[PERF] Lazily import heavy modules to speed up import times

daft/.ruff.toml

@@ -0,0 +1,13 @@
+extend = "../.ruff.toml"
+
+[lint]
+extend-select = [
+  "TID253",  # banned-module-level-imports, derived from flake8-tidy-imports
+  "TCH"  # flake8-type-checking
+]
+
+[lint.flake8-tidy-imports]
+# Ban certain modules from being imported at module level, instead requiring
+# that they're imported lazily (e.g., within a function definition,
+# with daft.lazy_import.LazyImport, or with TYPE_CHECKING).
+banned-module-level-imports = ["daft.unity_catalog", "fsspec", "numpy", "pandas", "PIL", "pyarrow", "ray", "xml"]

daft/arrow_utils.py

@@ -2,7 +2,7 @@
 
 import sys
 
-import pyarrow as pa
+from daft.dependencies import pa
 
 
 def ensure_array(arr: pa.Array) -> pa.Array:
@@ -34,13 +34,21 @@ class _FixEmptyStructArrays:
     Python layer before going through ffi into Rust.
     """
 
-    EMPTY_STRUCT_TYPE = pa.struct([])
-    SINGLE_FIELD_STRUCT_TYPE = pa.struct({"": pa.null()})
-    SINGLE_FIELD_STRUCT_VALUE = {"": None}
+    @staticmethod
+    def get_empty_struct_type():
+        return pa.struct([])
+
+    @staticmethod
+    def get_single_field_struct_type():
+        return pa.struct({"": pa.null()})
+
+    @staticmethod
+    def get_single_field_struct_value():
+        return {"": None}
 
     def ensure_table(table: pa.Table) -> pa.Table:
         empty_struct_fields = [
-            (i, f) for (i, f) in enumerate(table.schema) if f.type == _FixEmptyStructArrays.EMPTY_STRUCT_TYPE
+            (i, f) for (i, f) in enumerate(table.schema) if f.type == _FixEmptyStructArrays.get_empty_struct_type()
         ]
         if not empty_struct_fields:
             return table
@@ -49,19 +57,19 @@ def ensure_table(table: pa.Table) -> pa.Table:
         return table
 
     def ensure_chunked_array(arr: pa.ChunkedArray) -> pa.ChunkedArray:
-        if arr.type != _FixEmptyStructArrays.EMPTY_STRUCT_TYPE:
+        if arr.type != _FixEmptyStructArrays.get_empty_struct_type():
             return arr
         return pa.chunked_array([_FixEmptyStructArrays.ensure_array(chunk) for chunk in arr.chunks])
 
     def ensure_array(arr: pa.Array) -> pa.Array:
         """Recursively converts empty struct arrays to single-field struct arrays"""
-        if arr.type == _FixEmptyStructArrays.EMPTY_STRUCT_TYPE:
+        if arr.type == _FixEmptyStructArrays.get_empty_struct_type():
             return pa.array(
                 [
-                    _FixEmptyStructArrays.SINGLE_FIELD_STRUCT_VALUE if valid.as_py() else None
+                    _FixEmptyStructArrays.get_single_field_struct_value() if valid.as_py() else None
                     for valid in arr.is_valid()
                 ],
-                type=_FixEmptyStructArrays.SINGLE_FIELD_STRUCT_TYPE,
+                type=_FixEmptyStructArrays.get_single_field_struct_type(),
             )
 
         elif isinstance(arr, pa.StructArray):
@@ -77,10 +85,10 @@ def ensure_array(arr: pa.Array) -> pa.Array:
 
 def remove_empty_struct_placeholders(arr: pa.Array):
     """Recursively removes the empty struct placeholders placed by _FixEmptyStructArrays.ensure_array"""
-    if arr.type == _FixEmptyStructArrays.SINGLE_FIELD_STRUCT_TYPE:
+    if arr.type == _FixEmptyStructArrays.get_single_field_struct_type():
         return pa.array(
             [{} if valid.as_py() else None for valid in arr.is_valid()],
-            type=_FixEmptyStructArrays.EMPTY_STRUCT_TYPE,
+            type=_FixEmptyStructArrays.get_empty_struct_type(),
         )
 
     elif isinstance(arr, pa.StructArray):

daft/daft/__init__.pyi

@@ -3,8 +3,6 @@ import datetime
 from enum import Enum
 from typing import TYPE_CHECKING, Any, Callable, Iterator
 
-import pyarrow
-
 from daft.dataframe.display import MermaidOptions
 from daft.execution import physical_plan
 from daft.io.scan import ScanOperator
@@ -994,7 +992,7 @@ class PyDataType:
     def tensor(dtype: PyDataType, shape: tuple[int, ...] | None = None) -> PyDataType: ...
     @staticmethod
     def python() -> PyDataType: ...
-    def to_arrow(self, cast_tensor_type_for_ray: builtins.bool | None = None) -> pyarrow.DataType: ...
+    def to_arrow(self, cast_tensor_type_for_ray: builtins.bool | None = None) -> pa.DataType: ...
     def is_numeric(self) -> builtins.bool: ...
     def is_image(self) -> builtins.bool: ...
     def is_fixed_shape_image(self) -> builtins.bool: ...
@@ -1271,11 +1269,11 @@ class PyCatalog:
 
 class PySeries:
     @staticmethod
-    def from_arrow(name: str, pyarrow_array: pyarrow.Array) -> PySeries: ...
+    def from_arrow(name: str, pyarrow_array: pa.Array) -> PySeries: ...
     @staticmethod
     def from_pylist(name: str, pylist: list[Any], pyobj: str) -> PySeries: ...
     def to_pylist(self) -> list[Any]: ...
-    def to_arrow(self) -> pyarrow.Array: ...
+    def to_arrow(self) -> pa.Array: ...
     def __abs__(self) -> PySeries: ...
     def __add__(self, other: PySeries) -> PySeries: ...
     def __sub__(self, other: PySeries) -> PySeries: ...
@@ -1456,10 +1454,10 @@ class PyTable:
     def concat(tables: list[PyTable]) -> PyTable: ...
     def slice(self, start: int, end: int) -> PyTable: ...
     @staticmethod
-    def from_arrow_record_batches(record_batches: list[pyarrow.RecordBatch], schema: PySchema) -> PyTable: ...
+    def from_arrow_record_batches(record_batches: list[pa.RecordBatch], schema: PySchema) -> PyTable: ...
     @staticmethod
     def from_pylist_series(dict: dict[str, PySeries]) -> PyTable: ...
-    def to_arrow_record_batch(self) -> pyarrow.RecordBatch: ...
+    def to_arrow_record_batch(self) -> pa.RecordBatch: ...
     @staticmethod
     def empty(schema: PySchema | None = None) -> PyTable: ...
 
@@ -1476,7 +1474,7 @@ class PyMicroPartition:
     @staticmethod
     def from_tables(tables: list[PyTable]) -> PyMicroPartition: ...
     @staticmethod
-    def from_arrow_record_batches(record_batches: list[pyarrow.RecordBatch], schema: PySchema) -> PyMicroPartition: ...
+    def from_arrow_record_batches(record_batches: list[pa.RecordBatch], schema: PySchema) -> PyMicroPartition: ...
     @staticmethod
     def concat(tables: list[PyMicroPartition]) -> PyMicroPartition: ...
     def slice(self, start: int, end: int) -> PyMicroPartition: ...

daft/dataframe/preview.py

@@ -1,8 +1,10 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+from typing import TYPE_CHECKING
 
-from daft.table import MicroPartition
+if TYPE_CHECKING:
+    from daft.table import MicroPartition
 
 
 @dataclass(frozen=True)

daft/datatype.py

@@ -1,14 +1,14 @@
 from __future__ import annotations
 
-import builtins
 from typing import TYPE_CHECKING
 
-import pyarrow as pa
-
 from daft.context import get_context
 from daft.daft import ImageMode, PyDataType, PyTimeUnit
+from daft.dependencies import pa
 
 if TYPE_CHECKING:
+    import builtins
+
     import numpy as np
 
 
@@ -501,25 +501,40 @@ def __hash__(self) -> int:
         return self._dtype.__hash__()
 
 
-class DaftExtension(pa.ExtensionType):
-    def __init__(self, dtype, metadata=b""):
-        # attributes need to be set first before calling
-        # super init (as that calls serialize)
-        self._metadata = metadata
-        super().__init__(dtype, "daft.super_extension")
+_EXT_TYPE_REGISTERED = False
+_STATIC_DAFT_EXTENSION = None
 
-    def __reduce__(self):
-        return type(self).__arrow_ext_deserialize__, (self.storage_type, self.__arrow_ext_serialize__())
 
-    def __arrow_ext_serialize__(self):
-        return self._metadata
+def _ensure_registered_super_ext_type():
+    global _EXT_TYPE_REGISTERED
+    global _STATIC_DAFT_EXTENSION
+    if not _EXT_TYPE_REGISTERED:
 
-    @classmethod
-    def __arrow_ext_deserialize__(cls, storage_type, serialized):
-        return cls(storage_type, serialized)
+        class DaftExtension(pa.ExtensionType):
+            def __init__(self, dtype, metadata=b""):
+                # attributes need to be set first before calling
+                # super init (as that calls serialize)
+                self._metadata = metadata
+                super().__init__(dtype, "daft.super_extension")
+
+            def __reduce__(self):
+                return type(self).__arrow_ext_deserialize__, (self.storage_type, self.__arrow_ext_serialize__())
+
+            def __arrow_ext_serialize__(self):
+                return self._metadata
+
+            @classmethod
+            def __arrow_ext_deserialize__(cls, storage_type, serialized):
+                return cls(storage_type, serialized)
+
+        _STATIC_DAFT_EXTENSION = DaftExtension
+        pa.register_extension_type(DaftExtension(pa.null()))
+        import atexit
 
+        atexit.register(lambda: pa.unregister_extension_type("daft.super_extension"))
+        _EXT_TYPE_REGISTERED = True
 
-pa.register_extension_type(DaftExtension(pa.null()))
-import atexit
 
-atexit.register(lambda: pa.unregister_extension_type("daft.super_extension"))
+def get_super_ext_type():
+    _ensure_registered_super_ext_type()
+    return _STATIC_DAFT_EXTENSION

daft/delta_lake/delta_lake_scan.py

@@ -2,7 +2,7 @@
 
 import logging
 import os
-from collections.abc import Iterator
+from typing import TYPE_CHECKING
 
 from deltalake.table import DeltaTable
 
@@ -20,6 +20,9 @@
 from daft.io.scan import PartitionField, ScanOperator
 from daft.logical.schema import Schema
 
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
 logger = logging.getLogger(__name__)
 
 

daft/dependencies.py

@@ -0,0 +1,32 @@
+from typing import TYPE_CHECKING
+
+from daft.lazy_import import LazyImport
+
+if TYPE_CHECKING:
+    import xml.etree.ElementTree as ET
+
+    import fsspec
+    import numpy as np
+    import pandas as pd
+    import PIL.Image as pil_image
+    import pyarrow as pa
+    import pyarrow.csv as pacsv
+    import pyarrow.dataset as pads
+    import pyarrow.fs as pafs
+    import pyarrow.json as pajson
+    import pyarrow.parquet as pq
+else:
+    ET = LazyImport("xml.etree.ElementTree")
+
+    fsspec = LazyImport("fsspec")
+    np = LazyImport("numpy")
+    pd = LazyImport("pandas")
+    pil_image = LazyImport("PIL.Image")
+    pa = LazyImport("pyarrow")
+    pacsv = LazyImport("pyarrow.csv")
+    pads = LazyImport("pyarrow.dataset")
+    pafs = LazyImport("pyarrow.fs")
+    pajson = LazyImport("pyarrow.json")
+    pq = LazyImport("pyarrow.parquet")
+
+unity_catalog = LazyImport("daft.unity_catalog")

daft/execution/execution_step.py

@@ -1,15 +1,12 @@
 from __future__ import annotations
 
 import itertools
-import pathlib
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Generic, Protocol
 
 from daft.context import get_context
-from daft.daft import FileFormat, IOConfig, JoinType, ResourceRequest, ScanTask
+from daft.daft import ResourceRequest
 from daft.expressions import Expression, ExpressionsProjection, col
-from daft.logical.map_partition_ops import MapPartitionOp
-from daft.logical.schema import Schema
 from daft.runners.partitioning import (
     Boundaries,
     MaterializedResult,
@@ -20,9 +17,15 @@
 from daft.table import MicroPartition, table_io
 
 if TYPE_CHECKING:
+    import pathlib
+
     from pyiceberg.schema import Schema as IcebergSchema
     from pyiceberg.table import TableProperties as IcebergTableProperties
 
+    from daft.daft import FileFormat, IOConfig, JoinType, ScanTask
+    from daft.logical.map_partition_ops import MapPartitionOp
+    from daft.logical.schema import Schema
+
 
 ID_GEN = itertools.count()
 

daft/execution/native_executor.py

@@ -6,14 +6,14 @@
     NativeExecutor as _NativeExecutor,
 )
 from daft.daft import PyDaftExecutionConfig
-from daft.logical.builder import LogicalPlanBuilder
-from daft.runners.partitioning import (
-    MaterializedResult,
-    PartitionT,
-)
 from daft.table import MicroPartition
 
 if TYPE_CHECKING:
+    from daft.logical.builder import LogicalPlanBuilder
+    from daft.runners.partitioning import (
+        MaterializedResult,
+        PartitionT,
+    )
     from daft.runners.pyrunner import PyMaterializedResult
 
 

daft/execution/physical_plan.py

@@ -17,7 +17,6 @@
 import itertools
 import logging
 import math
-import pathlib
 from collections import deque
 from typing import (
     TYPE_CHECKING,
@@ -30,7 +29,7 @@
 )
 
 from daft.context import get_context
-from daft.daft import FileFormat, IOConfig, JoinType, ResourceRequest
+from daft.daft import ResourceRequest
 from daft.execution import execution_step
 from daft.execution.execution_step import (
     Instruction,
@@ -41,7 +40,6 @@
     SingleOutputPartitionTask,
 )
 from daft.expressions import ExpressionsProjection
-from daft.logical.schema import Schema
 from daft.runners.partitioning import (
     MaterializedResult,
     PartitionT,
@@ -53,9 +51,14 @@
 T = TypeVar("T")
 
 if TYPE_CHECKING:
+    import pathlib
+
     from pyiceberg.schema import Schema as IcebergSchema
     from pyiceberg.table import TableProperties as IcebergTableProperties
 
+    from daft.daft import FileFormat, IOConfig, JoinType
+    from daft.logical.schema import Schema
+
 
 # A PhysicalPlan that is still being built - may yield both PartitionTaskBuilders and PartitionTasks.
 InProgressPhysicalPlan = Iterator[Union[None, PartitionTask[PartitionT], PartitionTaskBuilder[PartitionT]]]

daft/execution/rust_physical_plan_shim.py

@@ -17,12 +17,13 @@
 from daft.logical.map_partition_ops import MapPartitionOp
 from daft.logical.schema import Schema
 from daft.runners.partitioning import PartitionT
-from daft.table import MicroPartition
 
 if TYPE_CHECKING:
     from pyiceberg.schema import Schema as IcebergSchema
     from pyiceberg.table import TableProperties as IcebergTableProperties
 
+    from daft.table import MicroPartition
+
 
 def scan_with_tasks(
     scan_tasks: list[ScanTask],