Github user xwu0226 commented on the issue:
https://github.com/apache/spark/pull/14509
I see. So the purpose of `spark.sql.streaming.schemaInference` is to
allow/disallow Spark SQL to infer schema on streaming file datasource. A bit
confusion here. Many thanks!
For CSV file format, I also tried the following on a csv file that has
header line for column names:
```
scala> spark.conf.set("spark.sql.streaming.schemaInference", true)
scala> val in =
spark.readStream.format("csv").load("/Users/xinwu/spark-test/data/csv1")
in: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string]
scala> val in = spark.readStream.format("csv").option("header",
true).load("/Users/xinwu/spark-test/data/csv1")
in: org.apache.spark.sql.DataFrame = [signal: string, flash: string]
scala> val in = spark.readStream.format("csv").option("header",
true).option("inferSchema", true).load("/Users/xinwu/spark-test/data/csv1")
in: org.apache.spark.sql.DataFrame = [signal: string, flash: int]
```
And the output looks expected on master branch's code. So I think there is
nothing wrong here so far. It was my misunderstanding of the purpose of
`spark.sql.streaming.schemaInference`. Unless you think differently, I will
close this PR.
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