Details
Description
Consider a jsonl file with 3 records. The third record has a value of type string, instead of int.
echo '{"field": 1}
{"field": 2}
{"field": "3"}' >/tmp/sample.json
Create a dataframe from this file, with a schema that contains "_corrupt_record" so that corrupt records are kept.
import org.apache.spark.sql.types._ val schema = new StructType() .add("field", ByteType) .add("_corrupt_record", StringType) val file = "/tmp/sample.json" val dfFromFile = spark.read.schema(schema).json(file)
Run the following lines from a spark-shell:
scala> dfFromFile.show(false) +-----+---------------+ |field|_corrupt_record| +-----+---------------+ |1 |null | |2 |null | |null |{"field": "3"} | +-----+---------------+ scala> dfFromFile.filter($"_corrupt_record".isNotNull).count() res1: Long = 0 scala> dfFromFile.filter($"_corrupt_record".isNull).count() res2: Long = 3
The expected result is 1 corrupt record and 2 valid records, but the actual one is 0 corrupt record and 3 valid records.
The bug is not reproduced if we create a dataframe from a RDD:
scala> val rdd = sc.textFile(file) rdd: org.apache.spark.rdd.RDD[String] = /tmp/sample.json MapPartitionsRDD[92] at textFile at <console>:28 scala> val dfFromRdd = spark.read.schema(schema).json(rdd) dfFromRdd: org.apache.spark.sql.DataFrame = [field: tinyint, _corrupt_record: string] scala> dfFromRdd.show(false) +-----+---------------+ |field|_corrupt_record| +-----+---------------+ |1 |null | |2 |null | |null |{"field": "3"} | +-----+---------------+ scala> dfFromRdd.filter($"_corrupt_record".isNotNull).count() res5: Long = 1 scala> dfFromRdd.filter($"_corrupt_record".isNull).count() res6: Long = 2
Attachments
Issue Links
- is duplicated by
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SPARK-22580 Count after filtering uncached CSV for isnull(columnNameOfCorruptRecord) always 0
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- Resolved
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- links to
