blackboard udf

What is UDF in spark?

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What is user defined functions (UDF)?

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Can I use UDF for performance optimization?

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What is the use of UDF in Python?

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Sample Pyspark Dataframe

Let’s create a dataframe, and the theme of this dataframe is going to be the name of the student, along with his/her raw scores in a test out of 100.

Creating Sample Function

Now, we have to make a function. So, for understanding, we will make a simple function that will split the columns and check, that if the traversing object in that column (is getting equal to ‘J' (Capital J) or ‘C' (Capital C) or ‘M' (Capital M), so it will be converting the second letter of that word, with its capital version.

Making UDF from Sample function

Now, we will convert it to our UDF function, which will, in turn, reduce our workload on data. For this, we are using lambda inside UDF.

Using UDF over Dataframe

The next thing we will use here, is the withcolumn (), remember that withcolumn () will return a full dataframe. So we will use our existing df dataframe only, and the returned value will be stored in df only (basically we will append it).

UDF with annotations

Now, a short and smart way of doing this is to use “ANNOTATIONS” (or decorators). This will create our UDF function in less number of steps. For this, all we have to do use @ sign ( decorator) in front of udf function, and give the return type of the function in its argument part,i.e assign returntype as Intergertype (), StringType (), etc.

What is a UDF in Spark?

In Spark, you create UDF by creating a function in a language you prefer to use for Spark.

How to create a UDF in Spark?

In Spark, you create UDF by creating a function in a language you prefer to use for Spark. For example, if you are using Spark with scala, you create a UDF in scala language and wrap it with udf () function or register it as udf to use it on DataFrame and SQL respectively.

Can you use UDF in Spark?

Performance concern using UDF. UDF’s are a black box to Spark hence it can’t apply optimization and you will lose all the optimization Spark does on Dataframe/Dataset. When possible you should use Spark SQL built-in functions as these functions provide optimization.