CODEWITHSUNDEEP
javascalaapache-spark
Rahul Sharma
Rahul Sharma

Reputation: 5824

Apache Spark: how to call UDF over dataset in Java?

What is exact translation of below Scala code snippet in Java?

import org.apache.spark.sql.functions.udf 

def upper(s:String) : String = {
    s.toUpperCase
}
val toUpper = udf(upper _)
peopleDS.select(peopleDS(“name”), toUpper(peopledS(“name”))).show

Please fill below missing statement in Java:

import org.apache.spark.sql.api.java.UDF1;

UDF1 toUpper = new UDF1<String, String>() {
    public String call(final String str) throws Exception {
        return str.toUpperCase();
    }
};

peopleDS.select(peopleDS.col("name"), /* how to run  toUpper("name")) ? */.show();

NOTE

Register UDF, then call using selectExpr works for me, but I need something similar to the showen above.

Working example:

sqlContext.udf().register(
    "toUpper",
    (String s) -> s.toUpperCase(),
    DataTypes.StringType
);
peopleDF.selectExpr("toUpper(name)","name").show();

Upvotes: 4

Views: 11353

Answers (2)

abaghel
abaghel

Reputation: 15297

In Java calling UDF without registration is not possible. Please check the following discussion:

Below is your UDF:

private static UDF1 toUpper = new UDF1<String, String>() {
    public String call(final String str) throws Exception {
        return str.toUpperCase();
    }
};

Register the UDF and you can use callUDF function.

import static org.apache.spark.sql.functions.callUDF;
import static org.apache.spark.sql.functions.col;

sqlContext.udf().register("toUpper", toUpper, DataTypes.StringType);
peopleDF.select(col("name"),callUDF("toUpper", col("name"))).show();

Upvotes: 7

Arun
Arun

Reputation: 9

Input csv:

+-------+--------+------+
|   name| address|salary|
+-------+--------+------+
|   Arun|  Indore|     1|
|Shubham|  Indore|     2|
| Mukesh|Hariyana|     3|
|   Arun|  Bhopal|     4|
|Shubham|Jabalpur|     5|
| Mukesh|  Rohtak|     6|
+-------+--------+------+

import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.functions;
import org.apache.spark.sql.api.java.UDF1;
import org.apache.spark.sql.types.DataTypes;

public static void main(String[] args) {
        SparkConf sparkConf = new SparkConf().setAppName("test").setMaster("local");
        SparkSession sparkSession = new SparkSession(new SparkContext(sparkConf));

        Dataset<Row> dataset = sparkSession.read().option("header", "true")
                .csv("C:\\Users\\Desktop\\Spark\\user.csv");

        /**Create udf*/
        UDF1<String, String> toLower = new UDF1<String, String>() {
            @Override
            public String call(String str) throws Exception {
                return str.toLowerCase();
            }
        };

        /**Register udf*/
        sparkSession.udf().register("toLower", toLower, DataTypes.StringType);

        /**call udf using functions.callUDF method*/
        dataset.select(dataset.col("name"),dataset.col("salary"), 
        functions.callUDF("toLower",dataset.col("address")).alias("address")).show();

}

Output :
+-------+------+--------+
|   name|salary| address|
+-------+------+--------+
|   Arun|     1|  indore|
|Shubham|     2|  indore|
| Mukesh|     3|hariyana|
|   Arun|     4|  bhopal|
|Shubham|     5|jabalpur|
| Mukesh|     6|  rohtak|
+-------+------+--------+

Upvotes: 1

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