{"id":33179057,"url":"https://github.com/traindb-project/traindb","last_synced_at":"2025-11-20T21:03:20.302Z","repository":{"id":39161455,"uuid":"404206934","full_name":"traindb-project/traindb","owner":"traindb-project","description":"ML model-based approximate query processing 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CI with Maven](https://github.com/traindb-project/traindb/actions/workflows/maven.yml/badge.svg)](https://github.com/traindb-project/traindb/actions/workflows/maven.yml)\n[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/traindb-project/traindb/blob/main/examples/traindb_tutorial.ipynb)\n\n# \u003cimg src=\"traindb-project/images/traindb_logo.png\" alt=\"TrainDB\" width=\"200\" /\u003e\n\nTrainDB is an ML model-based approximate query processing engine that aims to answer time-consuming analytical queries in a few seconds.\nTrainDB will provide SQL-like query interface and support various DBMS data sources.\n\n[Docs(English)](https://traindb-doc.readthedocs.io/en/latest/) • [Docs(Korean)](https://traindb-doc.readthedocs.io/ko/latest/) • [Tutorial(Colab)](https://colab.research.google.com/github/traindb-project/traindb/blob/main/examples/traindb_tutorial.ipynb)\n\n## Requirements\n\n* Java 11+\n* Maven 3.x\n* SQLite3 (or other DBMS for catalog store, supported by datanucleus)\n\nFor python environment setup, see README in our [traindb-model](https://github.com/traindb-project/traindb-model) repository.\n\n## Install\n\n### Download\n\n```console\n$ git clone --recurse-submodules https://github.com/traindb-project/traindb.git\n```\n\n### Build\n\n```console\n$ cd traindb\n$ mvn package\n```\n\nThen, you can find traindb-x.y-SNAPSHOT.tar.gz in traindb-assembly/target directory.\n\n```console\n$ tar xvfz traindb-assembly/target/traindb-x.y-SNAPSHOT.tar.gz\n```\n\n## Run\n\n### Example\n\nNow, you can execute SQL statements using the command line interface.\\\nYou need to put JDBC driver for your DBMS into the directory included in CLASSPATH.\n\n```console\n$ cd traindb-assembly/target/traindb-x.y-SNAPSHOT\n$ bin/trsql\nsqlline\u003e !connect jdbc:traindb:\u003cdbms\u003e://\u003chost\u003e\nEnter username for jdbc:traindb:\u003cdbms\u003e://localhost: \u003cusername\u003e \nEnter password for jdbc:traindb:\u003cdbms\u003e://localhost: \u003cpassword\u003e\n0: jdbc:traindb:\u003cdbms\u003e://\u003chost\u003e\u003e\n```\n\nYou can train ML models and run approximate queries like the following example.\n```\n0: jdbc:traindb:\u003cdbms\u003e://\u003chost\u003e\u003e CREATE MODELTYPE tablegan FOR SYNOPSIS AS LOCAL CLASS 'TableGAN' IN '$TRAINDB_PREFIX/models/TableGAN.py';\nNo rows affected (0.255 seconds)\n0: jdbc:traindb:\u003cdbms\u003e://\u003chost\u003e\u003e TRAIN MODEL tgan MODELTYPE tablegan ON \u003cschema\u003e.\u003ctable\u003e(\u003ccolumn 1\u003e, \u003ccolumn 2\u003e, ...);\nepoch 1 step 50 tensor(1.1035, grad_fn=\u003cSubBackward0\u003e) tensor(0.7770, grad_fn=\u003cNegBackward\u003e) None\nepoch 1 step 100 tensor(0.8791, grad_fn=\u003cSubBackward0\u003e) tensor(0.9682, grad_fn=\u003cNegBackward\u003e) None\n...\n0: jdbc:traindb:\u003cdbms\u003e://\u003chost\u003e\u003e CREATE SYNOPSIS \u003csynopsis\u003e FROM MODEL tgan LIMIT \u003c# of rows to generate\u003e;\n...\n0: jdbc:traindb:\u003cdbms\u003e://\u003chost\u003e\u003e SELECT APPROXIMATE avg(\u003ccolumn\u003e) FROM \u003cschema\u003e.\u003ctable\u003e;\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftraindb-project%2Ftraindb","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftraindb-project%2Ftraindb","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftraindb-project%2Ftraindb/lists"}