您可以考虑静态方法。简而言之：

import static org.apache.spark.sql.functions.expr;
...
df = df
    .withColumn("time_spent", expr("end - start"))
    .drop("start")
    .drop("end");
``` `expr()` 将计算列中的值。
下面是正确导入的完整示例。抱歉，这个示例的大部分内容是关于创建Dataframe的。

package net.jgp.books.sparkInAction.ch12.lab990Others;

import static org.apache.spark.sql.functions.expr;

import java.util.ArrayList;
import java.util.List;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

/**

• Use of expr().
• @author jgp
  */
  public class ExprApp {

/**

• main() is your entry point to the application.
• @param args
  */
  public static void main(String[] args) {
  ExprApp app = new ExprApp();
  app.start();
  }

/**

• The processing code.
  */
  private void start() {
  // Creates a session on a local master
  SparkSession spark = SparkSession.builder()
  .appName("All joins!")
  .master("local")
  .getOrCreate();

StructType schema = DataTypes.createStructType(new StructField[] {
    DataTypes.createStructField(
        "title",
        DataTypes.StringType,
        false),
    DataTypes.createStructField(
        "start",
        DataTypes.IntegerType,
        false),
    DataTypes.createStructField(
        "end",
        DataTypes.IntegerType,
        false) });

List<Row> rows = new ArrayList<Row>();
rows.add(RowFactory.create("bla", 10, 30));
Dataset<Row> df = spark.createDataFrame(rows, schema);
df.show();

df = df
    .withColumn("time_spent", expr("end - start"))
    .drop("start")
    .drop("end");
df.show();

}
}