On Monday, October 8, 2018 7:17:08 PM CEST Robert Schweikert wrote:
On 10/8/18 12:07 PM, Alberto Planas Dominguez wrote:
[Dropping a very unproductive content]
I am really trying hard to leave out "color commentary" and adjectives, it would be nice to see the effort reciprocated.
Indeed. Sorry if my choose of words are not correct. Maybe in Spanish I would use something more neutral. [...]
When cloud-init is loaded, Python will read all the `import`s and the required subtree of pyc will be generated before the execution of _any_ Python code. You are not compiling only the pyc from cloud-init, but for all the dependencies that are required.
Unless there are some lazy load in cloud-init based on something like stevedore (that I do not see), or is full for `import`s inside functions and methods, the pyc generation of the required subtree will be the first thing that Python will do.
I think we are in agreement, problem appears to be that we have a different idea about what
"create the initials pyc needed for cloud-init:"
means. For me this arrived as what you stated, i.e. "pyc needed for cloud-init" which says nothing about dependencies. For you this statement appears to imply that the pyc files for the dependencies were also generated in this test.
More explicit and concise communication would certainly help.
I see, I hope that now is clear. The pyc subtree is generated before any Python code runs, as they are generates as soon the `import` statement is parsed. [...]
In any case your second comparison appears to making a leap that I, at this point do not agree with. You are equating the generation of pyc code in a "hot system" to the time it takes to load everything in a "cold system". A calculation of percentage contribution of pyc creation in a "cold system" would only be valid if that scenario were tested. Which we have not done, but would certainly not be too difficult to test.
I do not get the point. At the end we measured the proportion of the time Python spend generating the pyc for cloud-init and all the dependencies needed for the service, in relation with the overall time that cloud init spend during the initialization of the service.
I am not sure what do you mean by hot and clod here, as I removed all the pyc from site-packages to have a measure of the relation of the generation of the pyc over the time that cloud init uses to start the service.
OK, maybe I can explain this better. We agree that there is a significant difference in the cloud-init execution time between initial start up of the VM vs. a reboot, even if the cloud-init cache (not the pyc files) is cleared. This implies that something is working behind the scene to our advantage and makes a VM reboot faster w.r.t. cloud-init execution when compared to the start a new instance scenario. Given that we do not know what this "makes it work faster" part is, we should not draw any conclusion that the pyc build will take equally as short/long of a time on initial start up as it takes in a "reboot the VM" scenario. This will have to be tested.
I understand now. Looks like that the version of cloud init in place for sle12 sp3 do not have the `clear` command. In any case I inspected site-packages and all the pycs are there after the reboot, so I can consider the time of generating pycs to be a constant time, not related about if or not the cloud- init cache is already populated or not, as this will affect the non-constant time of the total time that cloud-init needs to start. In any case I will look into that.
The cost is amortized, and the corner case, IMHO, is more yours than mine. Your case is a fresh boot of a just installed EC2 VM. I agree that there is a penalty of ~10% (or a 0.54% in my SLE12 SP3 OpenStack case), but this is only for this first boot.
Which is a problem for those users that start a lot of instances to throw them away and start new instances the next time they are needed. This would be a typical autoscaling use case or a typical test use case.
Correct. The 0.205s will be added for each new fresh VM. Am I correct to assume that also this is an scenario where the resize in the initial boot is happening? If so, the overall impact is much less that the 10% that we are talking about, and more close to the %0.5 that I measured in OpenStack. The data I presented as an example was generated with a 10GB image size for the creation of an instance with a 10GB root volume size. So there is a, what should be a negligible, contribution from the growpart script, which is called by cloud-init and runs in a subprocess.
I attribute "negligible" in this case as growpart will exit very fast is no resizing is required. It still take time for process start up etc. but again I consider this as negligible.
But you are correct, by increasing the time it takes for other things cloud-init calls, such as root volume resize, one can decrease the percentage of time allocated to pyc creation.
If I would want to take this to an extreme I could start a process during user data processing that runs for several minutes and thus I could make an argument that pyc creation takes almost no time. However, that would be misleading.
(Read the next as a joke) This point is were my set of words becomes too scarce to choose a colorless word to answer this argument. But I understand the idea behind it :-)
I think in an effort to arrive as close as reasonably possible at the "real cost" of the pyc generation for the cloud-init example, we should minimize the externally executed processes by cloud-init, such as minimizing the runtime for growpart, which is done by not manipulating the instance root volume size as compared to the image size.
Very true, removing all that is slow during the boot time will make the pyc time more relevant in the total amount. IMHO this is a very far time in the future, if happens. But I encourage you and all the cloud stakeholders to make this argument relevant, as this will make the distribution very attractive for Cloud.
It is relatively easy to calculate a cost for this with some estimates. If my test for my application needs 2000 (an arbitrary number I picked) test instances, and every test instance takes .2 seconds longer to boot, to use your number, than the total time penalty is ~6.7 seconds. If this test uses an instance type that costs me $10 per hour the slow down costs me ~ $1.1 every time I run my test. So if the test case runs once a week it would amount to ~$57 per year.
Imagine the cost of the resize of the kiwi operation, must be around some thousands dollars.
But you are right. If there is a weekly re-escalation of 2000 instances during the 54 weeks of a year, you can measure the cost of the pyc generation.
Is in my understanding that CPU cost is cheaper in relation with network transfer and storage. Can we measure the savings of network and storage here? Not in the Public Cloud, there is no data. Network data into the framework is always free and the size of the root volumes in our images is already 10GB (30GB in Azure) and thus offers up ample space for the pyc files. Meaning there is no gain if the actual disk space used by the packages we install is smaller and we have more empty space in the 10GB (30GB in Azure) image.
So uploading ISO / qcow2 images and storing them for a long period is free? [...]
Or better, the user can add a `python -m compileall` in the kiwi config.sh, that will populate /var/cache for the cloud images only.
Well, I could turn around and state that this is a "hack" and "...we don't want any of your proposed hacks.." in our image creation process. Hopefully sounds familiar.... ;)
Indeed, the hack is breaking the content of the RPM removing files from the places where zypper put in (/usr). There is no hack making /var/cache hot before the service runs :-)
Anyway, on a more serious note, if we can resolve the security concerns and properly handle the upgrade mechanism while not generating multiple packages I am not categorically opposed to such an addition in our Public Cloud image builds.
Thanks! I see the security problem, indeed. I will work to provide an answer to this problem and propose it here. Meanwhile the image from [1] is providing some of the pieces that I talk in the first email. This is the image that I am using for another project related with Salt, but as today: * Python 3.7 is installed (in TW we still have 3.6) * Python 3.7 contains the patch from 3.8 to enable storage of pycache in a different file system * I added two shim loaders (written in shell), that replace python3.7 and python3.7m, to enable the 3.8 feature * As a hack, I removed all the pycache from the ISO (I know, I know ...), so all is generated under demand on /var/cache [1] https://build.opensuse.org/project/monitor/home:aplanas:Images? arch_x86_64=1&defaults=0&repo_images=1&succeeded=1
I think that we need a productive argumentation here.
And I thought we were having that for the most part. (My contribution to color commentary)
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