dataframe - Writing UDF for looks up in the Map in java giving Unsupported literal type class java.util.HashMap

Question

I am using java8 with spark v2.4.1

I am trying to use UDF for look up using a Map as show below

Data :

+-----+-----+-----+
|code1|code2|code3|
+-----+-----+-----+
|1    |7    |  5  |
|2    |7    |  4  |
|3    |7    |  3  |
|4    |7    |  2  |
|5    |7    |  1  |
+-----+-----+-----+

Expected Data :

+-----+-----+-----+
|code1|code2|code3|
+-----+-----+-----+
|1    |7    |51   |
|2    |7    |41   |
|3    |7    |31   |
|4    |7    |21   |
|5    |7    |11   |
+-----+-----+-----+

Map<Integer,Integer> map= new HashMap<>();
      map.put(1,11);
      map.put(2,21);
      map.put(3,31);
      map.put(4,41);
      map.put(5,51);



public static UDF2 userDefinedFunction= new UDF2<java.util.Map<Integer, Integer> ,Integer, Integer>() 
 {
        private static final long serialVersionUID = 1L;
        
        @Override
        public Integer call(java.util.Map<Integer, Integer> map, Integer score) throws Exception {
            return map.get(score);
        }
    };


  Dataset<Row> resultDs= dataDs.withColumn("code3",
           functions.callUDF("userDefinedFunction",col("code3"),lit(map) ) )

Error :

java.lang.RuntimeException: Unsupported literal type class java.util.HashMap

What is wrong here ? how to pass/handle HashMap parameter in UDFs with JavaAPI

Data :

    List<String[]> stringAsList = new ArrayList<>();
    stringAsList.add(new String[] { "1","7","5" });
    stringAsList.add(new String[] { "2","7","4" });
    stringAsList.add(new String[] { "3","7","3" });
    stringAsList.add(new String[] { "4","7","2" });
    stringAsList.add(new String[] { "5","7","1" });
    
    JavaSparkContext sparkContext = new JavaSparkContext(sparkSession.sparkContext());
    JavaRDD<Row> rowRDD = sparkContext.parallelize(stringAsList).map((String[] row) -> RowFactory.create(row));

   
    StructType schema = DataTypes
            .createStructType(new StructField[] {
                    DataTypes.createStructField("code1", DataTypes.StringType, false),
                    DataTypes.createStructField("code2", DataTypes.StringType, false),
                    DataTypes.createStructField("code3", DataTypes.StringType, false)
                  
            });

    Dataset<Row> dataDf= sparkSession.sqlContext().createDataFrame(rowRDD, schema).toDF();

    
    Dataset<Row> dataDs = dataDf
                    .withColumn("code1", col("code1").cast(DataTypes.IntegerType))
                    .withColumn("code2", col("code2").cast(DataTypes.IntegerType))
                    .withColumn("code3", col("code3").cast(DataTypes.IntegerType))
                      ;

Answer 1

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