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relational database engine that incorporates some of the latest findings in database theory.","author":{"name":"David Luis Wiegandt","email":"wigandtd@hu-berlin.de","url":"https://dlw93.github.io"},"readme":"# sigmaDB\n\n[![NPM](https://img.shields.io/npm/v/@sigma-db/core)](https://www.npmjs.com/package/@sigma-db/core)\n[![Dependencies](https://david-dm.org/sigma-db/core/status.svg)](https://david-dm.org/sigma-db/core)\n[![Build Status](https://dev.azure.com/dlw/sigmaDB/_apis/build/status/sigmaDB?branchName=master)](https://dev.azure.com/dlw/sigmaDB/_build/latest?definitionId=2&branchName=master)\n![node version](https://img.shields.io/node/v/@sigma-db/core)\n\n*sigmaDB* is a relational database engine that aims to incorporate some of the latest findings in database theory.\nWhile many of the proposed approaches are provably optimal in some *theoretical* sense, it usually remains an open question how the performance would be in *practice*.\nOne such approach is the *Tetris* join algorithm introduced in [Joins via Geometric Resolutions: Worst Case and Beyond](http://doi.org/10.1145/2967101) by *Khamis et al*, which we implement to evaluate joins.\n\nWe developed sigmaDB in a manner that facilitates easy modification of almost any aspect of the database engine, be it query parsing, transaction logging or join evaluation.\nStill, please keep in mind that this is a *research project* and as such lacks many features of a full-fledged RDBMS (cf. [Limitations](#limitations)).\n\n## Build and Installation\n\n### Prerequisites\n\nIn order to run sigmaDB, you need [Node.js](https://nodejs.org) 14 or newer to be present on your system.\n\n### Installation\n\nTo install the package from [npm](https://www.npmjs.com/), run `npm install -g @sigma-db/core`.\nThereafter, you can run sigmaDB from the command line with `sigma --database=</path/to/database>`.\n\n### Custom Build\n\nIf you want to clone the repository and build sigmaDB from source by yourself, follow these teps:\n\n* Clone the project with `git clone https://dev.azure.com/dlw/sigmaDB/_git/core`.\n* From within the project directory, run `npm install` to download build dependencies and build the project.\n* To make the package accessible from other projects and the command line, run `npm link`.\n\nYou can now use sigmaDB as described in [Installation](#installation).\n\n## Query Language\n\nWe discern four types of queries, whose syntax we outline by example:\n\n1. To **create** a new relation *Employee* with attributes *id*, *name*, *salary* and *boss* of types `int`, `string`, `int` and `int`, respectively, write `Employee: (id: int, name: string, salary: int, boss: int)`. Supported data types are `int`, `string`, `char` and `bool`.\n2. To **insert** a tuple into the (existing) relation *Employee*, write `Employee(id=1, name=\"Sam\", salary=4200, boss=0)`. Alternatively, the less verbose syntax `Employee(1, \"Sam\", 4200, 0)` can be used. Please note that in the latter case, the order of the attributes matters, while it does not in the former.\n3. To **select** all employees' IDs whose salary is 4,200, write either `(name=x) <- Employee(name=x, salary=4200)` or `(name=y) <- Employee(x, y, 4200, z)`. Again, the order the attributes appear in only matters for the second form. In addition, attributes that are not required to formulate the query may be omitted in the named syntax, while they have to be explicitly mentioned in the unnamed syntax.\n4. To print the **schema** of the database or a specific relation, write `?` or `<rel>?`, respectively, where `<rel>` is the name of the relation to get the schema of.\n\nIn addition, we allow `<rel>!` as a shortcut for a **select**-query that simply yields `<rel>` itself.\n\n## Usage as a Library\n\nThe following script **creates** a database with two relations *Employee* and *Division*, **inserts** some tuples and **selects** all employees and their respective division head.\n\n```Prolog\n% create tables\nEmployee: (\n    id:       int,\n    name:     string(32),\n    salary:   int,\n    division: int\n)\nDivision: (\n    id:   int,\n    name: string(64),\n    head: int\n)\n\n% insert into tables\nDivision(0, \"Research and Development\", 1)\nDivision(1, \"Marketing\", 0)\nEmployee(0, \"Jack Walls\", 8360, 1)\nEmployee(1, \"Nicole Smith\", 7120, 0)\nEmployee(2, \"Joan Waters\", 2700, 0)\nEmployee(3, \"David Brown\", 4200, 1)\nEmployee(4, \"Marc Wilson\", 4200, 1)\n\n% ask a query\nOrder(master=x, servant=y) <-\n    Employee(name=x, division=z, id=u),\n    Employee(name=y, division=z),\n    Division(id=z, head=u)\n```\n\nAssuming the script is stored in a file named `employees.cqs`, we can evaluate it using *sigmaDB* as follows:\n\n```TypeScript\nimport { pipeline } from \"stream\";\nimport { Engine, Instance, Parser } from \"@sigma-db/core\";\n\nconst database = Instance.create();\nconst parser = Parser.create({ schema: database.schema });\nconst engine = Engine.create({ instance: database });\n\npipeline(\n    process.stdin,\n    parser,\n    engine,\n    process.stdout,\n    err => err && socket.write(err.message),\n);\n\ndatabase.close();\n```\n\nNow, run the above server and feed in some input by running `cat employees.cqs | node test.js` assuming `test.js` to be the result of the compiled TypeScript script above.\n\n## Limitations\n\nThere are quite a few functionalities essential to a real-world DBMS we do *not* support as of now, including:\n\n* aggregation queries\n* concurrent access\n* parallel/distributed query evaluation\n* ...and much more\n","readmeFilename":"README.md","license":"MIT","repository":{"type":"git","url":"https://dev.azure.com/dlw/sigmaDB/_git/core"},"keywords":["database theory","relational databases","conjunctive queries","DBMS"]}