GitHub user gatorsmile opened a pull request:
https://github.com/apache/spark/pull/16326
[SPARK-18915] [SQL] Automatic Table Repair when Creating a Partitioned Data
Source Table with a Specified Path
### What changes were proposed in this pull request?
In Spark 2.1 (the default of `spark.sql.hive.manageFilesourcePartitions` is
set to `true`), if we create a parititoned data source table given a specified
path, it returns nothing when we try to query it. To get the data, we have to
manually issue a DDL to repair the table.
In Spark 2.0, it can return the data stored in the specified path, without
repairing the table. In Spark 2.1, if we set
`spark.sql.hive.manageFilesourcePartitions` to false, the behavior is the same
as Spark 2.0.
Below is the output of Spark 2.1.
```Scala
scala> spark.range(5).selectExpr("id as fieldOne", "id as
partCol").write.partitionBy("partCol").mode("overwrite").saveAsTable("test")
scala> spark.sql("desc formatted test").show(50, false)
+----------------------------+----------------------------------------------------------------------+-------+
|col_name |data_type
|comment|
+----------------------------+----------------------------------------------------------------------+-------+
...
|Location:
|file:/Users/xiaoli/IdeaProjects/sparkDelivery/bin/spark-warehouse/test| |
|Table Type: |MANAGED
| |
...
|Partition Provider: |Catalog
| |
+----------------------------+----------------------------------------------------------------------+-------+
scala> spark.sql(s"create table newTab (fieldOne long, partCol int) using
parquet options (path
'file:/Users/xiaoli/IdeaProjects/sparkDelivery/bin/spark-warehouse/test')
partitioned by (partCol)")
res3: org.apache.spark.sql.DataFrame = []
scala> spark.table("newTab").show()
+--------+-------+
|fieldOne|partCol|
+--------+-------+
+--------+-------+
```
This PR is to make it consistent with the behavior of Spark 2.0. no matter
whether `spark.sql.hive.manageFilesourcePartitions` is `true` or `false`. It
repairs the table when creating such a table. After the change, the behavior
becomes consistent with what we did for CTAS of partitioned data source tables.
### How was this patch tested?
Modified the existing test case.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/gatorsmile/spark testtt
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/16326.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #16326
----
commit 40abcc281344923b33886243c83358f5084c2489
Author: gatorsmile <gatorsm...@gmail.com>
Date: 2016-12-18T03:55:32Z
fix.
commit 3942c4ea53b199a476855a3f39087d893a4e900a
Author: gatorsmile <gatorsm...@gmail.com>
Date: 2016-12-18T03:56:51Z
fix.
----
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