sqlglot/sqlglot/diff.py

from collections import defaultdict
from dataclasses import dataclass
from heapq import heappop, heappush

from sqlglot import Dialect
from sqlglot import expressions as exp
from sqlglot.helper import ensure_list


@dataclass(frozen=True)
class Insert:
    """Indicates that a new node has been inserted"""

    expression: exp.Expression


@dataclass(frozen=True)
class Remove:
    """Indicates that an existing node has been removed"""

    expression: exp.Expression


@dataclass(frozen=True)
class Move:
    """Indicates that an existing node's position within the tree has changed"""

    expression: exp.Expression


@dataclass(frozen=True)
class Update:
    """Indicates that an existing node has been updated"""

    source: exp.Expression
    target: exp.Expression


@dataclass(frozen=True)
class Keep:
    """Indicates that an existing node hasn't been changed"""

    source: exp.Expression
    target: exp.Expression


def diff(source, target):
    """
    Returns the list of changes between the source and the target expressions.

    Examples:
        >>> diff(parse_one("a + b"), parse_one("a + c"))
        [
            Remove(expression=(COLUMN this: (IDENTIFIER this: b, quoted: False))),
            Insert(expression=(COLUMN this: (IDENTIFIER this: c, quoted: False))),
            Keep(
                source=(ADD this: ...),
                target=(ADD this: ...)
            ),
            Keep(
                source=(COLUMN this: (IDENTIFIER this: a, quoted: False)),
                target=(COLUMN this: (IDENTIFIER this: a, quoted: False))
            ),
        ]

    Args:
        source (sqlglot.Expression): the source expression.
        target (sqlglot.Expression): the target expression against which the diff should be calculated.

    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.
    """
    return ChangeDistiller().diff(source.copy(), target.copy())


LEAF_EXPRESSION_TYPES = (
    exp.Boolean,
    exp.DataType,
    exp.Identifier,
    exp.Literal,
)


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=0.6, t=0.6):
        self.f = f
        self.t = t
        self._sql_generator = Dialect().generator()

    def diff(self, source, target):
        self._source = source
        self._target = target
        self._source_index = {id(n[0]): n[0] for n in source.bfs()}
        self._target_index = {id(n[0]): n[0] for n in target.bfs()}
        self._unmatched_source_nodes = set(self._source_index)
        self._unmatched_target_nodes = set(self._target_index)
        self._bigram_histo_cache = {}

        matching_set = self._compute_matching_set()
        return self._generate_edit_script(matching_set)

    def _generate_edit_script(self, matching_set):
        edit_script = []
        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 matching_set:
            source_node = self._source_index[kept_source_node_id]
            target_node = self._target_index[kept_target_node_id]
            if not isinstance(source_node, LEAF_EXPRESSION_TYPES) or source_node == target_node:
                edit_script.extend(self._generate_move_edits(source_node, target_node, matching_set))
                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, target, matching_set):
        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: (l, r) in matching_set))

        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(self._source_index[a]))

        return move_edits

    def _compute_matching_set(self):
        leaves_matching_set = self._compute_leaf_matching_set()
        matching_set = leaves_matching_set.copy()

        ordered_unmatched_source_nodes = {
            id(n[0]): None for n in self._source.bfs() if id(n[0]) in self._unmatched_source_nodes
        }
        ordered_unmatched_target_nodes = {
            id(n[0]): None for n in self._target.bfs() if id(n[0]) 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_leaves(source_node)}
                    target_leaf_ids = {id(l) for l in _get_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):
        candidate_matchings = []
        source_leaves = list(_get_leaves(self._source))
        target_leaves = list(_get_leaves(self._target))
        for source_leaf in source_leaves:
            for target_leaf in target_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,
                                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, target):
        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):
        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(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_leaves(expression):
    has_child_exprs = False

    for a in expression.args.values():
        nodes = ensure_list(a)
        for node in nodes:
            if isinstance(node, exp.Expression):
                has_child_exprs = True
                yield from _get_leaves(node)

    if not has_child_exprs:
        yield expression


def _is_same_type(source, target):
    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 _expression_only_args(expression):
    args = []
    if expression:
        for a in expression.args.values():
            args.extend(ensure_list(a))
    return [a for a in args if isinstance(a, exp.Expression)]


def _lcs(seq_a, seq_b, equal):
    """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] = []
            elif equal(seq_a[i - 1], seq_b[j - 1]):
                lcs_result[i][j] = lcs_result[i - 1][j - 1] + [seq_a[i - 1]]
            else:
                lcs_result[i][j] = (
                    lcs_result[i - 1][j]
                    if len(lcs_result[i - 1][j]) > len(lcs_result[i][j - 1])
                    else lcs_result[i][j - 1]
                )

    return lcs_result[len_a][len_b]