a href="https://pulp.plan.io/issues/5701" target="_blank">https://pulp.plan.io/issues/5701</a><br></div><div><br></div><div>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. <a href="https://www.cybertec-postgresql.com/en/avoid-or-for-better-performance/" target="_blank">https://www.cybertec-postgresql.com/en/avoid-or-for-better-performance/</a></div><div><br></div><div>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.</div><div><br></div><div>I would imagine that if we fix our queries, we'll both fix the memory consumption and improve performance at the same time.<br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Nov 26, 2019 at 11:57 AM Fabricio Aguiar <<a href="mailto:fabricio.aguiar@redhat.com" target="_blank">fabricio.aguiar@redhat.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hi everyone,<div><br></div><div>I've been investigating memory consumption for syncing on RPM plugin - <a href="https://pulp.plan.io/issues/5688" target="_blank">issue 5688</a>, and I want to share what I've found so far.</div><div><br></div><div>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.</div><div><br></div><div>As I shared on some notes here: <a href="https://pulp.plan.io/issues/5688" target="_blank">https://pulp.plan.io/issues/5688</a></div><div>The problem seems to affect only RHEL, I did not see a large consumption of memory for the following repos:</div><div>- CentOS 6: <a href="http://mirror.centos.org/centos-6/6.10/os/x86_64/" target="_blank">http://mirror.centos.org/centos-6/6.10/os/x86_64/</a></div><div>- CentOS 7: <a href="http://mirror.linux.duke.edu/pub/centos/7/os/x86_64/" target="_blank">http://mirror.linux.duke.edu/pub/centos/7/os/x86_64/</a></div><div>- CentOS 8 AppStrem: <a href="http://mirror.linux.duke.edu/pub/centos/8/AppStream/x86_64/os/" target="_blank">http://mirror.linux.duke.edu/pub/centos/8/AppStream/x86_64/os/</a></div><div>- CentOS 8 BaseOS: <a href="http://mirror.linux.duke.edu/pub/centos/8/BaseOS/x86_64/os/" target="_blank">http://mirror.linux.duke.edu/pub/centos/8/BaseOS/x86_64/os/</a></div><div>- EPEL 7: <a href="https://dl.fedoraproject.org/pub/epel/7/x86_64/" target="_blank">https://dl.fedoraproject.org/pub/epel/7/x86_64/</a></div><div><br></div><div>As stated on the issue, with RHEL 7 we can observe the problem, for my tests, I used the following repo: <a href="http://cdn.stage.redhat.com/content/dist/rhel/server/7/7Server/x86_64/os/" target="_blank">http://cdn.stage.redhat.com/content/dist/rhel/server/7/7Server/x86_64/os/</a></div><div><br></div><div>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.</div><div><br></div><div>Utilizing py-spy I got those SVG profiles attached, you can find them hosted here:<br>Sync - <a href="https://sendeyo.com/up/d/90a8ae4c8f" target="_blank">https://sendeyo.com/up/d/90a8ae4c8f</a></div><div>Re-sync - <a href="https://sendeyo.com/up/d/4c855bcce3" target="_blank">https://sendeyo.com/up/d/4c855bcce3</a><br><br>With the following branch, I was able to take some <a href="https://docs.python.org/3/library/tracemalloc.html" target="_blank">tracemalloc</a> snapshots (every 30 seconds):</div><div><a href="https://github.com/pulp/pulp_rpm/commit/f3f079010cfe81b7f5cf3ef94c2402b1ccf7d90c" target="_blank">https://github.com/pulp/pulp_rpm/commit/f3f079010cfe81b7f5cf3ef94c2402b1ccf7d90c</a><br clear="all"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><br></div><div><b>Tracemalloc for Sync at peak of memory consumption:</b></div><div><pre style="color:rgb(51,51,51);margin-top:0px;margin-bottom:0px;line-height:16.25px">#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</pre></div><div dir="ltr"><br></div><div dir="ltr"><br></div><div dir="ltr"><b>Tracemalloc for Re-sync at peak of memory consumption:</b><br></div><div dir="ltr"><pre style="color:rgb(51,51,51);margin-top:0px;margin-bottom:0px;line-height:16.25px">#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</pre></div><div dir="ltr"><br></div><div dir="ltr"><br></div><div dir="ltr">The snapshots can be found here:</div><div dir="ltr"><br><div class="gmail_chip gmail_drive_chip" style="width:396px;height:18px;max-height:18px;background-color:rgb(245,245,245);padding:5px;color:rgb(34,34,34);font-family:arial;font-style:normal;font-weight:bold;font-size:13px;border:1px solid rgb(221,221,221);line-height:1"><a href="https://drive.google.com/drive/folders/1mdJtdGDvbwq_jLc5m_T8VBjjb-bDRDmS" style="display:inline-block;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;text-decoration:none;padding:1px 0px;border:medium none;width:100%" target="_blank"><img style="vertical-align: bottom; border: medium none;" src="https://drive-thirdparty.googleusercontent.com/16/type/application/vnd.google-apps.folder"> <span dir="ltr" style="color:rgb(17,85,204);text-decoration:none;vertical-align:bottom">memory_data</span></a></div><br></div><div><b>Memory consumption over time:</b></div><div><div><img src="cid:ii_k3g3pplk2" alt="sync.png" width="542" height="368"><br></div></div><div><br></div><div><div><img src="cid:ii_k3g3q3px3" alt="resync.png" width="542" height="407"><br></div></div><div><br></div><div><div><img src="cid:ii_k3g3qfnr4" alt="sync_and_resync.png" width="542" height="197"><br></div></div><div dir="ltr"><br>Best regards,</div><div dir="ltr"><span style="color:rgb(0,0,0);font-family:RedHatText,sans-serif;font-size:14px;font-weight:700;text-transform:capitalize">Fabricio</span><span style="color:rgb(0,0,0);font-family:RedHatText,sans-serif;font-size:14px;font-weight:700;text-transform:capitalize"> </span><span style="color:rgb(0,0,0);font-family:RedHatText,sans-serif;font-size:14px;font-weight:700;text-transform:capitalize">Aguiar</span><div>Software Engineer, Pulp Project</div><div><a href="https://www.redhat.com/" style="color:rgb(0,136,206);font-family:RedHatText,sans-serif;font-size:12px;margin:0px" target="_blank">Red Hat Brazil - Latam</a><br></div><div>+55 11 999652368</div><div><img src="https://marketing-outfit-prod-images.s3-us-west-2.amazonaws.com/f5445ae0c9ddafd5b2f1836854d7416a/Logo-RedHat-Email.png" width="96" height="22"></div></div></div></div></div></div></div>
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