Aggregate improvements and SQL compatibility

dask_sql/physical/rel/logical/join.py

@@ -100,6 +100,23 @@ def convert(
                 f"common_{i}": df_rhs_renamed.iloc[:, index]
                 for i, index in enumerate(rhs_on)
             }
+
+            # SQL compatibility: when joining on columns that
+            # contain NULLs, pandas will actually happily
+            # keep those NULLs. That is however not compatible with
+            # SQL, so we get rid of them here
+            if join_type in ["inner", "right"]:
+                df_lhs_filter = reduce(
+                    operator.and_,
+                    [~df_lhs_renamed.iloc[:, index].isna() for index in lhs_on],
+                )
+                df_lhs_renamed = df_lhs_renamed[df_lhs_filter]
+            if join_type in ["inner", "left"]:
+                df_rhs_filter = reduce(
+                    operator.and_,
+                    [~df_rhs_renamed.iloc[:, index].isna() for index in rhs_on],
+                )
+                df_rhs_renamed = df_rhs_renamed[df_rhs_filter]
         else:
             # We are in the complex join case
             # where we have no column to merge on

dask_sql/physical/rel/logical/sort.py

@@ -135,7 +135,7 @@ def _sort_first_column(
         col = df[first_sort_column]
         is_na = col.isna().persist()
         if is_na.any().compute():
-            df_is_na = df[is_na].reset_index(drop=True)
+            df_is_na = df[is_na].reset_index(drop=True).repartition(1)
             df_not_is_na = (
                 df[~is_na]
                 .set_index(first_sort_column, drop=False)

dask_sql/physical/rex/core/call.py

@@ -240,6 +240,21 @@ def null(self, df: SeriesOrScalar,) -> SeriesOrScalar:
         return pd.isna(df) or df is None or np.isnan(df)
 
 
+class IsNotDistinctOperation(Operation):
+    """The is not distinct operator"""
+
+    def __init__(self):
+        super().__init__(self.not_distinct)
+
+    def not_distinct(self, lhs: SeriesOrScalar, rhs: SeriesOrScalar) -> SeriesOrScalar:
+        """
+        Returns true where `lhs` is not distinct from `rhs` (or both are null).
+        """
+        is_null = IsNullOperation()
+
+        return (is_null(lhs) & is_null(rhs)) | (lhs == rhs)
+
+
 class RegexOperation(Operation):
     """An abstract regex operation, which transforms the SQL regex into something python can understand"""
 
@@ -627,6 +642,8 @@ class RexCallPlugin(BaseRexPlugin):
         "-": ReduceOperation(operation=operator.sub, unary_operation=lambda x: -x),
         "/": ReduceOperation(operation=SQLDivisionOperator()),
         "*": ReduceOperation(operation=operator.mul),
+        "is distinct from": NotOperation().of(IsNotDistinctOperation()),
+        "is not distinct from": IsNotDistinctOperation(),
         # special operations
         "cast": lambda x: x,
         "case": CaseOperation(),

docs/pages/sql.rst

@@ -199,14 +199,16 @@ Limitatons
 
 ``dask-sql`` is still in early development, therefore exist some limitations:
 
-* Not all operations and aggregations are implemented already, most prominently: ``WINDOW`` is not implemented so far.
-* ``GROUP BY`` aggregations can not use ``DISTINCT``
+Not all operations and aggregations are implemented already, most prominently: ``WINDOW`` is not implemented so far.
 
 .. note::
 
     Whenever you find a not already implemented operation, keyword
     or functionality, please raise an issue at our `issue tracker <https://github.com/nils-braun/dask-sql/issues>`_ with your use-case.
 
+Dask/pandas and SQL treat null-values (or nan) differently on sorting, grouping and joining.
+``dask-sql`` tries to follow the SQL standard as much as possible, so results might be different to what you expect from Dask/pandas.
+
 Apart from those functional limitations, there is a operation which need special care: ``ORDER BY```.
 Normally, ``dask-sql`` calls create a ``dask`` data frame, which gets only computed when you call the ``.compute()`` member.
 Due to internal constraints, this is currently not the case for ``ORDER BY``.
@@ -218,4 +220,5 @@ Including this operation will trigger a calculation of the full data frame alrea
     The data inside ``dask`` is partitioned, to distribute it over the cluster.
     ``head`` will only return the first N elements from the first partition - even if N is larger than the partition size.
     As a benefit, calling ``.head(N)`` is typically faster than calculating the full data sample with ``.compute()``.
-    ``LIMIT`` on the other hand will always return the first N elements - no matter on how many partitions they are scattered - but will also need to precalculate the first partition to find out, if it needs to have a look into all data or not.
+    ``LIMIT`` on the other hand will always return the first N elements - no matter on how many partitions they are scattered -
+    but will also need to precalculate the first partition to find out, if it needs to have a look into all data or not.