tag	fb0de12a5243b60f5feaaa556aa964fa443cae53
tagger	Aaron Mihalik <aaron.mihalik@gmail.com>	Fri Oct 28 17:57:27 2016 -0400
object	66d8b7f060bddeeb7c50cb0918f98ce3b265c564

commit	66d8b7f060bddeeb7c50cb0918f98ce3b265c564	[log] [tgz]
author	Aaron Mihalik <aaron.mihalik@gmail.com>	Fri Oct 21 01:05:10 2016 -0400
committer	Aaron Mihalik <aaron.mihalik@gmail.com>	Fri Oct 21 01:05:10 2016 -0400
tree	6db6f31d586c8e7419f91cb071ee0c095d32d298
parent	c06c1073cbe9340e67f8c1aadc894f9105e752fc [diff]

tree: 6db6f31d586c8e7419f91cb071ee0c095d32d298

README.md

RYA

Overview

RYA is a scalable RDF Store that is built on top of a Columnar Index Store (such as Accumulo). It is implemented as an extension to OpenRdf to provide easy query mechanisms (SPARQL, SERQL, etc) and Rdf data storage (RDF/XML, NTriples, etc).

RYA stands for RDF y(and) Accumulo.

Rya Manual

A copy of the Rya Manual is located here. The material in the manual and below may be out of sync.

Upgrade Path

Since the data encodings changed in the 3.2.2 release, you will need to run the Upgrade322Tool MapReduce job to perform the upgrade.

Build the project with -Pmr to build the mapreduce artifacts
Make sure to clone the rya tables before doing the upgrade
Run

hadoop jar accumulo.rya-mr.jar org.apache.rya.accumulo.mr.upgrade.Upgrade322Tool -Dac.instance={} -Dac.username={} -Dac.pwd={}

Quick Start VM

A quickstart Vagrant VM is availible here

Quick Start

This tutorial will outline the steps needed to get quickly started with the Rya store using the web based endpoint.

Prerequisites

Columnar Store (either Accumulo) The tutorial will go forward using Accumulo
Rya code (Git: git://git.apache.org/incubator-rya.git)
Maven 3.0 +

Building from Source

Using Git, pull down the latest code from the url above.

Run the command to build the code mvn clean install

If all goes well, the build should be successful and a war should be produced in web/web.rya/target/web.rya.war

Note: To perform a build of the geomesa/lucene indexing, run the build with the profile ‘indexing’ mvn clean install -P indexing

Note: If you are building on windows, you will need hadoop-common 2.6.0's winutils.exe and hadoop.dll. You can download it from here. This build requires the Visual C++ Redistributable for Visual Studio 2015 (x64). Also you will need to set your path and Hadoop home using the commands below:

set HADOOP_HOME=c:\hadoop-common-2.6.0-bin
set PATH=%PATH%;c:\hadoop-common-2.6.0-bin\bin

Deployment Using Tomcat

Unwar the above war into the webapps directory.

To point the web.rya war to the appropriate Accumulo instance, make a properties file environment.properties and put it in the classpath. Here is an example:

instance.name=accumulo  #Accumulo instance name
instance.zk=localhost:2181  #Accumulo Zookeepers
instance.username=root  #Accumulo username
instance.password=secret  #Accumulo pwd
rya.tableprefix=triplestore_  #Rya Table Prefix
rya.displayqueryplan=true  #To display the query plan

Start the Tomcat server. ./bin/startup.sh

Usage

Load Data

Web REST endpoint

The War sets up a Web REST endpoint at http://server/web.rya/loadrdf that allows POST data to get loaded into the Rdf Store. This short tutorial will use Java code to post data.

First, you will need data to load and will need to figure out what format that data is in.

For this sample, we will use the following N-Triples:

<http://mynamespace/ProductType1> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://mynamespace/ProductType> .
<http://mynamespace/ProductType1> <http://www.w3.org/2000/01/rdf-schema#label> "Thing" .
<http://mynamespace/ProductType1> <http://purl.org/dc/elements/1.1/publisher> <http://mynamespace/Publisher1> .

Save this file somewhere $RDF_DATA

Second, use the following Java code to load data to the REST endpoint:

import java.io.BufferedReader;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.io.OutputStream;
import java.net.URL;
import java.net.URLConnection;

public class LoadDataServletRun {

    public static void main(String[] args) {
        try {
            final InputStream resourceAsStream = Thread.currentThread().getContextClassLoader()
                    .getResourceAsStream("$RDF_DATA");
            URL url = new URL("http://server/web.rya/loadrdf" +
                    "?format=N-Triples" +
                    "");
            URLConnection urlConnection = url.openConnection();
            urlConnection.setRequestProperty("Content-Type", "text/plain");
            urlConnection.setDoOutput(true);

            final OutputStream os = urlConnection.getOutputStream();

            int read;
            while((read = resourceAsStream.read()) >= 0) {
                os.write(read);
            }
            resourceAsStream.close();
            os.flush();

            BufferedReader rd = new BufferedReader(new InputStreamReader(
                    urlConnection.getInputStream()));
            String line;
            while ((line = rd.readLine()) != null) {
                System.out.println(line);
            }
            rd.close();
            os.close();
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

Compile and run this code above, changing the references for $RDF_DATA and the url that your Rdf War is running at.

The default “format” is RDF/XML, but these formats are supported : RDFXML, NTRIPLES, TURTLE, N3, TRIX, TRIG.

Bulk Loading data

Bulk loading data is done through Map Reduce jobs

Bulk Load RDF data

This Map Reduce job will read a full file into memory and parse it into statements. The statements are saved into the store. Here is an example for storing in Accumulo:

hadoop jar target/accumulo.rya-3.0.4-SNAPSHOT-shaded.jar org.apache.rya.accumulo.mr.fileinput.BulkNtripsInputTool -Dac.zk=localhost:2181 -Dac.instance=accumulo -Dac.username=root -Dac.pwd=secret -Drdf.tablePrefix=triplestore_ -Dio.sort.mb=64 /tmp/temp.ntrips

Options:

rdf.tablePrefix : The tables (spo, po, osp) are prefixed with this qualifier. The tables become: (rdf.tablePrefix)spo,(rdf.tablePrefix)po,(rdf.tablePrefix)osp
ac.* : Accumulo connection parameters
rdf.format : See RDFFormat from openrdf, samples include (Trig, N-Triples, RDF/XML)
io.sort.mb : Higher the value, the faster the job goes. Just remember that you will need this much ram at least per mapper

The argument is the directory/file to load. This file needs to be loaded into HDFS before running.

Direct OpenRDF API

Here is some sample code to load data directly through the OpenRDF API. (Loading N-Triples data) You will need at least accumulo.rya-, rya.api, rya.sail.impl on the classpath and transitive dependencies. I find that Maven is the easiest way to get a project dependency tree set up.

final RdfCloudTripleStore store = new RdfCloudTripleStore();
AccumuloRdfConfiguration conf = new AccumuloRdfConfiguration();
AccumuloRyaDAO dao = new AccumuloRdfDAO();
Connector connector = new ZooKeeperInstance("instance", "zoo1,zoo2,zoo3").getConnector("user", "password");
dao.setConnector(connector);
conf.setTablePrefix("rya_");
dao.setConf(conf);
store.setRdfDao(dao);

Repository myRepository = new RyaSailRepository(store);
myRepository.initialize();
RepositoryConnection conn = myRepository.getConnection();

//load data from file
final File file = new File("ntriples.ntrips");
conn.add(new FileInputStream(file), file.getName(),
        RDFFormat.NTRIPLES, new Resource[]{});

conn.commit();

conn.close();
myRepository.shutDown();

Query Data

Web JSP endpoint

Open a url to http://server/web.rya/sparqlQuery.jsp. This simple form can run Sparql.

Web REST endpoint

The War sets up a Web REST endpoint at http://server/web.rya/queryrdf that allows GET requests with queries.

For this sample, we will assume you already loaded data from the [loaddata.html] tutorial

Save this file somewhere $RDF_DATA

Second, use the following Java code to load data to the REST endpoint:

import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.net.URL;
import java.net.URLConnection;
import java.net.URLEncoder;

public class QueryDataServletRun {

    public static void main(String[] args) {
        try {
            String query = "select * where {\n" +
                                "<http://mynamespace/ProductType1> ?p ?o.\n" +
                                "}";

            String queryenc = URLEncoder.encode(query, "UTF-8");

            URL url = new URL("http://server/rdfTripleStore/queryrdf?query=" + queryenc);
            URLConnection urlConnection = url.openConnection();
            urlConnection.setDoOutput(true);

            BufferedReader rd = new BufferedReader(new InputStreamReader(
                    urlConnection.getInputStream()));
            String line;
            while ((line = rd.readLine()) != null) {
                System.out.println(line);
            }
            rd.close();
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

Compile and run this code above, changing the url that your Rdf War is running at.

Direct Code

Here is a code snippet for directly running against Accumulo with the code. You will need at least accumulo.rya.jar, rya.api, rya.sail.impl on the classpath and transitive dependencies. I find that Maven is the easiest way to get a project dependency tree set up.

Connector connector = new ZooKeeperInstance("instance", "zoo1,zoo2,zoo3").getConnector("user", "password");

final RdfCloudTripleStore store = new RdfCloudTripleStore();
AccumuloRyaDAO crdfdao = new AccumuloRyaDAO();
crdfdao.setConnector(connector);

AccumuloRdfConfiguration conf = new AccumuloRdfConfiguration();
conf.setTablePrefix("rts_");
conf.setDisplayQueryPlan(true);
crdfdao.setConf(conf);
store.setRdfDao(crdfdao);

InferenceEngine inferenceEngine = new InferenceEngine();
inferenceEngine.setRdfDao(crdfdao);
inferenceEngine.setConf(conf);
store.setInferenceEngine(inferenceEngine);

Repository myRepository = new RyaSailRepository(store);
myRepository.initialize();

String query = "select * where {\n" +
                    "<http://mynamespace/ProductType1> ?p ?o.\n" +
                    "}";
RepositoryConnection conn = myRepository.getConnection();
System.out.println(query);
TupleQuery tupleQuery = conn.prepareTupleQuery(
        QueryLanguage.SPARQL, query);
ValueFactory vf = ValueFactoryImpl.getInstance();

TupleQueryResultHandler writer = new SPARQLResultsXMLWriter(System.out);
tupleQuery.evaluate(new TupleQueryResultHandler() {

    int count = 0;

    @Override
    public void startQueryResult(List<String> strings) throws TupleQueryResultHandlerException {
    }

    @Override
    public void endQueryResult() throws TupleQueryResultHandlerException {
    }

    @Override
    public void handleSolution(BindingSet bindingSet) throws TupleQueryResultHandlerException {
        System.out.println(bindingSet);
    }
});

conn.close();
myRepository.shutDown();