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"""
.. include:: ../posts/sql_diff.md
----
"""
from __future__ import annotations
import typing as t
from collections import defaultdict
from dataclasses import dataclass
from heapq import heappop, heappush
from itertools import chain
from sqlglot import Dialect, expressions as exp
from sqlglot.helper import seq_get
if t.TYPE_CHECKING:
from collections.abc import Iterator, Sequence
from sqlglot.dialects.dialect import DialectType
@dataclass(frozen=True)
class Insert:
"""Indicates that a new node has been inserted"""
expression: exp.Expr
@dataclass(frozen=True)
class Remove:
"""Indicates that an existing node has been removed"""
expression: exp.Expr
@dataclass(frozen=True)
class Move:
"""Indicates that an existing node's position within the tree has changed"""
source: exp.Expr
target: exp.Expr
@dataclass(frozen=True)
class Update:
"""Indicates that an existing node has been updated"""
source: exp.Expr
target: exp.Expr
@dataclass(frozen=True)
class Keep:
"""Indicates that an existing node hasn't been changed"""
source: exp.Expr
target: exp.Expr
if t.TYPE_CHECKING:
from sqlglot._typing import T
Edit = t.Union[Insert, Remove, Move, Update, Keep]
def diff(
source: exp.Expr,
target: exp.Expr,
matchings: list[tuple[exp.Expr, exp.Expr]] | None = None,
delta_only: bool = False,
**kwargs: t.Any,
) -> list[Edit]:
"""
Returns the list of changes between the source and the target expressions.
Examples:
>>> from sqlglot import parse_one
>>> diff(parse_one("a + b"), parse_one("a + c")) # doctest: +SKIP
[...]
Args:
source: the source expression.
target: the target expression against which the diff should be calculated.
matchings: the list of pre-matched node pairs which is used to help the algorithm's
heuristics produce better results for subtrees that are known by a caller to be matching.
Note: expression references in this list must refer to the same node objects that are
referenced in the source / target trees.
delta_only: excludes all `Keep` nodes from the diff.
kwargs: additional arguments to pass to the ChangeDistiller instance.
Returns:
the list of Insert, Remove, Move, Update and Keep objects for each node in the source and the
target expression trees. This list represents a sequence of steps needed to transform the source
expression tree into the target one.
"""
matchings = matchings or []
def compute_node_mappings(
old_nodes: tuple[exp.Expr, ...], new_nodes: tuple[exp.Expr, ...]
) -> dict[int, exp.Expr]:
node_mapping = {}
for old_node, new_node in zip(reversed(old_nodes), reversed(new_nodes)):
new_node._hash = hash(new_node)
node_mapping[id(old_node)] = new_node
return node_mapping
# if the source and target have any shared objects, that means there's an issue with the ast
# the algorithm won't work because the parent / hierarchies will be inaccurate
source_nodes = tuple(source.walk())
target_nodes = tuple(target.walk())
source_ids = {id(n) for n in source_nodes}
target_ids = {id(n) for n in target_nodes}
copy = (
len(source_nodes) != len(source_ids)
or len(target_nodes) != len(target_ids)
or source_ids & target_ids
)
source_copy = source.copy() if copy else source
target_copy = target.copy() if copy else target
try:
# We cache the hash of each new node here to speed up equality comparisons. If the input
# trees aren't copied, these hashes will be evicted before returning the edit script.
if copy and matchings:
source_mapping = compute_node_mappings(source_nodes, tuple(source_copy.walk()))
target_mapping = compute_node_mappings(target_nodes, tuple(target_copy.walk()))
matchings = [(source_mapping[id(s)], target_mapping[id(t)]) for s, t in matchings]
else:
for node in chain(reversed(source_nodes), reversed(target_nodes)):
node._hash = hash(node)
edit_script = ChangeDistiller(**kwargs).diff(
source_copy,
target_copy,
matchings=matchings,
delta_only=delta_only,
)
finally:
if not copy:
for node in chain(source_nodes, target_nodes):
node._hash = None
return edit_script
# The expression types for which Update edits are allowed.
UPDATABLE_EXPRESSION_TYPES = (
exp.Alias,
exp.Boolean,
exp.Column,
exp.DataType,
exp.Lambda,
exp.Literal,
exp.Table,
exp.Window,
)
IGNORED_LEAF_EXPRESSION_TYPES = (exp.Identifier,)
class ChangeDistiller:
"""
The implementation of the Change Distiller algorithm described by Beat Fluri and Martin Pinzger in
their paper https://ieeexplore.ieee.org/document/4339230, which in turn is based on the algorithm by
Chawathe et al. described in http://ilpubs.stanford.edu:8090/115/1/1995-46.pdf.
"""
def __init__(self, f: float = 0.6, t: float = 0.6, dialect: DialectType = None) -> None:
self.f = f
self.t = t
self._sql_generator = Dialect.get_or_raise(dialect).generator(comments=False)
def diff(
self,
source: exp.Expr,
target: exp.Expr,
matchings: list[tuple[exp.Expr, exp.Expr]] | None = None,
delta_only: bool = False,
) -> list[Edit]:
matchings = matchings or []
pre_matched_nodes = {id(s): id(t) for s, t in matchings}
self._source = source
self._target = target
self._source_index = {
id(n): n for n in self._source.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
}
self._target_index = {
id(n): n for n in self._target.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
}
self._unmatched_source_nodes = set(self._source_index) - set(pre_matched_nodes)
self._unmatched_target_nodes = set(self._target_index) - set(pre_matched_nodes.values())
self._bigram_histo_cache: dict[int, defaultdict[str, int]] = {}
matching_set = self._compute_matching_set() | set(pre_matched_nodes.items())
return self._generate_edit_script(dict(matching_set), delta_only)
def _generate_edit_script(self, matchings: dict[int, int], delta_only: bool) -> list[Edit]:
edit_script: list[Edit] = []
for removed_node_id in self._unmatched_source_nodes:
edit_script.append(Remove(self._source_index[removed_node_id]))
for inserted_node_id in self._unmatched_target_nodes:
edit_script.append(Insert(self._target_index[inserted_node_id]))
for kept_source_node_id, kept_target_node_id in matchings.items():
source_node = self._source_index[kept_source_node_id]
target_node = self._target_index[kept_target_node_id]
identical_nodes = source_node == target_node
if not isinstance(source_node, UPDATABLE_EXPRESSION_TYPES) or identical_nodes:
if identical_nodes:
source_parent = source_node.parent
target_parent = target_node.parent
if (
(source_parent and not target_parent)
or (not source_parent and target_parent)
or (
source_parent
and target_parent
and matchings.get(id(source_parent)) != id(target_parent)
)
):
edit_script.append(Move(source=source_node, target=target_node))
else:
edit_script.extend(
self._generate_move_edits(source_node, target_node, matchings)
)
source_non_expression_leaves = dict(_get_non_expression_leaves(source_node))
target_non_expression_leaves = dict(_get_non_expression_leaves(target_node))
if source_non_expression_leaves != target_non_expression_leaves:
edit_script.append(Update(source_node, target_node))
elif not delta_only:
edit_script.append(Keep(source_node, target_node))
else:
edit_script.append(Update(source_node, target_node))
return edit_script
def _generate_move_edits(
self, source: exp.Expr, target: exp.Expr, matchings: dict[int, int]
) -> list[Move]:
source_args = [id(e) for e in _expression_only_args(source)]
target_args = [id(e) for e in _expression_only_args(target)]
args_lcs = set(
_lcs(source_args, target_args, lambda l, r: matchings.get(t.cast(int, l)) == r)
)
move_edits = []
for a in source_args:
if a not in args_lcs and a not in self._unmatched_source_nodes:
move_edits.append(
Move(source=self._source_index[a], target=self._target_index[matchings[a]])
)
return move_edits
def _compute_matching_set(self) -> set[tuple[int, int]]:
leaves_matching_set = self._compute_leaf_matching_set()
matching_set = leaves_matching_set.copy()
ordered_unmatched_source_nodes = {
id(n): None for n in self._source.bfs() if id(n) in self._unmatched_source_nodes
}
ordered_unmatched_target_nodes = {
id(n): None for n in self._target.bfs() if id(n) in self._unmatched_target_nodes
}
for source_node_id in ordered_unmatched_source_nodes:
for target_node_id in ordered_unmatched_target_nodes:
source_node = self._source_index[source_node_id]
target_node = self._target_index[target_node_id]
if _is_same_type(source_node, target_node):
source_leaf_ids = {id(l) for l in _get_expression_leaves(source_node)}
target_leaf_ids = {id(l) for l in _get_expression_leaves(target_node)}
max_leaves_num = max(len(source_leaf_ids), len(target_leaf_ids))
if max_leaves_num:
common_leaves_num = sum(
1 if s in source_leaf_ids and t in target_leaf_ids else 0
for s, t in leaves_matching_set
)
leaf_similarity_score = common_leaves_num / max_leaves_num
else:
leaf_similarity_score = 0.0
adjusted_t = (
self.t if min(len(source_leaf_ids), len(target_leaf_ids)) > 4 else 0.4
)
if leaf_similarity_score >= 0.8 or (
leaf_similarity_score >= adjusted_t
and self._dice_coefficient(source_node, target_node) >= self.f
):
matching_set.add((source_node_id, target_node_id))
self._unmatched_source_nodes.remove(source_node_id)
self._unmatched_target_nodes.remove(target_node_id)
ordered_unmatched_target_nodes.pop(target_node_id, None)
break
return matching_set
def _compute_leaf_matching_set(self) -> set[tuple[int, int]]:
candidate_matchings: list[tuple[float, int, int, exp.Expr, exp.Expr]] = []
source_expression_leaves = list(_get_expression_leaves(self._source))
target_expression_leaves = list(_get_expression_leaves(self._target))
for source_leaf in source_expression_leaves:
for target_leaf in target_expression_leaves:
if _is_same_type(source_leaf, target_leaf):
similarity_score = self._dice_coefficient(source_leaf, target_leaf)
if similarity_score >= self.f:
heappush(
candidate_matchings,
(
-similarity_score,
-_parent_similarity_score(source_leaf, target_leaf),
len(candidate_matchings),
source_leaf,
target_leaf,
),
)
# Pick best matchings based on the highest score
matching_set = set()
while candidate_matchings:
_, _, _, source_leaf, target_leaf = heappop(candidate_matchings)
if (
id(source_leaf) in self._unmatched_source_nodes
and id(target_leaf) in self._unmatched_target_nodes
):
matching_set.add((id(source_leaf), id(target_leaf)))
self._unmatched_source_nodes.remove(id(source_leaf))
self._unmatched_target_nodes.remove(id(target_leaf))
return matching_set
def _dice_coefficient(self, source: exp.Expr, target: exp.Expr) -> float:
source_histo = self._bigram_histo(source)
target_histo = self._bigram_histo(target)
total_grams = sum(source_histo.values()) + sum(target_histo.values())
if not total_grams:
return 1.0 if source == target else 0.0
overlap_len = 0
overlapping_grams = set(source_histo) & set(target_histo)
for g in overlapping_grams:
overlap_len += min(source_histo[g], target_histo[g])
return 2 * overlap_len / total_grams
def _bigram_histo(self, expression: exp.Expr) -> defaultdict[str, int]:
if id(expression) in self._bigram_histo_cache:
return self._bigram_histo_cache[id(expression)]
expression_str = self._sql_generator.generate(expression)
count = max(0, len(expression_str) - 1)
bigram_histo: defaultdict[str, int] = defaultdict(int)
for i in range(count):
bigram_histo[expression_str[i : i + 2]] += 1
self._bigram_histo_cache[id(expression)] = bigram_histo
return bigram_histo
def _get_expression_leaves(expression: exp.Expr) -> Iterator[exp.Expr]:
has_child_exprs = False
for node in expression.iter_expressions():
if not isinstance(node, IGNORED_LEAF_EXPRESSION_TYPES):
has_child_exprs = True
yield from _get_expression_leaves(node)
if not has_child_exprs:
yield expression
def _get_non_expression_leaves(expression: exp.Expr) -> Iterator[tuple[str, t.Any]]:
for arg, value in expression.args.items():
if (
value is None
or isinstance(value, exp.Expr)
or (isinstance(value, list) and isinstance(seq_get(value, 0), exp.Expr))
):
continue
yield (arg, value)
def _is_same_type(source: exp.Expr, target: exp.Expr) -> bool:
if type(source) is type(target):
if isinstance(source, exp.Join):
return source.args.get("side") == target.args.get("side")
if isinstance(source, exp.Anonymous):
return source.this == target.this
return True
return False
def _parent_similarity_score(source: exp.Expr | None, target: exp.Expr | None) -> int:
if source is None or target is None or type(source) is not type(target):
return 0
return 1 + _parent_similarity_score(source.parent, target.parent)
def _expression_only_args(expression: exp.Expr) -> Iterator[exp.Expr]:
yield from (
arg
for arg in expression.iter_expressions()
if not isinstance(arg, IGNORED_LEAF_EXPRESSION_TYPES)
)
def _lcs(
seq_a: Sequence[T], seq_b: Sequence[T], equal: t.Callable[[T, T], bool]
) -> Sequence[T | None]:
"""Calculates the longest common subsequence"""
len_a = len(seq_a)
len_b = len(seq_b)
lcs_result = [[None] * (len_b + 1) for i in range(len_a + 1)]
for i in range(len_a + 1):
for j in range(len_b + 1):
if i == 0 or j == 0:
lcs_result[i][j] = [] # type: ignore
elif equal(seq_a[i - 1], seq_b[j - 1]):
lcs_result[i][j] = lcs_result[i - 1][j - 1] + [seq_a[i - 1]] # type: ignore
else:
lcs_result[i][j] = (
lcs_result[i - 1][j]
if len(lcs_result[i - 1][j]) > len(lcs_result[i][j - 1]) # type: ignore
else lcs_result[i][j - 1]
)
return lcs_result[len_a][len_b] # type: ignore