Apache Spark:如何通过Java中的数据集调用UDF

Question

java中的Scala代码段的确切翻译是什么？

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

请在Java中填写以下缺少语句：

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();

---

注意

注册UDF，然后使用selectExpr调用对我有用，但是我需要类似于上面显示的东西。

工作示例：

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

Answer 1

在java中不可注册UDF。请检查以下讨论：

• 在没有注册的Java中使用UDF

---

下面是您的UDF：

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

注册UDF，您可以使用callUDF函数。

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();

Answer 2

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|
+-------+------+--------+