Spark allows one to define the column format as StructType or list. By
default Spark assumes that all fields are nullable when creating a
dataframe.
To change nullability you need to provide the structure of the columns.
Assume that I have created an RDD in the form
rdd = sc.parallelize(Range). \
map(lambda x: (x, usedFunctions.clustered(x,numRows), \
usedFunctions.scattered(x,numRows), \
usedFunctions.randomised(x,numRows), \
usedFunctions.randomString(50), \
usedFunctions.padString(x," ",50), \
usedFunctions.padSingleChar("x",4000)))
For the above I create a schema with StructType as below:
Schema = StructType([ *StructField("ID", IntegerType(), False*),
StructField("CLUSTERED", FloatType(), True),
StructField("SCATTERED", FloatType(), True),
StructField("RANDOMISED", FloatType(), True),
StructField("RANDOM_STRING", StringType(), True),
StructField("SMALL_VC", StringType(), True),
StructField("PADDING", StringType(), True)
])
Note that the first column ID is defined as NOT NULL
Then I can create a dataframe df as below
df= spark.createDataFrame(rdd, schema = Schema)
df.printSchema()
root
|-- ID: integer (nullable = false)
|-- CLUSTERED: float (nullable = true)
|-- SCATTERED: float (nullable = true)
|-- RANDOMISED: float (nullable = true)
|-- RANDOM_STRING: string (nullable = true)
|-- SMALL_VC: string (nullable = true)
|-- PADDING: string (nullable = true)
HTH
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On Thu, 14 Oct 2021 at 12:50, [email protected]
<[email protected]> wrote:
> Gurus,
>
> I have an RDD in PySpark that I can convert to DF through
>
> df = rdd.toDF()
>
> However, when I do
>
> df.printSchema()
>
> I see the columns as nullable. = true by default
>
> root
> |-- COL-1: long (nullable = true)
> |-- COl-2: double (nullable = true)
> |-- COl-3: string (nullable = true)
>
> What would be the easiest way to make COL-1 NOT NULLABLE
>
> Thanking you
>
