Details
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Bug
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Status: Resolved
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Major
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Resolution: Fixed
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2.0.0
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Ubuntu Linux 14.04
Description
Spark-2.0.0 seems to have some problems reading a parquet dataset generated by 1.6.2.
In [80]: spark.read.parquet('/path/to/data') ... AnalysisException: u'Unable to infer schema for ParquetFormat at /path/to/data. It must be specified manually;'
The dataset is ~150G and partitioned by _locality_code column. None of the partitions are empty. I have narrowed the failing dataset to the first 32 partitions of the data:
In [82]: spark.read.parquet(*subdirs[:32])
...
AnalysisException: u'Unable to infer schema for ParquetFormat at /path/to/data/_locality_code=AQ,/path/to/data/_locality_code=AI. It must be specified manually;'
Interestingly, it works OK if you remove any of the partitions from the list:
In [83]: for i in range(32): spark.read.parquet(*(subdirs[:i] + subdirs[i+1:32]))
Another strange thing is that the schemas for the first and the last 31 partitions of the subset are identical:
In [84]: spark.read.parquet(*subdirs[:31]).schema.fields == spark.read.parquet(*subdirs[1:32]).schema.fields Out[84]: True
Which got me interested and I tried this:
In [87]: spark.read.parquet(*([subdirs[0]] * 32)) ... AnalysisException: u'Unable to infer schema for ParquetFormat at /path/to/data/_locality_code=AQ,/path/to/data/_locality_code=AQ. It must be specified manually;' In [88]: spark.read.parquet(*([subdirs[15]] * 32)) ... AnalysisException: u'Unable to infer schema for ParquetFormat at /path/to/data/_locality_code=AX,/path/to/data/_locality_code=AX. It must be specified manually;' In [89]: spark.read.parquet(*([subdirs[31]] * 32)) ... AnalysisException: u'Unable to infer schema for ParquetFormat at /path/to/data/_locality_code=BE,/path/to/data/_locality_code=BE. It must be specified manually;'
If I read the first partition, save it in 2.0 and try to read in the same manner, everything is fine:
In [100]: spark.read.parquet(subdirs[0]).write.parquet('spark-2.0-test') 16/08/09 11:03:37 WARN ParquetRecordReader: Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl In [101]: df = spark.read.parquet(*(['spark-2.0-test'] * 32))
I have originally posted it to user mailing list, but with the last discoveries this clearly seems like a bug.
