Jonas Amrich created ARROW-1459: ----------------------------------- Summary: [Python] PyArrow fails to load partitioned parquet files with non-primitive types Key: ARROW-1459 URL: https://issues.apache.org/jira/browse/ARROW-1459 Project: Apache Arrow Issue Type: Bug Components: Python Affects Versions: 0.6.0 Reporter: Jonas Amrich
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): {noformat} >>> 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] {noformat} When the data is loaded using Spark or coalesced into one partition, everything works as expected: {noformat} >>> 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] {noformat} -- This message was sent by Atlassian JIRA (v6.4.14#64029)