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# Rust Compiler Performance Monitoring & Benchmarking
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- This repository contains two primary crates: ` collector ` and ` site ` . Collector gathers data for each
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- bors commit and the site displays the data and provides a GitHub bot for on-demand benchmarking.
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-
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- The primary required setup is to provide a folder with a ` retries ` file and a ` times ` folder. Data
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- is gathered into https://github.com/rust-lang/rustc-timing by Rust Infrastructure; cloning
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- that is the best approach for working on the frontend.
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+ This repository contains two primary crates:
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+ * [ ` collector ` ] ( ./collector ) : gathers data for each bors commit
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+ * [ ` site ` ] ( ./site ) : [ displays] ( https://perf.rust-lang.org ) the data and provides a GitHub bot for on-demand benchmarking
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Additional documentation on running and setting up the frontend and backend can
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be found in the ` README ` files in the ` collector ` and ` site ` directories.
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- rustc-perf
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- ----------
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+ # rustc-perf Site
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This is the website for Rust compiler performance monitoring. The website
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contains a backend to process the raw data and expose it to the frontend,
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which displays graphs to the user.
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This also contains a GitHub bot to trigger on-demand benchmarking.
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- Setup
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- -----
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+ ## Setup
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The site launches on port 2346 by default, which can be overridden by setting
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the ` PORT ` environment variable.
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```
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- sudo apt-get install git
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git clone https://github.com/rust-lang/rustc-perf.git
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```
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- Launching
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- ---------
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+ ## Launching
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- If you've collected data locally, you will likely want to point the site at that
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- local database.
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+ If you've collected data locally, you will likely want to point the site at a
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+ local database. By default, a database called results.db located at the root of
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+ the project will be used. You can optionally pass a path to a database
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+ if you don't want to use the default.
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```
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cargo run --bin site --release <database>
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```
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- The release flag is on purpose, reducing startup time 15x from roughly 15
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+ The ` -- release` flag is on purpose, reducing startup time 15x from roughly 15
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seconds to 1; this is on the production database and smaller data sets are
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likely to load considerably faster.
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