AQuery++ Database is a cross-platform, In-Memory Column-Store Database that incorporates compiled query execution. (**Note**: If you encounter any problems, feel free to contact me via ys3540@nyu.edu)
- 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`)
## CIMS Computer Lab (Only for NYU affiliates who have access)
1. Clone this git repo in CIMS.
2. Download the [patch](https://drive.google.com/file/d/1YkykhM6u0acZ-btQb4EUn4jAEXPT81cN/view?usp=sharing)
3. Decompress the patch to any directory and execute script inside by typing (`source ./cims.sh`). Please use the source command or `. ./cims.sh` (dot space) to execute the script because it contains configurations for environment variables.
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)
There're multiple options to run AQuery on Windows. But for better consistency I recommend using a simulated Linux environment such as **Windows Subsystem for Linux** (1 or 2), **Docker** or **Linux Virtual Machines**. You can also use the native toolchain from Microsoft Visual Studio or gcc from Winlabs/Cygwin/MinGW.
- Install WSL2 from [Microsoft Store](https://aka.ms/wslstore)
- Install Ubuntu 22.04 LTS from [Microsoft Store](https://apps.microsoft.com/store/detail/ubuntu-22041-lts/9PN20MSR04DW), you can use your favorite distro but make sure you know how to install MonetDB on it.
- Select Linux and then ubuntu in [MonetDB Easy Setup](https://www.monetdb.org/easy-setup/)
- Install Python 3.6 or above
- Install required packages in requirements.txt by `python3 -m pip install -r requirements.txt`
- Download latest winlibs toolchain from the [official website](https://winlibs.com/)
- Since winlibs is linked with native windows runtime libraries (UCRT or MSVCRT), it offers better interoperatibility with other libraries built with MSVC such as python and monetdb.
- Other steps can be either the same as Visual Studio or Cygwin/Mingw (below) without ABI break.
- Copy or link `mingw64/libexec/gcc/<arch>/<version>/liblto-plugin.dll` to `mingw64/lib/bfd-plugins/` For Link time optimization support on gcc-ar and gcc-ranlib
5. The libraries and headers for Monetdb are already included in msc-plugins, however you can also choose to download them from [Monetdb Easy Setup](https://www.monetdb.org/easy-setup/) and put them in the same place.
1. Install gcc and python3 using its **builtin package manager** instead of the one from python.org or windows store. (For Msys2, `pacman -S gcc python3`). Otherwise, ABI breakage may happen.
- If you're using an arm-based mac (e.g. M1, M2 processors). Please go to the Application folder and right-click on the Terminal app, select 'Get Info' and ensure that the 'Open using Rosetta' option is unchecked. See the section below for more notes for arm-based macs.
- In theory, AQuery++ can work on both native arm64 and x86_64 through Rosetta. But for maximum performance, running native is preferred.
- However, they can't be mixed up, i.e. make sure every component, `python` , `C++ compiler`, `monetdb` library and system commandline utilities such as `uname` should have the same architecture.
- Use the script `./arch-check.sh` to check if relevant binaries all have the same architecture.
- In the case where binaries have different architectures, install the software with desired architecture and make an alias or link to ensure the newly installed binary is referred to.
- Because I can't get access to an arm-based mac to fully test this setup, there might still be issues. Please open an issue if you encounter any problems.
### Linux
- Install monetdb, see [Monetdb Easy Setup](https://www.monetdb.org/easy-setup/) for instructions.
- Install python3, C++ compiler and git. (For Ubuntu, run `apt update && apt install -y python3 python3-pip clang-14 libmonetdbe-dev git `)
- Note for anaconda users: the system libraries included in anaconda might differ from the ones your compiler is using. In this case, you might get errors similar to:
>ImportError: libstdc++.so.6: version `GLIBCXX_3.4.26' not found
In this case, upgrade anaconda or your compiler or use the python from your OS or package manager instead. Or (**NOT recommended**) copy/link the library from your system (e.g. /usr/lib/x86_64-linux-gnu/libstdc++.so.6) to anaconda's library directory (e.g. ~/Anaconda3/lib/).
-`exec`: execute last parsed statement(s) with Hybrid Execution Engine. Hybrid Execution Engine decouples the query into two parts. The standard SQL (MonetDB dialect) part is executed by an Embedded version of Monetdb and everything else is executed by a post-process module which is generated by AQuery++ Compiler in C++ and then compiled and executed.
- String Types: `STRING` and `TEXT` are variable-length strings with unlimited length. `VARCHAR(n)` is for strings with upper-bound limits.
- Integer Types: `INT` and `INTEGER` are 32-bit integers, `SMALLINT` is for 16-bit integers, `TINYINT` is for 8-bit integers and `BIGINT` is 64-bit integers. On Linux and macOS, `HGEINT` is 128-bit integers.
- Floating-Point Types: `REAL` denotes 32-bit floating point numbers while `DOUBLE` denotes 64-bit floating point numbers.
- Temporal Types: `DATE` only supports the format of `yyyy-mm-dd`, and `TIME` uses 24-hour format and has the form of `hh:mm:ss:ms` the milliseconds part can range from 0 to 999, `TIMESTAMP` has the format of `yyyy-mm-dd hh:mm:ss:ms`. When importing data from CSV files, please make sure the spreadsheet software (if they were used) doesn't change the format of the date and timestamp by double-checking the file with a plain-text editor.
- Boolean Type: `BOOLEAN` is a boolean type with values `TRUE` and `FALSE`.
## Load Data:
- Use query like `LOAD DATA INFILE <filename> INTO <table_name> [OPTIONS <options>]`
- File name is the relative path to the AQuery root directory (where prompy.py resides)
- The query is first processed by the AQuery Compiler which is composed of a frontend that parses the query into AST and a backend that generates target code that delivers the query.
- Front end of AQuery++ Compiler is built on top of [mo-sql-parsing](https://github.com/klahnakoski/mo-sql-parsing) with modifications to handle AQuery dialect and extension.
- Backend of AQuery++ Compiler generates target code dependent on the Execution Engine. It can either be the C++ code for AQuery Execution Engine or sql and C++ post-processor for Hybrid Engine or k9 for the k9 Engine.
- AQuery++ supports different execution engines thanks to the decoupled compiler structure.
- Hybrid Execution Engine: decouples the query into two parts. The sql-compliant part is executed by an Embedded version of Monetdb and everything else is executed by a post-process module which is generated by AQuery++ Compiler in C++ and then compiled and executed.
