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README.md

Sage: a SPARQL query engine for public Linked Data providers

Build Status PyPI version

SaGe is a SPARQL query engine for public Linked Data providers that implements Web preemption. The SPARQL engine includes a smart Sage client and a Sage SPARQL query server hosting RDF datasets (hosted using HDT). This repository contains the Python implementation of the SaGe SPARQL query server.

SPARQL queries are suspended by the web server after a fixed quantum of time and resumed upon client request. Using Web preemption, Sage ensures stable response times for query execution and completeness of results under high load.

The complete approach and experimental results are available in a Research paper accepted at The Web Conference 2019, available here. Thomas Minier, Hala Skaf-Molli and Pascal Molli. "SaGe: Web Preemption for Public SPARQL Query services" in Proceedings of the 2019 World Wide Web Conference (WWW'19), San Francisco, USA, May 13-17, 2019.

We appreciate your feedback/comments/questions to be sent to our mailing list or our issue tracker on github.

Table of contents

Installation

Installation using pip (with the HDT backend)

Installation in a virtualenv is strongly advised!

Requirements:

  • pip
  • gcc/clang with c++11 support
  • Python Development headers

You should have the Python.h header available on your system.
For example, for Python 3.6, install the python3.6-dev package on Debian/Ubuntu systems.

The core engine of the SaGe SPARQL query server with HDT as a backend can be installed as follows:

pip install sage-engine[hdt]

Manual installation (with the HDT backend)

Additional requirements:

  • git
  • npm (shipped with Node.js on most systems)
git clone https://github.com/sage-org/sage-engine
cd sage-engine/
pip install -r requirements.txt
pip install -e .[hdt]

Getting started

Server configuration

A Sage server is configured using a configuration file in YAML syntax. You will find below a minimal working example of such configuration file. A full example is available in the config_examples/ directory

name: SaGe Test server
maintainer: Chuck Norris
quota: 75
max_results: 2000
datasets:
-
  name: dbpedia
  description: DBPedia
  backend: hdt-file
  file: datasets/dbpedia.2016.hdt

The quota and max_results fields are used to set the maximum time quantum and the maximum number of results allowed per request, respectively.

Each entry in the datasets field declare a RDF dataset with a name, description, backend and options specific to this backend. Currently, only the hdt-file backend is supported, which allow a Sage server to load RDF datasets from HDT files. Sage uses pyHDT to load and query HDT files.

Starting the server

The sage executable, installed alongside the Sage server, allows to easily start a Sage server from a configuration file using Gunicorn, a Python WSGI HTTP Server.

# launch Sage server with 4 workers on port 8000
sage my_config.yaml -w 4 -p 8000

The full usage of the sage executable is detailed below:

usage: sage [-h] [-p P] [-w W] [--log-level LEVEL] config

Launch the Sage server using a configuration file

positional arguments:
  config             Path to the configuration file

optional arguments:
  -h, --help         show this help message and exit
  -p P, --port P     The port to bind (default: 8000)
  -w W, --workers W  The number of server workers (default: 4)
  --log-level LEVEL  The granularity of log outputs (default: info)

SaGe Docker image

The Sage server is also available through a Docker image. In order to use it, do not forget to mount in the container the directory that contains you configuration file and your datasets.

docker pull callidon/sage
docker run -v path/to/config-file:/opt/data/ -p 8000:8000 callidon/sage sage /opt/data/config.yaml -w 4 -p 8000

Documentation

To generate the documentation, you must install the following dependencies

pip install sphinx sphinx_rtd_theme sphinxcontrib-httpdomain

Then, navigate in the docs directory and generate the documentation

cd docs/
make html
open build/html/index.html

Copyright 2017-2019 - GDD Team, LS2N, University of Nantes

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