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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0.6.0
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None
Description
When reading partitioned parquet files (tested with those produced by Spark), that contain lists, the resulting table seems to contain data loaded only from one partition. Primitive types seems to be loaded correctly.
It can be reproduced using following code (arrow 0.6.0, spark 2.1.1):
>>> df = spark.createDataFrame(list(zip(np.arange(10).tolist(), np.arange(20).reshape((10,2)).tolist())))
>>> df.toPandas()
_1 _2
0 0 [0, 1]
1 1 [2, 3]
2 2 [4, 5]
3 3 [6, 7]
4 4 [8, 9]
5 5 [10, 11]
6 6 [12, 13]
7 7 [14, 15]
8 8 [16, 17]
9 9 [18, 19]
>>> df.repartition(2).write.parquet('df_parts.parquet')
>>> pq.read_table('df_parts.parquet').to_pandas()
_1 _2
0 0 [0, 1]
1 2 [4, 5]
2 4 [8, 9]
3 6 [12, 13]
4 8 [16, 17]
5 1 [0, 1]
6 3 [4, 5]
7 5 [8, 9]
8 7 [12, 13]
9 9 [16, 17]
When the data is loaded using Spark or coalesced into one partition, everything works as expected:
>>> spark.read.parquet('df_parts.parquet').toPandas()
_1 _2
0 1 [2, 3]
1 3 [6, 7]
2 5 [10, 11]
3 7 [14, 15]
4 9 [18, 19]
5 0 [0, 1]
6 2 [4, 5]
7 4 [8, 9]
8 6 [12, 13]
9 8 [16, 17]
>>> df.coalesce(1).write.parquet('df_single.parquet')
>>> pq.read_table('df_single.parquet').to_pandas()
_1 _2
0 0 [0, 1]
1 1 [2, 3]
2 2 [4, 5]
3 3 [6, 7]
4 4 [8, 9]
5 5 [10, 11]
6 6 [12, 13]
7 7 [14, 15]
8 8 [16, 17]
9 9 [18, 19]