I’ve previously blogged about a case where tests on .NET Core on Linux were much, much slower than the same tests on Windows. Today I’ve got a very similar problem – but I suspect the cause isn’t going to be the same.
This is my reasonably raw log – skip to the end for a summary of what I found, what went well, and what went badly. Apologies for skipping around in terms of past/present/future tense – being consistent in that respect would be distracting enough to make the rest worse, I think.
Step 0: notice the problem
I spotted this problem because I happened to be looking at the Travis and AppVeyor builds, moving to .NET Core 2.0. Looking at the test runs, I saw that the tests for .NET Core 1.0 were completing in about 15 seconds on AppVeyor (Windows), and 133 seconds on Travis Linux). My experience is that AppVeyor seems to use slightly less powerful hardware than Travis, making this a very odd result.
I looked at several builds, and it seemed consistent across them – very odd.
Step 1: file an issue
My main focus at that point wasn’t to investigate performance – it was to get the build working with .NET Core 2. However, I didn’t want to forget about the problem, so I filed an issue in the Noda Time repo. At this point, I have no idea where the problem is, so the Noda Time repo is the most obvious place to put it. There’s no evidence that it’s a problem with NUnit or .NET Core itself, for example.
Step 2: reproduce locally
I have two machines at home that I use for running the automated benchmarks. One (bagpuss) runs Windows 10, the other (gabriel) runs Ubuntu 16.04. Both are Intel NuCs with i5-5250U processors – I bought one much later than the other, but looked hard to find as close to identical hardware as I could, to enable cross-OS benchmark comparisons without dual booting or virtualization.
I ran the tests on both machines, and copied the TestResult.xml output files to my main dev box for analysis.
First interesting point: the difference isn’t as marked. The tests take 77 seconds on gabriel and 29 seconds on bagpuss. That’s still a lot, but very different to what we’re seeing in CI. It’s possible that the Travis machine is twice as slow as gabriel/bagpuss and that the AppVeyor machine is twice as fast, but that seems unlikely.
Deem that temporarily out of scope as it’s harder to investigate than the fixed-hardware, easy-access problem I have now. File another issue and I can come back to it.
Step 3: Look for individual test case discrepancies
If we’re really lucky, there’ll be a single test case that has a much longer duration on gabriel than on bagpuss, and I can then delve into it.
- Create a small console app that loads the two XML files, finds all the
test-caseelements and puts them in a dictionary from fullname to duration (the latter parsed as a dictionary)
- Oops: some full names aren’t unique (due to using parameters that don’t override ToString, I suspect).
- Load into a lookup instead, then convert that into a Dictionary by taking the first test from each group. Unlikely to affect the results
- For each test that’s in both result sets, find the ratio of gabriel/bagpuss
- Dump the top 20 ratios, and the slowest 10 tests from each set for good measure.
Okay, lots of text handling tests with ratios of more than 2:1, but they’re all very small durations. I doubt that they’ll be responsible for everything, and could easily just be “measuring small amounts of time is flaky”. Nothing to blame yet.
Point to note: this console app was always less than 50 lines of code. LINQ to XML makes this sort of thing really easy.
Step 4: Look for test fixture discrepancies
If individual tests don’t show a massive change, what about test fixtures? Let’s change the program to load
test-suite elements where
type="TestFixture" which will get us the durations as a per-test-class level. We’ll start by dumping the slowest 10 tests for each environment… and wow, we have a result!
NodaTime.Test.TimeZones.TzdbDateTimeZoneSourceTest takes 65 seconds on Gabriel and 17 seconds on Bagpuss! That’s out of a total of 77 and 29 seconds respectively – so removing those would take us to 12 seconds on each machine.
Concern: that’s assuming no multithreading is going on. I don’t think NUnit parallelizes by default, but it would definitely make things trickier to reason about.
Step 5: check the logs in more detail
Given that we haven’t seen any very slow individual tests, my guess at this point is that the slowness is going to be in test fixture setup. I haven’t looked at the code yet. Let’s have a look at the XML logs for that one test in more detail.
Yikes: all the difference is in GuessZoneIdByTransitionsUncached. That’s a single method, but it’s parameterized for all system time zones.
At this point, a memory stirs: I’ve looked at this before. I have a distinct memory of being in an airport lounge. At the time I wasn’t trying to work out the OS discrepancy so much as why this one test was taking up over half the total unit test time, even on Windows.
Looking at the code for
GuessZoneIdByTransitionsUncached, I see I was even diligent enough to create an issue and keep a bit of a log of what I found. Hooray! So, now we know why this is slow in general. But why is it so much slower on gabriel than on bagpuss?
The answer is that gabriel appears to have rather more system time zones than bagpuss: it’s running 424 tests instead of 135. If they ran each test equally fast, it would take bagpuss about 53 seconds to gabriel’s 65 – that’s not so bad.
Step 6: test on CI again
In one fell swoop, we can confirm that this is the problem on Travis and potentially work out why AppVeyor is so fast. If we only run this one test fixture, we can look at how many tests are running on each machine (and in which environments, as I now run the tests on net45 (AppVeyor only), netcoreapp1.0 and netcoreapp2.0.
That’s really easy to do – create a PR which just changes the test filter, let CI run it, then delete the PR.
- Travis, both environments run 424 tests – 95 seconds for netcoreapp1.0, 92 seconds for netcoreapp2.0
- AppVeyor, all environments roughly equal, 135 tests – about 3 seconds
That certainly shows that this is taking the majority of the time on Travis, but we still have two questions:
- How is AppVeyor so much faster than my Core i7 development machine, where the same tests take about 15 seconds?
- Travis is still only doing about 3 times as much work as AppVeyor, but it’s taking 30 times as long. Why?
Step 7: spot flag differences
I look at the Travis script. I look at the AppVeyor script. I look at what I’ve been running locally. There’s a difference… on AppVeyor, I’m running with
-c Release, whereas locally I’ve used the default (debug). Can a debug build make that much difference?
- Test locally: running on my dev box with
-c Releasebrings the time down from 15s to 3.9s. Yikes!
- Update the Travis script to run with
-c Releaseas well
Now the tests take about 30 seconds on Travis. That’s still three times as long as it would be on Windows for the same number of time zones, but it’s much, much closer.
Run the tests with the same flags on gabriel and bagpuss: gabriel takes 19s, bagpuss takes 4.3s. Taking the number of time zones into account, that suggests that netcoreapp1.0 on Linux is about 40% slower than netcoreapp1.0 on Windows for each test. I can investigate that separately, but it does sound like Travis is just using slower hardware than AppVeyor, contrary to my previous suspicions.
Step 8: update Travis script and merge
Even though this one test takes a large proportion of the test time on Linux (where there are three times as many time zones to run through), it’s not a gating factor. Correctness beats speed in this case – I can live with waiting a few minutes for a complete CI test run.
We still have some investigation to do, but we can already make things definitely better by running the release build on Travis. On a new branch – not the one I’ve been investigating on – create a commit that just changes Travis to use
-c Release. Observe the total time go down from 133 seconds to 57 seconds. Yay.
Points to note
- When you’re running performance tests, don’t compare release with debug builds. Doh. This was a rookie mistake.
- Reproducing a CI problem locally is key: it’s much, much easier to work with local machines than CI. It’s a faster turnaround, and you get easier access to detailed results.
- Having multiple machines with the same hardware is really useful for performance comparisons.
- Don’t assume that what sounds like the same set of tests will run the same number of tests on different platforms – if a test is parameterized, check how many tests are actually running in each case.
- Ad-hoc tools are great – the console app I wrote for steps 3 and 4 was quick to write, and helped zoom in on the problem very simply.
Almost every diagnostic investigation seems to “end” with something else to look into. In this case, I want to find out why Linux is slower than Windows for the GuessTimeZoneTransitionsUncached test on a per-test basis. Is this a Noda Time difference, or a BCL difference? That can wait.
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