Case study in optimization post

_drafts/case-study-optimization.md

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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 the first conversations I had about programming went like this:
+
+> Programmers have it too easy these days. They should learn to develop
+> in low memory environments and be 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're given, because
+[what Intel giveth, Microsoft taketh away](http://exo-blog.blogspot.com/2007/09/what-intel-giveth-microsoft-taketh-away.html).
+My professional work has been focused on this kind of efficiency; low-latency financial markets force
+you to understand deeply *exactly* what your code is doing. As I've been experimenting with Rust for
+personal projects, I'm glad to see that it's possible to bring that mindset with me. There's flexibility for
+programming as if there was a garbage collector, and flexibility for the times when I really care about efficiency.
+
+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.
+
+# The Starting Line
+
+When I first started building the [dtparse] crate, my intention was to mirror as closely as possible the logic from
+the equivalent [Python library][dateutil]. Python, as you may know, is garbage collected. Very rarely is memory
+usage considered in Python, and so I likewise wasn't paying attention when `dtparse` was first being built.
+
+That works out well enough, and I'm not planning on tuning the crate for memory usage.
+But every so often I 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 very quickly turned from "fun weekend project" into
+"I have literally no idea what my computer is doing."
+
+Instead, let's highlight another (easier) way you can make sense of your 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 what your code is actually doing. We have to
+instead compile our programs with some special options to make it work.
+
+Specifically, in `lib.rs` or `main.rs`, make sure you add this:
+
+```rust
+use std::alloc::System;
+
+#[global_allocator]
+static GLOBAL: System = System;
+```
+
+Or look [here](https://blog.rust-lang.org/2018/08/02/Rust-1.28.html) for another example.
+
+# Running heaptrack
+
+Assuming you've installed heaptrack <span style="font-size: .6em;">(Homebrew in Mac, package manager in Linux, ??? in Windows)</span>,
+all that's left is to fire it up:
+
+```
+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:
+
+![heaptrack](/assets/images/2018-10-heaptrack/heaptrack-before.png)
+
+---
+
+And even these pretty colors:
+
+![pretty colors](/assets/images/2018-10-heaptrack/heaptrack-flamegraph.png)
+
+# Reading Flamegraphs
+
+We're going to focus on the heap ["flamegraph"](http://www.brendangregg.com/flamegraphs.html),
+which is the last picture I showed above. Normally these charts are used to show how much time
+you spend executing different functions, but the focus for now is to show how much memory
+was allocated during those functions.
+
+I'm not going to spend too much time on how to read flamegraphs, but the idea is this:
+The width of the bar is how much memory was allocated by that function, and all functions
+that it calls.
+
+For example, we can see that all executions happened during the `main` function:
+
+![allocations in main](/assets/images/2018-10-heaptrack/heaptrack-main-colorized.png)
+
+...and within that, all allocations happened during `dtparse::parse`:
+
+![allocations in dtparse](/assets/images/2018-10-heaptrack/heaptrack-dtparse-colorized.png)
+
+...and within *that*, allocations happened in two main places:
+
+![allocations in parseinfo](/assets/images/2018-10-heaptrack/heaptrack-parseinfo-colorized.png)
+
+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?**
+
+![pretty colors](/assets/images/2018-10-heaptrack/heaptrack-flamegraph-default.png)
+
+# Optimizing dtparse
+
+See, I knew that there were some allocations that happen during the `dtparse::parse` method,
+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<FixedOffset>)> {
+    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)
+
+---
+
+The issue is that I keep on creating a new `Parser` every time you call the `parse()` function!
+
+Now this is a bit excessive, but was necessary at the time because `Parser.parse()` used `&mut self`.
+In order to properly parse a string, the parser itself required mutable state.
+
+So, I put some time in to
+[make the parser immutable](https://github.com/bspeice/dtparse/commit/741afa34517d6bc1155713bbc5d66905fea13fad#diff-b4aea3e418ccdb71239b96952d9cddb6),
+and now I could re-use the same parser over and over. And would you believe it? No more allocations of default parsers:
+
+![allocations cleaned up](/assets/images/2018-10-heaptrack/heaptrack-flamegraph-after.png)
+
+In total, we went from requiring 2 MB of memory:
+
+![memory before](/assets/images/2018-10-heaptrack/heaptrack-closeup.png)
+
+All the way down to 300KB:
+
+![memory after](/assets/images/2018-10-heaptrack/heaptrack-closeup-after.png)
+
+# Conclusion
+
+In the end, you don't need to write a custom allocator to test memory performance. Rather, there are some
+pretty good tools that already exist you can make use of!
+
+**Use them.**
+
+Now 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