#2032

opteryx/compiled/cross_join/cython_cross_join.pyx

@@ -15,43 +15,54 @@ from libc.stdint cimport int32_t
 cpdef tuple build_rows_indices_and_column(cnp.ndarray column_data):
     cdef int32_t i, total_size = 0
     cdef int32_t length
-    cdef list flat_data
     cdef int32_t row_count = len(column_data)
     cdef int32_t *lengths = <int32_t *>malloc(row_count * sizeof(int32_t))
     if lengths is NULL:
         raise MemoryError("Failed to allocate memory for lengths array.")
 
+    # Calculate the total size and fill lengths array
     for i in range(row_count):
-        length = len(column_data[i])
+        length = column_data[i].shape[0]
         lengths[i] = length
         total_size += length
 
+    # If the total size is zero, return empty arrays
     if total_size == 0:
         free(lengths)
         return (np.array([], dtype=np.int32), np.array([], dtype=object))
 
-    flat_data = [''] * total_size
+    # Determine the dtype of the elements in the arrays, handling the case where the first element is None
+    element_dtype = object
+    for i in range(row_count):
+        if column_data[i] is not None:
+            element_dtype = column_data[i].dtype
+            break
+
+    # Preallocate arrays for indices and flat data
+    flat_data = np.empty(total_size, dtype=element_dtype)  # More efficient than list
     cdef int32_t *indices = <int32_t *>malloc(total_size * sizeof(int32_t))
     if indices is NULL:
-        raise MemoryError("Failed to allocate memory.")
+        free(lengths)
+        raise MemoryError("Failed to allocate memory for indices.")
 
     cdef int32_t start = 0
     cdef int32_t end = 0
-    cdef int32_t j = 0
 
+    # Flatten the data and fill indices
     for i in range(row_count):
         end = start + lengths[i]
+        flat_data[start:end] = column_data[i]  # NumPy handles the slicing and copying
         for j in range(start, end):
-            indices[j] = i 
-            flat_data[j] = column_data[i][j - start]
+            indices[j] = i
         start = end
-    free(lengths)
 
+    free(lengths)  # Free the lengths array
+
+    # Create a NumPy array from indices
     cdef cnp.int32_t[:] mv = <cnp.int32_t[:total_size]>indices
-    # Create a NumPy array that is a copy of the memoryview, 
-    # which in turn makes it safe to free the original indices memory.
-    np_array = np.array(mv, copy=True)
-    free(indices)  # Now it's safe to free indices since np_array has its own copy.
+    np_array = np.array(mv, copy=True)  # Copy the memoryview into a NumPy array
+    free(indices)  # Free the indices memory now that we've copied it
+
     return (np_array, flat_data)
 
 
@@ -78,20 +89,35 @@ cpdef tuple build_filtered_rows_indices_and_column(cnp.ndarray column_data, set
     """
     cdef int32_t i, index = 0, allocated_size
     cdef int32_t row_count = len(column_data)
-    cdef int32_t initial_alloc_size = 500
+    cdef int32_t initial_alloc_size = row_count * 2
     allocated_size = initial_alloc_size
     cdef int32_t *indices = <int32_t *>malloc(allocated_size * sizeof(int32_t))
-    cdef list flat_data = [None] * allocated_size
+    cdef int32_t *new_indices
+
     if indices is NULL:
         raise MemoryError("Failed to allocate memory for indices.")
 
+    # Determine the dtype of the elements in the arrays, handling the case where the first element is None
+    element_dtype = object
+    for i in range(row_count):
+        if column_data[i] is not None:
+            element_dtype = column_data[i].dtype
+            break
+
+    cdef flat_data = np.empty(allocated_size, dtype=element_dtype)
+
     for i in range(row_count):
         for value in column_data[i]:
             if value in valid_values:
                 if index == allocated_size:  # Check if we need to expand the memory allocation
-                    allocated_size += initial_alloc_size
-                    indices = <int32_t *>realloc(indices, allocated_size * sizeof(int32_t))
-                    flat_data.extend([None] * initial_alloc_size)  # Extend flat_data by the same amount
+                    allocated_size = allocated_size * 2  # Double the allocation size to reduce reallocations
+                    # Handle realloc for indices safely
+                    new_indices = <int32_t *>realloc(indices, allocated_size * sizeof(int32_t))
+                    if new_indices is NULL:
+                        free(indices)  # Free previously allocated memory to avoid memory leak
+                        raise MemoryError("Failed to reallocate memory for indices.")
+                    indices = new_indices
+                    flat_data = np.resize(flat_data, allocated_size)
                     if indices is NULL:
                         raise MemoryError("Failed to reallocate memory for indices.")
                 flat_data[index] = value

opteryx/functions/other_functions.py

@@ -45,7 +45,7 @@ def list_contains_any(array: numpy.ndarray, items: numpy.ndarray) -> numpy.ndarr
     res = numpy.empty(array.size, dtype=bool)
     for i, test_set in enumerate(array):
         # Using not to correctly capture overlap (isdisjoint is True when no common elements)
-        res[i] = not items_set.isdisjoint(test_set) if test_set is not None else False
+        res[i] = bool(set(test_set) & items_set) if test_set is not None else False
     return res
 
 

opteryx/operators/cross_join_node.py

@@ -125,12 +125,15 @@ def _cross_join_unnest_column(
                                 )
                             ]
                         )
-                        new_block = pyarrow.Table.from_arrays(
-                            [single_column_collector], schema=schema
-                        )
+                        arrow_array = pyarrow.array(single_column_collector)
+                        if arrow_array.type != target_column.arrow_field.type:
+                            arrow_array = arrow_array.cast(target_column.arrow_field.type)
+                        new_block = pyarrow.Table.from_arrays([arrow_array], schema=schema)
                         single_column_collector.clear()
+                        del arrow_array
                         statistics.time_cross_join_unnest += time.monotonic_ns() - start
                         yield new_block
+                        start = time.monotonic_ns()
                         at_least_once = True
                 else:
                     # Rebuild the block with the new column data if we have any rows to build for
@@ -139,13 +142,13 @@ def _cross_join_unnest_column(
                     block_size = MORSEL_SIZE_BYTES / (left_block.nbytes / left_block.num_rows)
                     block_size = int(block_size // 1000) * 1000
 
-                    for start in range(0, total_rows, block_size):
+                    for start_block in range(0, total_rows, block_size):
                         # Compute the end index for the current chunk
-                        end = min(start + block_size, total_rows)
+                        end_block = min(start_block + block_size, total_rows)
 
                         # Slice the current chunk of indices and new_column_data
-                        indices_chunk = indices[start:end]
-                        new_column_data_chunk = new_column_data[start:end]
+                        indices_chunk = indices[start_block:end_block]
+                        new_column_data_chunk = new_column_data[start_block:end_block]
 
                         # Create a new block using the chunk of indices
                         new_block = left_block.take(indices_chunk)
@@ -171,16 +174,18 @@ def _cross_join_unnest_column(
         if arrow_array.type != target_column.arrow_field.type:
             arrow_array = arrow_array.cast(target_column.arrow_field.type)
         new_block = pyarrow.Table.from_arrays([arrow_array], schema=schema)
-
+        statistics.time_cross_join_unnest += time.monotonic_ns() - start
         yield new_block
         at_least_once = True
+        start = time.monotonic_ns()
 
     if not at_least_once:
         # Create an empty table with the new schema
         schema = left_morsel.schema
         new_column = pyarrow.field(target_column.identity, pyarrow.string())
         new_schema = pyarrow.schema(list(schema) + [new_column])
         new_block = pyarrow.Table.from_batches([], schema=new_schema)
+        statistics.time_cross_join_unnest += time.monotonic_ns() - start
         yield new_block
 
 

opteryx/planner/binder/binder_visitor.py

@@ -831,11 +831,10 @@ def visit_project(self, node: Node, context: BindingContext) -> Tuple[Node, Bind
                         (n for n in node.columns if n.schema_column.identity == column.identity),
                         None,
                     )
-                    if node_column:
-                        # update the column reference with any AS aliases
-                        if node_column.alias:
-                            node_column.schema_column.aliases.append(node_column.alias)
-                            column.aliases.append(node_column.alias)
+                    # update the column reference with any AS aliases
+                    if node_column and node_column.alias:
+                        node_column.schema_column.aliases.append(node_column.alias)
+                        column.aliases.append(node_column.alias)
                 # update the schema with columns we have references to, removing redundant columns
                 schema.columns = schema_columns
                 for column in node.columns: