Home Big Data Introducing Python Consumer-Outlined Desk Capabilities (UDTFs)

Introducing Python Consumer-Outlined Desk Capabilities (UDTFs)

Introducing Python Consumer-Outlined Desk Capabilities (UDTFs)


Apache Spark™ 3.5 and Databricks Runtime 14.0 have introduced an thrilling function to the desk: Python user-defined desk capabilities (UDTFs). On this weblog put up, we’ll dive into what UDTFs are, why they’re highly effective, and the way you should use them.

What are Python user-defined desk capabilities (UDTFs)

A Python user-defined desk perform (UDTF) is a brand new type of perform that returns a desk as output as an alternative of a single scalar consequence worth. As soon as registered, they will seem within the FROM clause of a SQL question.

Every Python UDTF accepts zero or extra arguments, the place every argument could be a fixed scalar worth similar to an integer or string. The physique of the perform can examine the values of those arguments with the intention to make selections about what knowledge to return.

Why must you use Python UDTFs

Briefly, in order for you a perform that generates a number of rows and columns, and need to leverage the wealthy Python ecosystem, Python UDTFs are for you.

Python UDTFs vs Python UDFs

Whereas Python UDFs in Spark are designed to every settle for zero or extra scalar values as enter, and return a single worth as output, UDTFs supply extra flexibility. They’ll return a number of rows and columns, extending the capabilities of UDFs.

Python UDTFs vs SQL UDTFs

SQL UDTFs are environment friendly and versatile, however Python provides a richer set of libraries and instruments. For transformations or computations needing superior strategies (like statistical capabilities or machine studying inferences), Python stands out.

Find out how to create a Python UDTF

Let’s take a look at a fundamental Python UDTF:

from pyspark.sql.capabilities import udtf

@udtf(returnType="num: int, squared: int")
class SquareNumbers:
    def eval(self, begin: int, finish: int):
        for num in vary(begin, finish + 1):
            yield (num, num * num)

Within the above code, we have created a easy UDTF that takes two integers as inputs and produces two columns as output: the unique quantity and its sq..

Step one to implement a UDTF is to outline a category, on this case

class SquareNumbers:

Subsequent, it’s essential to implement the eval technique of the UDTF. That is the strategy that does the computations and returns rows, the place you outline the enter arguments of the perform.

def eval(self, begin: int, finish: int):
    for num in vary(begin, finish + 1):
        yield (num, num * num)

Word the usage of the yield assertion; A Python UDTF requires the return sort to be both a tuple or a Row object in order that the outcomes will be processed correctly.

Lastly, to mark the category as a UDTF, you should use the @udtf decorator and outline the return sort of the UDTF. Word the return sort should be a StructType with block-formatting or DDL string representing a StructType with block-formatting in Spark.

@udtf(returnType="num: int, squared: int")

Find out how to use a Python UDTF

In Python

You possibly can invoke a UDTF straight utilizing the category title.

from pyspark.sql.capabilities import lit

SquareNumbers(lit(1), lit(3)).present()

|  1|      1|
|  2|      4|
|  3|      9|


First, register the Python UDTF:

spark.udtf.register("square_numbers", SquareNumbers)

Then you should use it in SQL as a table-valued perform within the FROM clause of a question:

spark.sql("SELECT * FROM square_numbers(1, 3)").present()

|  1|      1|
|  2|      4|
|  3|      9|

Arrow-optimized Python UDTFs

Apache Arrow is an in-memory columnar knowledge format that permits for environment friendly knowledge transfers between Java and Python processes. It will possibly considerably enhance efficiency when the UDTF outputs many rows. Arrow-optimization will be enabled utilizing useArrow=True.

from pyspark.sql.capabilities import lit, udtf

@udtf(returnType="num: int, squared: int", useArrow=True)
class SquareNumbers:

Actual-World Use Case with LangChain

The instance above may really feel fundamental. Let’s dive deeper with a enjoyable instance, integrating Python UDTFs with LangChain.

from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from pyspark.sql.capabilities import lit, udtf

@udtf(returnType="key phrase: string")
class KeywordsGenerator:
    Generate an inventory of comma separated key phrases a couple of subject utilizing an LLM.
    Output solely the key phrases.
    def __init__(self):
        llm = OpenAI(model_name="gpt-4", openai_api_key=<your-key>)
        immediate = PromptTemplate(
            template="generate a few comma separated key phrases about {subject}. Output solely the key phrases."
        self.chain = LLMChain(llm=llm, immediate=immediate)

    def eval(self, subject: str):
        response = self.chain.run(subject)
        key phrases = [keyword.strip() for keyword in response.split(",")]
        for key phrase in key phrases:
            yield (key phrase, )

Now, you possibly can invoke the UDTF:

KeywordsGenerator(lit("apache spark")).present(truncate=False)

|key phrase            |
|Large Information           |
|Information Processing    |
|In-reminiscence Computing|
|Actual-Time Evaluation |
|Machine Studying   |
|Graph Processing   |
|Scalability        |
|Fault Tolerance    |
|RDD                |
|Datasets           |
|DataFrames         |
|Spark Streaming    |
|Spark SQL          |
|MLlib              |

Get Began with Python UDTFs As we speak

Whether or not you are seeking to carry out advanced knowledge transformations, enrich your datasets, or just discover new methods to research your knowledge, Python UDTFs are a useful addition to your toolkit. Strive this pocket book and see the documentation for extra data.

Future Work

This performance is simply the start of the Python UDTF platform. Many extra options are at the moment in growth in Apache Spark to develop into out there in future releases. For instance, it can develop into potential to assist:

  • A polymorphic evaluation whereby UDTF calls might dynamically compute their output schemas in response to the precise arguments supplied for every name (together with the varieties of supplied enter arguments and the values of any literal scalar arguments).
  • Passing total enter relations to UDTF calls within the SQL FROM clause utilizing the TABLE key phrase. This can work with direct catalog desk references in addition to arbitrary desk subqueries. Will probably be potential to specify customized partitioning of the enter desk in every question to outline which subsets of rows of the enter desk might be consumed by the identical occasion of the UDTF class within the eval technique.
  • Performing arbitrary initialization for any UDTF name simply as soon as at question scheduling time and propagating that state to all future class situations for future consumption. Because of this the UDTF output desk schema returned by the preliminary static “analyze” technique might be consumable by all future __init__ calls for a similar question.
  • Many extra attention-grabbing options!


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