Table of Contents

1. About The Project
   • Features
   • Built with
   • Benchmark
2. Installation
   • With precompiled binaries
   • From source
   • With cargo
3. Usage
   • Providers
   • Options
   • Generation details
4. Contributing
5. License

# What is FakeLake ? FakeLake is a command line tool that generates fake data from a YAML schema. It can generate millions of rows in seconds, and is order of magnitude faster than popular Python generators (see benchmarks). FakeLake is actively developed and maintained by [SOMA](https://www.linkedin.com/company/soma-smart/mycompany/) in Paris 🦊. ```mermaid flowchart TD subgraph Z["How it works"] direction LR Y[YAML file description] --> F F[FakeLake] --> O[Output file in CSV, Parquet, ...] end ``` Any feedback is welcome! ## Features - Very fast - Easy to use - Small memory footprint - Small binary size - Robust / no unsafe code - No dependencies - Cross-platform (Windows, Linux, Mac OS X) - MIT license ## Built with ## Benchmark Benchmark of FakeLake, Mimesis and Faker:
- Goal: Generate 1 million rows with one column: random string (length 10) - Specs: Windows, AMD Ryzen 5 7530U, 8Go RAM, SSD | Command | Mean [ms] | Min [ms] | Max [ms] | Relative | |:---|---:|---:|---:|---:| | `fakelake generate bench\fakelake_input.yaml` | 252.8 ± 3.3 | 249.0 | 260.0 | 1.00 | | `python bench\mimesis_bench.py` | 3374.9 ± 21.3 | 3353.0 | 3426.2 | 13.35 ± 0.19 | | `python bench\faker_bench.py` | 13552.7 ± 340.5 | 13336.4 | 14446.4 | 53.62 ± 1.52 | Build the benchmark yourself with scripts/benchmark.sh # Installation ## Simple way : With precompiled binaries Download the latest release from [here](https://github.com/soma-smart/Fakelake/releases) ```bash $ tar -xvf Fakelake__.tar.gz $ ./fakelake --help ``` ## From source ```bash $ git clone $ cd fakelake $ cargo build --release $ ./target/release/fakelake --help ``` # How to use it Generate from one or multiple files ```bash $ fakelake generate tests/parquet_all_options.yaml $ fakelake generate tests/parquet_all_options.yaml tests/csv_all_options.yaml ```
The configuration file used contains a list of columns, with a specified provider (for the column behavior), as well as some options. There is also an info structure to define the output. ```yaml columns: - name: id provider: Increment.integer start: 42 presence: 0.8 - name: company_email provider: Person.email domain: soma-smart.com - name: created provider: Random.Date.date format: "%Y-%m-%d" after: 2000-02-15 before: 2020-07-17 - name: name provider: Random.String.alphanumeric info: output_name: all_options output_format: parquet rows: 1_234_567 ``` ## Providers A provider follows a naming rule as "Category.\.provider".
Few examples: - Person.email - Increment.integer - Random.String.alphanumeric ## Options There is two types of options: - Options linked to the provider (date and format) - Options linked to the column (% presence) ## Generation Details There is three optional fields: - output_name: To specify the location and name of the output - output_format: To specify the generated format (we support Parquet and CSV for now) - rows: To specify the number of rows to generate # Contributing Contributions are welcome! Feel free to submit pull requests. 1. Fork the Project 2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`) 3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`) 4. Push to the Branch (`git push origin feature/AmazingFeature`) 5. Open a Pull Request # License Distributed under the MIT License. See `LICENSE.txt` for more information.