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272 lines (205 loc) · 10.6 KB
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import time
from typing import List, Tuple
import logging
from itertools import product
from runtype import dataclass
from .utils import safezip, Vector
from data_diff.sqeleton.utils import ArithString, split_space
from data_diff.sqeleton.databases import Database, DbPath, DbKey, DbTime
from data_diff.sqeleton.schema import Schema, create_schema
from data_diff.sqeleton.queries import Count, Checksum, SKIP, table, this, Expr, min_, max_, Code
from data_diff.sqeleton.queries.extras import ApplyFuncAndNormalizeAsString, NormalizeAsString
logger = logging.getLogger("table_segment")
RECOMMENDED_CHECKSUM_DURATION = 20
def split_key_space(min_key: DbKey, max_key: DbKey, count: int) -> List[DbKey]:
assert min_key < max_key
if max_key - min_key <= count:
count = 1
if isinstance(min_key, ArithString):
assert type(min_key) is type(max_key)
checkpoints = min_key.range(max_key, count)
else:
checkpoints = split_space(min_key, max_key, count)
assert all(min_key < x < max_key for x in checkpoints)
return [min_key] + checkpoints + [max_key]
def int_product(nums: List[int]) -> int:
p = 1
for n in nums:
p *= n
return p
def split_compound_key_space(mn: Vector, mx: Vector, count: int) -> List[List[DbKey]]:
"""Returns a list of split-points for each key dimension, essentially returning an N-dimensional grid of split points."""
return [split_key_space(mn_k, mx_k, count) for mn_k, mx_k in safezip(mn, mx)]
def create_mesh_from_points(*values_per_dim: list) -> List[Tuple[Vector, Vector]]:
"""Given a list of values along each axis of N dimensional space,
return an array of boxes whose start-points & end-points align with the given values,
and together consitute a mesh filling that space entirely (within the bounds of the given values).
Assumes given values are already ordered ascending.
len(boxes) == ∏i( len(i)-1 )
Example:
::
>>> d1 = 'a', 'b', 'c'
>>> d2 = 1, 2, 3
>>> d3 = 'X', 'Y'
>>> create_mesh_from_points(d1, d2, d3)
[
[('a', 1, 'X'), ('b', 2, 'Y')],
[('a', 2, 'X'), ('b', 3, 'Y')],
[('b', 1, 'X'), ('c', 2, 'Y')],
[('b', 2, 'X'), ('c', 3, 'Y')]
]
"""
assert all(len(v) >= 2 for v in values_per_dim), values_per_dim
# Create tuples of (v1, v2) for each pair of adjacent values
ranges = [list(zip(values[:-1], values[1:])) for values in values_per_dim]
assert all(a <= b for r in ranges for a, b in r)
# Create a product of all the ranges
res = [tuple(Vector(a) for a in safezip(*r)) for r in product(*ranges)]
expected_len = int_product(len(v) - 1 for v in values_per_dim)
assert len(res) == expected_len, (len(res), expected_len)
return res
@dataclass
class TableSegment:
"""Signifies a segment of rows (and selected columns) within a table
Parameters:
database (Database): Database instance. See :meth:`connect`
table_path (:data:`DbPath`): Path to table in form of a tuple. e.g. `('my_dataset', 'table_name')`
key_columns (Tuple[str]): Name of the key column, which uniquely identifies each row (usually id)
update_column (str, optional): Name of updated column, which signals that rows changed.
Usually updated_at or last_update. Used by `min_update` and `max_update`.
extra_columns (Tuple[str, ...], optional): Extra columns to compare
min_key (:data:`Vector`, optional): Lowest key value, used to restrict the segment
max_key (:data:`Vector`, optional): Highest key value, used to restrict the segment
min_update (:data:`DbTime`, optional): Lowest update_column value, used to restrict the segment
max_update (:data:`DbTime`, optional): Highest update_column value, used to restrict the segment
where (str, optional): An additional 'where' expression to restrict the search space.
case_sensitive (bool): If false, the case of column names will adjust according to the schema. Default is true.
"""
# Location of table
database: Database
table_path: DbPath
# Columns
key_columns: Tuple[str, ...]
update_column: str = None
extra_columns: Tuple[str, ...] = ()
# Restrict the segment
min_key: Vector = None
max_key: Vector = None
min_update: DbTime = None
max_update: DbTime = None
where: str = None
case_sensitive: bool = True
_schema: Schema = None
def __post_init__(self):
if not self.update_column and (self.min_update or self.max_update):
raise ValueError("Error: the min_update/max_update feature requires 'update_column' to be set.")
if self.min_key is not None and self.max_key is not None and self.min_key >= self.max_key:
raise ValueError(f"Error: min_key expected to be smaller than max_key! ({self.min_key} >= {self.max_key})")
if self.min_update is not None and self.max_update is not None and self.min_update >= self.max_update:
raise ValueError(
f"Error: min_update expected to be smaller than max_update! ({self.min_update} >= {self.max_update})"
)
def _where(self):
return f"({self.where})" if self.where else None
def _with_raw_schema(self, raw_schema: dict) -> "TableSegment":
schema = self.database._process_table_schema(self.table_path, raw_schema, self.relevant_columns, self._where())
return self.new(_schema=create_schema(self.database, self.table_path, schema, self.case_sensitive))
def with_schema(self) -> "TableSegment":
"Queries the table schema from the database, and returns a new instance of TableSegment, with a schema."
if self._schema:
return self
return self._with_raw_schema(self.database.query_table_schema(self.table_path))
def get_schema(self):
return self.database.query_table_schema(self.table_path)
def _make_key_range(self):
if self.min_key is not None:
for mn, k in safezip(self.min_key, self.key_columns):
yield mn <= this[k]
if self.max_key is not None:
for k, mx in safezip(self.key_columns, self.max_key):
yield this[k] < mx
def _make_update_range(self):
if self.min_update is not None:
yield self.min_update <= this[self.update_column]
if self.max_update is not None:
yield this[self.update_column] < self.max_update
@property
def source_table(self):
return table(*self.table_path, schema=self._schema)
def make_select(self):
return self.source_table.where(
*self._make_key_range(), *self._make_update_range(), Code(self._where()) if self.where else SKIP
)
def get_values(self) -> list:
"Download all the relevant values of the segment from the database"
select = self.make_select().select(*self._relevant_columns_repr)
return self.database.query(select, List[Tuple])
def choose_checkpoints(self, count: int) -> List[List[DbKey]]:
"Suggests a bunch of evenly-spaced checkpoints to split by, including start, end."
assert self.is_bounded
# Take Nth root of count, to approximate the appropriate box size
count = int(count ** (1 / len(self.key_columns))) or 1
return split_compound_key_space(self.min_key, self.max_key, count)
def segment_by_checkpoints(self, checkpoints: List[List[DbKey]]) -> List["TableSegment"]:
"Split the current TableSegment to a bunch of smaller ones, separated by the given checkpoints"
return [self.new_key_bounds(min_key=s, max_key=e) for s, e in create_mesh_from_points(*checkpoints)]
def new(self, **kwargs) -> "TableSegment":
"""Creates a copy of the instance using 'replace()'"""
return self.replace(**kwargs)
def new_key_bounds(self, min_key: Vector, max_key: Vector) -> "TableSegment":
if self.min_key is not None:
assert self.min_key <= min_key, (self.min_key, min_key)
assert self.min_key < max_key
if self.max_key is not None:
assert min_key < self.max_key
assert max_key <= self.max_key
return self.replace(min_key=min_key, max_key=max_key)
@property
def relevant_columns(self) -> List[str]:
extras = list(self.extra_columns)
if self.update_column and self.update_column not in extras:
extras = [self.update_column] + extras
return list(self.key_columns) + extras
@property
def _relevant_columns_repr(self) -> List[Expr]:
return [NormalizeAsString(this[c]) for c in self.relevant_columns]
def count(self) -> int:
"""Count how many rows are in the segment, in one pass."""
return self.database.query(self.make_select().select(Count()), int)
def count_and_checksum(self) -> Tuple[int, int]:
"""Count and checksum the rows in the segment, in one pass."""
start = time.monotonic()
q = self.make_select().select(Count(), Checksum(self._relevant_columns_repr))
count, checksum = self.database.query(q, tuple)
duration = time.monotonic() - start
if duration > RECOMMENDED_CHECKSUM_DURATION:
logger.warning(
"Checksum is taking longer than expected (%.2f). "
"We recommend increasing --bisection-factor or decreasing --threads.",
duration,
)
if count:
assert checksum, (count, checksum)
return count or 0, int(checksum) if count else None
def query_key_range(self) -> Tuple[tuple, tuple]:
"""Query database for minimum and maximum key. This is used for setting the initial bounds."""
# Normalizes the result (needed for UUIDs) after the min/max computation
select = self.make_select().select(
ApplyFuncAndNormalizeAsString(this[k], f) for k in self.key_columns for f in (min_, max_)
)
result = tuple(self.database.query(select, tuple))
if any(i is None for i in result):
raise ValueError("Table appears to be empty")
# Min/max keys are interleaved
min_key, max_key = result[::2], result[1::2]
assert len(min_key) == len(max_key)
return min_key, max_key
@property
def is_bounded(self):
return self.min_key is not None and self.max_key is not None
def approximate_size(self):
if not self.is_bounded:
raise RuntimeError("Cannot approximate the size of an unbounded segment. Must have min_key and max_key.")
diff = self.max_key - self.min_key
assert all(d > 0 for d in diff)
return int_product(diff)
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