Feature identifiers

[tschaub]

Feature identifiers

[tschaub]

If we want to support this, I think a simple new (and optional) field in our geo metadata where you can say which column is the ID column would be sufficient? (similarly to what GDAL does now, i.e. "fid":"fid")

I was envisioning the same. Though unless space is a concern, I think “id_column” is a bit friendlier and fits well with “primary_column” (nit).

[rouault]

Do GDAL types not map 1:1 to Arrow types?

at 99%, but they are subtelties. Like GDAL can have a hint for the maximum width of a string, or JSON or UUID "subtypes" for strings. Those are generally not essential metadata, but GDAL can write them for perfect round-tripping of its abstraction model.

[jorisvandenbossche]

I would personally not follow (or be inspired by) the pandas' metadata here. The information in there is very pandas specific.

If we want to support this, I think a simple new (and optional) field in our geo metadata where you can say which column is the ID column would be sufficient? (similarly to what GDAL does now, i.e. "fid":"fid")

One other thing to note is that pandas stores its metadata in two places: once in the file metadata under the pandas key, but again in the file metadata within the Arrow schema metadata. Arrow also encodes its schema in a file-level metadata key named 'ARROW:schema' and is a base64 encoding of a custom binary format (I forget exactly where this layout is defined, @jorisvandenbossche maybe would know?) so that it's possible to exactly round trip Arrow data through Parquet without losing metadata like for extension types.

@kylebarron yeah, this duplication of the pandas metadata in both the normal Parquet metadata and inside the serialized schema is not super ideal. There is a JIRA about this: https://issues.apache.org/jira/browse/ARROW-14303
The format itself is the IPC message for a schema, and then base64 encoded: https://arrow.apache.org/docs/dev/cpp/parquet.html#serialization-details
But, as you mention yourself, I don't think it is interesting to look at this place to put FID information, as that is not readily available for Parquet readers that are not based on an Arrow library.

[rouault]

GDAL currently uses an extension gdal:schema metadata domain where it puts information such as the fid column name or GDAL specific typing.
e.g:

{"fid":"fid","columns":{"AREA":{"type":"Real"},"EAS_ID":{"type":"Integer64"},"PRFEDEA":{"type":"String","width":16}}}

[kylebarron]

I think several libraries have come up with their own custom metadata to solve this problem; not sure there's any "native" Parquet solution. For example, Pandas adds its own metadata for its index columns (essentially equivalent to a feature id column) and data types so that it's reliably able to round-trip data.

import pandas as pd
import pyarrow.parquet as pq
import json

df = pd.DataFrame({'a': [2, 3, 4, 5], 'b': ['a', 'b', 'c', 'd']}).set_index('a')
df.to_parquet('test.parquet')

meta = pq.read_metadata('test.parquet')
json.loads(meta.metadata[b'pandas'])

{'index_columns': ['a'],
 'column_indexes': [{'name': None,
   'field_name': None,
   'pandas_type': 'unicode',
   'numpy_type': 'object',
   'metadata': {'encoding': 'UTF-8'}}],
 'columns': [{'name': 'b',
   'field_name': 'b',
   'pandas_type': 'unicode',
   'numpy_type': 'object',
   'metadata': None},
  {'name': 'a',
   'field_name': 'a',
   'pandas_type': 'int64',
   'numpy_type': 'int64',
   'metadata': None}],
 'creator': {'library': 'pyarrow', 'version': '7.0.0'},
 'pandas_version': '1.3.1'}

[cholmes]

Interesting. Seems like it might be good for GeoParquet to at least make a recommendation for reliable FID. Perhaps just use what Pandas does, and then GeoPandas and GDAL could align?

[TomAugspurger]

I'll note that pandas uses a list of fields for index_columns, to support its MultiIndex (I think similar to a composite key in some flavors of SQL). It looks like GDAL uses a string, at least in Even's example.

[rouault]

It looks like GDAL uses a string, at least in Even's example.

yes, GDAL only supports a single numeric column as feature identifier

[kylebarron]

Perhaps just use what Pandas does, and then GeoPandas and GDAL could align?

I'd be hesitant to mimic the Pandas metadata exactly because it's very Python-specific, at least the pandas_type and numpy_type.

One other thing to note is that pandas stores its metadata in two places: once in the file metadata under the pandas key, but again in the file metadata within the Arrow schema metadata. Arrow also encodes its schema in a file-level metadata key named 'ARROW:schema' and is a base64 encoding of a custom binary format (I forget exactly where this layout is defined, @jorisvandenbossche maybe would know?) so that it's possible to exactly round trip Arrow data through Parquet without losing metadata like for extension types.

# continuing from above
meta.metadata[b'ARROW:schema']
# b'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'

# Function to read and parse the above buffer
arrow_schema = pq.read_schema('test.parquet')
print(arrow_schema)
# b: string
# a: int64
# -- schema metadata --
# pandas: '{"index_columns": ["a"], "column_indexes": [{"name": null, "fiel' ...

print(arrow_schema.metadata)
# {b'pandas': b'{"index_columns": ["a"], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "b", "field_name": "b", "pandas_type": "unicode", "numpy_type": "object", "metadata": null}, {"name": "a", "field_name": "a", "pandas_type": "int64", "numpy_type": "int64", "metadata": null}], "creator": {"library": "pyarrow", "version": "7.0.0"}, "pandas_version": "1.3.1"}'}

Having metadata in the arrow schema might make it more interoperable than in the pandas-specific metadata, but not usable for non-Arrow-based readers (the Java world I think). If we want to be able to round-trip from e.g. GeoJSON which has feature identifiers, maybe it would make sense to add an option to the geoparquet-specific metadata describing an id column (though we'd have to guard against our metadata being out of sync with other metadata)

[kylebarron]

GDAL currently uses an extension gdal:schema metadata domain where it puts information such as the fid column name or GDAL specific typing. e.g:

Other than the fid, is there a reason why GDAL can't reuse the Arrow schema metadata, given that GDAL is using the Arrow C++ libraries to read/write Parquet? Do GDAL types not map 1:1 to Arrow types?

[cholmes]

Call 10/24 says we should add some 'best practice' that says parquet doesn't have a primary key, so it's not part of this spec. GDAL should do what it does, if other systems are also interest in roundtripping a feature id then we'd consider it as part of the spec.