mirror-github-bspeice/speice.io
1
0
Fork 0
mirror of https://github.com/bspeice/speice.io synced 2024-12-22 16:48:10 -05:00
Code Issues Releases Wiki Activity

Final version

Browse Source
This commit is contained in:
Bradlee Speice 2018-10-07 22:01:30 -04:00
parent 86501a24ac
commit 6133a58849
1 changed files with 16 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

32
_drafts/case-study-optimization.md
View File

@ -1,7 +1,7 @@
---
layout: post
title: "A Case Study in Heaptrack"
description: "...because you don't need no garbage collection "
description: "...because you don't need no garbage collection"
category:
tags: []
---
@ -17,15 +17,15 @@ One of my earliest conversations about programming went like this:
[graphing calculator](https://education.ti.com/en/products/calculators/graphing-calculators/ti-84-plus-se)
packing a whole 24KB of RAM. By the way, *what are you doing on my lawn?*
The principle remains though: be efficient with the resources you're given, because
The principle remains though: be efficient with the resources you have, because
[what Intel giveth, Microsoft taketh away](http://exo-blog.blogspot.com/2007/09/what-intel-giveth-microsoft-taketh-away.html).
My professional work is focused on this kind of efficiency; low-latency financial markets demand that
you understand at a deep level *exactly* what your code is doing. As I continue experimenting with Rust for
personal projects, it's exciting to bring a utilitarian mindset with me: there's flexibility for the times I pretend
to have a garbage collector, and flexibility for the times that I really care about efficiency.
to have a garbage collector, and flexibility for the times that I really care about how memory is used.
This post is a (small) case study in how I went from the former to the latter. And it's an attempt to prove how easy
it is for you to do the same.
This post is a (small) case study in how I went from the former to the latter. And ultimately, it's intended
to be a starting toolkit to empower analysis of your own code.
# Curiosity
@ -33,7 +33,7 @@ When I first started building the [dtparse] crate, my intention was to mirror as
the equivalent [Python library][dateutil]. Python, as you may know, is garbage collected. Very rarely is memory
usage considered in Python, and I likewise wasn't paying too much attention when `dtparse` was first being built.
That works out well enough, and I'm not planning on making that `dtparse` hyper-efficient.
This lackadaisical approach to memory works well enough, and I'm not planning on making `dtparse` hyper-efficient.
But every so often, I've wondered: "what exactly is going on in memory?" With the advent of Rust 1.28 and the
[Global Allocator trait](https://doc.rust-lang.org/std/alloc/trait.GlobalAlloc.html), I had a really great idea:
*build a custom allocator that allows you to track your own allocations.* That way, you can do things like
@ -47,10 +47,10 @@ Instead, I'll highlight a separate path I took to make sense of my memory usage:
This is the hardest part of the post. Because Rust uses
[its own allocator](https://github.com/rust-lang/rust/pull/27400#issue-41256384) by default,
`heaptrack` is unable to properly record what your code is doing out of the box. Instead,
we compile our programs with some special options to make it work.
`heaptrack` is unable to properly record unmodified Rust code. To remedy this, we'll make use
of the `#[global_allocator]` attribute.
Specifically, in `lib.rs` or `main.rs`, make sure you add this:
Specifically, in `lib.rs` or `main.rs`, add this:
```rust
use std::alloc::System;
@ -86,8 +86,8 @@ And even these pretty colors:
To make sense of our memory usage, we're going to focus on that last picture - it's called
a ["flamegraph"](http://www.brendangregg.com/flamegraphs.html). These charts are typically
used to show how much time you spend executing different functions, but they're used here
to show how much memory was allocated during those functions.
used to show how much time your program spends executing each function, but they're used here
to show how much memory was allocated during those functions instead.
For example, we can see that all executions happened during the `main` function:
@ -128,9 +128,9 @@ pub fn parse(timestr: &str) -> ParseResult<(NaiveDateTime, Option<FixedOffset>)>
---
Because `Parser::parse` requires a mutable reference to itself, I have to create a new parser
every time it receives a string. This seems excessive! We'd rather have an immutable parser
that can be re-used, and avoid needing to allocate memory in the first place.
Because `Parser::parse` requires a mutable reference to itself, I have to create a new `Parser::default`
every time it receives a string. This is excessive! We'd rather have an immutable parser
that can be re-used, and avoid allocating memory in the first place.
Armed with that information, I put some time in to
[make the parser immutable](https://github.com/bspeice/dtparse/commit/741afa34517d6bc1155713bbc5d66905fea13fad#diff-b4aea3e418ccdb71239b96952d9cddb6).
@ -138,11 +138,11 @@ Now that I can re-use the same parser over and over, the allocations disappear:
![allocations cleaned up](/assets/images/2018-10-heaptrack/heaptrack-flamegraph-after.png)
In total, we went from requiring 2 MB of memory:
In total, we went from requiring 2 MB of memory in [version 1.0.2](https://crates.io/crates/dtparse/1.0.2):
![memory before](/assets/images/2018-10-heaptrack/heaptrack-closeup.png)
All the way down to 300KB:
All the way down to 300KB in [version 1.0.3](https://crates.io/crates/dtparse/1.0.3):
![memory after](/assets/images/2018-10-heaptrack/heaptrack-closeup-after.png)