Use a Java UDF with Apache Hive in HDInsight

Article
07/12/2024

Learn how to create a Java-based user-defined function (UDF) that works with Apache Hive. The Java UDF in this example converts a table of text strings to all-lowercase characters.

Prerequisites

A Hadoop cluster on HDInsight. See Get Started with HDInsight on Linux.
Java Developer Kit (JDK) version 8
Apache Maven properly installed according to Apache. Maven is a project build system for Java projects.
The URI scheme for your clusters primary storage. This would be wasb:// for Azure Storage, abfs:// for Azure Data Lake Storage Gen2 or adl:// for Azure Data Lake Storage Gen1. If secure transfer is enabled for Azure Storage, the URI would be wasbs://. See also, secure transfer.
A text editor or Java IDE

Important

If you create the Python files on a Windows client, you must use an editor that uses LF as a line ending. If you are not sure whether your editor uses LF or CRLF, see the Troubleshooting section for steps on removing the CR character.

Test environment

The environment used for this article was a computer running Windows 10. The commands were executed in a command prompt, and the various files were edited with Notepad. Modify accordingly for your environment.

From a command prompt, enter the commands below to create a working environment:

IF NOT EXIST C:\HDI MKDIR C:\HDI
cd C:\HDI

Create an example Java UDF

Create a new Maven project by entering the following command:

mvn archetype:generate -DgroupId=com.microsoft.examples -DartifactId=ExampleUDF -DarchetypeArtifactId=maven-archetype-quickstart -DinteractiveMode=false

This command creates a directory named exampleudf, which contains the Maven project.

Once the project has been created, delete the exampleudf/src/test directory that was created as part of the project by entering the following command:
```
cd ExampleUDF
rmdir /S /Q "src/test"
```

Open pom.xml by entering the command below:

notepad pom.xml

Then replace the existing <dependencies> entry with the following XML:

<dependencies>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>2.7.3</version>
        <scope>provided</scope>
    </dependency>
    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-exec</artifactId>
        <version>1.2.1</version>
        <scope>provided</scope>
    </dependency>
</dependencies>

These entries specify the version of Hadoop and Hive included with HDInsight 3.6. You can find information on the versions of Hadoop and Hive provided with HDInsight from the HDInsight component versioning document.

Add a <build> section before the </project> line at the end of the file. This section should contain the following XML:

<build>
    <plugins>
        <!-- build for Java 1.8. This is required by HDInsight 3.6  -->
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>3.3</version>
            <configuration>
                <source>1.8</source>
                <target>1.8</target>
            </configuration>
        </plugin>
        <!-- build an uber jar -->
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-shade-plugin</artifactId>
            <version>3.2.1</version>
            <configuration>
                <!-- Keep us from getting a can't overwrite file error -->
                <transformers>
                    <transformer
                            implementation="org.apache.maven.plugins.shade.resource.ApacheLicenseResourceTransformer">
                    </transformer>
                    <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer">
                    </transformer>
                </transformers>
                <!-- Keep us from getting a bad signature error -->
                <filters>
                    <filter>
                        <artifact>*:*</artifact>
                        <excludes>
                            <exclude>META-INF/*.SF</exclude>
                            <exclude>META-INF/*.DSA</exclude>
                            <exclude>META-INF/*.RSA</exclude>
                        </excludes>
                    </filter>
                </filters>
            </configuration>
            <executions>
                <execution>
                    <phase>package</phase>
                    <goals>
                        <goal>shade</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

These entries define how to build the project. Specifically, the version of Java that the project uses and how to build an uberjar for deployment to the cluster.

Save the file once the changes have been made.

Enter the command below to create and open a new file ExampleUDF.java:

notepad src/main/java/com/microsoft/examples/ExampleUDF.java

Then copy and paste the Java code below into the new file. Then close the file.

package com.microsoft.examples;

import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.UDF;
import org.apache.hadoop.io.*;

// Description of the UDF
@Description(
    name="ExampleUDF",
    value="returns a lower case version of the input string.",
    extended="select ExampleUDF(deviceplatform) from hivesampletable limit 10;"
)
public class ExampleUDF extends UDF {
    // Accept a string input
    public String evaluate(String input) {
        // If the value is null, return a null
        if(input == null)
            return null;
        // Lowercase the input string and return it
        return input.toLowerCase();
    }
}

This code implements a UDF that accepts a string value, and returns a lowercase version of the string.

Build and install the UDF

In the commands below, replace sshuser with the actual username if different. Replace mycluster with the actual cluster name.

Compile and package the UDF by entering the following command:
```
mvn compile package
```
This command builds and packages the UDF into the exampleudf/target/ExampleUDF-1.0-SNAPSHOT.jar file.
Use the scp command to copy the file to the HDInsight cluster by entering the following command:
```
scp ./target/ExampleUDF-1.0-SNAPSHOT.jar [email protected]:
```
Connect to the cluster using SSH by entering the following command:
```
ssh [email protected]
```
From the open SSH session, copy the jar file to HDInsight storage.
```
hdfs dfs -put ExampleUDF-1.0-SNAPSHOT.jar /example/jars
```

Use the UDF from Hive

Start the Beeline client from the SSH session by entering the following command:
```
beeline -u 'jdbc:hive2://localhost:10001/;transportMode=http'
```
This command assumes that you used the default of admin for the login account for your cluster.

Once you arrive at the jdbc:hive2://localhost:10001/> prompt, enter the following to add the UDF to Hive and expose it as a function.

ADD JAR wasbs:///example/jars/ExampleUDF-1.0-SNAPSHOT.jar;
CREATE TEMPORARY FUNCTION tolower as 'com.microsoft.examples.ExampleUDF';

Use the UDF to convert values retrieved from a table to lower case strings.

SELECT tolower(state) AS ExampleUDF, state FROM hivesampletable LIMIT 10;

This query selects the state from the table, convert the string to lower case, and then display them along with the unmodified name. The output appears similar to the following text:

+---------------+---------------+--+
|  exampleudf   |     state     |
+---------------+---------------+--+
| california    | California    |
| pennsylvania  | Pennsylvania  |
| pennsylvania  | Pennsylvania  |
| pennsylvania  | Pennsylvania  |
| colorado      | Colorado      |
| colorado      | Colorado      |
| colorado      | Colorado      |
| utah          | Utah          |
| utah          | Utah          |
| colorado      | Colorado      |
+---------------+---------------+--+

Troubleshooting

When running the hive job, you may come across an error similar to the following text:

Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: [Error 20001]: An error occurred while reading or writing to your custom script. It may have crashed with an error.

This problem may be caused by the line endings in the Python file. Many Windows editors default to using CRLF as the line ending, but Linux applications usually expect LF.

You can use the following PowerShell statements to remove the CR characters before uploading the file to HDInsight:

# Set $original_file to the Python file path
$text = [IO.File]::ReadAllText($original_file) -replace "`r`n", "`n"
[IO.File]::WriteAllText($original_file, $text)

Next steps

For other ways to work with Hive, see Use Apache Hive with HDInsight.

For more information on Hive User-Defined Functions, see Apache Hive Operators and User-Defined Functions section of the Hive wiki at apache.org.

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