topic Re: Using Spark SQL to alter a column of an existing lakehouse table in Data Engineering

Using Spark SQL to alter a column of an existing lakehouse table

pmscorca — Tue, 09 Jul 2024 12:28:01 GMT

Hi,

I try to use a Spark SQL code to alter the data type and the lenght of a column of an existing lakehouse table.

This syntax goes in error

%%sql ALTER TABLE MYTABLE ALTER COLUMN mycolumn varchar(10)

Any helps to me, please? Thanks

Unfortunately, I cannot use an equivalent T-SQL statement against a lakehouse.

Re: Using Spark SQL to alter a column of an existing lakehouse table

Anonymous — Wed, 10 Jul 2024 04:09:16 GMT

Hi @pmscorca ,

In Apache Spark SQL, you cannot directly change the data type of an existing column using the ALTER TABLE command.

You need to create a new DataFrame with the changed column type and then overwrite the existing table with the new DataFrame.

Here is a sample PySpark code:

from pyspark.sql.functions import col # Load the existing table into a DataFrame df = spark.table("MYTABLE") # Cast the column to the desired type df = df.withColumn("mycolumn", col("mycolumn").cast("string")) # Overwrite the existing table df.write.mode('overwrite').saveAsTable("MYTABLE")

Replace "MYTABLE" and "mycolumn" with the name of the table and column, and "string" with the desired data type. Note that the length parameter does not apply when casting to a string in Spark.

One small note, remember to back up your data before proceeding.

Best Regards,
Yang
Community Support Team

If there is any post helps, then please consider Accept it as the solution to help the other members find it more quickly.
If I misunderstand your needs or you still have problems on it, please feel free to let us know. Thanks a lot!

Re: Using Spark SQL to alter a column of an existing lakehouse table

frithjof_v — Wed, 10 Jul 2024 07:15:59 GMT

This worked for me when I tried it a couple of months ago:

spark.read.table("tableName")\

.withColumn("columnName", col("columnName").cast("columnType"))\

.write\

.mode("overwrite")\

.option("overwriteSchema", "true")\

.saveAsTable("tableName")

https://community.fabric.microsoft.com/t5/General-Discussion/Dropping-and-recreating-lakehouse-table/m-p/3835426/highlight/true#M6312

Re: Using Spark SQL to alter a column of an existing lakehouse table

pmscorca — Wed, 10 Jul 2024 15:07:01 GMT

Hi,

I've tried to use the cast method and I've noticed that if I try to pass from a string data type to a int data type working with an empty table I've an error about the data type incompatibility.
I'd like to know which are the data types supported by the cast method.

I'd like to cast to a varchar with a specified lenght.

I'd like that the MS official documentation could report more useful and reusable spark codes to implement the notebooks.

Thanks

Re: Using Spark SQL to alter a column of an existing lakehouse table

Anonymous — Thu, 11 Jul 2024 01:29:26 GMT

Hi @pmscorca ,

Thanks for the reply from @frithjof_v .

The types supported by Cast() method include: "string", "boolean", "byte", "short", "int", "long", "float", "double", "decimal", "date", "timestamp".

Here is an official document about Cast() method:

Column.Cast(String) Method (Microsoft.Spark.Sql) - .NET for Apache Spark | Microsoft Learn

In Spark SQL, there is no direct way to cast a string to a varchar with a specified length.

Here is an alternative:

You can use withColumn and cast:

from pyspark.sql.functions import col df = spark.sql("SELECT * FROM MYTABLE") df = df.withColumn("mycolumn", col("mycolumn").cast("new_data_type"))

If you have any other questions, please feel free to contact me.

Best Regards,
Yang
Community Support Team

Re: Using Spark SQL to alter a column of an existing lakehouse table

pmscorca — Thu, 11 Jul 2024 05:09:06 GMT

Hi,

however does it exist a manner to cast to a varchar with a specified length by a pyspark code, Scala code, etc.? Thanks

Re: Using Spark SQL to alter a column of an existing lakehouse table

pmscorca — Thu, 11 Jul 2024 09:22:21 GMT

Hi,

I can use this PySpark code successfully:

sql_sample_alter_table = """ ALTER TABLE sql_sample_table ALTER COLUMN descr TYPE VARCHAR(100) """ spark.sql(sql_sample_alter_table)