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https://issues.apache.org/jira/browse/SPARK-7192?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14566735#comment-14566735
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Josh Rosen commented on SPARK-7192:
-----------------------------------
AFAIK in Python 2.6+ Python will support arbitrary precision integers
automatically. See
https://stackoverflow.com/questions/538551/handling-very-large-numbers-in-python
Do you have an example of where we're losing precision in the Hive -> Python
conversion? If so, it would be great if you could post some sample code
demonstrating that.
> Pyspark casts hive bigint to int
> --------------------------------
>
> Key: SPARK-7192
> URL: https://issues.apache.org/jira/browse/SPARK-7192
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 1.3.0
> Reporter: Tamas Jambor
>
> It seems that pyspark reads bigint from hive and stores it as an int:
> >> hive_ctx = HiveContext(sc)
> >> data = hive_ctx.sql("select col1, col2 from dataset1")
> >> data
> DataFrame[col1: int, col2: bigint]
> >> c_t = [type(v) for v in data.collect()[0]]
> >> c_t
> [int, int]
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