add column check10

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CompilerConfiguration">
+    <annotationProcessing>
+      <profile name="Maven default annotation processors profile" enabled="true">
+        <sourceOutputDir name="target/generated-sources/annotations" />
+        <sourceTestOutputDir name="target/generated-test-sources/test-annotations" />
+        <outputRelativeToContentRoot value="true" />
+      </profile>
+    </annotationProcessing>
+  </component>
+</project>
\ No newline at end of file

 <?xml version="1.0" encoding="UTF-8"?>
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
 <project version="4">
-  <component name="JavaScriptSettings">
-    <option name="languageLevel" value="ES6" />
+  <component name="ExternalStorageConfigurationManager" enabled="true" />
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_1_8" project-jdk-name="1.8" project-jdk-type="JavaSDK">
+    <output url="file://$PROJECT_DIR$/out" />
   </component>
   </component>
 </project>
 </project>
\ No newline at end of file

 <?xml version="1.0" encoding="UTF-8"?>
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
 <project version="4">
   <component name="VcsDirectoryMappings">
   <component name="VcsDirectoryMappings">
-    <mapping directory="" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
   </component>
   </component>
 </project>
 </project>
\ No newline at end of file

@@ -16,6 +16,13 @@
             <artifactId>spark-core_2.11</artifactId>
             <artifactId>spark-core_2.11</artifactId>
             <version>2.3.0</version>
             <version>2.3.0</version>
         </dependency>
         </dependency>
+        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.11</artifactId>
+            <version>2.3.0</version>
+        </dependency>
+
 
 
     </dependencies>
     </dependencies>
     
     

+import com.oracle.jrockit.jfr.DataType;
 import org.apache.spark.SparkConf;
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.JavaSparkContext;
-import scala.Tuple2;
+import org.apache.spark.sql.api.java.UDF1;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.*;
+import org.apache.spark.sql.types.LongType;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.types.TimestampType;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.functions.*;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.SparkSession;
+
+
+import java.util.Calendar;
+
+import java.sql.Time;
+import java.sql.Timestamp;
 
 
 import java.util.Arrays;
 import java.util.Arrays;
 import java.util.List;
 import java.util.List;
 
 
+class data {
+    private int ip;
+    private int app;
+    private int device;
+    private int os;
+    private int channel;
+    //private int date click_time;
+    //private int date a
+}
+
 public class MapExample {
 public class MapExample {
 
 
     static SparkConf conf = new SparkConf().setMaster("local[*]").setAppName("Cesco");
     static SparkConf conf = new SparkConf().setMaster("local[*]").setAppName("Cesco");
     static JavaSparkContext sc = new JavaSparkContext(conf);
     static JavaSparkContext sc = new JavaSparkContext(conf);
-    
+
     public static void main(String[] args) throws Exception {
     public static void main(String[] args) throws Exception {
-        
-        // Parallelized with 2 partitions
-        JavaRDD<String> x = sc.parallelize(
-                Arrays.asList("spark", "rdd", "example", "sample", "example"),
-                2);
-
-        // Word Count Map Example
-        JavaRDD<Tuple2<String, Integer>> y1 = x.map(e -> new Tuple2<>(e, 1));
-        List<Tuple2<String, Integer>> list1 = y1.collect();
-
-        // Another example of making tuple with string and it's length
-        JavaRDD<Tuple2<String, Integer>> y2 = x.map(e -> new Tuple2<>(e, e.length()));
-        List<Tuple2<String, Integer>> list2 = y2.collect();
-        
-        System.out.println(list1);
+
+        SparkSession spark = SparkSession
+                .builder()
+                .appName("Java Spark SQL basic example")
+                .config("spark.some.config.option", "some-value")
+                .getOrCreate();
+
+
+        /*StructType schema = new StructType()
+                .add("ip","int")
+                .add("app","int")
+                .add("device","int")
+                .add("os","int")
+                .add("channel","int")
+                .add("click_time","datetime2")
+                .add("attributed_time","datetime2")
+                .add("is_attributed","int");*/
+        Dataset<Row> df2 = spark.read().format("csv")
+                .option("sep", ",")
+                .option("inferSchema","true")
+                .option("header", "true")
+                .load("train_sample.csv");
+
+        Dataset<Row> df=df2.select("ip","click_time");
+        df.createOrReplaceTempView("data");
+
+        Dataset<Row> mindf = spark.sql("select ip, min(click_time) as first_click_time from data group by ip order by ip");
+        mindf.createOrReplaceTempView("mindf");
+
+        Dataset<Row> df3 = spark.sql("select * from data natural join mindf order by ip,click_time");
+        df3.createOrReplaceTempView("df3");
+
+        //df3.na().fill("2020-01-01 00:00");
+        Dataset<Row> newdf = df3.withColumn("utc_click_time",df3.col("click_time").cast("long"));
+        newdf = newdf.withColumn("utc_fclick_time",df3.col("first_click_time").cast("long"));
+        newdf=newdf.drop("click_time"); newdf=newdf.drop("first_click_time");
+
+        newdf = newdf.withColumn("within_ten",((newdf.col("utc_click_time").minus(newdf.col("utc_fclick_time"))).divide(60)));
+
+        newdf = newdf.withColumn("check_ten",newdf.col("within_ten").gt((long)600));
+        Dataset<Row> newdf2=newdf.select("ip","check_ten");
+
+        //newdf = newdf.withColumn("check_ten",newdf.col("within_ten").notEqual((long)0));
+        //newdf = newdf.withColumn("check_ten",(newdf.col("within_ten") -> newdf.col("within_ten") < 11 ? 1 : 0));
+
+        newdf2.show();
+
+        /*Dataset<Row> df4= spark.sql("select ip, cast((convert(bigint,click_time)) as decimal)  from df3");
+        spark.udf().register("timestamp_diff",new UDF1<DataTypes.LongType,DataTypes.LongType>(){
+            public long call(long arg1,long arg2) throws Exception{
+                long arg3 = arg1-arg2;
+                return arg3;
+            }
+        }, DataTypes.LongType);
+
+        df3.withColumn("term",df3.col("click_time").cast("timestamp")-df3.col("first_click_time").cast("timestamp"));
+        Dataset<Row> df4=df3.toDF();
+        df4 = df4.withColumn("Check10Min", df3.select(df4("click_time").minus(df4("first_click_time")));*/
+
     }
     }
-}
+}
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