A User-Defined Function (UDF) in Hive is a function that is defined by the user and can be used in Hive queries like built-in functions. UDFs enable users to extend the functionality of Hive by defining their own custom functions.
In this example, we will create a UDF in Hive using Python that takes a string as input and returns the number of words in the string. We will name our UDF “freshers_in_wordcount”. Here are the steps:
Step 1: Set up the Environment
Before we begin, we need to make sure that we have the necessary tools installed on our system. We will be using Python 3 and Apache Hive. Here’s how to set up the environment:
1. Install Python 3 on your system.
2. Install the PyHive library using pip:
pip install PyHive
3. Install the Hive JDBC driver on your system. You can download it from the Apache Hive website (https://hive.apache.org/downloads.html).
4. Add the Hive JDBC driver to your system’s CLASSPATH by running the following command:
export CLASSPATH=$CLASSPATH:/path/to/hive-jdbc.jar
Replace “/path/to/hive-jdbc.jar” with the actual path to the Hive JDBC driver on your system.
Step 2: Create the Python Script
Now that we have set up our environment, we can create the Python script that defines our UDF. Here’s the code:
from pyhive import hive
def freshers_in_wordcount(text):
words = text.split()
return len(words)
conn = hive.Connection(host='localhost', port=10000, username='hiveuser')
cursor = conn.cursor()
cursor.execute("""
CREATE FUNCTION freshers_in_wordcount AS 'myudfs.wordcount.WordCount' USING JAR 'hdfs:///path/to/udf.jar'
""")
conn.close()
This script defines the “freshers_in_wordcount” function that takes a string as input and returns the number of words in the string. It uses the PyHive library to connect to Hive and create the UDF.
Step 3: Build and Deploy the UDF JAR File
In order to use our UDF in Hive, we need to build a JAR file that contains our Python code. Here’s how to do it:
1. Create a new directory for the UDF and change into it:
mkdir myudfs
cd myudfs
2. Create a new Python file named “wordcount.py” in this directory with the following code:
import sys
from pyhive import hive
def freshers_in_wordcount(text):
words = text.split()
return len(words)
if __name__ == '__main__':
conn = hive.Connection(host='localhost', port=10000, username='hiveuser')
cursor = conn.cursor()
cursor.execute("""
ADD FILE /path/to/myudfs/wordcount.py;
""")
cursor.execute("""
CREATE TEMPORARY FUNCTION freshers_in_wordcount AS 'wordcount.WordCount'
""")
cursor.execute("""
SELECT freshers_in_wordcount('hello world') FROM mytable
""")
results = cursor.fetchone()
print(results)
conn.close()
This file defines the “freshers_in_wordcount” function and creates a temporary function in Hive that calls this function. It also includes a test query that uses the UDF to count the words in the string “hello world”.
3. Build the JAR file using the following command:
jar -cvf myudfs.jar wordcount.py
This command creates a JAR file named “myudfs.jar” that contains the “wordcount.py” file.
4. Upload the JAR file to HDFS using the following command:
hadoop fs -put myudfs.jar /path/to/udf.jar
Replace “/path/to/udf.jar” with the actual path where you want to store the JAR file in HDFS.
Step 4: Register the UDF in Hive
Now that we have built and deployed our UDF JAR file, we can register the UDF in Hive using the Python script we created earlier. Here’s how to do it:
1. Run the Python script using the following command:
python register_udf.py
This command connects to Hive using the PyHive library and creates the UDF using the “CREATE FUNCTION” HiveQL statement.
Verify that the UDF has been registered by running the following command in the Hive CLI:
DESCRIBE FUNCTION EXTENDED freshers_in_wordcount;
This command should display information about the “freshers_in_wordcount” function, including its input and output types.
Step 5: Use the UDF in Hive Queries
Now that our UDF is registered in Hive, we can use it in Hive queries like any other built-in function. Here’s an example query:
SELECT freshers_in_wordcount('hello world') FROM mytable;
This query calls our UDF with the string “hello world” as input and returns the number of words in the string. You can replace “mytable” with the name of your own table.
You have now successfully implemented a UDF in Hive using Python. You can modify the Python code to implement your own custom functions and register them as UDFs in Hive.
Hive important pages to refer
