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author | Sean Zhong <seanzhong@databricks.com> | 2016-09-06 22:20:55 +0800 |
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committer | Wenchen Fan <wenchen@databricks.com> | 2016-09-06 22:20:55 +0800 |
commit | bc2767df2666ff615e7f44e980555afab06dd8a3 (patch) | |
tree | 9351e11c0d0abad856f637bf05d2930c3d80fa13 /dev/run-tests-jenkins.py | |
parent | 39d538dddf7d44bf4603c966d0f7b2c92f1e951a (diff) | |
download | spark-bc2767df2666ff615e7f44e980555afab06dd8a3.tar.gz spark-bc2767df2666ff615e7f44e980555afab06dd8a3.tar.bz2 spark-bc2767df2666ff615e7f44e980555afab06dd8a3.zip |
[SPARK-17374][SQL] Better error messages when parsing JSON using DataFrameReader
## What changes were proposed in this pull request?
This PR adds better error messages for malformed record when reading a JSON file using DataFrameReader.
For example, for query:
```
import org.apache.spark.sql.types._
val corruptRecords = spark.sparkContext.parallelize("""{"a":{, b:3}""" :: Nil)
val schema = StructType(StructField("a", StringType, true) :: Nil)
val jsonDF = spark.read.schema(schema).json(corruptRecords)
```
**Before change:**
We silently replace corrupted line with null
```
scala> jsonDF.show
+----+
| a|
+----+
|null|
+----+
```
**After change:**
Add an explicit warning message:
```
scala> jsonDF.show
16/09/02 14:43:16 WARN JacksonParser: Found at least one malformed records (sample: {"a":{, b:3}). The JSON reader will replace
all malformed records with placeholder null in current PERMISSIVE parser mode.
To find out which corrupted records have been replaced with null, please use the
default inferred schema instead of providing a custom schema.
Code example to print all malformed records (scala):
===================================================
// The corrupted record exists in column _corrupt_record.
val parsedJson = spark.read.json("/path/to/json/file/test.json")
+----+
| a|
+----+
|null|
+----+
```
###
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes #14929 from clockfly/logwarning_if_schema_not_contain_corrupted_record.
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