# AQuery++ Database
## Introduction

AQuery++ Database is a cross-platform, In-Memory Column-Store Database that incorporates compiled query execution.

## Architecture 
![Architecture](./docs/arch-hybrid.svg)

### AQuery Compiler
- 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.
### Execution Engines
- AQuery++ supports different execution engines thanks to the decoupled compiler structure.
- AQuery Execution Engine: executes query by compiling the query plan to C++ code. Doesn't support joins and udf functions. 
- 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.
- K9 Execution Engine: (discontinued).
  
## Roadmap
- [x] SQL Parser -> AQuery Parser (Front End)
- [x] AQuery-C++ Compiler (Back End)
   -  [x] Schema and Data Model 
   -  [x] Data acquisition/output from/to csv file
- [ ] Execution Engine
   -  [x] Projections and single-group Aggregations 
   -  [x] Group by Aggregations
   -  [x] Filters
   -  [x] Order by
   -  [x] Assumption
   -  [x] Flatten
   -  [x] UDFs (Hybrid Engine only)
   -  [ ] User Module
   -  [ ] Triggers 
   -  [x] Join (Hybrid Engine only)
   -  [ ] Subqueries 
- [ ] Query Optimization
  - [x] Selection/Order by push-down
  - [ ] Join Optimization

## TODO:

- [ ] User Module load syntax parsing (fn definition/registration)
- [ ] User Module test
- [ ] Interval based triggers
- [ ] Bug fixes: type deduction misaligned in Hybrid Engine
- [ ] Limitation: putting ColRefs back to monetdb. 
- [ ] C++ Meta-Programming: Eliminate template recursions as much as possible.

# Installation
## Requirements
1. Recent version of Linux, Windows or MacOS, with recent C++ compiler that has C++17 (1z) support.
     - GCC: 9.0 or above (g++ 7.x, 8.x fail to handle variadic template expansion due to a compiler bug)
     - Clang: 5.0 or above (Recommended)
     - MSVC: 2017 or later

2. Monetdb for Hybrid Engine
   - On windows, the required libraries and headers are already included in the repo.
   - On Linux, see [Monetdb Easy Setup](https://www.monetdb.org/easy-setup/) for instructions.
   - On MacOS, Monetdb can be easily installed in homebrew `brew install monetdb`.

3. Python 3.6 or above and install required packages in requirements.txt by `python3 -m pip install -r requirements.txt` 
## Usage
`python3 prompt.py` will launch the interactive command prompt. The server binary will be autometically rebuilt and started.
#### Commands:
- `<sql statement>`: parse sql statement
- `f <filename>`: parse all sql statements in file
- `dbg` start debugging session 
- `print`: printout parsed sql statements
- `exec`: execute last parsed statement(s) with AQuery Execution Engine. AQuery Execution Engine executes query by compiling it to C++ code and then executing it.
  
- `xexec`: execute last parsed statement(s) with Hybrid Execution Engine. 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.
- `r`: run the last generated code snippet
- `save <OPTIONAL: filename>`: save current code snippet. will use random filename if not specified.
- `exit`: quit the prompt
#### Example:
   `f moving_avg.a` <br>
   `xexec`