Many thanks all, especially to Mich. That is what I was looking for.
On Friday, 15 October 2021, 09:28:24 BST, Mich Talebzadeh
<mich.talebza...@gmail.com> wrote:
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, ashok34...@yahoo.com.INVALID
<ashok34...@yahoo.com.invalid> 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
