Add support for QBit data type

CHANGELOG.md

@@ -22,8 +22,11 @@ instead of being passed as ClickHouse server settings. This is in conjunction wi
 The supported method of passing ClickHouse server settings is to prefix such arguments/query parameters with`ch_`.
 
 ## UNRELEASED
+
 ### Bug Fixes
+
 ### Improvements
+- Add support for QBit data type. Closes [#570](https://github.com/ClickHouse/clickhouse-connect/issues/570)
 
 ## 0.10.0, 2025-11-14
 

clickhouse_connect/cc_sqlalchemy/datatypes/sqltypes.py

@@ -497,3 +497,20 @@ def __init__(self, *params, type_def: TypeDef = None):
                 values += (x,)
             type_def = TypeDef(values=values)
         super().__init__(type_def)
+
+
+class QBit(ChSqlaType, UserDefinedType):
+    python_type = list
+
+    def __init__(self, element_type: str = None, dimension: int = None, type_def: TypeDef = None):
+        """
+        QBit constructor for bit-transposed vector types
+        :param element_type: Element type (BFloat16, Float32, or Float64)
+        :param dimension: Number of elements in the vector
+        :param type_def: TypeDef from parse_name function (used during reflection)
+        """
+        if not type_def:
+            if not element_type or not dimension:
+                raise ArgumentError("QBit requires element_type and dimension parameters")
+            type_def = TypeDef(values=(element_type, dimension))
+        super().__init__(type_def)

clickhouse_connect/datatypes/__init__.py

@@ -5,6 +5,7 @@
 import clickhouse_connect.datatypes.string
 import clickhouse_connect.datatypes.temporal
 import clickhouse_connect.datatypes.geometric
+import clickhouse_connect.datatypes.vector
 import clickhouse_connect.datatypes.dynamic
 import clickhouse_connect.datatypes.registry
 import clickhouse_connect.datatypes.postinit

clickhouse_connect/datatypes/vector.py

@@ -0,0 +1,276 @@
+import logging
+from math import ceil, nan
+from struct import pack, unpack
+from typing import Any, Sequence
+
+from clickhouse_connect.datatypes.base import ClickHouseType, TypeDef
+from clickhouse_connect.datatypes.registry import get_from_name
+from clickhouse_connect.driver.ctypes import data_conv
+from clickhouse_connect.driver.insert import InsertContext
+from clickhouse_connect.driver.options import np
+from clickhouse_connect.driver.query import QueryContext
+from clickhouse_connect.driver.types import ByteSource
+
+logger = logging.getLogger(__name__)
+
+if np is None:
+    logger.info("NumPy not detected. Install NumPy to see 10-30x performance gains with QBit columns.")
+
+
+class QBit(ClickHouseType):
+    """
+    QBit type - represents bit-transposed vectors for efficient vector search operations.
+
+    Syntax: QBit(element_type, dimension)
+    - element_type: BFloat16, Float32, or Float64
+    - dimension: Number of elements per vector
+
+    Over the Native protocol, ClickHouse transmits QBit columns as bit-transposed Tuples.
+
+    Requires:
+        - SET allow_experimental_qbit_type = 1
+        - Server version >=25.10
+    """
+
+    __slots__ = (
+        "element_type",
+        "dimension",
+        "_bits_per_element",
+        "_bytes_per_fixedstring",
+        "_tuple_type",
+    )
+
+    python_type = list
+    _BIT_SHIFTS = [1 << i for i in range(8)]
+    _ELEMENT_BITS = {"BFloat16": 16, "Float32": 32, "Float64": 64}
+
+    def __init__(self, type_def: TypeDef):
+        super().__init__(type_def)
+
+        self.element_type = type_def.values[0]
+        if self.element_type not in self._ELEMENT_BITS:
+            raise ValueError(f"Unsupported QBit element type '{self.element_type}'. Supported types: BFloat16, Float32, Float64.")
+
+        self.dimension = type_def.values[1]
+        if self.dimension <= 0:
+            raise ValueError(f"QBit dimension must be greater than 0. Got: {self.dimension}.")
+
+        self._name_suffix = f"({self.element_type}, {self.dimension})"
+        self._bits_per_element = self._ELEMENT_BITS.get(self.element_type, 32)
+        self._bytes_per_fixedstring = ceil(self.dimension / 8)
+
+        # Create the underlying Tuple type for bit-transposed representation
+        # E.g., for Float32 with dim=8: Tuple(FixedString(1), FixedString(1), ... x32)
+        fixedstring_type = f"FixedString({self._bytes_per_fixedstring})"
+        tuple_types = ", ".join([fixedstring_type] * self._bits_per_element)
+        tuple_type_name = f"Tuple({tuple_types})"
+        self._tuple_type = get_from_name(tuple_type_name)
+        self.byte_size = self._bits_per_element * self._bytes_per_fixedstring
+
+    def read_column_prefix(self, source: ByteSource, ctx: QueryContext):
+        return self._tuple_type.read_column_prefix(source, ctx)
+
+    def read_column_data(self, source: ByteSource, num_rows: int, ctx: QueryContext, read_state: Any) -> Sequence:
+        """Read bit-transposed Tuple data and convert to flat float vectors."""
+        if num_rows == 0:
+            return []
+
+        null_map = None
+        if self.nullable:
+            null_map = source.read_bytes(num_rows)
+
+        tuple_data = self._tuple_type.read_column_data(source, num_rows, ctx, read_state)
+        vectors = [self._untranspose_row(t) for t in tuple_data]
+        if self.nullable:
+            return data_conv.build_nullable_column(vectors, null_map, self._active_null(ctx))
+        return vectors
+
+    def write_column_prefix(self, dest: bytearray):
+        self._tuple_type.write_column_prefix(dest)
+
+    def write_column_data(self, column: Sequence, dest: bytearray, ctx: InsertContext):
+        """Convert flat float vectors to bit-transposed Tuple data and write."""
+        if len(column) == 0:
+            return
+
+        if self.nullable:
+            dest += bytes([1 if x is None else 0 for x in column])
+
+        null_tuple = tuple(b"\x00" * self._bytes_per_fixedstring for _ in range(self._bits_per_element))
+        tuple_column = [null_tuple if row is None else self._transpose_row(row) for row in column]
+
+        self._tuple_type.write_column_data(tuple_column, dest, ctx)
+
+    def _active_null(self, ctx: QueryContext):
+        """Return context-appropriate null value for nullable QBit columns."""
+        if ctx.use_none:
+            return None
+        if ctx.use_extended_dtypes:
+            return nan
+        return None
+
+    def _values_to_words(self, values: list[float]) -> Sequence[int]:
+        """Convert float values to integer words using batch struct processing."""
+        count = len(values)
+
+        if self.element_type == "BFloat16":
+            # BFloat16 is the top 16 bits of a Float32 (truncate mantissa)
+            raw_ints = unpack(f"<{count}I", pack(f"<{count}f", *values))
+            return [(x >> 16) & 0xFFFF for x in raw_ints]
+
+        fmt_char = "I" if self.element_type == "Float32" else "Q"
+        float_char = "f" if self.element_type == "Float32" else "d"
+
+        return unpack(f"<{count}{fmt_char}", pack(f"<{count}{float_char}", *values))
+
+    def _words_to_values(self, words: list[int]) -> list[float]:
+        """Convert integer words to float values using batch unpacking."""
+        count = len(words)
+
+        if self.element_type == "BFloat16":
+            # Pad BFloat16 words with zeros to reconstruct valid Float32s
+            shifted_words = [(w & 0xFFFF) << 16 for w in words]
+            return list(unpack(f"<{count}f", pack(f"<{count}I", *shifted_words)))
+
+        if self.element_type == "Float32":
+            return list(unpack(f"<{count}f", pack(f"<{count}I", *words)))
+
+        # Float64
+        return list(unpack(f"<{count}d", pack(f"<{count}Q", *words)))
+
+    def _untranspose_row(self, bit_planes: tuple):
+        """Convert bit-transposed tuple to flat float vector."""
+        if np is not None:
+            return self._untranspose_row_numpy(bit_planes)
+
+        words = [0] * self.dimension
+        bit_shifts = self._BIT_SHIFTS
+        dim = self.dimension
+
+        # Iterate Planes (MSB -> LSB)
+        for bit_idx, bit_plane_bytes in enumerate(bit_planes):
+            bit_pos = self._bits_per_element - 1 - bit_idx
+            mask = 1 << bit_pos
+
+            # Iterate Bytes in Plane
+            for byte_idx, byte_val in enumerate(bit_plane_bytes):
+                # if byte is 0, skip processing 8 bits
+                if byte_val == 0:
+                    continue
+
+                base_elem_idx = byte_idx << 3  # Each byte encodes 8 elements
+
+                # Extract set bits from this byte
+                for bit_in_byte in range(8):
+                    if byte_val & bit_shifts[bit_in_byte]:
+                        elem_idx = base_elem_idx + bit_in_byte
+                        if elem_idx < dim:
+                            words[elem_idx] |= mask  # Accumulate bit at position bit_pos
+
+        return self._words_to_values(words)
+
+    def _untranspose_row_numpy(self, bit_planes: tuple) -> list[float]:
+        """Vectorized numpy operations version of _untranspose_row"""
+        # 1. Convert tuple of bytes to a single uint8 array
+        total_bytes = b"".join(bit_planes)
+        planes_uint8 = np.frombuffer(total_bytes, dtype=np.uint8)
+        planes_uint8 = planes_uint8.reshape(self._bits_per_element, -1)
+
+        # 2. Unpack bits to get the boolean/integer matrix
+        bits_matrix: "np.ndarray" = np.unpackbits(planes_uint8, axis=1, bitorder="little")
+
+        # 3. Trim padding if necessary
+        if bits_matrix.shape[1] != self.dimension:  # pylint: disable=no-member
+            bits_matrix = bits_matrix[:, : self.dimension]  # pylint: disable=invalid-sequence-index
+
+        # 4. Reconstruct the integer words
+        if self.element_type == "Float64":
+            int_dtype = np.uint64
+            final_dtype = np.float64
+        else:
+            # Float32 and BFloat16 use 32-bit containers
+            int_dtype = np.uint32
+            final_dtype = np.float32
+
+        # Accumulate bits into integers
+        words = np.zeros(self.dimension, dtype=int_dtype)
+
+        for i in range(self._bits_per_element):
+            # MSB is at index 0
+            shift = self._bits_per_element - 1 - i
+
+            # If the bit row is 1, add 2^shift to the word
+            # Cast bits to the target int type before shifting to avoid overflow
+            words |= bits_matrix[i].astype(int_dtype) << shift
+
+        # 5. Interpret as Floats
+        if self.element_type == "BFloat16":
+            # Shift back up to the top 16 bits of a Float32
+            # Cast to uint32 first to ensure safe shifting
+            words = words.astype(np.uint32) << 16
+            return words.view(np.float32).tolist()
+
+        return words.view(final_dtype).tolist()
+
+    def _transpose_row(self, values: list[float]) -> tuple:
+        """Convert flat float vector to bit-transposed tuple."""
+        if len(values) != self.dimension:
+            raise ValueError(f"Vector dimension mismatch: expected {self.dimension}, got {len(values)}")
+
+        # If numpy is available, use the fast path
+        if np is not None:
+            if isinstance(values, np.ndarray):
+                return self._transpose_row_numpy(values)
+
+            # If numpy is available but user supplied python list, convert to np array anyway for
+            #  huge performance gains.
+            dtype = np.float64 if self.element_type == "Float64" else np.float32
+            return self._transpose_row_numpy(np.array(values, dtype=dtype))
+
+        words = self._values_to_words(values)
+        bit_planes = []
+        bit_shifts = self._BIT_SHIFTS
+        bytes_per_fs = self._bytes_per_fixedstring
+
+        for bit_idx in range(self._bits_per_element):
+            bit_pos = self._bits_per_element - 1 - bit_idx
+            mask = 1 << bit_pos
+            plane = bytearray(bytes_per_fs)
+
+            for elem_idx, word in enumerate(words):
+                if word & mask:
+                    plane[elem_idx >> 3] |= bit_shifts[elem_idx & 7]
+
+            bit_planes.append(bytes(plane))
+
+        return tuple(bit_planes)
+
+    def _transpose_row_numpy(self, vector: "np.ndarray") -> tuple:
+        """Fast path for numpy arrays using vectorized operations."""
+        # Cast to int view
+        if self.element_type == "BFloat16":
+            # Numpy doesn't have bfloat16. Input is Float32 so just
+            #  discard the bottom 16 bits.
+            v_float = vector.astype(np.float32, copy=False)
+            # View as uint32, shift right 16, cast to uint16
+            v_int = (v_float.view(np.uint32) >> 16).astype(np.uint16)
+
+        elif self.element_type == "Float32":
+            # Ensure it is 32-bit float first (handles float64->32 downcast safely)
+            v_float = vector.astype(np.float32, copy=False)
+            v_int = v_float.view(np.uint32)
+
+        else:  # Float64
+            v_float = vector.astype(np.float64, copy=False)
+            v_int = v_float.view(np.uint64)
+
+        bits = self._bits_per_element
+        masks = (1 << np.arange(bits - 1, -1, -1, dtype=v_int.dtype)).reshape(-1, 1)
+
+        # Extract bits: (Bits, Dim)
+        # v_int broadcasted to (1, Dim)
+        bits_extracted = (v_int & masks) != 0
+
+        packed = np.packbits(bits_extracted.view(np.uint8), axis=1, bitorder="little")
+
+        return tuple(row.tobytes() for row in packed)

tests/integration_tests/test_client.py

@@ -234,7 +234,7 @@ def test_temporary_tables(test_client: Client):
     test_client.insert_df('temp_test_table', df, settings=session_settings)
     df = test_client.query_df('SELECT * FROM temp_test_table', settings=session_settings)
     assert len(df['field1']) == 4
-    test_client.command('DROP TABLE temp_test_table', settings=session_settings)
+    test_client.command('DROP TABLE IF EXISTS temp_test_table', settings=session_settings)
 
 
 def test_str_as_bytes(test_client: Client, table_context: Callable):

tests/integration_tests/test_sqlalchemy/test_ddl.py

@@ -11,7 +11,7 @@
 from clickhouse_connect import common
 from clickhouse_connect.cc_sqlalchemy.datatypes.sqltypes import Int8, UInt16, Decimal, Enum16, Float64, Boolean, \
     FixedString, String, UInt64, UUID, DateTime, DateTime64, LowCardinality, Nullable, Array, AggregateFunction, \
-    UInt32, IPv4
+    UInt32, IPv4, QBit
 from clickhouse_connect.cc_sqlalchemy import final
 from clickhouse_connect.cc_sqlalchemy.ddl.custom import CreateDatabase, DropDatabase
 from clickhouse_connect.cc_sqlalchemy.ddl.tableengine import engine_map, ReplacingMergeTree
@@ -181,3 +181,34 @@ def test_final_modifier_error_cases(test_engine: Engine, test_db: str):
 
         test_table.drop(conn)
         other_table.drop(conn)
+
+
+def test_qbit_table(test_engine: Engine, test_db: str, test_table_engine: str, test_config: TestConfig):
+    """Test QBit type DDL and basic operations"""
+    common.set_setting('invalid_setting_action', 'drop')
+    with test_engine.begin() as conn:
+        if test_config.cloud:
+            pytest.skip('QBit type requires allow_experimental_qbit_type setting, but settings are locked in cloud')
+
+        if not conn.connection.driver_connection.client.min_version('25.10'):
+            pytest.skip('QBit type requires ClickHouse version 25.10+')
+
+        conn.execute(text('SET allow_experimental_qbit_type = 1'))
+
+        table_cls = engine_map[test_table_engine]
+        metadata = MetaData(schema=test_db)
+        conn.execute(text('DROP TABLE IF EXISTS qbit_test'))
+
+        table = db.Table('qbit_test', metadata,
+                        db.Column('id', UInt32),
+                        db.Column('vector', QBit('Float32', 8)),
+                        db.Column('embedding', QBit('Float32', 128)),
+                        table_cls('id'))
+        table.create(conn)
+
+        result = conn.execute(text("SHOW CREATE TABLE qbit_test"))
+        create_sql = result.fetchone()[0]
+        assert 'QBit(Float32, 8)' in create_sql
+        assert 'QBit(Float32, 128)' in create_sql
+
+        conn.execute(text('DROP TABLE IF EXISTS qbit_test'))