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https://issues.apache.org/jira/browse/SPARK-25199?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16592704#comment-16592704
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Maxim Gekk commented on SPARK-25199:
------------------------------------
I wasn't able to reproduce the issue on the current master:
{code}
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.4.0-SNAPSHOT
/_/
Using Python version 2.7.15 (default, Aug 22 2018 16:36:18)
>>> df = spark.read.format("csv").option("header",
>>> "true").option("inferSchema", "true").load("tmp/csv/*.csv")
>>> df.printSchema()
root
|-- a: integer (nullable = true)
|-- b: integer (nullable = true)
{code}
for two csv files but one of them is empty:
{code:java}
tree -h ./csv
./csv
├── [ 8] 1.csv
└── [ 0] 2.csv
{code}
> InferSchema "all Strings" if one of many CSVs is empty
> ------------------------------------------------------
>
> Key: SPARK-25199
> URL: https://issues.apache.org/jira/browse/SPARK-25199
> Project: Spark
> Issue Type: Bug
> Components: Input/Output
> Affects Versions: 2.2.1
> Environment: I discovered this on AWS Glue, which uses Spark 2.2.1
> Reporter: Neil McGuigan
> Priority: Minor
> Labels: newbie
>
> Spark can load multiple CSV files in one read:
> df = spark.read.format("csv").option("header", "true").option("inferSchema",
> "true").load("/*.csv")
> However, if one of these files is empty (though it has a header), Spark will
> set all column types to "String"
> Spark should skip a file for inference if it contains no (non-header) rows
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