On this minisite you can keep track of our progress improving Haskell's interactive editing experience. Each Friday we'll post a new update about what has been going on.

#### The performance of ghcide 0.2.0

Posted on June 12, 2020 by Pepe Iborra

You may have tried ghcide in the past and given up after running out of memory. If this was your experience, you should be aware that ghcide v0.2.0 was released earlier this month with a number of very noticeable efficiency improvements. Perhaps after reading this post you will consider giving it another try.

In case you don’t have time much, this is what the heap size looks like while working on the Cabal project (230 modules) over different versions of ghcide:

The graph shows that ghcide v0.2.0 is much more frugal and doesn’t’ leak. These improvements apply to haskell-language-server as well, which builds on a version of ghcide that includes these benefits.

## Background

A few months ago I started using ghcide 0.0.6. It worked fine for small packages, but our codebase at work has a few hundreds of modules and ghcide would happily grow >50GB of RAM. While the development server I was using had RAM to spare, the generation 1 garbage collector pauses were multi-second and making ghcide unresponsive. Thus one of my early contributions to the project was to use better default GC settings to great effect.

Work to improve the situation started during the Bristol Hackathon that Matthew Pickering organised, where some of us set to teach ghcide to leverage .hi and .hie files produced by GHC to reduce the memory usage. Version 0.2.0 is the culmination of those efforts, allowing it to handle much larger projects with ease.

In parallel, Matthew Pickering and Neil Mitchell spent some long hours chasing and plugging a number of space leaks that were causing an unbounded memory growth while editing files or requesting hover data. While there’s probably still some leaks left, the situation has improved dramatically with the new release.

## Benchmarks

One thing that became clear recently is that a benchmark suite is needed, both to better understand the performance impact of a change and to prevent the introduction of space leaks that are very costly to diagnose once they get in.

Usually the main hurdle with defining a benchmark suite is ensuring that the experiments do reproduce faithfully the real world scenario that is being analysed. Thankfully, the fantastic lsp-test package makes this relatively easy in this case. An experiment in the new and shiny ghcide benchmark suite looks like follows:

Currently the benchmark suite has the following experiments covering the most common actions:

- edit
- hover
- hover after edit
- getDefinition
- completions after edit
- code actions
- code actions after edit
- documentSymbols after edit

The benchmark unpacks the Cabal 3.0.0.0 package (using Cabal of course), then starts ghcide and uses Luke’s lsp-test package to simulate opening the Distribution.Version module and repeating the experiment a fixed number of times, collecting time and space metrics along the way. It’s not using criterion or anything sophisticated yet, so there is plenty of margin for improvement. But at least it provides a tool to detect performance regressions as well as to decide which improvements are worth pursuing.

## Performance over time

Now that we have a standard set of experiments, we can give an answer to the question:

How did the performance change since the previous release of ghcide?

I have put together a little Shake script to checkout, benchmark and compare a set of git commit ids automatically. It is able to produce simple graphs of memory usage over time too. I have used it to generate all the graphs included in this blog post, and to gain insights about the performance of ghcide over time that have already led to new performance improvements, as explained in the section about interfaces a few lines below.

The script is currently still in pull request.

## Performance of past ghcide versions

The graph of live bytes over time shown at the beginning of the post was for the hover after edit experiment. It’s reproduced below for convenience.

The graph shows very clearly that versions 0.0.5, 0.0.6 and 0.1.0 of ghcide contained a roughly constant space leak that caused the huge memory usage that caused many people including myself struggle with ghcide. As ghcide became faster in v0.1.0 the leak became faster too, making the situation perhaps even worse.

The good news is that not only does v0.2.0 leak nearly zero bytes in this experiment, but it also sits at a much lower memory footprint. Gone are the days of renting EC2 16xlarge instances just to be able to run ghcide in your project!

The graph also shows that v0.1.0 became 2.5X faster than the previous version, whereas v0.2.0 regressed in speed by a roughly similar amount. I wasn’t aware of this fact before running these experiments, and I don’t think any of the usual ghcide contributors was either. By pointing the performance over time script to a bunch of commits in between the 0.1.0 and 0.2.0 version tags, it was relatively easy to track this regression down to the change that introduced interface files. More details a few lines below.

## The HashMap space leak

The graph below compares the live bytes over time in the hover after edit experiment before and after Neil’s PR fixing the ‘Hashable’ instances to avoid a space leak on collisions.

The graph shows clearly that the fix makes a huge difference in the memory footprint, but back when Neil sent his PR, the benefits were not so obvious. Neil said at the time:

This patch and the haskell-lsp-types one should definitely show up in a benchmark, if we had one. Likely on the order of 2-10%, given the profiling I did, but given how profiling adjusts times, very hard to predict. Agreed, without a benchmark its hard to say for sure.

And then followed up with:

Since I had the benchmark around I ran it. 9.10s, in comparison to 9.77s before.

That statement doesn’t really illustrate the massive space benefits of this fix. A good benchmark suite must show not only time but also space metrics, which are often just as important or even more.

## The switch to interfaces

ghcide versions prior to 0.2.0 would load, parse and typecheck all the dependencies of a module, and then cache those in memory for the rest of the session. That’s very wasteful given that GHC supports separate compilation to avoid exactly this, writing a so called “interface” or .hi file down to disk with the results of parsing and typechecking a module. And indeed, GHCi, ghcid and other build tools including GHC itself leverage these interface files to avoid re-typechecking a module unless it’s strictly necessary. In version 0.2.0 we have rearranged some of the internals to take advantage of interface files for typechecking and other tasks: if an interface file is available and up-to-date it will be reused, otherwise the module is typechecked and a new interface file is generated. 0.2.0 also leverages extended interface (.hie) files for similar purposes.

The graph below shows the accumulated impact of the switch to interface files, which was spread over several PRs for ease of review, using the get definition experiment compared with the previous version:

On a first impression this looks like a net win: shorter execution time and lower memory usage, as one would expect from such a change. But when we look at another experiment, hover after edit, the tables turn as the experiment takes almost twice as long as previously, while consuming even more memory:

We can explain the memory usage as the result of a space leak undoing any win from using interface files, but there is no such explanation for the loss in performance. This is a good reminder that one experiment is not enough, a good benchmark suite must cover all the relevant use cases, or as many as possible and practical.

As it turns out, the switch to interfaces introduced a serious performance regression in the code that collects the Haddocks for the set of spans of a module, something that went completely undetected until now. Thankfully, with the knowledge that better performance is available, it is much easier for any competent programmer to stare at their screen comparing, measuring and bisecting until eventually such performance is recovered. This pull request to ghcide does so for this particular performance regression.

## Conclusion

ghcide is a very young project with plenty of low hanging fruit for the catch. With a benchmark suite in place, the project is now in a better position to accept contributions without the fear of incurring into performance regressions or introducing new space leaks.