Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/16928#discussion_r120027093
--- Diff: python/pyspark/sql/readwriter.py ---
@@ -191,10 +191,13 @@ def json(self, path, schema=None,
primitivesAsString=None, prefersDecimal=None,
:param mode: allows a mode for dealing with corrupt records during
parsing. If None is
set, it uses the default value, ``PERMISSIVE``.
- * ``PERMISSIVE`` : sets other fields to ``null`` when it
meets a corrupted \
- record and puts the malformed string into a new field
configured by \
- ``columnNameOfCorruptRecord``. When a schema is set by
user, it sets \
- ``null`` for extra fields.
+ * ``PERMISSIVE`` : sets other fields to ``null`` when it
meets a corrupted \
+ record, and puts the malformed string into a field
configured by \
+ ``columnNameOfCorruptRecord``. To keep corrupt records,
an user can set \
+ a string type field named ``columnNameOfCorruptRecord``
in an user-defined \
+ schema. If a schema does not have the field, it drops
corrupt records during \
+ parsing. When inferring a schema, it implicitly adds a \
+ ``columnNameOfCorruptRecord`` field in an output schema.
--- End diff --
@gatorsmile, I just got to my laptop.
I checked when the length of tokens are more than the schema it fills the
malformed column. with the data below:
```
a,a
```
(BTW, it looks respecting `spark.sql.columnNameOfCorruptRecord` ?)
```scala
scala> spark.read.schema("a string, _corrupt_record
string").csv("test.csv").show()
+---+---------------+
| a|_corrupt_record|
+---+---------------+
| a| a,a|
+---+---------------+
```
```scala
scala> spark.conf.set("spark.sql.columnNameOfCorruptRecord", "abc")
scala> spark.read.schema("a string, abc string").csv("test.csv").show()
+---+---+
| a|abc|
+---+---+
| a|a,a|
+---+---+
```
And, I found another bug (when the length is less then the schema):
with data
```
a
a
a
a
a
```
```scala
scala> spark.read.schema("a string, b string, _corrupt_record
string").csv("test.csv").show()
```
prints ...
```
17/06/05 09:45:26 ERROR Executor: Exception in task 0.0 in stage 5.0 (TID 5)
java.lang.NullPointerException
at
scala.collection.immutable.StringLike$class.stripLineEnd(StringLike.scala:89)
at scala.collection.immutable.StringOps.stripLineEnd(StringOps.scala:29)
at
org.apache.spark.sql.execution.datasources.csv.UnivocityParser.org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$getCurrentInput(UnivocityParser.scala:56)
at
org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:211)
at
org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:211)
at
org.apache.spark.sql.execution.datasources.FailureSafeParser$$anonfun$2.apply(FailureSafeParser.scala:50)
at
org.apache.spark.sql.execution.datasources.FailureSafeParser$$anonfun$2.apply(FailureSafeParser.scala:43)
at
org.apache.spark.sql.execution.datasources.FailureSafeParser.parse(FailureSafeParser.scala:64)
at
org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$parseIterator$1.apply(UnivocityParser.scala:312)
at
org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$parseIterator$1.apply(UnivocityParser.scala:312)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at
org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:105)
at
org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:177)
at
org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:105)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:236)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:230)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
It looks `getCurrentInput` produces `null` as the input is all parsed.
Another thing I would like to leave is, JSON produces `null` in the columns
and put the contents in the malformed column:
With the input:
```json
{"a": 1, "b": "a"}
```
```scala
scala> spark.read.json("test.json").show()
+---+---+
| a| b|
+---+---+
| 1| a|
+---+---+
```
```scala
scala> spark.read.schema("a string, b int, _corrupt_record
string").json("test.json").show()
+----+----+------------------+
| a| b| _corrupt_record|
+----+----+------------------+
|null|null|{"a": 1, "b": "a"}|
+----+----+------------------+
```
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