Add SQLite mixed-type compatibility for _get_table_columns_and_types

cytotable/convert.py

@@ -35,7 +35,9 @@ def _get_table_columns_and_types(source: Dict[str, Any]) -> List[Dict[str, str]]
 
     import pathlib
 
-    from cytotable.utils import _duckdb_reader
+    import duckdb
+
+    from cytotable.utils import _duckdb_reader, _sqlite_mixed_type_query_to_parquet
 
     source_path = source["source_path"]
     source_type = str(pathlib.Path(source_path).suffix).lower()
@@ -49,12 +51,12 @@ def _get_table_columns_and_types(source: Dict[str, Any]) -> List[Dict[str, str]]
 
     # query top 5 results from table and use pragma_storage_info() to
     # gather duckdb interpreted data typing
-    select_query = f"""
+    select_query = """
         /* we create an in-mem table for later use with the pragma_storage_info call
         as this call only functions with materialized tables and not views or related */
         CREATE TABLE column_details AS
             (SELECT *
-            FROM {select_source}
+            FROM &select_source
             LIMIT 5
             );
 
@@ -69,8 +71,39 @@ def _get_table_columns_and_types(source: Dict[str, Any]) -> List[Dict[str, str]]
         WHERE segment_type != 'VALIDITY';
         """
 
-    # perform the query and create a list of dictionaries with the column data for table
-    return _duckdb_reader().execute(select_query).arrow().to_pylist()
+    # attempt to read the data to parquet from duckdb
+    # with exception handling to read mixed-type data
+    # using sqlite3 and special utility function
+    try:
+        # isolate using new connection to read data with chunk size + offset
+        # and export directly to parquet via duckdb (avoiding need to return data to python)
+        # perform the query and create a list of dictionaries with the column data for table
+        return (
+            _duckdb_reader()
+            .execute(select_query.replace("&select_source", select_source))
+            .arrow()
+            .to_pylist()
+        )
+
+    except duckdb.Error as e:
+        # if we see a mismatched type error
+        # run a more nuanced query through sqlite
+        # to handle the mixed types
+        if "Mismatch Type Error" in str(e) and source_type == ".sqlite":
+            arrow_data_tbl = _sqlite_mixed_type_query_to_parquet(
+                source_path=str(source["source_path"]),
+                table_name=str(source["table_name"]),
+                chunk_size=5,
+                offset=0,
+            )
+            return (
+                _duckdb_reader()
+                .execute(select_query.replace("&select_source", "arrow_data_tbl"))
+                .arrow()
+                .to_pylist()
+            )
+        else:
+            raise
 
 
 @python_app
@@ -238,6 +271,7 @@ def _source_chunk_to_parquet(
 
     import duckdb
     from cloudpathlib import AnyPath
+    from pyarrow import parquet
 
     from cytotable.utils import _duckdb_reader, _sqlite_mixed_type_query_to_parquet
 
@@ -292,13 +326,17 @@ def _source_chunk_to_parquet(
             "Mismatch Type Error" in str(e)
             and str(AnyPath(source["source_path"]).suffix).lower() == ".sqlite"
         ):
-            result_filepath = _sqlite_mixed_type_query_to_parquet(
-                source_path=str(source["source_path"]),
-                table_name=str(source["table_name"]),
-                chunk_size=chunk_size,
-                offset=offset,
-                result_filepath=result_filepath,
+            parquet.write_table(
+                table=_sqlite_mixed_type_query_to_parquet(
+                    source_path=str(source["source_path"]),
+                    table_name=str(source["table_name"]),
+                    chunk_size=chunk_size,
+                    offset=offset,
+                ),
+                where=result_filepath,
             )
+        else:
+            raise
 
     # return the filepath for the chunked output file
     return result_filepath

cytotable/utils.py

@@ -162,12 +162,11 @@ def _sqlite_mixed_type_query_to_parquet(
     table_name: str,
     chunk_size: int,
     offset: int,
-    result_filepath: str,
 ) -> str:
     """
     Performs SQLite table data extraction where one or many
     columns include data values of potentially mismatched type
-    such that the data may be exported to Arrow and a Parquet file.
+    such that the data may be exported to Arrow for later use.
 
     Args:
         source_path: str:
@@ -178,17 +177,14 @@ def _sqlite_mixed_type_query_to_parquet(
             Row count to use for chunked output.
         offset: int:
             The offset for chunking the data from source.
-        dest_path: str:
-            Path to store the output data.
 
     Returns:
-        str:
-           The resulting filepath for the table exported to parquet.
+        pyarrow.Table:
+           The resulting arrow table for the data
     """
     import sqlite3
 
     import pyarrow as pa
-    import pyarrow.parquet as parquet
 
     # open sqlite3 connection
     with sqlite3.connect(source_path) as conn:
@@ -234,14 +230,8 @@ def _sqlite_mixed_type_query_to_parquet(
             for row in cursor.fetchall()
         ]
 
-    # write results to a parquet file
-    parquet.write_table(
-        table=pa.Table.from_pylist(results),
-        where=result_filepath,
-    )
-
-    # return filepath
-    return result_filepath
+    # return arrow table with results
+    return pa.Table.from_pylist(results)
 
 
 def _cache_cloudpath_to_local(path: Union[str, AnyPath]) -> pathlib.Path:

tests/test_convert.py

@@ -970,36 +970,58 @@ def test_sqlite_mixed_type_query_to_parquet(
         # run a more nuanced query through sqlite
         # to handle the mixed types
         if "Mismatch Type Error" in str(duckdb_exc):
-            result = _sqlite_mixed_type_query_to_parquet(
-                source_path=example_sqlite_mixed_types_database,
-                table_name=table_name,
-                chunk_size=2,
-                offset=0,
-                result_filepath=result_filepath,
+            parquet.write_table(
+                table=_sqlite_mixed_type_query_to_parquet(
+                    source_path=example_sqlite_mixed_types_database,
+                    table_name=table_name,
+                    chunk_size=2,
+                    offset=0,
+                ),
+                where=result_filepath,
             )
 
     # check schema names
-    assert parquet.read_schema(where=result).names == [
+    assert parquet.read_schema(where=result_filepath).names == [
         "col_integer",
         "col_text",
         "col_blob",
         "col_real",
     ]
     # check schema types
-    assert parquet.read_schema(where=result).types == [
+    assert parquet.read_schema(where=result_filepath).types == [
         pa.int64(),
         pa.string(),
         pa.binary(),
         pa.float64(),
     ]
     # check the values per column
-    assert parquet.read_table(source=result).to_pydict() == {
+    assert parquet.read_table(source=result_filepath).to_pydict() == {
         "col_integer": [1, None],
         "col_text": ["sample", "sample"],
         "col_blob": [b"sample_blob", b"another_blob"],
         "col_real": [0.5, None],
     }
 
+    # run full convert on mixed type database
+    result = convert(
+        source_path=example_sqlite_mixed_types_database,
+        dest_path=result_filepath,
+        dest_datatype="parquet",
+        source_datatype="sqlite",
+        compartments=[table_name],
+        join=False,
+    )
+
+    # assert that the single table result looks like the following dictionary
+    assert parquet.read_table(
+        source=result["Tbl_a.sqlite"][0]["table"][0]
+    ).to_pydict() == {
+        "Tbl_a_col_integer": [1, None],
+        "Tbl_a_col_text": ["sample", "sample"],
+        "Tbl_a_col_blob": [b"sample_blob", b"another_blob"],
+        "Tbl_a_col_real": [0.5, None],
+    }
+
 
 def test_convert_hte_cellprofiler_csv(
     get_tempdir: str,