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Sqljs upgrade and benchmark improvements (#103)

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* Update to sql.js 1.7.0

* Update to emsdk 3.0.1, replace/remove deprecated/irrelevant settings

- Renamed .bc extension to .o
- Remove deprecated INLINING_LIMIT setting
- Remove SINGLE_FILE

* Update SQLite to 3.39.3

* Collect and plot CPU and RSS charts from the benchmark containers

* Move procpath commands to a playbook, plot only top 2 RSS & CPU usage

* Optimise for size, put -flto for both compile and link
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saaj
2023-03-04 17:00:46 +01:00
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parent 3c456ef135
commit e4b117ffb9
11 changed files with 333 additions and 249 deletions
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lib/sql-js/benchmark/README.md
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# SQLite WebAssembly build micro-benchmark
This directory contains a micro-benchmark for evaluating SQLite
WebAssembly builds performance on typical SQL queries, run from
`make.sh` script. It can also serve as a smoke test.
This directory contains a micro-benchmark for evaluating SQLite WebAssembly
builds performance on read and write SQL queries, run from `make.sh` script. If
the script has permission to `nice` processes and [Procpath][1] is installed,
e.g. it is run with `sudo -E env PATH=$PATH ./make.sh`, it'll `renice` all
processes running inside the benchmark containers. It can also serve as a smoke
test (e.g. for memory leaks).
The benchmark operates on a set of SQLite WebAssembly builds expected
in `lib/build-$NAME` directories each containing `sql-wasm.js` and
`sql-wasm.wasm`. Then it creates a Docker image for each, and runs
the benchmark in Firefox and Chromium using Karma in the container.
The benchmark operates on a set of SQLite WebAssembly builds expected in
`lib/build-$NAME` directories each containing `sql-wasm.js` and
`sql-wasm.wasm`. Then it creates a Docker image for each, and runs the
benchmark in Firefox and Chromium using Karma in the container.
After successful run, the benchmark result of each build is contained
in `build-$NAME-result.json`. The JSON result files can be analysed
using `result-analysis.ipynb` Jupyter notebook.
After successful run, the benchmark produces the following per each build:
- `build-$NAME-result.json`
- `build-$NAME.sqlite` (if Procpath is installed)
- `build-$NAME.svg` (if Procpath is installed)
These files can be analysed using `result-analysis.ipynb` Jupyter notebook.
The SVG is a chart with CPU and RSS usage of each test container (i.e. Chromium
run, then Firefox run per container).
[1]: https://pypi.org/project/Procpath/