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https://issues.apache.org/jira/browse/SPARK-22221?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16195750#comment-16195750
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Li Jin edited comment on SPARK-22221 at 10/7/17 3:32 PM:
---------------------------------------------------------
Per [~leif] 's comment here about struct type:
https://issues.apache.org/jira/browse/SPARK-21187?focusedCommentId=16098522&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-16098522
I think really consider turning spark struct into a pandas multi index because
they are semantically very similar, and multi index is much faster than, say,
map object in Pandas.
Pandas:
{code:java}
In [29]: df
Out[29]:
name
first last
0 Reynold Xin
1 Bryan Cutler
In [28]: df.assign(full_name=df['name']['first'] + df['name']['last'])
Out[28]:
name full_name
first last
0 Reynold Xin ReynoldXin
1 Bryan Cutler BryanCutler
{code}
Spark:
{code}
In [27]: result.printSchema()
root
|-- name: struct (nullable = false)
| |-- first: string (nullable = true)
| |-- last: string (nullable = true)
In [31]: result.withColumn('full_name', concat(result['name']['first'],
result['name']['last'])).show()
+--------------+-----------+
| name| full_name|
+--------------+-----------+
| [Reynold,Xin]| ReynoldXin|
|[Bryan,Cutler]|BryanCutler|
+--------------+-----------+
{code}
was (Author: icexelloss):
Also we might want really consider Leif comment here about struct type:
https://issues.apache.org/jira/browse/SPARK-21187?focusedCommentId=16098522&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-16098522
I think really consider turning spark struct into a pandas multi index because
they are semantically very similar, and multi index is much faster than, say,
map object in Pandas.
Pandas:
{code:java}
In [29]: df
Out[29]:
name
first last
0 Reynold Xin
1 Bryan Cutler
In [28]: df.assign(full_name=df['name']['first'] + df['name']['last'])
Out[28]:
name full_name
first last
0 Reynold Xin ReynoldXin
1 Bryan Cutler BryanCutler
{code}
Spark:
{code}
In [27]: result.printSchema()
root
|-- name: struct (nullable = false)
| |-- first: string (nullable = true)
| |-- last: string (nullable = true)
In [31]: result.withColumn('full_name', concat(result['name']['first'],
result['name']['last'])).show()
+--------------+-----------+
| name| full_name|
+--------------+-----------+
| [Reynold,Xin]| ReynoldXin|
|[Bryan,Cutler]|BryanCutler|
+--------------+-----------+
{code}
> Add User Documentation for Working with Arrow in Spark
> ------------------------------------------------------
>
> Key: SPARK-22221
> URL: https://issues.apache.org/jira/browse/SPARK-22221
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark, SQL
> Affects Versions: 2.3.0
> Reporter: Bryan Cutler
>
> There needs to be user facing documentation that will show how to enable/use
> Arrow with Spark, what the user should expect, and describe any differences
> with similar existing functionality.
> A comment from Xiao Li on https://github.com/apache/spark/pull/18664
> Given the users/applications contain the Timestamp in their Dataset and their
> processing algorithms also need to have the codes based on the corresponding
> time-zone related assumptions.
> * For the new users/applications, they first enabled Arrow and later hit an
> Arrow bug? Can they simply turn off spark.sql.execution.arrow.enable? If not,
> what should they do?
> * For the existing users/applications, they want to utilize Arrow for better
> performance. Can they just turn on spark.sql.execution.arrow.enable? What
> should they do?
> Note Hopefully, the guides/solutions are user-friendly. That means, it must
> be very simple to understand for most users.
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