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Posted to issues@iceberg.apache.org by GitBox <gi...@apache.org> on 2022/02/07 00:03:03 UTC
[GitHub] [iceberg] jun-he commented on a change in pull request #3450: [Python] support iceberg transforms in python
jun-he commented on a change in pull request #3450:
URL: https://github.com/apache/iceberg/pull/3450#discussion_r800252653
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File path: python/src/iceberg/transforms.py
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@@ -0,0 +1,583 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import re
+import struct
+from decimal import Decimal
+from enum import Enum
+from typing import Callable, Optional
+from uuid import UUID
+
+import mmh3 # type: ignore
+
+from iceberg.types import (
+ BinaryType,
+ DateType,
+ DecimalType,
+ FixedType,
+ IntegerType,
+ LongType,
+ StringType,
+ TimestampType,
+ TimestamptzType,
+ TimeType,
+ Type,
+ UUIDType,
+)
+from iceberg.utils import transform_util
+
+
+class Transform:
+ """Transform base class for concrete transforms.
+
+ A base class to transform values and project predicates on partition values.
+ This class is not used directly. Instead, use one of module method to create the child classes.
+
+ Args:
+ transform_string (str): name of the transform type
+ repr_string (str): string representation of a transform instance
+ """
+
+ def __init__(self, transform_string: str, repr_string: str):
+ self._transform_string = transform_string
+ self._repr_string = repr_string
+
+ def __repr__(self):
+ return self._repr_string
+
+ def __str__(self):
+ return self._transform_string
+
+ def __call__(self, value):
+ return self.apply(value)
+
+ def apply(self, value):
+ raise NotImplementedError()
+
+ def can_transform(self, target: Type) -> bool:
+ return False
+
+ def result_type(self, source: Type) -> Type:
+ return source
+
+ def preserves_order(self) -> bool:
+ return False
+
+ def satisfies_order_of(self, other) -> bool:
+ return self == other
+
+ def to_human_string(self, value) -> str:
+ if value is None:
+ return "null"
+ return str(value)
+
+ def dedup_name(self) -> str:
+ return self._transform_string
+
+
+class BaseBucketTransform(Transform):
+ """Base Transform class to transform a value into a bucket partition value
+
+ Transforms are parameterized by a number of buckets. Bucket partition transforms use a 32-bit
+ hash of the source value to produce a positive value by mod the bucket number.
+
+ Args:
+ source_type (Type): An Iceberg Type of IntegerType, LongType, DecimalType, DateType, TimeType,
+ TimestampType, TimestamptzType, StringType, BinaryType, FixedType, UUIDType.
+ num_buckets (int): The number of buckets.
+ """
+
+ def __init__(self, source_type: Type, num_buckets: int):
+ super().__init__(
+ f"bucket[{num_buckets}]",
+ f"transforms.bucket(source_type={repr(source_type)}, num_buckets={num_buckets})",
+ )
+ self._num_buckets = num_buckets
+
+ @property
+ def num_buckets(self) -> int:
+ return self._num_buckets
+
+ def hash(self, value) -> int:
+ raise NotImplementedError()
+
+ def apply(self, value) -> Optional[int]:
+ if value is None:
+ return None
+
+ return (self.hash(value) & IntegerType.max) % self._num_buckets
+
+ def can_transform(self, target: Type) -> bool:
+ raise NotImplementedError()
+
+ def result_type(self, source: Type) -> Type:
+ return IntegerType()
+
+
+class BucketIntegerTransform(BaseBucketTransform):
+ """Transforms a value of IntegerType or DateType into a bucket partition value
+
+ Example:
+ >>> transform = BucketIntegerTransform(100)
+ >>> transform.apply(34)
+ 79
+ """
+
+ def can_transform(self, target: Type) -> bool:
+ return type(target) in {IntegerType, DateType}
+
+ def hash(self, value) -> int:
+ return mmh3.hash(struct.pack("q", value))
+
+
+class BucketLongTransform(BaseBucketTransform):
+ """Transforms a value of LongType, TimeType, TimestampType, or TimestamptzType
+ into a bucket partition value
+
+ Example:
+ >>> transform = BucketLongTransform(100)
+ >>> transform.apply(81068000000)
+ 59
+ """
+
+ def can_transform(self, target: Type) -> bool:
+ return type(target) in {LongType, TimeType, TimestampType, TimestamptzType}
+
+ def hash(self, value) -> int:
+ return mmh3.hash(struct.pack("q", value))
+
+
+class BucketDoubleTransform(BaseBucketTransform):
+ """Transforms a value of FloatType or DoubleType into a bucket partition value.
+
+ Note that bucketing by Double is not allowed by the spec, but this has the hash implementation.
+
+ Example:
+ >>> transform = BucketDoubleTransform(8)
+ >>> transform.hash(1.0)
+ -142385009
+ """
+
+ def hash(self, value) -> int:
+ return mmh3.hash(struct.pack("d", value))
+
+
+class BucketDecimalTransform(BaseBucketTransform):
+ """Transforms a value of DecimalType into a bucket partition value.
+
+ Example:
+ >>> transform = BucketDecimalTransform(100)
+ >>> transform.apply(Decimal("14.20"))
+ 59
+ """
+
+ def can_transform(self, target: Type) -> bool:
+ return isinstance(target, DecimalType)
+
+ def hash(self, value: Decimal) -> int:
+ return mmh3.hash(transform_util.decimal_to_bytes(value))
+
+
+class BucketStringTransform(BaseBucketTransform):
+ """Transforms a value of StringType into a bucket partition value.
+
+ Example:
+ >>> transform = BucketStringTransform(100)
+ >>> transform.apply("iceberg")
+ 89
+ """
+
+ def can_transform(self, target: Type) -> bool:
+ return isinstance(target, StringType)
+
+ def hash(self, value: str) -> int:
+ return mmh3.hash(value)
+
+
+class BucketFixedTransform(BaseBucketTransform):
+ """Transforms a value of FixedType into a bucket partition value.
+
+ Example:
+ >>> transform = BucketFixedTransform(128)
+ >>> transform.apply(b"foo")
+ 32
+ """
+
+ def can_transform(self, target: Type) -> bool:
+ return isinstance(target, FixedType)
+
+ def hash(self, value: bytearray) -> int:
+ return mmh3.hash(value)
+
+
+class BucketBinaryTransform(BaseBucketTransform):
+ """Transforms a value of BinaryType into a bucket partition value.
+
+ Example:
+ >>> transform = BucketBinaryTransform(128)
+ >>> transform.apply(b"\x00\x01\x02\x03")
+ 57
+ """
+
+ def can_transform(self, target: Type) -> bool:
+ return isinstance(target, BinaryType)
+
+ def hash(self, value: bytes) -> int:
+ return mmh3.hash(value)
+
+
+class BucketUUIDTransform(BaseBucketTransform):
+ """Transforms a value of UUIDType into a bucket partition value.
+
+ Example:
+ >>> transform = BucketUUIDTransform(100)
+ >>> transform.apply(UUID("f79c3e09-677c-4bbd-a479-3f349cb785e7"))
+ 40
+ """
+
+ def can_transform(self, target: Type) -> bool:
+ return isinstance(target, UUIDType)
+
+ def hash(self, value: UUID) -> int:
+ return mmh3.hash(
+ struct.pack(
+ ">QQ",
+ (value.int >> 64) & 0xFFFFFFFFFFFFFFFF,
+ value.int & 0xFFFFFFFFFFFFFFFF,
+ )
+ )
+
+
+class DateTimeTransform(Transform):
+ """Base transform class for transforms of DateType, TimestampType, and TimestamptzType."""
+
+ class Granularity(Enum):
+ def __init__(
+ self, order: int, result_type: Type, human_string: Callable[[int], str]
+ ):
+ self.order = order
+ self.result_type = result_type
+ self.human_string = human_string
+
+ YEAR = 3, IntegerType(), transform_util.human_year
+ MONTH = 2, IntegerType(), transform_util.human_month
+ DAY = 1, DateType(), transform_util.human_day
+ HOUR = 0, IntegerType(), transform_util.human_hour
+
+ _DATE_APPLY_FUNCS = {
+ Granularity.YEAR: transform_util.years_for_days,
+ Granularity.MONTH: transform_util.months_for_days,
+ Granularity.DAY: lambda d: d,
+ }
+
+ _TIMESTAMP_APPLY_FUNCS = {
+ Granularity.YEAR: transform_util.years_for_ts,
+ Granularity.MONTH: transform_util.months_for_ts,
+ Granularity.DAY: transform_util.days_for_ts,
+ Granularity.HOUR: transform_util.hours_for_ts,
+ }
+
+ def __init__(self, source_type: Type, name: str):
+ super().__init__(name, f"transforms.{name}(source_type={repr(source_type)})")
+
+ self._type = source_type
+ try:
+ self._granularity = DateTimeTransform.Granularity[name.upper()]
+
+ if isinstance(source_type, DateType):
+ self._apply = DateTimeTransform._DATE_APPLY_FUNCS[self._granularity]
+ elif type(source_type) in {TimestampType, TimestamptzType}:
+ self._apply = DateTimeTransform._TIMESTAMP_APPLY_FUNCS[
+ self._granularity
+ ]
+ else:
+ raise KeyError
+ except KeyError:
+ raise ValueError(f"Cannot partition type {source_type} by {name}")
+
+ def __eq__(self, other):
+ if type(self) is type(other):
+ return self._type == other._type and self._granularity == other._granularity
+ return False
+
+ def can_transform(self, target: Type) -> bool:
+ if isinstance(self._type, DateType):
+ return isinstance(target, DateType)
+ else: # self._type is either TimestampType or TimestamptzType
+ return not isinstance(target, DateType)
+
+ def apply(self, value: int) -> int:
+ return self._apply(value)
+
+ def result_type(self, source_type: Type) -> Type:
+ return self._granularity.result_type
+
+ def preserves_order(self) -> bool:
+ return True
+
+ def satisfies_order_of(self, other: Transform) -> bool:
+ if self == other:
+ return True
+
+ if isinstance(other, DateTimeTransform):
+ return self._granularity.order <= other._granularity.order
+
+ return False
+
+ def to_human_string(self, value) -> str:
+ if value is None:
+ return "null"
+ return self._granularity.human_string(value)
+
+ def dedup_name(self) -> str:
+ return "time"
+
+
+class IdentityTransform(Transform):
+ def __init__(self, source_type: Type):
+ super().__init__(
+ "identity",
+ f"transforms.identity(source_type={repr(source_type)})",
+ )
+ self._type = source_type
+
+ def apply(self, value):
+ return value
+
+ def can_transform(self, target: Type) -> bool:
+ return target.is_primitive
+
+ def preserves_order(self) -> bool:
+ return True
+
+ def satisfies_order_of(self, other: Transform) -> bool:
+ return other.preserves_order()
+
+ def to_human_string(self, value) -> str:
+ if value is None:
+ return "null"
+ elif isinstance(self._type, DateType):
+ return transform_util.human_day(value)
+ elif isinstance(self._type, TimeType):
+ return transform_util.human_time(value)
+ elif isinstance(self._type, TimestampType):
+ return transform_util.human_timestamp(value)
+ elif isinstance(self._type, TimestamptzType):
+ return transform_util.human_timestamptz(value)
+ elif isinstance(self._type, FixedType):
+ return transform_util.base64encode(value)
+ elif isinstance(self._type, BinaryType):
+ return transform_util.base64encode(value)
+ else:
+ return str(value)
+
+
+class TruncateTransform(Transform):
+ """A transform for truncating a value to a specified width.
+
+ Args:
+ source_type (Type): An Iceberg Type of IntegerType, LongType, StringType, or BinaryType
+ width (int): The truncate width
+
+ Raises:
+ ValueError: If a type is provided that is incompatible with a Truncate transform
+ """
+
+ _VALID_TYPES = {IntegerType, LongType, StringType, BinaryType, DecimalType}
+
+ def __init__(self, source_type: Type, width: int):
+ if type(source_type) not in TruncateTransform._VALID_TYPES:
+ raise ValueError(f"Cannot truncate type: {source_type}")
+
+ super().__init__(
+ f"truncate[{width}]",
+ f"transforms.truncate(source_type={repr(source_type)}, width={width})",
+ )
+ self._type = source_type
+ self._width = width
+
+ if type(self._type) in {IntegerType, LongType}:
+ self._apply = lambda v, w: v - v % w
+ elif type(self._type) in {StringType, BinaryType}:
+ self._apply = lambda v, w: v[0 : min(w, len(v))]
Review comment:
SG, moved both into transform_util. Also, the other option is to add truncate method to types, then we don't need `if ... elif ... else` here.
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