Spark SQL: How to call UDF from DataFrame operation using JAVA

Question

I would like to know how to call UDF function from function of domain-specific language(DSL) in Spark SQL using JAVA.

I have UDF function (just for example):

UDF2 equals = new UDF2<String, String, Boolean>() {
   @Override
   public Boolean call(String first, String second) throws Exception {
       return first.equals(second);
   }
};

I've registered it to sqlContext

sqlContext.udf().register("equals", equals, DataTypes.BooleanType);

When I run following query, my UDF is called and I get a result.

sqlContext.sql("SELECT p0.value FROM values p0 WHERE equals(p0.value, 'someString')");

I would transfrom this query using functions of domain specific language in Spark SQL, and I am not sure how to do it.

valuesDF.select("value").where(???);

I found that there exists callUDF() function where one of its parameters is Function2 fnctn but not UDF2. How can I use UDF and functions from DSL?

HR.AD · Answer 1 · 2015-12-08T10:53:04.380

4

I found a solution with which I am half-satisfied. It is possible to call UDF as a Column Condition such as:

valuesDF.filter("equals(columnName, 'someString')").select("columnName");

But I still wonder if it is possible to call UDF directly.

Edit:

Btw, it is possible to call udf directly e.g:

df.where(callUdf("equals", scala.collection.JavaConversions.asScalaBuffer(
                        Arrays.asList(col("columnName"), col("otherColumnName"))
                    ).seq())).select("columnName");

import of org.apache.spark.sql.functions is required.

edited Dec 08 '15 at 10:53

answered Nov 23 '15 at 15:42

HR.AD

121
2
9

That's interesting, a subtle difference from the scala API! – Ewan Leith Nov 24 '15 at 10:13

score 0 · Answer 2 · answered Nov 20 '15 at 15:32

0

When querying a dataframe, you should just be able to execute the UDF using something like this:

sourceDf.filter(equals(col("columnName"), "someString")).select("columnName")

where col("columnName") is the column you want to compare.

answered Nov 20 '15 at 15:32

Ewan Leith

1,655
11
10

I assumed that it should work like you described, but it doesn't work. I got this exception java.lang.RuntimeException: Uncompilable source code - Erroneous tree type: – HR.AD Nov 20 '15 at 18:00
Ok it ^^ was error in my NetBeans... Your solution does not work. It is because: `method equals in class Object cannot be applied to given types; | required: Object | found: Column,String | reason: actual and formal argument lists differ in length` Also calling equals.call(col("columnName"), "someString") is not solution, since call() require String, String as parameter and col() returns Column. Does anyone have any suggestion how to deal with UDFs? – HR.AD Nov 22 '15 at 22:59
Hmm, sorry I don't know then, that's an odd one! That works in the Scala API, but I can't get it to work in the Java API either. – Ewan Leith Nov 23 '15 at 10:13

AEDWIP · Answer 3 · 2016-01-21T23:23:38.140

Here is working code example. It works with Spark 1.5.x and 1.6.x. The trick to calling UDF's from within a pipeline transformer is to use the sqlContext() on the DataFrame to register your UDF

@Test
public void test() {
    // https://issues.apache.org/jira/browse/SPARK-12484
    logger.info("BEGIN");

    DataFrame df = createData();        
    final String tableName = "myTable";
    sqlContext.registerDataFrameAsTable(df, tableName);

    logger.info("print schema");
    df.printSchema();
    logger.info("original data before we applied UDF");
    df.show();

    MyUDF udf = new MyUDF();
    final String udfName = "myUDF";
    sqlContext.udf().register(udfName, udf, DataTypes.StringType);

    String fmt = "SELECT *, %s(%s) as transformedByUDF FROM %s";
    String stmt = String.format(fmt, udfName, tableName+".labelStr", tableName); 
    logger.info("AEDWIP stmt:{}", stmt);
    DataFrame udfDF = sqlContext.sql(stmt);
    Row[] results = udfDF.head(3);
    for (Row row : results) {
        logger.info("row returned by applying UDF {}", row);
    }

    logger.info("AEDWIP udfDF schema");
    udfDF.printSchema();
    logger.info("AEDWIP udfDF data");
    udfDF.show();


    logger.info("END");
}

DataFrame createData() {
    Features f1 = new Features(1, category1);
    Features f2 = new Features(2, category2);
    ArrayList<Features> data = new ArrayList<Features>(2);
    data.add(f1);
    data.add(f2);
    //JavaRDD<Features> rdd = javaSparkContext.parallelize(Arrays.asList(f1, f2));
    JavaRDD<Features> rdd = javaSparkContext.parallelize(data);
    DataFrame df = sqlContext.createDataFrame(rdd, Features.class);
    return df;
}

class MyUDF implements UDF1<String, String> {
    private static final long serialVersionUID = 1L;

    @Override
    public String call(String s) throws Exception {
        logger.info("AEDWIP s:{}", s);
        String ret = s.equalsIgnoreCase(category1) ?  category1 : category3;
        return ret;
    }
}

public class Features implements Serializable{
    private static final long serialVersionUID = 1L;
    int id;
    String labelStr;

    Features(int id, String l) {
        this.id = id;
        this.labelStr = l;
    }

    public int getId() {
        return id;
    }

    public void setId(int id) {
        this.id = id;
    }

    public String getLabelStr() {
        return labelStr;
    }

    public void setLabelStr(String labelStr) {
        this.labelStr = labelStr;
    }
}

this is the output

+---+--------+
| id|labelStr|
+---+--------+
|  1|   noise|
|  2|     ack|
+---+--------+

root
 |-- id: integer (nullable = false)
 |-- labelStr: string (nullable = true)
 |-- transformedByUDF: string (nullable = true)

+---+--------+----------------+
| id|labelStr|transformedByUDF|
+---+--------+----------------+
|  1|   noise|           noise|
|  2|     ack|          signal|
+---+--------+----------------+

Spark SQL: How to call UDF from DataFrame operation using JAVA

3 Answers3

Linked