신은섭(Shin Eun Seop)

Merge branch 'feature/#3' into calcDelta

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<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
......@@ -28,16 +28,15 @@
<artifactId>spark-csv_2.11</artifactId>
<version>1.5.0</version>
</dependency>
</dependencies>
</dependencies>
<!--maven-compiler-plugin-->
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<version>3.6.1</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
......@@ -45,4 +44,6 @@
</plugin>
</plugins>
</build>
</project>
\ No newline at end of file
......
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.expressions.Window;
import org.apache.spark.sql.expressions.WindowSpec;
import static org.apache.spark.sql.functions.col;
import static org.apache.spark.sql.functions.count;
import static org.apache.spark.sql.functions.sum;
public class AvgAdvTime {
public static void main(String[] args) throws Exception {
// Start Spark Session
SparkSession spark = SparkSession
.builder()
.master("local")
.appName("Java Spark SQL basic example")
.getOrCreate();
// Read SCV to DataSet
Dataset<Row> df = spark.read().format("csv")
.option("inferSchema", "true")
.option("header", "true")
.load("train_sample.csv");
// cast timestamp to long
Dataset<Row> newdf = df.withColumn("utc_click_time", df.col("click_time").cast("long"));
newdf = newdf.withColumn("utc_attributed_time", df.col("attributed_time").cast("long"));
newdf = newdf.drop("click_time").drop("attributed_time");
// set Window partition by 'ip' and 'app' order by 'utc_click_time' select rows between 1st row to current row
WindowSpec w = Window.partitionBy("ip", "app")
.orderBy("utc_click_time")
.rowsBetween(Window.unboundedPreceding(), Window.currentRow());
// aggregation
newdf = newdf.withColumn("cum_count_click", count("utc_click_time").over(w));
newdf = newdf.withColumn("cum_sum_attributed", sum("is_attributed").over(w));
newdf = newdf.withColumn("avg_efficient", col("cum_sum_attributed").divide(col("cum_count_click")));
// print example
newdf.where("ip == '5348' and app == '19'").show();
newdf.printSchema();
}
}
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