AQuery++ Database is a cross-platform, In-Memory Column-Store Database that incorporates compiled query execution.
## Requirements
## Docker (Recommended):
- See installation instructions from [docker.com](https://www.docker.com). Run **docker desktop** to start docker engine.
- In AQuery root directory, type `make docker` to build the docker image from scratch.
- For Arm-based Mac users, you would have to build and run the **x86_64** docker image because MonetDB doesn't offer official binaries for arm64 Linux. (Run `docker buildx build --platform=linux/amd64 -t aquery .` instead of `make docker`)
- Finally run the image in **interactive** mode (`docker run -it --rm aquery`)
- If there is a need to access the system shell, type `dbg` to activate python interpreter and type `os.system('sh')` to launch a shell.
## Native Installation:
### Requirements
1. Recent version of Linux, Windows or MacOS, with recent C++ compiler that has C++17 (1z) support. (however c++20 is recommended if available for heterogeneous lookup on unordered containers)
- GCC: 9.0 or above (g++ 7.x, 8.x fail to handle fold-expressions due to a compiler bug)
- Clang: 5.0 or above (Recommended)
@ -17,15 +25,9 @@ AQuery++ Database is a cross-platform, In-Memory Column-Store Database that inco
3. Python 3.6 or above and install required packages in requirements.txt by `python3 -m pip install -r requirements.txt`
## Installation
### Installation
AQuery is tested on mainstream operating systems such as Windows, macOS and Linux
### Docker (Recommended):
- See installation instructions from [docker.com](https://www.docker.com). Run **docker desktop** to start docker engine.
- In AQuery root directory, type `make docker` to build the docker image from scratch.
- For Arm-based Mac users, you would have to build and run the **x86_64** docker image because MonetDB doesn't offer official binaries for arm64 Linux. (Run `docker buildx build --platform=linux/amd64 -t aquery .` instead of `make docker`)
- Finally run the image in **interactive** mode (`docker run -it --rm aquery`)
- If there is a need to access the system shell, type `dbg` to activate python interpreter and type `os.system('sh')` to launch a shell.
### Windows
There're multiple options to run AQuery on Windows. You can use the native toolchain from Microsoft Visual Studio or gcc from Cygwin/MinGW or run it under Windows Subsystem for Linux.
@ -86,6 +88,7 @@ There're multiple options to run AQuery on Windows. You can use the native toolc
- no options: show statistics for all queries so far.
- `on` : statistics will be shown for every future query.
- `off`: statistics will not be shown for every future query.
- `script <filename>`: use automated testing script, this will execute all commands in the script
- `dbg` start python interactive interpreter at the current context.
- `print`: print parsed AQuery statements (AST in JSON form)
- `save <OPTIONAL: filename>`: save current code snippet. will use random filename if not specified.
@ -97,7 +100,10 @@ There're multiple options to run AQuery on Windows. You can use the native toolc
See ./tests/ for more examples.
### Automated Testing Scripts
- A series of commands can be put in a script file and execute using `script` command.
