[
https://issues.apache.org/jira/browse/SPARK-30006?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hasil Sharma updated SPARK-30006:
---------------------------------
Description:
printSchema doesn't give a consistent output in following example.
```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_1 = rdd.map(lambda x: Row(name=x[0], age=int(x[1])))
df1 = spark.createDataFrame(people_1)
print(df1.printSchema())
df2 = df1.select("name", "age")
print(df2.printSchema())
```
first print outputs
```root
|– age: long (nullable = true)|
|– name: string (nullable = true)|```
second print outputs
```root
|– name: string (nullable = true)|
|– age: long (nullable = true)|```
Expectation: The output should be same because the column names are same.
was:
printSchema doesn't give a consistent output in following example.
```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_1 = rdd.map(lambda x: Row(name=x[0], age=int(x[1])))
df1 = spark.createDataFrame(people_1)
print(df1.printSchema())
df2 = df1.select("name", "age")
print(df2.printSchema())```
first print outputs
```
```
second print outputs
```
root
|– name: string (nullable = true)|
|– age: long (nullable = true)|
```
Expectation: The output should be same because the column names are same.
> 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.
>
> ```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_1 = rdd.map(lambda x: Row(name=x[0], age=int(x[1])))
> df1 = spark.createDataFrame(people_1)
> print(df1.printSchema())
> df2 = df1.select("name", "age")
> print(df2.printSchema())
> ```
>
> first print outputs
> ```root
> |– age: long (nullable = true)|
> |– name: string (nullable = true)|```
>
> second print outputs
> ```root
> |– name: string (nullable = true)|
> |– age: long (nullable = true)|```
> Expectation: The output should be same because the column names are same.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
