HyukjinKwon commented on code in PR #47253:
URL: https://github.com/apache/arrow/pull/47253#discussion_r2667845476
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python/pyarrow/parquet/core.py:
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@@ -90,6 +90,69 @@ def _check_filters(filters, check_null_strings=True):
return filters
+def _map_spark_to_arrow_types(datatype: pa.DataType) -> str | None:
+ lookup = {
+ "NA": "null",
+ "BOOL": "boolean",
+ "INT8": "byte",
+ "INT16": "short",
+ **dict.fromkeys(
+ ["UINT8", "UINT16", "UINT32", "UINT64", "INT32"], "integer"),
+ "INT64": "long",
+ **dict.fromkeys(["HALF_FLOAT", "FLOAT"], "float"),
+ "DOUBLE": "double",
+ "BINARY": "binary",
+ "STRING": "string",
+ **dict.fromkeys(
+ ["DECIMAL" + str(2 ** i) for i in range(5, 9)], "decimal"),
+ **dict.fromkeys(
+ ["LIST", "LARGE_LIST", "LIST_VIEW", "LARGE_LIST_VIEW",
"FIXED_SIZE_LIST"],
+ "array",
+ ),
+ "MAP": "map",
+ **dict.fromkeys(["DATE32", "DATE64"], "date"),
+ "TIMESTAMP": "timestamp",
+ "INTERVAL_MONTH_DAY_NANO": "Calendar Interval", # TODO: Correct this
Review Comment:
TBH, I think it's too much to handle Spark specific cases like this in
PyArrow. Spark is even adding more types such as variant type, and more
interval types.
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