Identifying Hotspots in the PostgreSQL Build Process

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Identifying Hotspots in the PostgreSQL Build Process
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Software developers rely on a fast and correct build system to compile their source code changes and produce modified deliverables for testing and deployment. The scale and complexity of the PostgreSQL build process makes build performance an important topic to discuss and address. In this talk, we will introduce a new build performance analysis technique which identifies "build hotspots", i.e., files that are slow to rebuild (by analyzing a build dependency graph), yet change often (by analyzing version control history). We will discuss the identified hotspots in the 9.2.4 release of PostgreSQL
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we will see some of the churches the not at all and you don't have to go and we the we it the action is what we want a major and 1 of them at a high everybody on mining chain 1st of all it is is not some opportunity for me because I am a PhD student from Canada and most of the conferences I attend the research this is like up actual stuff is happening the of yeah so things for welcoming me in my talk is about to have identifying hot spots in the post Wilson should before I start here's my Twitter handle
and this would be really awesome for me and my future research how you guys could actually take a look at my Twitter handle I just we a bank and it's a survey devolver survey will take maybe 5 minutes of your time to fill out and it's just about how you interact with build systems in these sorts of things that
really help me during my future work if you can show that for anyways back to the task at hand so we did some work on identifying hot spots in poster and find it it's usually useful for me to take a few minutes to explain what I mean by the old system forest thinking because this kind of overlapping terms everybody thinks something different C so so if we imagine that this part of my goal while blocks is the source code of the software project and this spaceship is what we want wanted to our customers while the build system is the set of
order dependent instructions the take from walks to the actual situation warrants offer terms of makefiles figuration scripts these sorts of things that describe how source code text files documentation files are translated into deliverables in package the and we've been using build systems developers have for quite some time now and since the seventies the original make tool came out in this concept of the developers work cycle of thinking of the current thinking of something like change the editing code to implement the change running to build to integrate the changes that the and then testing what you produce and then you circle back around half the this model all builds were thought of as equals so any time around there was just doing the build step but in reality some builds are more equal than others it's so some builds actually take longer than I was and this 1 reason xk college kind of highlights that saying that 1 of the number 1 reason for developers slacking off this the code is compiled and waiting for the detection and got some quotes from gtk developers who say you know that this is really small and preventing them from being able to get the job done so slowly builds a actually impacting people that's the impression so working people do you tend to address build performance and so refactoring is a contact so if we imagine that we have for file system very simple system made up foresee files in there we notice that this particular file when we change it takes a while to to reintegrate is 1st we need to recompile this object than 3 archive it and then it gets linked into to co before refactoring this source tree for commands trade when you change utilize that's however if we look at this graph and we find out that utilize the goalie ever used in the 1st level it's never used in the 2nd 1 we can actually be factor this going so it's something like this were now if you feudal that is changed only 1 compile commands treated and then 1 deliverables wait so now
only 2 commands are interested in checking should compile time I but the question is now emerging imagine working real system this the where do I focus laughter where should I look for gains to the fact and we thought the most obvious 1 is to look for the file so that is the slowest to you go from the turtles the so we came up with that I use a two-step approach to find the to finding the slope parts of the 1st step we extract the dependency graph from the software system and the way that we do that all explained the next slide the 2nd step is we can always that graph to find out where the decision files so I'm going to each step in the morning and in the 1st thing the 1st step for constructing the dependency graph so we use this tool called my cell which is a tool that will positive debug trace and the builds and they'll understand recursion as well and piece together the dependency graph so what I'm showing here is actually pose 90 2 . 4 which from the ancient for you guys know about and that's the version that we studied and this is the dependency graph and so on the outside edges our out the source files so these are the ones that there's no command to generate and then as you were closer and that's where things are getting compiled and linked in the source of so yet we have this tool to build this graph how in the graph we don't have a cost for energy so we could look at that graph and just say the Costa Rican compiling a file is just all the edges and get tree but instead we decided to do something a little bit more practical and look at how how much each edge costs so how long each compile each link everything costs so we did 10 clean rebuilds most grass and finding each command and then we take the median those and that's the cost for each edge so no doubt a nice way of finding out how how much it costs to rebuild far so we said let's try this so the factory systems and was formed the slowly building up so before I die
then here's some characteristics of those graphs produces the following year highlighted in yellow suppose responded to for we also what did she lived and worked in cheat and I'm showing in blue the number of edges or sorry the number of nodes so these are files this . se files that all files executables libraries these sorts of things and then the red of bars are edges dependencies in the rules that trigger so you can see we looked at 3 systems with 3 different orders of magnitude event so I appeared qt was the largest and number 2 . 7 million edges in the dependency graph posters has been 121 thousand and then dual for circles with smallest she was 120 so yeah so we said now let's go through each 1 of the source file so that the nodes on the outside of that big graph that have no images end traced through the graph to see how much it would cost to rebuild each flight and then we plot the graphs that look like this so on the left hand side of the see following an incentive header files In each point on the graph is 1 file and we sorted according to the hilt cost so the stuff on the far right is the stuff that took longest path so we saw some interesting things here like for example down here is where we would expect most . si files to be trigonal 1 compiled and maybe a couple of links but there were these jumps between the Jedi where some see false actually triggered several of them on the walls in several when commands as well so we dug in and found that they had some code generators there were a few change that's the file and regenerating some code and recompiling rankings and stuff so yeah there was some interesting stuff there on here in the header files you can see there again steps some of these header files were included in the source code generator which meant that they would treat the same sort of the and as you'd expect on the Y axis at the top here see false if you touch them tend to rebuild very maybe 12 seconds this time whereas if the if you touching header files you pay a higher price the this is what PostgreSQL like which was much more of a lot what we expect touching CVOs never really took more than 2 sheets of but we found that if you did touch on some of the bad pedophiles it take more time 3 minutes to recompile and where we tend to Q 2 years is worse and these guys are in trouble is that the there some c files that actually took almost 3 minutes to compile and there's some pedophiles and took over 2 hours to pass so if you if you're unlucky enough to have to touch 1 of those in the the effects whatever you work but in any case we said this is great you know we can sort the staff can find the father really slow rebuild all we have to do to find this stuff you should focus on for refactoring is just draw a simple life I don't wait more than 100 seconds from close to build so everything above this line the I need to look at the that or maybe a more patient in the essay and this the last patients and 50 seconds but either way all we have to do with strong it's a great so let's take an arbitrary line something we felt was bad was 90 seconds that books 90 seconds seems to be how long people say no versatile tool so we said was that these pedophiles or files that took longer than 90 seconds to the you but merely the as of these files do you think any of the elements in the get scary so listen to all of you know none of them are hot spots we don't care what's been going on painful and then we ask them why because none of these files change that often so we went back to the drawing
board originally said with the little files maybe it's not the slow files that of because they might only rarely change so the only really change people don't have to pay the price of the so maybe we should be looking at the files that change the most often the reason OK and I would find the files that change most often returns to and the version
control system and we did some simple might be just with for example for each file the number of changes that have to assumed the files and change the light in the past will continue to change in future so then we said OK with those same grants from line there what we found is that many of these calls already built 3 so we thought OK that's not gonna work either so focusing on the files that change the most often also is insufficient because they may already be optimal so much you can do the factor a file that builds in lessons so what we suggest is that instead of trying 1 of the other you should focus on both so we call these files build hot the the files that we build slowly but also they change frequently so people have to pay that cost the building lot so we took this two-step approach that we originally had and turned it into a three-step so we added a 3rd step we took the version control of stats that mind integrated with the stuff we pulled from penalty graph to get what we call logical this is a pretty simple concept just take to rebuild cost and the number of changes for each file and plot them against each other and that's so each 1 of these dots would represent 1 files in your system and we split into quadrants so based on thresholds this page your project we say I'm not willing to wait more than 90 seconds seconds 20 minutes whatever it is that's use threshold for the cost and then you pick a threshold for the number of changes so 10 changes in the past and what or 100 changes in the past 1 that becomes your threshold and then we just say focus on this red area those the files that you want to those are the files that you want factors so it's a great now we've got a metal detector go try not to the sources and see what kind of hot spots and so for this we have to pick some thresholds like I mentioned we've been playing with 90 seconds because we have these books that that's a pretty good threshold and we also need to take a number of changes to the threshold and for that we take the media so anything that changes more than the median number of changes for filing project we consider that the frequently change so some of the things that we found using quadrant plots for the 3 systems 3 open source this recently been exploring hour so about 7 per cent of GTK this energy lives on codebase was identified as a hotspot using those thresholds so about
65 g the files and some some of things we found that you don't actually have to be factor all 65 sometimes that all bottle neck into 1 place and there were some main culprits reason that you could you could focus but noted here is that the maximum amount of time it takes to rebuild is about 2 and a half to know minutes there from produce well returned to duty aboard organized from the 2 hour 1 build we found that there were a lot of hot spots 732 herself but is roughly 80 per cent of the source code also on par with what we're seeing in uh and she would have as far as the main culprits we started trying to manually analyze this stuff to find something called respectively just 732 files was too much for us as outsiders looking and really familiar with the system so instead what we did is we found that the components of just directories that had the highest hotspot concentrations so like of the files in that directory how many of them to point is hot spots and some of them with really high percentages would call a library for the use XML patterns but again that they had a really bad scenario where you could have files rebuilding very very slow there we turned oppose grass and that we found only 2 per cent of the files you actually hot spots so the job to those of you who were close to problems where you go I 27 hotspots still from 20 so hot spots here and in the worst case it was just just over 3 minutes to go in some of these hotspots and so some of the main culprits IdentiFinder here is far follows so actually if you if you survive the internally goes to the heart of the actions the so can a shift now to what we've learned from doing all these studies enhanced by is it the kind of highlighted this transitive trend for for hot spot in this fucking make turn and so if we imagine that we have of an object file the gets went into a huge list of 2 peoples huge list of a deal Olsen executables and this file changes frequently we were displayed as a hot spot so this this this C file here would be considered a hot spot but if the sea floor actually ended up including header file this header file can actually change his header file can trigger the same amount of elected so it's going end up having to recompile object while we make everything and now this header file also changes frequently it's also going to be a hot spot but that's not it this header file is also included in another header and that the file includes several other his house and so those are also hotspots garbled call a super hot spot is it changing this final lecture truthfully hotspots to be compiled then keeps getting content and the further out you go so the 1st we have here is that you should try to limit the use of these header file Hobson header files that just include the header files because they tend to accumulate builds the activity and if they change frequently now these become hotspots the so just to quickly summarize what I'm talking about so we started out saying what I meant by builds the set of order dependent tasks that have to be executed in order to translate the source code and documentation into something that you customers users and developers reliability and quick so that they can make their edits change their deliverables and test them quickly to keep the feedback fast but there are some files that and taking longer than week and they can really interfere with developers so what we set out to do is come up with a way of finding these files so that you can refactor and improve their performance so we 1st started out trying to find just the files rebuilt slowly that didn't work out so well we tried the files that change the most often which also didn't work out so well when they were combined so we call these files build hotspots in the what are the ones that build slowly but also taken 1 uh change change very frequently and came up with an approach to find these files based on thresholds the finally we did we did some open-source studies using our approach found some hotspots and found some general trends and we think it helps avoid making spots so that's a that's all about the same the hot spots now and happy to take questions from the guys at the same time they remind you again that we put a survey and it's a you can find it might for the length of the really helpful to me and happy to feed back the results to you guys if you could take the 5 minutes of been suggested and should be and without is there any questions for a change I will go on and on the part some yeah this was just single process and 1 of the things we want look at is using this this graph you can actually find how mate with pairwise things you can actually find the problems that the lot of that you you have can fire and not that of all the all the time even when using recursive make that you had any problems with that all the customer OK we call this a lot and I know the present there are the all the the I guess there's 1 other thing before we go into questions any further so my supervisor at the supervisory series at the back end is organizing a workshop on release engineering so maybe he can tell you a couple words about to look the cost of the the the the the the community the the the what is the book that exist in the it have to do use a by the use of the the war the you know that sort of thing and have a year you In this and the other is the the you been the share of the it will also be the on the and I believe so that the there was yeah yeah the problem the group of the journal of the American Society 3 but when you go the part of this the only thing in the of the process of
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