Stuart Reynolds created SPARK-21392:
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Summary: Unable to infer schema when loading Parquet file
Key: SPARK-21392
URL: https://issues.apache.org/jira/browse/SPARK-21392
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 2.1.1
Environment: Spark 2.1.1. python 2.7.6
Reporter: Stuart Reynolds
The following boring code works
{{ response = "mi_or_chd_5"
colname = "f_1000"
outcome = sqlc.sql("""select eid,{response} as response
from outcomes
where {response} IS NOT NULL""".format(response=response))
outcome.write.parquet(response, mode="overwrite")
col = sqlc.sql("""select eid,{colname} as {colname}
from baseline_denull
where {colname} IS NOT NULL""".format(colname=colname))
col.write.parquet(colname, mode="overwrite")
>>> print outcome.schema
StructType(List(StructField(eid,IntegerType,true),StructField(response,ShortType,true)))
>>> print col.schema
StructType(List(StructField(eid,IntegerType,true),StructField(f_1000,DoubleType,true)))
}}.
But then,
{{
outcome2 = sqlc.read.parquet(response) # fail
col2 = sqlc.read.parquet(colname) # fail
}}.
fails with:
{{ AnalysisException: u'Unable to infer schema for Parquet. It must be
specified manually.;'
}}.
in
{{
/usr/local/lib/python2.7/dist-packages/pyspark-2.1.0+hadoop2.7-py2.7.egg/pyspark/sql/utils.pyc
in deco(*a, **kw)
}}.
The documentation for parquet says the format is self describing, and the full
schema was available when the parquet file was saved. What gives?
Seems related to: https://issues.apache.org/jira/browse/SPARK-16975, but which
claims it was fixed in 2.0.1, 2.1.0. (Current bug is 2.1.1)
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