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Merge branch 'release/0.1.1'
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README.md

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Modifiers := (LIMITk)? (ORDER BY(ASC|DESC)? Var)? (GROUP BYVar+)?
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```
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### File Storage format
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The previous three files can be stored either locally, in HDFS on in an AWS S3 bucket. For the latter, make sure to have your credentials ([see](https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/setup-credentials.html)) stored in ~/.aws/credentials (C:\Users\USERNAME\.aws\credentials on Windows), in the following form:
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```
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[default]
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aws_access_key_id=...
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aws_secret_access_key=...
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```
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## Usage
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The usage of the Semantic Data Lake is documented under the respective SANSA-Query [datalake component](https://github.com/SANSA-Stack/SANSA-Query/tree/feature/datalake/sansa-query-spark/src/main/scala/net/sansa_stack/query/spark/datalake).
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The usage of the Semantic Data Lake is documented under the respective SANSA-Query [datalake component](https://github.com/SANSA-Stack/SANSA-Query/tree/develop/sansa-query-spark/src/main/scala/net/sansa_stack/query/spark/datalake).
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## How to Contribute
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We always welcome new contributors to the project! Please see [our contribution guide](http://sansa-stack.net/contributing-to-sansa/) for more details on how to get started contributing to SANSA.

build.sbt

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pom.xml

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<groupId>net.sansa-stack</groupId>
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<artifactId>sansa-datalake-parent_2.11</artifactId>
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<version>0.1.0</version>
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<version>0.1.1</version>
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<packaging>pom</packaging>
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<name>SANSA Stack - DataLake Layer - Parent</name>
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<scala.binary.version>2.11</scala.binary.version>
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<scala.version.suffix>_${scala.binary.version}</scala.version.suffix>
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<spark.version>2.4.0</spark.version>
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<spark.version>2.4.3</spark.version>
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<jena.version>3.9.0</jena.version>
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<scalastyle.config.path>${project.basedir}/scalastyle-config.xml</scalastyle.config.path>
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<version>${jena.version}</version>
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</dependency>
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<dependency>
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<groupId>com.typesafe.scala-logging</groupId>
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<artifactId>scala-logging_${scala.binary.version}</artifactId>
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<version>3.5.0</version>
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</dependency>
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<dependency>
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<groupId>de.javakaffee</groupId>
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<artifactId>kryo-serializers</artifactId>
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</configuration>
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</plugin>
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<!--This plugin's configuration is used to store Eclipse m2e settings
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<!--This plugin's configuration is used to store Eclipse m2e settings
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only. It has no influence on the Maven build itself. -->
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<plugin>
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<groupId>org.eclipse.m2e</groupId>

sansa-datalake-spark/pom.xml

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<parent>
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<groupId>net.sansa-stack</groupId>
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<artifactId>sansa-datalake-parent_2.11</artifactId>
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<version>0.1.0</version>
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<version>0.1.1</version>
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</parent>
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<dependencies>
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-core_${scala.binary.version}</artifactId>
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</dependency>
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-sql_${scala.binary.version}</artifactId>
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<scope>provided</scope>
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</dependency>
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<!-- Test -->
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<dependency>
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<groupId>junit</groupId>
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<artifactId>jena-arq</artifactId>
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</dependency>
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<dependency>
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<groupId>com.typesafe.scala-logging</groupId>
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<artifactId>scala-logging_${scala.binary.version}</artifactId>
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</dependency>
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<dependency>
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<groupId>com.typesafe</groupId>
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<artifactId>config</artifactId>
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<artifactId>mysql-connector-java</artifactId>
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</dependency>
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<dependency>
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<groupId>com.amazonaws</groupId>
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<artifactId>aws-java-sdk</artifactId>
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<version>1.9.6</version>
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</dependency>
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<!-- Loggings -->
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<dependency>
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<groupId>com.typesafe.scala-logging</groupId>
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<artifactId>scala-logging_${scala.binary.version}</artifactId>
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<version>3.9.0</version>
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</dependency>
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<dependency>
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<groupId>ch.qos.logback</groupId>
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<artifactId>logback-classic</artifactId>
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<version>1.2.3</version>
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<scope>test</scope>
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</dependency>
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</dependencies>
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<build>
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log4j.rootLogger=OFF, console
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# A1 is set to be a ConsoleAppender.
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log4j.appender.console=org.apache.log4j.ConsoleAppender
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# A1 uses PatternLayout.
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log4j.appender.console.layout=org.apache.log4j.PatternLayout
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log4j.appender.console.layout.ConversionPattern=%-4r [%t] %-5p [%c] %x %m%n
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log4j.logger.org.apache.spark = ERROR
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log4j.logger.org.spark_project.jetty.server = ERROR
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log4j.logger.org.apache.parquet = ERROR

sansa-datalake-spark/src/main/scala/net/sansa_stack/datalake/spark/Main.scala

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val hadoopConfig = spark.conf
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val executor : SparkExecutor = new SparkExecutor(spark, mappingsFile)
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// val finalResults = executor.getType()
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val run = new Run[DataFrame](executor)
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run.application(queryFile, mappingsFile, configFile)
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}
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}

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