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https://issues.apache.org/jira/browse/SPARK-30006?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16981407#comment-16981407
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Kun edited comment on SPARK-30006 at 11/25/19 9:26 AM:
-------------------------------------------------------
In the constructor of *pyspark.sql.Row*, the fields get sorted by their names.
So the order changes.
Is the sorting by design?
{code:python}
class Row(tuple):
def __new__(self, *args, **kwargs):
if args and kwargs:
raise ValueError("Can not use both args "
"and kwargs to create Row")
if kwargs:
# create row objects
names = sorted(kwargs.keys())
row = tuple.__new__(self, [kwargs[n] for n in names])
row.__fields__ = names
row.__from_dict__ = True
return row
else:
# create row class or objects
return tuple.__new__(self, args)
{code}
was (Author: konjac):
In the constructor of *pyspark.sql.Row*, the fields order get sorted by the
name. So the order changes.
Is the sorting by design?
{code:python}
class Row(tuple):
def __new__(self, *args, **kwargs):
if args and kwargs:
raise ValueError("Can not use both args "
"and kwargs to create Row")
if kwargs:
# create row objects
names = sorted(kwargs.keys())
row = tuple.__new__(self, [kwargs[n] for n in names])
row.__fields__ = names
row.__from_dict__ = True
return row
else:
# create row class or objects
return tuple.__new__(self, args)
{code}
> printSchema indeterministic output
> ----------------------------------
>
> Key: SPARK-30006
> URL: https://issues.apache.org/jira/browse/SPARK-30006
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.4.4
> Reporter: Hasil Sharma
> Priority: Minor
>
> printSchema doesn't give a consistent output in following example.
>
> {code:python}
> from pyspark.sql import SparkSession
> from pyspark.sql import Row
> spark = SparkSession.builder.appName("new-session").getOrCreate()
> l = [('Ankit',25),('Jalfaizy',22),('saurabh',20),('Bala',26)]
> rdd = spark.sparkContext.parallelize(l)
> people = rdd.map(lambda x: Row(name=x[0], age=int(x[1])))
> df1 = spark.createDataFrame(people)
> print(df1.printSchema())
> df2 = df1.select("name", "age")
> print(df2.printSchema())
> {code}
>
> first print outputs
> {noformat}
> root
> |– age: long (nullable = true)
> |– name: string (nullable = true)
> {noformat}
>
> second print outputs
> {noformat}
> root
> |– name: string (nullable = true)
> |– age: long (nullable = true)
> {noformat}
> Expectation: The output should be same because the column names are same.
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