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Joris Van den Bossche commented on ARROW-16339: ----------------------------------------------- Note: I recently created an issue about _field-level_ metadata (ARROW-15548), but so this issue is about schema-level (for Arrow) / file-level (for Parquet) metadata. The above is a long description with examples, but trying to summarize the findings, and questions to answer: - Do we generally want to map the schema-level metadata from Arrow with Parquet file-level metadata? (I think the answer is yes?) - When _reading_, and the file metadata does not contain a "ARROW:schema" key, we actually already do map the Parquet file metadata to resulting Arrow schema metadata (this is OK) - When _writing_, the {{store_schema}} flag seems to also influence whether we store schema metadata key/values in the Parquet file. This might be a bug? (or at least unintended behaviour?) - When _reading_, and the file metadata does contain both an "ARROW:schema" key and other keys, we ignore the other keys. Should we merge the keys from the metadata in the serialized "ARROW:schema" schema with the other keys in the Parquet FileMetaData key_value_metadata? (those could of course be duplicative / conflicting) cc [~apitrou] [~emkornfield] > [C++][Parquet] Parquet FileMetaData key_value_metadata not always mapped to > Arrow Schema metadata > ------------------------------------------------------------------------------------------------- > > Key: ARROW-16339 > URL: https://issues.apache.org/jira/browse/ARROW-16339 > Project: Apache Arrow > Issue Type: Improvement > Components: C++, Parquet, Python > Reporter: Joris Van den Bossche > Priority: Major > > Context: I ran into this issue when reading Parquet files created by GDAL > (using the Arrow C++ APIs, [https://github.com/OSGeo/gdal/pull/5477]), which > writes files that have custom key_value_metadata, but without storing > ARROW:schema in those metadata (cc [~paleolimbot] > — > Both in reading and writing files, I expected that we would map Arrow > {{Schema::metadata}} with Parquet {{{}FileMetaData::key_value_metadata{}}}. > But apparently this doesn't (always) happen out of the box, and only happens > through the "ARROW:schema" field (which stores the original Arrow schema, and > thus the metadata stored in this schema). > For example, when writing a Table with schema metadata, this is not stored > directly in the Parquet FileMetaData (code below is using branch from > ARROW-16337 to have the {{store_schema}} keyword): > {code:python} > import pyarrow as pa > import pyarrow.parquet as pq > table = pa.table({'a': [1, 2, 3]}, metadata={"key": "value"}) > pq.write_table(table, "test_metadata_with_arrow_schema.parquet") > pq.write_table(table, "test_metadata_without_arrow_schema.parquet", > store_schema=False) > # original schema has metadata > >>> table.schema > a: int64 > -- schema metadata -- > key: 'value' > # reading back only has the metadata in case we stored ARROW:schema > >>> pq.read_table("test_metadata_with_arrow_schema.parquet").schema > a: int64 > -- schema metadata -- > key: 'value' > # and not if ARROW:schema is absent > >>> pq.read_table("test_metadata_without_arrow_schema.parquet").schema > a: int64 > {code} > It seems that if we store the ARROW:schema, we _also_ store the schema > metadata separately. But if {{store_schema}} is False, we also stop writing > those metadata (not fully sure if this is the intended behaviour, and that's > the reason for the above output): > {code:python} > # when storing the ARROW:schema, we ALSO store key:value metadata > >>> pq.read_metadata("test_metadata_with_arrow_schema.parquet").metadata > {b'ARROW:schema': b'/////7AAAAAQAAAAAAAKAA4ABgAFAA...', > b'key': b'value'} > # when not storing the schema, we also don't store the key:value > >>> pq.read_metadata("test_metadata_without_arrow_schema.parquet").metadata > >>> is None > True > {code} > On the reading side, it seems that we generally do read custom key/value > metadata into schema metadata. We don't have the pyarrow APIs at the moment > to create such a file (given the above), but with a small patch I could > create such a file: > {code:python} > # a Parquet file with ParquetFileMetaData::metadata that ONLY has a custom key > >>> pq.read_metadata("test_metadata_without_arrow_schema2.parquet").metadata > {b'key': b'value'} > # this metadata is now correctly mapped to the Arrow schema metadata > >>> pq.read_schema("test_metadata_without_arrow_schema2.parquet") > a: int64 > -- schema metadata -- > key: 'value' > {code} > But if you have a file that has both custom key/value metadata and an > "ARROW:schema" key, we actually ignore the custom keys, and only look at the > "ARROW:schema" one. > This was the case that I ran into with GDAL, where I have a file with both > keys, but where the custom "geo" key is not also included in the serialized > arrow schema in the "ARROW:schema" key: > {code:python} > # includes both keys in the Parquet file > >>> pq.read_metadata("test_gdal.parquet").metadata > {b'geo': b'{"version":"0.1.0","...', > b'ARROW:schema': b'/////3gBAAAQ...'} > # the "geo" key is lost in the Arrow schema > >>> pq.read_table("test_gdal.parquet").schema.metadata is None > True > {code} -- This message was sent by Atlassian Jira (v8.20.7#820007)