Creating User-Defined Functions (UDFs) for DataFrames in Java¶

Introduction¶

You can call Snowpark APIs to create user-defined functions (UDFs) for lambda expressions in Java, and you can call these UDFs to process the data in your DataFrame.

When you use the Snowpark API to create a UDF, the Snowpark library serializes and uploads the code for your UDF to a stage. When you call the UDF, the Snowpark library executes your function on the server, where the data is located. As a result, the data doesn’t need to be transferred to the client in order for the function to process the data.

In your custom code, you can also call code that is packaged in JAR files (for example, Java classes for a third-party library).

You can create a UDF for your custom code in one of two ways:

• You can create an anonymous UDF and assign the function to a variable. As long as this variable is in scope, you can use this variable to call the UDF.

  import com.snowflake.snowpark_java.types.*;
  ...
  
  // Create and register an anonymous UDF (doubleUdf)
  // that takes in an integer argument and returns an integer value.
  UserDefinedFunction doubleUdf =
    Functions.udf((Integer x) -> x + x, DataTypes.IntegerType, DataTypes.IntegerType);
  // Call the anonymous UDF.
  DataFrame df = session.table("sample_product_data");
  DataFrame dfWithDoubleQuantity = df.withColumn("doubleQuantity", doubleUdf.apply(Functions.col("quantity")));
  dfWithDoubleQuantity.show();
  

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• You can create a named UDF and call the UDF by name. You can use this if, for example, you need to call a UDF by name or use the UDF in a subsequent session.

  import com.snowflake.snowpark_java.types.*;
  ...
  
  // Create and register a permanent named UDF ("doubleUdf")
  // that takes in an integer argument and returns an integer value.
  UserDefinedFunction doubleUdf =
    session
      .udf()
      .registerPermanent(
        "doubleUdf",
        (Integer x) -> x + x,
        DataTypes.IntegerType,
        DataTypes.IntegerType,
        "mystage");
  // Call the named UDF.
  DataFrame df = session.table("sample_product_data");
  DataFrame dfWithDoubleQuantity = df.withColumn("doubleQuantity", Functions.callUDF("doubleUdf", Functions.col("quantity")));
  dfWithDoubleQuantity.show();
  

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The rest of this topic explains how to create UDFs.

Data Types Supported for Arguments and Return Values¶

In order to create a UDF for a Java lambda, you must use the supported data types listed below for the arguments and return value of your method:

Specifying Dependencies for a UDF¶

In order to define a UDF through the Snowpark API, you must call Session.addDependency() for any files that contain any classes and resources that your UDF depends on (e.g. JAR files, resource files, etc.). (For details on reading resources from a UDF, see Reading Files from a UDF.)

The Snowpark library uploads these files to an internal stage and adds the files to the classpath when executing your UDF.

The following example demonstrates how to add a JAR file in a stage as a dependency:

// Add a JAR file that you uploaded to a stage.
session.addDependency("@my_stage/<path>/my-library.jar");

The following examples demonstrate how to add dependencies for JAR files and resource files:

// Add a JAR file on your local machine.
session.addDependency("/<path>/my-library.jar");

// Add a directory of resource files.
session.addDependency("/<path>/my-resource-dir/");

// Add a resource file.
session.addDependency("/<path>/my-resource.xml");

You should not need to specify the following dependencies:

• Your Java runtime libraries.

  These libraries are already available in the runtime environment on the server where your UDFs are executed.

• The Snowpark JAR file.

  The Snowpark library automatically attempts to detect and upload the Snowpark JAR file to the server.

  To prevent the library from repeatedly uploading the Snowpark JAR file to the server:

  1. Upload the Snowpark JAR file to a stage.

     For example, the following command uploads the Snowpark JAR file to the stage @mystage. The PUT command compresses the JAR file and names the resulting file snowpark-1.14.0.jar.gz.

     -- Put the Snowpark JAR file in a stage.
     PUT file:///<path>/snowpark-1.14.0.jar @mystage

  2. Call addDependency to add the Snowpark JAR file in the stage as a dependency.

     For example, to add the Snowpark JAR file uploaded by the previous command:

     // Add the Snowpark JAR file that you uploaded to a stage.
     session.addDependency("@mystage/snowpark-1.14.0.jar.gz");

     Note that the specified path to the JAR file includes the .gz filename extension, which was added by the PUT command.

• The JAR file or directory with the currently running application.

  The Snowpark library automatically attempts to detect and upload these dependencies.

  If the Snowpark library is unable to detect these dependencies automatically, the library reports an error, and you must call addDependency to add these dependencies manually.

If it takes too long for the dependencies to be uploaded to the stage, the Snowpark library reports a timeout exception. To configure the maximum amount of time that the Snowpark library should wait, set the snowpark_request_timeout_in_seconds property when creating the session.

Creating an Anonymous UDF¶

To create an anonymous UDF, you can either:

• Call the Functions.udf static method, passing in the lambda expression and the DataTypes fields (or objects constructed by the methods of that class) representing the data types of the inputs and output.

• Call the registerTemporary method in the UDFRegistration class, passing in the lambda expression and the DataTypes fields (or objects constructed by the methods of that class) representing the data types of the inputs and output.

  You can access an instance of the UDFRegistration class by calling the udf method of the Session object.

  When calling registerTemporary, use a method signature that does not have a name parameter. (Because you are creating an anonymous UDF, you do not specify a name for the UDF.)

These methods return a UserDefinedFunction object, which you can use to call the UDF. (See Calling scalar user-defined functions (UDFs).)

The following example creates an anonymous UDF:

import com.snowflake.snowpark_java.types.*;
...

// Create and register an anonymous UDF
// that takes in an integer argument and returns an integer value.
UserDefinedFunction doubleUdf =
  Functions.udf((Integer x) -> x + x, DataTypes.IntegerType, DataTypes.IntegerType);
// Call the anonymous UDF, passing in the "quantity" column.
// The example uses withColumn to return a DataFrame containing
// the UDF result in a new column named "doubleQuantity".
DataFrame df = session.table("sample_product_data");
DataFrame dfWithDoubleQuantity = df.withColumn("doubleQuantity", doubleUdf.apply(Functions.col("quantity")));
dfWithDoubleQuantity.show();

The following example creates an anonymous UDF that uses a custom class (LanguageDetector, which detects the language used in text). The example calls the anonymous UDF to detect the language in the text_data column in a DataFrame and creates a new DataFrame that includes an additional lang column with the language used.

import com.snowflake.snowpark_java.types.*;

// Import the package for your custom code.
// The custom code in this example detects the language of textual data.
import com.mycompany.LanguageDetector;

// If the custom code is packaged in a JAR file, add that JAR file as
// a dependency.
session.addDependency("$HOME/language-detector.jar");

// Create a detector
LanguageDetector detector = new LanguageDetector();

// Create an anonymous UDF that takes a string of text and returns the language used in that string.
// Note that this captures the detector object created above.
// Assign the UDF to the langUdf variable, which will be used to call the UDF.
UserDefinedFunction langUdf =
  Functions.udf(
    (String s) -> Option(detector.detect(s)).getOrElse("UNKNOWN"),
    DataTypes.StringType,
    DataTypes.StringType);

// Create a new DataFrame that contains an additional "lang" column that contains the language
// detected by the UDF.
DataFrame dfEmailsWithLangCol =
    dfEmails.withColumn("lang", langUdf(Functions.col("text_data")));

Creating and Registering a Named UDF¶

If you want to call a UDF by name (e.g. by using the Functions.callUDF static method) or if you need to use a UDF in subsequent sessions, you can create and register a named UDF. To do this, use one of the following methods in the UDFRegistration class:

• registerTemporary, if you just plan to use the UDF in the current session

• registerPermanent, if you plan to use the UDF in subsequent sessions

To access an object of the UDFRegistration class, call the udf method of the Session object.

When calling registerTemporary or registerPermanent method, pass in the lambda expression and the DataTypes fields (or objects constructed by the methods of that class) representing the data types of the inputs and output.

For example:

import com.snowflake.snowpark_java.types.*;
...
// Create and register a temporary named UDF
// that takes in an integer argument and returns an integer value.
UserDefinedFunction doubleUdf =
  session
    .udf()
    .registerTemporary(
      "doubleUdf",
      (Integer x) -> x + x,
      DataTypes.IntegerType,
      DataTypes.IntegerType);
// Call the named UDF, passing in the "quantity" column.
// The example uses withColumn to return a DataFrame containing
// the UDF result in a new column named "doubleQuantity".
DataFrame df = session.table("sample_product_data");
DataFrame dfWithDoubleQuantity = df.withColumn("doubleQuantity", Functions.callUDF("doubleUdf", Functions.col("quantity")));
dfWithDoubleQuantity.show();

registerPermanent creates a UDF that you can use in the current and subsequent sessions. When you call registerPermanent, you must also specify a location in an internal stage location where the JAR files for the UDF and its dependencies will be uploaded.

For example:

import com.snowflake.snowpark_java.types.*;
...

// Create and register a permanent named UDF
// that takes in an integer argument and returns an integer value.
// Specify that the UDF and dependent JAR files should be uploaded to
// the internal stage named mystage.
UserDefinedFunction doubleUdf =
  session
    .udf()
    .registerPermanent(
      "doubleUdf",
      (Integer x) -> x + x,
      DataTypes.IntegerType,
      DataTypes.IntegerType,
      "mystage");
// Call the named UDF, passing in the "quantity" column.
// The example uses withColumn to return a DataFrame containing
// the UDF result in a new column named "doubleQuantity".
DataFrame df = session.table("sample_product_data");
DataFrame dfWithDoubleQuantity = df.withColumn("doubleQuantity", Functions.callUDF("doubleUdf", Functions.col("quantity")));
dfWithDoubleQuantity.show();

Using Objects That Are Not Serializable¶

When you create a UDF for a lambda expression, the Snowpark library serializes the lambda closure and sends it to the server for execution.

If an object captured by the lambda closure is not serializable, the Snowpark library throws an java.io.NotSerializableException exception.

Exception in thread "main" java.io.NotSerializableException: <YourObjectName>

If this occurs, you must make the object serializable.

Writing Initialization Code for a UDF¶

If your UDF requires initialization code or context, you can provide this through values captured as part of the UDF closure.

The following example uses a separate class to initialize the context needed by two UDFs.

• The first UDF creates a new instance of the class within the lambda, so the initialization is performed every time the UDF is invoked.

• The second UDF captures an instance of the class generated in your client program. The context generated on the client is serialized and is used by the UDF. Note that the context class must be serializable for this approach to work.

import com.snowflake.snowpark_java.*;
import com.snowflake.snowpark_java.types.*;
import java.io.Serializable;

// Context needed for a UDF.
class Context {
  double randomInt = Math.random();
}

// Serializable context needed for the UDF.
class SerContext implements Serializable {
  double randomInt = Math.random();
}

class TestUdf {
  public static void main(String[] args) {
    // Create the session.
    Session session = Session.builder().configFile("/<path>/profile.properties").create();
    session.range(1, 10, 2).show();

    // Create a DataFrame with two columns ("c" and "d").
    DataFrame dummy =
      session.createDataFrame(
        new Row[]{
          Row.create(1, 1),
          Row.create(2, 2),
          Row.create(3, 3)
        },
        StructType.create(
          new StructField("c", DataTypes.IntegerType),
          new StructField("d", DataTypes.IntegerType))
        );
    dummy.show();

    // Initialize the context once per invocation.
    UserDefinedFunction udfRepeatedInit =
      Functions.udf(
        (Integer i) -> new Context().randomInt,
        DataTypes.IntegerType,
        DataTypes.DoubleType
      );
    dummy.select(udfRepeatedInit.apply(dummy.col("c"))).show();

    // Initialize the serializable context only once,
    // regardless of the number of times that the UDF is invoked.
    SerContext sC = new SerContext();
    UserDefinedFunction udfOnceInit =
      Functions.udf(
        (Integer i) -> sC.randomInt,
        DataTypes.IntegerType,
        DataTypes.DoubleType
      );
    dummy.select(udfOnceInit.apply(dummy.col("c"))).show();
    UserDefinedFunction udfOnceInit = udf((i: Int) => sC.randomInt);
  }
}

Reading Files from a UDF¶

As mentioned earlier, the Snowpark library uploads and executes UDFs on the server. If your UDF needs to read data from a file, you must ensure that the file is uploaded with the UDF.

In addition, if the content of the file remains the same between calls to the UDF, you can write your code to load the file once during the first call and not on subsequent calls. This can improve the performance of your UDF calls.

To set up a UDF to read a file:

1. Add the file to a JAR file.

   For example, if your UDF needs to use a file in a data/ subdirectory (data/hello.txt), run the jar command to add this file to a JAR file:

   # Create a new JAR file containing data/hello.txt.
   $ jar cvf <path>/myJar.jar data/hello.txt
   

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2. Specify that the JAR file is a dependency, which uploads the file to the server and adds the file to the classpath. See Specifying Dependencies for a UDF.

   For example:

   // Specify that myJar.jar contains files that your UDF depends on.
   session.addDependency("<path>/myJar.jar");
   

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3. In the UDF, call Class.forName().getResourceAsStream() to find the file in the classpath and read the file.

   To avoid adding a dependency on this, you can use Class.forName("com.snowflake.snowpark_java.DataFrame") (rather than getClass()) to get the Class object.

   For example, to read the data/hello.txt file:

   // Read data/hello.txt from myJar.jar.
   String resourceName = "/data/hello.txt";
   InputStream inputStream = Class.forName("com.snowflake.snowpark_java.DataFrame").getResourceAsStream(resourceName);
   

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   In this example, the resource name starts with a /, which indicates that this is the full path of the file in the JAR file. (In this case, the location of the file is not relative to the package of the class.)

Note

If you don’t expect the content of the file to change between UDF calls, read the file into a static field of your class, and read the file only if the field is not set.

The following example defines an object (UDFCode) with a function that will be used as a UDF (readFileFunc). The function reads the file data/hello.txt, which is expected to contain the string hello,. The function prepends this string to the string passed in as an argument.

import java.io.InputStream;
import java.nio.charset.StandardCharsets;

// Create a function class that reads a file.
class UDFCode {
  private static String fileContent = null;
  // The code in this block reads the file. To prevent this code from executing each time that the UDF is called,
  // The file content is cached in 'fileContent'.
  public static String readFile() {
    if (fileContent == null) {
      try {
        String resourceName = "/data/hello.txt";
        InputStream inputStream = Class.forName("com.snowflake.snowpark_java.DataFrame")
          .getResourceAsStream(resourceName);
        fileContent = new String(inputStream.readAllBytes(), StandardCharsets.UTF_8);
      } catch (Exception e) {
        fileContent = "Error while reading file";
      }
    }
    return fileContent;
  }
}

The next part of the example registers the function as an anonymous UDF. The example calls the UDF on the NAME column in a DataFrame. The example assumes that the data/hello.txt file is packaged in the JAR file myJar.jar.

import com.snowflake.snowpark_java.types.*;

// Add the JAR file as a dependency.
session.addDependency("<path>/myJar.jar");

// Create a new DataFrame with one column (NAME)
// that contains the name "Raymond".
DataFrame myDf = session.sql("select 'Raymond' NAME");

// Register the function that you defined earlier as an anonymous UDF.
UserDefinedFunction readFileUdf = session.udf().registerTemporary(
  (String s) -> UDFCode.readFile() + " : " + s, DataTypes.StringType, DataTypes.StringType);

// Call UDF for the values in the NAME column of the DataFrame.
myDf.withColumn("CONCAT", readFileUdf.apply(Functions.col("NAME"))).show();

Creating User-Defined Table Functions (UDTFs)¶

To create and register a UDTF in Snowpark, you must:

• Define a class for the UDTF.

• Create an instance of that class, and register that instance as a UDTF.

The next sections describe these steps in more detail.

For information on calling a UDTF, see Calling a UDTF.

import java.util.Map;
import com.snowflake.snowpark_java.*;
import com.snowflake.snowpark_java.types.*;
...

public Stream<Row> process(Map<String, String> stringMap, Map<String, Variant> varMap) {
  ...
}

import java.util.Map;
import com.snowflake.snowpark_java.types.*;
...

public StructType inputSchema() {
  return StructType.create(
      new StructField(
          "string_map",
          DataTypes.createMapType(DataTypes.StringType, DataTypes.StringType)),
      new StructField(
          "variant_map",
          DataTypes.createMapType(DataTypes.StringType, DataTypes.VariantType)));
}

import java.util.stream.Stream;
import com.snowflake.snowpark_java.types.*;
import com.snowflake.snowpark_java.udtf.*;

class MyRangeUdtf implements JavaUDTF2<Integer, Integer> {
  public StructType outputSchema() {
    return StructType.create(new StructField("C1", DataTypes.IntegerType));
  }

  // Because the process() method in this example does not pass in Map arguments,
  // implementing the inputSchema() method is optional.
  public StructType inputSchema() {
    return StructType.create(
            new StructField("start_value", DataTypes.IntegerType),
            new StructField("value_count", DataTypes.IntegerType));
  }

  public Stream<Row> endPartition() {
    return Stream.empty();
  }

  public Stream<Row> process(Integer start, Integer count) {
    Stream.Builder<Row> builder = Stream.builder();
    for (int i = start; i < start + count ; i++) {
      builder.add(Row.create(i));
    }
    return builder.build();
  }
}