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164 lines
6.9 KiB
Markdown
164 lines
6.9 KiB
Markdown
---
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layout: post
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title: "A Case Study in Heaptrack"
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description: "...because you don't need no garbage collection"
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category:
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tags: []
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---
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One of my earliest conversations about programming went like this:
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> Programmers have it too easy these days. They should learn to develop
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> in low memory environments and be more efficient.
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>
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> -- My Father (paraphrased)
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...though it's not like the first code I wrote was for a
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[graphing calculator](https://education.ti.com/en/products/calculators/graphing-calculators/ti-84-plus-se)
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packing a whole 24KB of RAM. By the way, *what are you doing on my lawn?*
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The principle remains though: be efficient with the resources you have, because
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[what Intel giveth, Microsoft taketh away](http://exo-blog.blogspot.com/2007/09/what-intel-giveth-microsoft-taketh-away.html).
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My professional work is focused on this kind of efficiency; low-latency financial markets demand that
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you understand at a deep level *exactly* what your code is doing. As I continue experimenting with Rust for
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personal projects, it's exciting to bring a utilitarian mindset with me: there's flexibility for the times I pretend
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to have a garbage collector, and flexibility for the times that I really care about how memory is used.
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This post is a (small) case study in how I went from the former to the latter. And ultimately, it's intended
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to be a starting toolkit to empower analysis of your own code.
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# Curiosity
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When I first started building the [dtparse] crate, my intention was to mirror as closely as possible
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the equivalent [Python library][dateutil]. Python, as you may know, is garbage collected. Very rarely is memory
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usage considered in Python, and I likewise wasn't paying too much attention when `dtparse` was first being built.
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This lackadaisical approach to memory works well enough, and I'm not planning on making `dtparse` hyper-efficient.
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But every so often, I've wondered: "what exactly is going on in memory?" With the advent of Rust 1.28 and the
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[Global Allocator trait](https://doc.rust-lang.org/std/alloc/trait.GlobalAlloc.html), I had a really great idea:
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*build a custom allocator that allows you to track your own allocations.* That way, you can do things like
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writing tests for both correct results and correct memory usage. I gave it a [shot][qadapt], but learned
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very quickly: **never write your own allocator**. It went from "fun weekend project" to
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"I have literally no idea what my computer is doing" at breakneck speed.
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Instead, I'll highlight a separate path I took to make sense of my memory usage: [heaptrack].
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# Turning on the System Allocator
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This is the hardest part of the post. Because Rust uses
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[its own allocator](https://github.com/rust-lang/rust/pull/27400#issue-41256384) by default,
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`heaptrack` is unable to properly record unmodified Rust code. To remedy this, we'll make use
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of the `#[global_allocator]` attribute.
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Specifically, in `lib.rs` or `main.rs`, add this:
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```rust
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use std::alloc::System;
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#[global_allocator]
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static GLOBAL: System = System;
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```
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...and that's it. Everything else comes essentially for free.
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# Running heaptrack
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Assuming you've installed heaptrack <span style="font-size: .6em;">(Homebrew in Mac, package manager in Linux, ??? in Windows)</span>,
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all that's left is to fire up your application:
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```
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heaptrack my_application
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```
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It's that easy. After the program finishes, you'll see a file in your local directory with a name
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like `heaptrack.my_appplication.XXXX.gz`. If you load that up in `heaptrack_gui`, you'll see
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something like this:
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---
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And even these pretty colors:
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# Reading Flamegraphs
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To make sense of our memory usage, we're going to focus on that last picture - it's called
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a ["flamegraph"](http://www.brendangregg.com/flamegraphs.html). These charts are typically
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used to show how much time your program spends executing each function, but they're used here
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to show how much memory was allocated during those functions instead.
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For example, we can see that all executions happened during the `main` function:
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...and within that, all allocations happened during `dtparse::parse`:
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...and within *that*, allocations happened in two different places:
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Now I apologize that it's hard to see, but there's one area specifically that stuck out
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as an issue: **what the heck is the `Default` thing doing?**
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# Optimizing dtparse
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See, I knew that there were some allocations during calls to `dtparse::parse`,
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but I was totally wrong about where the bulk of allocations occurred in my program.
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Let me post the code and see if you can spot the mistake:
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```rust
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/// Main entry point for using `dtparse`.
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pub fn parse(timestr: &str) -> ParseResult<(NaiveDateTime, Option<FixedOffset>)> {
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let res = Parser::default().parse(
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timestr, None, None, false, false,
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None, false,
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&HashMap::new(),
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)?;
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Ok((res.0, res.1))
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}
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```
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> [dtparse](https://github.com/bspeice/dtparse/blob/4d7c5dd99572823fa4a390b483c38ab020a2172f/src/lib.rs#L1286)
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---
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Because `Parser::parse` requires a mutable reference to itself, I have to create a new `Parser::default`
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every time it receives a string. This is excessive! We'd rather have an immutable parser
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that can be re-used, and avoid allocating memory in the first place.
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Armed with that information, I put some time in to
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[make the parser immutable](https://github.com/bspeice/dtparse/commit/741afa34517d6bc1155713bbc5d66905fea13fad#diff-b4aea3e418ccdb71239b96952d9cddb6).
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Now that I can re-use the same parser over and over, the allocations disappear:
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In total, we went from requiring 2 MB of memory in [version 1.0.2](https://crates.io/crates/dtparse/1.0.2):
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All the way down to 300KB in [version 1.0.3](https://crates.io/crates/dtparse/1.0.3):
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# Conclusion
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In the end, you don't need to write a custom allocator to be efficient with memory, great tools
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already exist to help you understand what your program is doing.
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**Use them.**
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Given that [Moore's Law](https://en.wikipedia.org/wiki/Moore%27s_law)
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is [dead](https://www.technologyreview.com/s/601441/moores-law-is-dead-now-what/), we've all got to
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do our part to take back what Microsoft stole.
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[dtparse]: https://crates.io/crates/dtparse
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[dateutil]: https://github.com/dateutil/dateutil
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[heaptrack]: https://github.com/KDE/heaptrack
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[qadapt]: https://crates.io/crates/qadapt
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