Building a StructType from a dataframe in pyspark

Question

I am new spark and python and facing this difficulty of building a schema from a metadata file that can be applied to my data file. Scenario: Metadata File for the Data file(csv format), contains the columns and their types: for example:

id,int,10,"","",id,"","",TRUE,"",0
created_at,timestamp,"","","",created_at,"","",FALSE,"",0

I have successfully converted this to a dataframe that looks like:

+--------------------+---------------+
|                name|           type|
+--------------------+---------------+
|                  id|  IntegerType()|
|          created_at|TimestampType()|
|          updated_at|   StringType()|

But when I try to convert this to a StructField format using this

fields = schemaLoansNew.map(lambda l:([StructField(l.name, l.type, 'true')]))

OR

schemaList = schemaLoansNew.map(lambda l: ("StructField(" + l.name + "," + l.type + ",true)")).collect()

And then later convert it to StructType, using

schemaFinal = StructType(schemaList)

I get the following error:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/mapr/spark/spark-1.4.1/python/pyspark/sql/types.py", line 372, in __init__
assert all(isinstance(f, DataType) for f in fields), "fields should be a list of DataType"
AssertionError: fields should be a list of DataType

I am stuck on this due to my lack of knowledge on Data Frames, can you please advise, how to proceed on this. once I have schema ready I want to use createDataFrame to apply to my data File. This process has to be done for many tables so I do not want to hardcode the types rather use the metadata file to build the schema and then apply to the RDD.

Thanks in advance.

Answer 1

Fields have argument have to be a list of DataType objects. This:

.map(lambda l:([StructField(l.name, l.type, 'true')]))

generates after collect a list of lists of tuples (Rows) of DataType (list[list[tuple[DataType]]]) not to mention that nullable argument should be boolean not a string.

Your second attempt:

.map(lambda l: ("StructField(" + l.name + "," + l.type + ",true)")).

generates after collect a list of str objects.

Correct schema for the record you've shown should look more or less like this:

from pyspark.sql.types import *

StructType([
    StructField("id", IntegerType(), True),
    StructField("created_at", TimestampType(), True),
    StructField("updated_at", StringType(), True)
])

Although using distributed data structures for task like this is a serious overkill, not to mention inefficient, you can try to adjust your first solution as follows:

StructType([
    StructField(name, eval(type), True) for (name, type) in  df.rdd.collect()
])

but it is not particularly safe (eval). It could be easier to build a schema from JSON / dictionary. Assuming you have function which maps from type description to canonical type name:

def get_type_name(s: str) -> str:
    """
    >>> get_type_name("int")
    'integer'
    """
    _map = {
        'int': IntegerType().typeName(),
        'timestamp': TimestampType().typeName(),
        # ...
    } 
    return _map.get(s, StringType().typeName())

You can build dictionary of following shape:

schema_dict = {'fields': [
    {'metadata': {}, 'name': 'id', 'nullable': True, 'type': 'integer'},
    {'metadata': {}, 'name': 'created_at', 'nullable': True, 'type': 'timestamp'}
], 'type': 'struct'}

and feed it to StructType.fromJson:

StructType.fromJson(schema_dict)

Answer 2

Below steps can be followed to change the Datatype Objects

data_schema=[
    StructField("age", IntegerType(), True),
    StructField("name", StringType(), True)
]



final_struct=StructType(fields=data_schema)

df=spark.read.json('/home/abcde/Python-and-Spark-for-Big-Data-master/Spark_DataFrames/people.json', schema=final_struct)



df.printSchema()

root
 |-- age: integer (nullable = true)
 |-- name: string (nullable = true)