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+---
+layout: post
+title: "A Case Study in Heaptrack"
+description: "...because you don't need no garbage collection"
+category:
+tags: []
+---
+
+One of my earliest conversations about programming went like this:
+
+> Programmers have it too easy these days. They should learn to develop in low memory environments
+> and be more efficient.
+>
+> -- My Father (paraphrased)
+
+...though it's not like the first code I wrote was for a
+[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 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 how memory is used.
+
+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
+
+When I first started building the [dtparse] crate, my intention was to mirror as closely as possible
+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.
+
+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 writing tests for both correct results and correct memory usage. I gave it a
+[shot][qadapt], but learned very quickly: **never write your own allocator**. It went from "fun
+weekend project" to "I have literally no idea what my computer is doing" at breakneck speed.
+
+Instead, I'll highlight a separate path I took to make sense of my memory usage: [heaptrack].
+
+# Turning on the System Allocator
+
+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 unmodified Rust code. To remedy this, we'll make use of the
+`#[global_allocator]` attribute.
+
+Specifically, in `lib.rs` or `main.rs`, add this:
+
+```rust
+use std::alloc::System;
+
+#[global_allocator]
+static GLOBAL: System = System;
+```
+
+...and that's it. Everything else comes essentially for free.
+
+# Running heaptrack
+
+Assuming you've installed heaptrack (Homebrew in Mac, package manager
+in Linux, ??? in Windows), all that's left is to fire up your application:
+
+```
+heaptrack my_application
+```
+
+It's that easy. After the program finishes, you'll see a file in your local directory with a name
+like `heaptrack.my_appplication.XXXX.gz`. If you load that up in `heaptrack_gui`, you'll see
+something like this:
+
+
+
+---
+
+And even these pretty colors:
+
+
+
+# Reading Flamegraphs
+
+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 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:
+
+
+
+...and within that, all allocations happened during `dtparse::parse`:
+
+
+
+...and within _that_, allocations happened in two different places:
+
+
+
+Now I apologize that it's hard to see, but there's one area specifically that stuck out as an issue:
+**what the heck is the `Default` thing doing?**
+
+
+
+# Optimizing dtparse
+
+See, I knew that there were some allocations during calls to `dtparse::parse`, but I was totally
+wrong about where the bulk of allocations occurred in my program. Let me post the code and see if
+you can spot the mistake:
+
+```rust
+/// Main entry point for using `dtparse`.
+pub fn parse(timestr: &str) -> ParseResult<(NaiveDateTime, Option)> {
+ let res = Parser::default().parse(
+ timestr, None, None, false, false,
+ None, false,
+ &HashMap::new(),
+ )?;
+
+ Ok((res.0, res.1))
+}
+```
+
+> [dtparse](https://github.com/bspeice/dtparse/blob/4d7c5dd99572823fa4a390b483c38ab020a2172f/src/lib.rs#L1286)
+
+---
+
+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).
+Now that I can re-use the same parser over and over, the allocations disappear:
+
+
+
+In total, we went from requiring 2 MB of memory in
+[version 1.0.2](https://crates.io/crates/dtparse/1.0.2):
+
+
+
+All the way down to 300KB in [version 1.0.3](https://crates.io/crates/dtparse/1.0.3):
+
+
+
+# Conclusion
+
+In the end, you don't need to write a custom allocator to be efficient with memory, great tools
+already exist to help you understand what your program is doing.
+
+**Use them.**
+
+Given that [Moore's Law](https://en.wikipedia.org/wiki/Moore%27s_law) is
+[dead](https://www.technologyreview.com/s/601441/moores-law-is-dead-now-what/), we've all got to do
+our part to take back what Microsoft stole.
+
+[dtparse]: https://crates.io/crates/dtparse
+[dateutil]: https://github.com/dateutil/dateutil
+[heaptrack]: https://github.com/KDE/heaptrack
+[qadapt]: https://crates.io/crates/qadapt
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diff --git a/blog/2018-10-08-case-study-optimization/index.mdx b/blog/2018-10-08-case-study-optimization/index.mdx
new file mode 100644
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--- /dev/null
+++ b/blog/2018-10-08-case-study-optimization/index.mdx
@@ -0,0 +1,169 @@
+---
+slug: 2018/10/case-study-optimization
+title: A case study in heaptrack
+date: 2018-10-08 12:00:00
+authors: [bspeice]
+tags: []
+---
+
+I remember early in my career someone joking that:
+
+> Programmers have it too easy these days. They should learn to develop in low memory environments
+> and be more efficient.
+
+...though it's not like the first code I wrote was for a
+[graphing calculator](https://web.archive.org/web/20180924060530/https://education.ti.com/en/products/calculators/graphing-calculators/ti-84-plus-se)
+packing a whole 24KB of RAM.
+
+But the principle remains: 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 how memory is used.
+
+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
+
+When I first started building the [dtparse] crate, my intention was to mirror as closely as possible
+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.
+
+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 writing tests for both correct results and correct memory usage. I gave it a
+[shot][qadapt], but learned very quickly: **never write your own allocator**. It went from "fun
+weekend project" to "I have literally no idea what my computer is doing" at breakneck speed.
+
+Instead, I'll highlight a separate path I took to make sense of my memory usage: [heaptrack].
+
+## Turning on the System Allocator
+
+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 unmodified Rust code. To remedy this, we'll make use of the
+`#[global_allocator]` attribute.
+
+Specifically, in `lib.rs` or `main.rs`, add this:
+
+```rust
+use std::alloc::System;
+
+#[global_allocator]
+static GLOBAL: System = System;
+```
+
+...and that's it. Everything else comes essentially for free.
+
+## Running heaptrack
+
+Assuming you've installed heaptrack (Homebrew in Mac, package manager
+in Linux, ??? in Windows), all that's left is to fire up your application:
+
+```
+heaptrack my_application
+```
+
+It's that easy. After the program finishes, you'll see a file in your local directory with a name
+like `heaptrack.my_appplication.XXXX.gz`. If you load that up in `heaptrack_gui`, you'll see
+something like this:
+
+
+
+---
+
+And even these pretty colors:
+
+
+
+## Reading Flamegraphs
+
+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 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:
+
+
+
+...and within that, all allocations happened during `dtparse::parse`:
+
+
+
+...and within _that_, allocations happened in two different places:
+
+
+
+Now I apologize that it's hard to see, but there's one area specifically that stuck out as an issue:
+**what the heck is the `Default` thing doing?**
+
+
+
+## Optimizing dtparse
+
+See, I knew that there were some allocations during calls to `dtparse::parse`, but I was totally
+wrong about where the bulk of allocations occurred in my program. Let me post the code and see if
+you can spot the mistake:
+
+```rust
+/// Main entry point for using `dtparse`.
+pub fn parse(timestr: &str) -> ParseResult<(NaiveDateTime, Option)> {
+ let res = Parser::default().parse(
+ timestr, None, None, false, false,
+ None, false,
+ &HashMap::new(),
+ )?;
+
+ Ok((res.0, res.1))
+}
+```
+
+> [dtparse](https://github.com/bspeice/dtparse/blob/4d7c5dd99572823fa4a390b483c38ab020a2172f/src/lib.rs#L1286)
+
+---
+
+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).
+Now that I can re-use the same parser over and over, the allocations disappear:
+
+
+
+In total, we went from requiring 2 MB of memory in
+[version 1.0.2](https://crates.io/crates/dtparse/1.0.2):
+
+
+
+All the way down to 300KB in [version 1.0.3](https://crates.io/crates/dtparse/1.0.3):
+
+
+
+## Conclusion
+
+In the end, you don't need to write a custom allocator to be efficient with memory, great tools
+already exist to help you understand what your program is doing.
+
+**Use them.**
+
+Given that [Moore's Law](https://en.wikipedia.org/wiki/Moore%27s_law) is
+[dead](https://www.technologyreview.com/s/601441/moores-law-is-dead-now-what/), we've all got to do
+our part to take back what Microsoft stole.
+
+[dtparse]: https://crates.io/crates/dtparse
+[dateutil]: https://github.com/dateutil/dateutil
+[heaptrack]: https://github.com/KDE/heaptrack
+[qadapt]: https://crates.io/crates/qadapt
diff --git a/docusaurus.config.ts b/docusaurus.config.ts
index 9667860..0f60ead 100644
--- a/docusaurus.config.ts
+++ b/docusaurus.config.ts
@@ -94,7 +94,7 @@ const config: Config = {
},
],
future: {
- experimental_faster: true
+ // experimental_faster: true
}
};