Fix TIMESTAMP / DATE scalars, add support for DATE column casting

dask_sql/mappings.py

@@ -1,5 +1,5 @@
 import logging
-from datetime import datetime, timedelta, timezone
+from datetime import timedelta
 from typing import Any
 
 import dask.array as da
@@ -77,7 +77,9 @@
     "VARCHAR": pd.StringDtype(),
     "CHAR": pd.StringDtype(),
     "STRING": pd.StringDtype(),  # Although not in the standard, makes compatibility easier
-    "DATE": np.dtype("<M8[ns]"),
+    "DATE": np.dtype(
+        "<M8[ns]"
+    ),  # TODO: ideally this would be np.dtype("<M8[D]") but that doesn't work for Pandas
     "TIMESTAMP": np.dtype("<M8[ns]"),
     "NULL": type(None),
 }
@@ -160,12 +162,11 @@ def sql_to_python_value(sql_type: str, literal_value: Any) -> Any:
         tz = literal_value.getTimeZone().getID()
         assert str(tz) == "UTC", "The code can currently only handle UTC timezones"
 
-        dt = datetime.fromtimestamp(
-            int(literal_value.getTimeInMillis()) / 1000, timezone.utc
-        )
-
-        return dt
+        dt = np.datetime64(literal_value.getTimeInMillis(), "ms")
 
+        if sql_type == "DATE":
+            return dt.astype("<M8[D]")
+        return dt.astype("<M8[ns]")
     elif sql_type.startswith("DECIMAL("):
         # We use np.float64 always, even though we might
         # be able to use a smaller type

dask_sql/physical/rex/core/call.py

@@ -224,14 +224,20 @@ def cast(self, operand, rex=None) -> SeriesOrScalar:
             return operand
 
         output_type = str(rex.getType())
-        output_type = sql_to_python_type(output_type.upper())
+        python_type = sql_to_python_type(output_type.upper())
 
-        return_column = cast_column_to_type(operand, output_type)
+        return_column = cast_column_to_type(operand, python_type)
 
         if return_column is None:
-            return operand
-        else:
-            return return_column
+            return_column = operand
+
+        # TODO: ideally we don't want to directly access the datetime dates,
+        # but Pandas can't truncate timezone datetimes and cuDF can't
+        # truncate datetimes
+        if output_type == "DATE":
+            return return_column.dt.strftime("%Y-%m-%d").astype(python_type)
+
+        return return_column
 
 
 class IsFalseOperation(Operation):

tests/integration/fixtures.py

@@ -86,11 +86,13 @@ def datetime_table():
     return pd.DataFrame(
         {
             "timezone": pd.date_range(
-                start="2014-08-01 09:00", freq="H", periods=3, tz="Europe/Berlin"
+                start="2014-08-01 09:00", freq="8H", periods=6, tz="Europe/Berlin"
+            ),
+            "no_timezone": pd.date_range(
+                start="2014-08-01 09:00", freq="8H", periods=6
             ),
-            "no_timezone": pd.date_range(start="2014-08-01 09:00", freq="H", periods=3),
             "utc_timezone": pd.date_range(
-                start="2014-08-01 09:00", freq="H", periods=3, tz="UTC"
+                start="2014-08-01 09:00", freq="8H", periods=6, tz="UTC"
             ),
         }
     )
@@ -116,6 +118,11 @@ def gpu_string_table(string_table):
     return cudf.from_pandas(string_table) if cudf else None
 
 
+@pytest.fixture()
+def gpu_datetime_table(datetime_table):
+    return cudf.from_pandas(datetime_table) if cudf else None
+
+
 @pytest.fixture()
 def c(
     df_simple,
@@ -131,6 +138,7 @@ def c(
     gpu_df,
     gpu_long_table,
     gpu_string_table,
+    gpu_datetime_table,
 ):
     dfs = {
         "df_simple": df_simple,
@@ -146,6 +154,7 @@ def c(
         "gpu_df": gpu_df,
         "gpu_long_table": gpu_long_table,
         "gpu_string_table": gpu_string_table,
+        "gpu_datetime_table": gpu_datetime_table,
     }
 
     # Lazy import, otherwise the pytest framework has problems

tests/integration/test_filter.py

@@ -1,4 +1,6 @@
+import dask.dataframe as dd
 import pandas as pd
+import pytest
 from pandas.testing import assert_frame_equal
 
 from dask_sql._compat import INT_NAN_IMPLEMENTED
@@ -70,7 +72,50 @@ def test_string_filter(c, string_table):
     )
 
 
-def test_filter_datetime(c):
+@pytest.mark.parametrize(
+    "input_table",
+    ["datetime_table", pytest.param("gpu_datetime_table", marks=pytest.mark.gpu),],
+)
+def test_filter_cast_date(c, input_table, request):
+    datetime_table = request.getfixturevalue(input_table)
+    return_df = c.sql(
+        f"""
+        SELECT * FROM {input_table} WHERE
+            CAST(timezone AS DATE) > DATE '2014-08-01'
+        """
+    )
+
+    expected_df = datetime_table[
+        datetime_table["timezone"]
+        .astype("<M8[ns]")
+        .dt.strftime("%Y-%m-%d")
+        .astype("<M8[ns]")
+        > pd.Timestamp("2014-08-01")
+    ]
+    dd.assert_eq(return_df, expected_df)
+
+
+@pytest.mark.parametrize(
+    "input_table",
+    ["datetime_table", pytest.param("gpu_datetime_table", marks=pytest.mark.gpu),],
+)
+def test_filter_cast_timestamp(c, input_table, request):
+    datetime_table = request.getfixturevalue(input_table)
+    return_df = c.sql(
+        f"""
+        SELECT * FROM {input_table} WHERE
+            CAST(timezone AS TIMESTAMP) >= TIMESTAMP '2014-08-01 23:00:00'
+        """
+    )
+
+    expected_df = datetime_table[
+        datetime_table["timezone"].astype("<M8[ns]")
+        >= pd.Timestamp("2014-08-01 23:00:00")
+    ]
+    dd.assert_eq(return_df, expected_df)
+
+
+def test_filter_year(c):
     df = pd.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})
 
     df["dt"] = pd.to_datetime(df)

tests/integration/test_select.py

@@ -1,3 +1,4 @@
+import dask.dataframe as dd
 import numpy as np
 import pandas as pd
 import pytest
@@ -118,6 +119,65 @@ def test_timezones(c, datetime_table):
     assert_frame_equal(result_df, datetime_table)
 
 
+@pytest.mark.parametrize(
+    "input_table",
+    ["datetime_table", pytest.param("gpu_datetime_table", marks=pytest.mark.gpu),],
+)
+def test_date_casting(c, input_table, request):
+    datetime_table = request.getfixturevalue(input_table)
+    result_df = c.sql(
+        f"""
+        SELECT
+            CAST(timezone AS DATE) AS timezone,
+            CAST(no_timezone AS DATE) AS no_timezone,
+            CAST(utc_timezone AS DATE) AS utc_timezone
+        FROM {input_table}
+        """
+    )
+
+    expected_df = datetime_table
+    expected_df["timezone"] = (
+        expected_df["timezone"]
+        .astype("<M8[ns]")
+        .dt.strftime("%Y-%m-%d")
+        .astype("<M8[ns]")
+    )
+    expected_df["no_timezone"] = (
+        expected_df["no_timezone"]
+        .astype("<M8[ns]")
+        .dt.strftime("%Y-%m-%d")
+        .astype("<M8[ns]")
+    )
+    expected_df["utc_timezone"] = (
+        expected_df["utc_timezone"]
+        .astype("<M8[ns]")
+        .dt.strftime("%Y-%m-%d")
+        .astype("<M8[ns]")
+    )
+
+    dd.assert_eq(result_df, expected_df)
+
+
+@pytest.mark.parametrize(
+    "input_table",
+    ["datetime_table", pytest.param("gpu_datetime_table", marks=pytest.mark.gpu),],
+)
+def test_timestamp_casting(c, input_table, request):
+    datetime_table = request.getfixturevalue(input_table)
+    result_df = c.sql(
+        f"""
+        SELECT
+            CAST(timezone AS TIMESTAMP) AS timezone,
+            CAST(no_timezone AS TIMESTAMP) AS no_timezone,
+            CAST(utc_timezone AS TIMESTAMP) AS utc_timezone
+        FROM {input_table}
+        """
+    )
+
+    expected_df = datetime_table.astype("<M8[ns]")
+    dd.assert_eq(result_df, expected_df)
+
+
 def test_multi_case_when(c):
     df = pd.DataFrame({"a": [1, 6, 7, 8, 9]})
     c.create_table("df", df)