Aggregation.java
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package detact;
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.*;
public class Aggregation {
public static void main(String[] args) {
if (args.length != 2) {
System.out.println("Usage: java -jar aggregation.jar <data_path> <result_path>");
System.exit(0);
}
String data_path = args[0];
String result_path = args[1];
//Create Session
SparkSession spark = SparkSession
.builder()
.appName("Detecting Fraud Clicks")
.master("local")
.config("spark.driver.memory", "2g")
.getOrCreate();
// detact.Aggregation
Aggregation agg = new Aggregation();
Dataset<Row> dataset = Utill.loadCSVDataSet(data_path, spark);
dataset = agg.changeTimestempToLong(dataset);
dataset = agg.averageValidClickCount(dataset);
dataset = agg.clickTimeDelta(dataset);
dataset = agg.countClickInTenMinutes(dataset);
// test
// dataset.where("ip == '5348' and app == '19'").show(10);
// Save to scv
Utill.saveCSVDataSet(dataset, result_path);
}
public Dataset<Row> changeTimestempToLong(Dataset<Row> dataset){
// cast timestamp to long
Dataset<Row> newDF = dataset.withColumn("utc_click_time", dataset.col("click_time").cast("long"));
newDF = newDF.withColumn("utc_attributed_time", dataset.col("attributed_time").cast("long"));
newDF = newDF.drop("click_time").drop("attributed_time");
return newDF;
}
public Dataset<Row> averageValidClickCount(Dataset<Row> dataset){
// 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
Dataset<Row> newDF = dataset.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_valid_click_count", col("cum_sum_attributed").divide(col("cum_count_click")));
newDF = newDF.drop("cum_count_click", "cum_sum_attributed");
return newDF;
}
public Dataset<Row> clickTimeDelta(Dataset<Row> dataset){
WindowSpec w = Window.partitionBy ("ip")
.orderBy("utc_click_time");
Dataset<Row> newDF = dataset.withColumn("lag(utc_click_time)", lag("utc_click_time",1).over(w));
newDF = newDF.withColumn("click_time_delta", when(col("lag(utc_click_time)").isNull(),
lit(0)).otherwise(col("utc_click_time")).minus(when(col("lag(utc_click_time)").isNull(),
lit(0)).otherwise(col("lag(utc_click_time)"))));
newDF = newDF.drop("lag(utc_click_time)");
return newDF;
}
public Dataset<Row> countClickInTenMinutes(Dataset<Row> dataset){
WindowSpec w = Window.partitionBy("ip")
.orderBy("utc_click_time")
.rangeBetween(Window.currentRow(),Window.currentRow()+600);
Dataset<Row> newDF = dataset.withColumn("count_click_in_ten_mins",
(count("utc_click_time").over(w)).minus(1));
return newDF;
}
}