Edit on GitHub

Semantic Diff for SQL

by Iaroslav Zeigerman

Motivation

Software is constantly changing and evolving, and identifying what has changed and reviewing those changes is an integral part of the development process. SQL code is no exception to this.

Text-based diff tools such as git diff, when applied to a code base, have certain limitations. First, they can only detect insertions and deletions, not movements or updates of individual pieces of code. Second, such tools can only detect changes between lines of text, which is too coarse for something as granular and detailed as source code. Additionally, the outcome of such a diff is dependent on the underlying code formatting, and yields different results if the formatting should change.

Consider the following diff generated by Git:

Git diff output

Semantically the query hasn’t changed. The two arguments b and c have been swapped (moved), posing no impact on the output of the query. Yet Git replaced the whole affected expression alongside a bulk of unrelated elements.

The alternative to text-based diffing is to compare Abstract Syntax Trees (AST) instead. The main advantage of ASTs are that they are a direct product of code parsing, which represents the underlying code structure at any desired level of granularity. Comparing ASTs may yield extremely precise diffs; changes such as code movements and updates can also be detected. Even more importantly, this approach facilitates additional use cases beyond eyeballing two versions of source code side by side.

The use cases I had in mind for SQL when I decided to embark on this journey of semantic diffing were the following:

  • Query similarity score. Identifying which parts the two queries have in common to automatically suggest opportunities for consolidation, creation of intermediate/staging tables, and so on.
  • Differentiating between cosmetic / structural changes and functional ones. For example when a nested query is refactored into a common table expression (CTE), this kind of change doesn’t have any functional impact on either a query or its outcome.
  • Automatic suggestions about the need to retroactively backfill data. This is especially important for pipelines that populate very large tables for which restatement is a runtime-intensive procedure. The ability to discern between simple code movements and actual modifications can help assess the impact of a change and make suggestions accordingly.

The implementation discussed in this post is now a part of the SQLGlot library. You can find a complete source code in the diff.py module. The choice of SQLglot was an obvious one due to its simple but powerful API, lack of external dependencies and, more importantly, extensive list of supported SQL dialects.

The Search for a Solution

When it comes to any diffing tool (not just a semantic one), the primary challenge is to match as many elements of compared entities as possible. Once such a set of matching elements is available, deriving a sequence of changes becomes an easy task.

If our elements have unique identifiers associated with them (for example, an element’s ID in DOM), the matching problem is trivial. However, the SQL syntax trees that we are comparing have neither unique keys nor object identifiers that can be used for the purposes of matching. So, how do we suppose to find pairs of nodes that are related?

To better illustrate the problem, consider comparing the following SQL expressions: SELECT a + b + c, d, e and SELECT a - b + c, e, f. Matching individual nodes from respective syntax trees can be visualized as follows:

Figure 1: Example of node matching for two SQL expression trees Figure 1: Example of node matching for two SQL expression trees.

By looking at the figure of node matching for two SQL expression trees above, we conclude that the following changes should be captured by our solution:

  • Inserted nodes: Sub and f. These are the nodes from the target AST which do not have a matching node in the source AST.
  • Removed nodes: Add and d. These are the nodes from the source AST which do not have a counterpart in the target AST.
  • Remaining nodes must be identified as unchanged.

It should be clear at this point that if we manage to match nodes in the source tree with their counterparts in the target tree, then computing the diff becomes a trivial matter.

Naïve Brute-Force

The naïve solution would be to try all different permutations of node pair combinations, and see which set of pairs performs the best based on some type of heuristics. The runtime cost of such a solution quickly reaches the escape velocity; if both trees had only 10 nodes each, the number of such sets would approximately be 10! ^ 2 = 3.6M ^ 2 ~= 13 * 10^12. This is a very bad case of factorial complexity (to be precise, it’s actually much worse - O(n! ^ 2) - but I couldn’t come up with a name for it), so there is little need to explore this approach any further.

Myers Algorithm

After the naïve approach was proven to be infeasible, the next question I asked myself was “how does git diff work?”. This question led me to discover the Myers diff algorithm [1]. This algorithm has been designed to compare sequences of strings. At its core, it’s looking for the shortest path on a graph of possible edits that transform the first sequence into the second one, while heavily rewarding those paths that lead to longest subsequences of unchanged elements. There’s a lot of material out there describing this algorithm in greater detail. I found James Coglan’s series of blog posts to be the most comprehensive.

Therefore, I had this “brilliant” (actually not) idea to transform trees into sequences by traversing them in topological order, and then applying the Myers algorithm on resulting sequences while using a custom heuristics when checking the equality of two nodes. Unsurprisingly, comparing sequences of strings is quite different from comparing hierarchical tree structures, and by flattening trees into sequences, we lose a lot of relevant context. This resulted in a terrible performance of this algorithm on ASTs. It often matched completely unrelated nodes, even when the two trees were mostly the same, and produced extremely inaccurate lists of changes overall. After playing around with it a little and tweaking my equality heuristics to improve accuracy, I ultimately scrapped the whole implementation and went back to the drawing board.

Change Distiller

The algorithm I settled on at the end was Change Distiller, created by Fluri et al. [2], which in turn is an improvement over the core idea described by Chawathe et al. [3].

The algorithm consists of two high-level steps:

  1. Finding appropriate matchings between pairs of nodes that are part of compared ASTs. Identifying what is meant by “appropriate” matching is also a part of this step.
  2. Generating the so-called “edit script” from the matching set built in the 1st step. The edit script is a sequence of edit operations (for example, insert, remove, update, etc.) on individual tree nodes, such that when applied as transformations on the source AST, it eventually becomes the target AST. In general, the shorter the sequence, the better. The length of the edit script can be used to compare the performance of different algorithms, though this is not the only metric that matters.

The rest of this section is dedicated to the Python implementation of the steps above using the AST implementation provided by the SQLGlot library.

Building the Matching Set

Matching Leaves

We begin composing the matching set by matching the leaf nodes. Leaf nodes are the nodes that do not have any children nodes (such as literals, identifiers, etc.). In order to match them, we gather all the leaf nodes from the source tree and generate a cartesian product with all the leaves from the target tree, while comparing pairs created this way and assigning them a similarity score. During this stage, we also exclude pairs that don’t pass basic matching criteria. Then, we pick pairs that scored the highest while making sure that each node is matched no more than once.

Using the example provided at the beginning of the post, the process of building an initial set of candidate matchings can be seen on Figure 2.

Figure 2: Building a set of candidate matchings between leaf nodes. The third item in each triplet represents a similarity score between two nodes. Figure 2: Building a set of candidate matchings between leaf nodes. The third item in each triplet represents a similarity score between two nodes.

First, let’s analyze the similarity score. Then, we’ll discuss matching criteria.

The similarity score proposed by Fluri et al. [2] is a dice coefficient applied to bigrams of respective node values. A bigram is a sequence of two adjacent elements from a string computed in a sliding window fashion:

def bigram(string):
    count = max(0, len(string) - 1)
    return [string[i : i + 2] for i in range(count)]

For reasons that will become clear shortly, we actually need to compute bigram histograms rather than just sequences:

from collections import defaultdict

def bigram_histo(string):
    count = max(0, len(string) - 1)
    bigram_histo = defaultdict(int)
    for i in range(count):
        bigram_histo[string[i : i + 2]] += 1
    return bigram_histo

The dice coefficient formula looks like following:

Dice Coefficient

Where X is a bigram of the source node and Y is a bigram of the second one. What this essentially does is count the number of bigram elements the two nodes have in common, multiply it by 2, and then divide by the total number of elements in both bigrams. This is where bigram histograms come in handy:

def dice_coefficient(source, target):
    source_histo = bigram_histo(source.sql())
    target_histo = bigram_histo(target.sql())

    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

To compute a bigram given a tree node, we first transform the node into its canonical SQL representation,so that the Literal(123) node becomes just “123” and the Identifier(“a”) node becomes just “a”. We also handle a scenario when strings are too short to derive bigrams. In this case, we fallback to checking the two nodes for equality.

Now when we know how to compute the similarity score, we can take care of the matching criteria for leaf nodes. In the original paper [2], the matching criteria is formalized as follows:

Matching criteria for leaf nodes

The two nodes are matched if two conditions are met:

  1. The node labels match (in our case labels are just node types).
  2. The similarity score for node values is greater than or equal to some threshold “f”. The authors of the paper recommend setting the value of “f” to 0.6.

With building blocks in place, we can now build a matching set for leaf nodes. First, we generate a list of candidates for matching:

from heapq import heappush, heappop

candidate_matchings = []
source_leaves = _get_leaves(self._source)
target_leaves = _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 = dice_coefficient(
                source_leaf, target_leaf
            )
            if similarity_score >= 0.6:
                heappush(
                    candidate_matchings,
                    (
                        -similarity_score,
                        len(candidate_matchings),
                        source_leaf,
                        target_leaf,
                    ),
                )

In the implementation above, we push each matching pair onto the heap to automatically maintain the correct order based on the assigned similarity score.

Finally, we build the initial matching set by picking leaf pairs with the highest score:

matching_set = set()
while candidate_matchings:
    _, _, source_leaf, target_leaf = heappop(candidate_matchings)
    if (
        source_leaf in unmatched_source_nodes
        and target_leaf in unmatched_target_nodes
    ):
        matching_set.add((source_leaf, target_leaf))
        unmatched_source_nodes.remove(source_leaf)
        unmatched_target_nodes.remove(target_leaf)

To finalize the matching set, we should now proceed with matching inner nodes.

Matching Inner Nodes

Matching inner nodes is quite similar to matching leaf nodes, with the following two distinctions:

  • Rather than ranking a set of possible candidates, we pick the first node pair that passes the matching criteria.
  • The matching criteria itself has been extended to account for the number of leaf nodes the pair of inner nodes have in common.

Figure 3: Matching inner nodes based on their type as well as how many of their leaf nodes have been previously matched. Figure 3: Matching inner nodes based on their type as well as how many of their leaf nodes have been previously matched.

Let’s start with the matching criteria. The criteria is formalized as follows:

Matching criteria for inner nodes

Alongside already familiar similarity score and node type criteria, there is a new one in the middle: the ratio of leaf nodes that the two nodes have in common must exceed some threshold “t”. The recommended value for “t” is also 0.6. Counting the number of common leaf nodes is pretty straightforward, since we already have the complete matching set for leaves. All we need to do is count how many matching pairs do leaf nodes from the two compared inner nodes form.

There are two additional heuristics associated with this matching criteria:

  • Inner node similarity weighting: if the similarity score between the node values doesn’t pass the threshold “f” but the ratio of common leaf nodes (“t”) is greater than or equal to 0.8, then the matching is considered successful.
  • The threshold “t” is reduced to 0.4 for inner nodes with the number of leaf nodes equal to 4 or less, in order to decrease the false negative rate for small subtrees.

We now only have to iterate through the remaining unmatched nodes and form matching pairs based on the outlined criteria:

leaves_matching_set = matching_set.copy()

for source_node in unmatched_source_nodes.copy():
    for target_node in unmatched_target_nodes:
        if _is_same_type(source_node, target_node):
            source_leaves = set(_get_leaves(source_node))
            target_leaves = set(_get_leaves(target_node))

            max_leaves_num = max(len(source_leaves), len(target_leaves))
            if max_leaves_num:
                common_leaves_num = sum(
                    1 if s in source_leaves and t in target_leaves 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 = (
                0.6
                if min(len(source_leaves), len(target_leaves)) > 4
                else 0.4
            )

            if leaf_similarity_score >= 0.8 or (
                leaf_similarity_score >= adjusted_t
                and dice_coefficient(source_node, target_node) >= 0.6
            ):
                matching_set.add((source_node, target_node))
                unmatched_source_nodes.remove(source_node)
                unmatched_target_nodes.remove(target_node)
                break

After the matching set is formed, we can proceed with generation of the edit script, which will be the algorithm’s output.

Generating the Edit Script

At this point, we should have the following 3 sets at our disposal:

  • The set of matched node pairs.
  • The set of remaining unmatched nodes from the source tree.
  • The set of remaining unmatched nodes from the target tree.

We can derive 3 kinds of edits from the matching set: either the node’s value was updated (Update), the node was moved to a different position within the tree (Move), or the node remained unchanged (Keep). Note that the Move case is not mutually exclusive with the other two. The node could have been updated or could have remained the same while at the same time its position within its parent node or the parent node itself could have changed. All unmatched nodes from the source tree are the ones that were removed (Remove), while unmatched nodes from the target tree are the ones that were inserted (Insert).

The latter two cases are pretty straightforward to implement:

edit_script = []

for removed_node in unmatched_source_nodes:
    edit_script.append(Remove(removed_node))
for inserted_node in unmatched_target_nodes:
    edit_script.append(Insert(inserted_node))

Traversing the matching set requires a little more thought:

for source_node, target_node in matching_set:
    if (
        not isinstance(source_node, LEAF_EXPRESSION_TYPES)
        or source_node == target_node
    ):
        move_edits = generate_move_edits(
            source_node, target_node, matching_set
        )
        edit_script.extend(move_edits)
        edit_script.append(Keep(source_node, target_node))
    else:
        edit_script.append(Update(source_node, target_node))

If a matching pair represents a pair of leaf nodes, we check if they are the same to decide whether an update took place. For inner node pairs, we also need to compare the positions of their respective children to detect node movements. Chawathe et al. [3] suggest applying the longest common subsequence (LCS) algorithm which, no surprise here, was described by Myers himself [1]. There is a small catch, however: instead of checking the equality of two children nodes, we need to check whether the two nodes form a pair that is a part of our matching set.

Now with this knowledge, the implementation becomes straightforward:

def generate_move_edits(source, target, matching_set):
    source_children = _get_child_nodes(source)
    target_children = _get_child_nodes(target)

    lcs = set(
        _longest_common_subsequence(
            source_children,
            target_children,
            lambda l, r: (l, r) in matching_set
        )
    )

    move_edits = []
    for node in source_children:
        if node not in lcs and node not in unmatched_source_nodes:
            move_edits.append(Move(node))

    return move_edits

I left out the implementation of the LCS algorithm itself here, but there are plenty of implementation choices out there that can be easily looked up.

Output

The implemented algorithm produces the output that resembles the following:

>>> from sqlglot import parse_one, diff
>>> diff(parse_one("SELECT a + b + c, d, e"), parse_one("SELECT a - b + c, e, f"))

Remove(Add)
Remove(Column(d))
Remove(Identifier(d))
Insert(Sub)
Insert(Column(f))
Insert(Identifier(f))
Keep(Select, Select)
Keep(Add, Add)
Keep(Column(a), Column(a))
Keep(Identifier(a), Identifier(a))
Keep(Column(b), Column(b))
Keep(Identifier(b), Identifier(b))
Keep(Column(c), Column(c))
Keep(Identifier(c), Identifier(c))
Keep(Column(e), Column(e))
Keep(Identifier(e), Identifier(e))

Note that the output above is abbreviated. The string representation of actual AST nodes is significantly more verbose.

The implementation works especially well when coupled with the SQLGlot’s query optimizer which can be used to produce canonical representations of compared queries:

>>> schema={"t": {"a": "INT", "b": "INT", "c": "INT", "d": "INT"}}
>>> source = """
... SELECT 1 + 1 + a
... FROM t
... WHERE b = 1 OR (c = 2 AND d = 3)
... """
>>> target = """
... SELECT 2 + a
... FROM t
... WHERE (b = 1 OR c = 2) AND (b = 1 OR d = 3)
... """
>>> optimized_source = optimize(parse_one(source), schema=schema)
>>> optimized_target = optimize(parse_one(target), schema=schema)
>>> edit_script = diff(optimized_source, optimized_target)
>>> sum(0 if isinstance(e, Keep) else 1 for e in edit_script)
0

Optimizations

The worst case runtime complexity of this algorithm is not exactly stellar: O(n^2 * log n^2). This is because of the leaf matching process, which involves ranking a cartesian product between all leaf nodes of compared trees. Unsurprisingly, the algorithm takes a considerable time to finish for bigger queries.

There are still a few basic things we can do in our implementation to help improve performance:

  • Refer to individual node objects using their identifiers (Python’s id()) instead of direct references in sets. This helps avoid costly recursive hash calculations and equality checks.
  • Cache bigram histograms to avoid computing them more than once for the same node.
  • Compute the canonical SQL string representation for each tree once while caching string representations of all inner nodes. This prevents redundant tree traversals when bigrams are computed.

At the time of writing only the first two optimizations have been implemented, so there is an opportunity to contribute for anyone who’s interested.

Alternative Solutions

This section is dedicated to solutions that I’ve investigated, but haven’t tried.

First, this section wouldn’t be complete without Tristan Hume’s blog post. Tristan’s solution has a lot in common with the Myers algorithm plus heuristics that is much more clever than what I came up with. The implementation relies on a combination of dynamic programming and A* search algorithm to explore the space of possible matchings and pick the best ones. It seemed to have worked well for Tistan’s specific use case, but after my negative experience with the Myers algorithm, I decided to try something different.

Another notable approach is the Gumtree algorithm by Falleri et al. [4]. I discovered this paper after I’d already implemented the algorithm that is the main focus of this post. In sections 5.2 and 5.3 of their paper, the authors compare the two algorithms side by side and claim that Gumtree is significantly better in terms of both runtime performance and accuracy when evaluated on 12 792 pairs of Java source files. This doesn’t surprise me, as the algorithm takes the height of subtrees into account. In my tests, I definitely saw scenarios in which this context would have helped. On top of that, the authors promise O(n^2) runtime complexity in the worst case which, given the Change Distiller's O(n^2 * log n^2), looks particularly tempting. I hope to try this algorithm out at some point, and there is a good chance you see me writing about it in my future posts.

Conclusion

The Change Distiller algorithm yielded quite satisfactory results in most of my tests. The scenarios in which it fell short mostly concerned identical (or very similar) subtrees located in different parts of the AST. In those cases, node mismatches were frequent and, as a result, edit scripts were somewhat suboptimal.

Additionally, the runtime performance of the algorithm leaves a lot to be desired. On trees with 1000 leaf nodes each, the algorithm takes a little under 2 seconds to complete. My implementation still has room for improvement, but this should give you a rough idea of what to expect. It appears that the Gumtree algorithm [4] can help address both of these points. I hope to find bandwidth to work on it soon and then compare the two algorithms side-by-side to find out which one performs better on SQL specifically. In the meantime, Change Distiller definitely gets the job done, and I can now proceed with applying it to some of the use cases I mentioned at the beginning of this post.

I’m also curious to learn whether other folks in the industry faced a similar problem, and how they approached it. If you did something similar, I’m interested to hear about your experience.

References

[1] Eugene W. Myers. An O(ND) Difference Algorithm and Its Variations. Algorithmica 1(2): 251-266 (1986)

[2] B. Fluri, M. Wursch, M. Pinzger, and H. Gall. Change Distilling: Tree differencing for fine-grained source code change extraction. IEEE Trans. Software Eng., 33(11):725–743, 2007.

[3] S.S. Chawathe, A. Rajaraman, H. Garcia-Molina, and J. Widom. Change Detection in Hierarchically Structured Information. Proc. ACM Sigmod Int’l Conf. Management of Data, pp. 493-504, June 1996

[4] Jean-Rémy Falleri, Floréal Morandat, Xavier Blanc, Matias Martinez, Martin Monperrus. Fine-grained and Accurate Source Code Differencing. Proceedings of the International Conference on Automated Software Engineering, 2014, Västeras, Sweden. pp.313-324, 10.1145/2642937.2642982. hal-01054552


  1"""
  2.. include:: ../posts/sql_diff.md
  3
  4----
  5"""
  6
  7from __future__ import annotations
  8
  9import typing as t
 10from collections import defaultdict
 11from dataclasses import dataclass
 12from heapq import heappop, heappush
 13
 14from sqlglot import Dialect, expressions as exp
 15from sqlglot.helper import ensure_list
 16
 17
 18@dataclass(frozen=True)
 19class Insert:
 20    """Indicates that a new node has been inserted"""
 21
 22    expression: exp.Expression
 23
 24
 25@dataclass(frozen=True)
 26class Remove:
 27    """Indicates that an existing node has been removed"""
 28
 29    expression: exp.Expression
 30
 31
 32@dataclass(frozen=True)
 33class Move:
 34    """Indicates that an existing node's position within the tree has changed"""
 35
 36    expression: exp.Expression
 37
 38
 39@dataclass(frozen=True)
 40class Update:
 41    """Indicates that an existing node has been updated"""
 42
 43    source: exp.Expression
 44    target: exp.Expression
 45
 46
 47@dataclass(frozen=True)
 48class Keep:
 49    """Indicates that an existing node hasn't been changed"""
 50
 51    source: exp.Expression
 52    target: exp.Expression
 53
 54
 55if t.TYPE_CHECKING:
 56    from sqlglot._typing import T
 57
 58    Edit = t.Union[Insert, Remove, Move, Update, Keep]
 59
 60
 61def diff(
 62    source: exp.Expression,
 63    target: exp.Expression,
 64    matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None,
 65    delta_only: bool = False,
 66    **kwargs: t.Any,
 67) -> t.List[Edit]:
 68    """
 69    Returns the list of changes between the source and the target expressions.
 70
 71    Examples:
 72        >>> diff(parse_one("a + b"), parse_one("a + c"))
 73        [
 74            Remove(expression=(COLUMN this: (IDENTIFIER this: b, quoted: False))),
 75            Insert(expression=(COLUMN this: (IDENTIFIER this: c, quoted: False))),
 76            Keep(
 77                source=(ADD this: ...),
 78                target=(ADD this: ...)
 79            ),
 80            Keep(
 81                source=(COLUMN this: (IDENTIFIER this: a, quoted: False)),
 82                target=(COLUMN this: (IDENTIFIER this: a, quoted: False))
 83            ),
 84        ]
 85
 86    Args:
 87        source: the source expression.
 88        target: the target expression against which the diff should be calculated.
 89        matchings: the list of pre-matched node pairs which is used to help the algorithm's
 90            heuristics produce better results for subtrees that are known by a caller to be matching.
 91            Note: expression references in this list must refer to the same node objects that are
 92            referenced in source / target trees.
 93        delta_only: excludes all `Keep` nodes from the diff.
 94        kwargs: additional arguments to pass to the ChangeDistiller instance.
 95
 96    Returns:
 97        the list of Insert, Remove, Move, Update and Keep objects for each node in the source and the
 98        target expression trees. This list represents a sequence of steps needed to transform the source
 99        expression tree into the target one.
100    """
101    matchings = matchings or []
102    matching_ids = {id(n) for pair in matchings for n in pair}
103
104    def compute_node_mappings(
105        original: exp.Expression, copy: exp.Expression
106    ) -> t.Dict[int, exp.Expression]:
107        return {
108            id(old_node): new_node
109            for old_node, new_node in zip(original.walk(), copy.walk())
110            if id(old_node) in matching_ids
111        }
112
113    source_copy = source.copy()
114    target_copy = target.copy()
115
116    node_mappings = {
117        **compute_node_mappings(source, source_copy),
118        **compute_node_mappings(target, target_copy),
119    }
120    matchings_copy = [(node_mappings[id(s)], node_mappings[id(t)]) for s, t in matchings]
121
122    return ChangeDistiller(**kwargs).diff(
123        source_copy,
124        target_copy,
125        matchings=matchings_copy,
126        delta_only=delta_only,
127    )
128
129
130# The expression types for which Update edits are allowed.
131UPDATABLE_EXPRESSION_TYPES = (
132    exp.Boolean,
133    exp.DataType,
134    exp.Literal,
135    exp.Table,
136    exp.Column,
137    exp.Lambda,
138)
139
140IGNORED_LEAF_EXPRESSION_TYPES = (exp.Identifier,)
141
142
143class ChangeDistiller:
144    """
145    The implementation of the Change Distiller algorithm described by Beat Fluri and Martin Pinzger in
146    their paper https://ieeexplore.ieee.org/document/4339230, which in turn is based on the algorithm by
147    Chawathe et al. described in http://ilpubs.stanford.edu:8090/115/1/1995-46.pdf.
148    """
149
150    def __init__(self, f: float = 0.6, t: float = 0.6) -> None:
151        self.f = f
152        self.t = t
153        self._sql_generator = Dialect().generator()
154
155    def diff(
156        self,
157        source: exp.Expression,
158        target: exp.Expression,
159        matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None,
160        delta_only: bool = False,
161    ) -> t.List[Edit]:
162        matchings = matchings or []
163        pre_matched_nodes = {id(s): id(t) for s, t in matchings}
164        if len({n for pair in pre_matched_nodes.items() for n in pair}) != 2 * len(matchings):
165            raise ValueError("Each node can be referenced at most once in the list of matchings")
166
167        self._source = source
168        self._target = target
169        self._source_index = {
170            id(n): n for n in self._source.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
171        }
172        self._target_index = {
173            id(n): n for n in self._target.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
174        }
175        self._unmatched_source_nodes = set(self._source_index) - set(pre_matched_nodes)
176        self._unmatched_target_nodes = set(self._target_index) - set(pre_matched_nodes.values())
177        self._bigram_histo_cache: t.Dict[int, t.DefaultDict[str, int]] = {}
178
179        matching_set = self._compute_matching_set() | {(s, t) for s, t in pre_matched_nodes.items()}
180        return self._generate_edit_script(matching_set, delta_only)
181
182    def _generate_edit_script(
183        self,
184        matching_set: t.Set[t.Tuple[int, int]],
185        delta_only: bool,
186    ) -> t.List[Edit]:
187        edit_script: t.List[Edit] = []
188        for removed_node_id in self._unmatched_source_nodes:
189            edit_script.append(Remove(self._source_index[removed_node_id]))
190        for inserted_node_id in self._unmatched_target_nodes:
191            edit_script.append(Insert(self._target_index[inserted_node_id]))
192        for kept_source_node_id, kept_target_node_id in matching_set:
193            source_node = self._source_index[kept_source_node_id]
194            target_node = self._target_index[kept_target_node_id]
195            if (
196                not isinstance(source_node, UPDATABLE_EXPRESSION_TYPES)
197                or source_node == target_node
198            ):
199                edit_script.extend(
200                    self._generate_move_edits(source_node, target_node, matching_set)
201                )
202                if not delta_only:
203                    edit_script.append(Keep(source_node, target_node))
204            else:
205                edit_script.append(Update(source_node, target_node))
206
207        return edit_script
208
209    def _generate_move_edits(
210        self, source: exp.Expression, target: exp.Expression, matching_set: t.Set[t.Tuple[int, int]]
211    ) -> t.List[Move]:
212        source_args = [id(e) for e in _expression_only_args(source)]
213        target_args = [id(e) for e in _expression_only_args(target)]
214
215        args_lcs = set(_lcs(source_args, target_args, lambda l, r: (l, r) in matching_set))
216
217        move_edits = []
218        for a in source_args:
219            if a not in args_lcs and a not in self._unmatched_source_nodes:
220                move_edits.append(Move(self._source_index[a]))
221
222        return move_edits
223
224    def _compute_matching_set(self) -> t.Set[t.Tuple[int, int]]:
225        leaves_matching_set = self._compute_leaf_matching_set()
226        matching_set = leaves_matching_set.copy()
227
228        ordered_unmatched_source_nodes = {
229            id(n): None for n in self._source.bfs() if id(n) in self._unmatched_source_nodes
230        }
231        ordered_unmatched_target_nodes = {
232            id(n): None for n in self._target.bfs() if id(n) in self._unmatched_target_nodes
233        }
234
235        for source_node_id in ordered_unmatched_source_nodes:
236            for target_node_id in ordered_unmatched_target_nodes:
237                source_node = self._source_index[source_node_id]
238                target_node = self._target_index[target_node_id]
239                if _is_same_type(source_node, target_node):
240                    source_leaf_ids = {id(l) for l in _get_leaves(source_node)}
241                    target_leaf_ids = {id(l) for l in _get_leaves(target_node)}
242
243                    max_leaves_num = max(len(source_leaf_ids), len(target_leaf_ids))
244                    if max_leaves_num:
245                        common_leaves_num = sum(
246                            1 if s in source_leaf_ids and t in target_leaf_ids else 0
247                            for s, t in leaves_matching_set
248                        )
249                        leaf_similarity_score = common_leaves_num / max_leaves_num
250                    else:
251                        leaf_similarity_score = 0.0
252
253                    adjusted_t = (
254                        self.t if min(len(source_leaf_ids), len(target_leaf_ids)) > 4 else 0.4
255                    )
256
257                    if leaf_similarity_score >= 0.8 or (
258                        leaf_similarity_score >= adjusted_t
259                        and self._dice_coefficient(source_node, target_node) >= self.f
260                    ):
261                        matching_set.add((source_node_id, target_node_id))
262                        self._unmatched_source_nodes.remove(source_node_id)
263                        self._unmatched_target_nodes.remove(target_node_id)
264                        ordered_unmatched_target_nodes.pop(target_node_id, None)
265                        break
266
267        return matching_set
268
269    def _compute_leaf_matching_set(self) -> t.Set[t.Tuple[int, int]]:
270        candidate_matchings: t.List[t.Tuple[float, int, int, exp.Expression, exp.Expression]] = []
271        source_leaves = list(_get_leaves(self._source))
272        target_leaves = list(_get_leaves(self._target))
273        for source_leaf in source_leaves:
274            for target_leaf in target_leaves:
275                if _is_same_type(source_leaf, target_leaf):
276                    similarity_score = self._dice_coefficient(source_leaf, target_leaf)
277                    if similarity_score >= self.f:
278                        heappush(
279                            candidate_matchings,
280                            (
281                                -similarity_score,
282                                -_parent_similarity_score(source_leaf, target_leaf),
283                                len(candidate_matchings),
284                                source_leaf,
285                                target_leaf,
286                            ),
287                        )
288
289        # Pick best matchings based on the highest score
290        matching_set = set()
291        while candidate_matchings:
292            _, _, _, source_leaf, target_leaf = heappop(candidate_matchings)
293            if (
294                id(source_leaf) in self._unmatched_source_nodes
295                and id(target_leaf) in self._unmatched_target_nodes
296            ):
297                matching_set.add((id(source_leaf), id(target_leaf)))
298                self._unmatched_source_nodes.remove(id(source_leaf))
299                self._unmatched_target_nodes.remove(id(target_leaf))
300
301        return matching_set
302
303    def _dice_coefficient(self, source: exp.Expression, target: exp.Expression) -> float:
304        source_histo = self._bigram_histo(source)
305        target_histo = self._bigram_histo(target)
306
307        total_grams = sum(source_histo.values()) + sum(target_histo.values())
308        if not total_grams:
309            return 1.0 if source == target else 0.0
310
311        overlap_len = 0
312        overlapping_grams = set(source_histo) & set(target_histo)
313        for g in overlapping_grams:
314            overlap_len += min(source_histo[g], target_histo[g])
315
316        return 2 * overlap_len / total_grams
317
318    def _bigram_histo(self, expression: exp.Expression) -> t.DefaultDict[str, int]:
319        if id(expression) in self._bigram_histo_cache:
320            return self._bigram_histo_cache[id(expression)]
321
322        expression_str = self._sql_generator.generate(expression)
323        count = max(0, len(expression_str) - 1)
324        bigram_histo: t.DefaultDict[str, int] = defaultdict(int)
325        for i in range(count):
326            bigram_histo[expression_str[i : i + 2]] += 1
327
328        self._bigram_histo_cache[id(expression)] = bigram_histo
329        return bigram_histo
330
331
332def _get_leaves(expression: exp.Expression) -> t.Iterator[exp.Expression]:
333    has_child_exprs = False
334
335    for node in expression.iter_expressions():
336        if not isinstance(node, IGNORED_LEAF_EXPRESSION_TYPES):
337            has_child_exprs = True
338            yield from _get_leaves(node)
339
340    if not has_child_exprs:
341        yield expression
342
343
344def _is_same_type(source: exp.Expression, target: exp.Expression) -> bool:
345    if type(source) is type(target):
346        if isinstance(source, exp.Join):
347            return source.args.get("side") == target.args.get("side")
348
349        if isinstance(source, exp.Anonymous):
350            return source.this == target.this
351
352        return True
353
354    return False
355
356
357def _parent_similarity_score(
358    source: t.Optional[exp.Expression], target: t.Optional[exp.Expression]
359) -> int:
360    if source is None or target is None or type(source) is not type(target):
361        return 0
362
363    return 1 + _parent_similarity_score(source.parent, target.parent)
364
365
366def _expression_only_args(expression: exp.Expression) -> t.List[exp.Expression]:
367    args: t.List[t.Union[exp.Expression, t.List]] = []
368    if expression:
369        for a in expression.args.values():
370            args.extend(ensure_list(a))
371    return [
372        a
373        for a in args
374        if isinstance(a, exp.Expression) and not isinstance(a, IGNORED_LEAF_EXPRESSION_TYPES)
375    ]
376
377
378def _lcs(
379    seq_a: t.Sequence[T], seq_b: t.Sequence[T], equal: t.Callable[[T, T], bool]
380) -> t.Sequence[t.Optional[T]]:
381    """Calculates the longest common subsequence"""
382
383    len_a = len(seq_a)
384    len_b = len(seq_b)
385    lcs_result = [[None] * (len_b + 1) for i in range(len_a + 1)]
386
387    for i in range(len_a + 1):
388        for j in range(len_b + 1):
389            if i == 0 or j == 0:
390                lcs_result[i][j] = []  # type: ignore
391            elif equal(seq_a[i - 1], seq_b[j - 1]):
392                lcs_result[i][j] = lcs_result[i - 1][j - 1] + [seq_a[i - 1]]  # type: ignore
393            else:
394                lcs_result[i][j] = (
395                    lcs_result[i - 1][j]
396                    if len(lcs_result[i - 1][j]) > len(lcs_result[i][j - 1])  # type: ignore
397                    else lcs_result[i][j - 1]
398                )
399
400    return lcs_result[len_a][len_b]  # type: ignore
@dataclass(frozen=True)
class Insert:
19@dataclass(frozen=True)
20class Insert:
21    """Indicates that a new node has been inserted"""
22
23    expression: exp.Expression

Indicates that a new node has been inserted

Insert(expression: sqlglot.expressions.Expression)
@dataclass(frozen=True)
class Remove:
26@dataclass(frozen=True)
27class Remove:
28    """Indicates that an existing node has been removed"""
29
30    expression: exp.Expression

Indicates that an existing node has been removed

Remove(expression: sqlglot.expressions.Expression)
@dataclass(frozen=True)
class Move:
33@dataclass(frozen=True)
34class Move:
35    """Indicates that an existing node's position within the tree has changed"""
36
37    expression: exp.Expression

Indicates that an existing node's position within the tree has changed

Move(expression: sqlglot.expressions.Expression)
@dataclass(frozen=True)
class Update:
40@dataclass(frozen=True)
41class Update:
42    """Indicates that an existing node has been updated"""
43
44    source: exp.Expression
45    target: exp.Expression

Indicates that an existing node has been updated

@dataclass(frozen=True)
class Keep:
48@dataclass(frozen=True)
49class Keep:
50    """Indicates that an existing node hasn't been changed"""
51
52    source: exp.Expression
53    target: exp.Expression

Indicates that an existing node hasn't been changed

def diff( source: sqlglot.expressions.Expression, target: sqlglot.expressions.Expression, matchings: Optional[List[Tuple[sqlglot.expressions.Expression, sqlglot.expressions.Expression]]] = None, delta_only: bool = False, **kwargs: Any) -> List[Union[Insert, Remove, Move, Update, Keep]]:
 62def diff(
 63    source: exp.Expression,
 64    target: exp.Expression,
 65    matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None,
 66    delta_only: bool = False,
 67    **kwargs: t.Any,
 68) -> t.List[Edit]:
 69    """
 70    Returns the list of changes between the source and the target expressions.
 71
 72    Examples:
 73        >>> diff(parse_one("a + b"), parse_one("a + c"))
 74        [
 75            Remove(expression=(COLUMN this: (IDENTIFIER this: b, quoted: False))),
 76            Insert(expression=(COLUMN this: (IDENTIFIER this: c, quoted: False))),
 77            Keep(
 78                source=(ADD this: ...),
 79                target=(ADD this: ...)
 80            ),
 81            Keep(
 82                source=(COLUMN this: (IDENTIFIER this: a, quoted: False)),
 83                target=(COLUMN this: (IDENTIFIER this: a, quoted: False))
 84            ),
 85        ]
 86
 87    Args:
 88        source: the source expression.
 89        target: the target expression against which the diff should be calculated.
 90        matchings: the list of pre-matched node pairs which is used to help the algorithm's
 91            heuristics produce better results for subtrees that are known by a caller to be matching.
 92            Note: expression references in this list must refer to the same node objects that are
 93            referenced in source / target trees.
 94        delta_only: excludes all `Keep` nodes from the diff.
 95        kwargs: additional arguments to pass to the ChangeDistiller instance.
 96
 97    Returns:
 98        the list of Insert, Remove, Move, Update and Keep objects for each node in the source and the
 99        target expression trees. This list represents a sequence of steps needed to transform the source
100        expression tree into the target one.
101    """
102    matchings = matchings or []
103    matching_ids = {id(n) for pair in matchings for n in pair}
104
105    def compute_node_mappings(
106        original: exp.Expression, copy: exp.Expression
107    ) -> t.Dict[int, exp.Expression]:
108        return {
109            id(old_node): new_node
110            for old_node, new_node in zip(original.walk(), copy.walk())
111            if id(old_node) in matching_ids
112        }
113
114    source_copy = source.copy()
115    target_copy = target.copy()
116
117    node_mappings = {
118        **compute_node_mappings(source, source_copy),
119        **compute_node_mappings(target, target_copy),
120    }
121    matchings_copy = [(node_mappings[id(s)], node_mappings[id(t)]) for s, t in matchings]
122
123    return ChangeDistiller(**kwargs).diff(
124        source_copy,
125        target_copy,
126        matchings=matchings_copy,
127        delta_only=delta_only,
128    )

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))
    ),
]
Arguments:
  • 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 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.

IGNORED_LEAF_EXPRESSION_TYPES = (<class 'sqlglot.expressions.Identifier'>,)
class ChangeDistiller:
144class ChangeDistiller:
145    """
146    The implementation of the Change Distiller algorithm described by Beat Fluri and Martin Pinzger in
147    their paper https://ieeexplore.ieee.org/document/4339230, which in turn is based on the algorithm by
148    Chawathe et al. described in http://ilpubs.stanford.edu:8090/115/1/1995-46.pdf.
149    """
150
151    def __init__(self, f: float = 0.6, t: float = 0.6) -> None:
152        self.f = f
153        self.t = t
154        self._sql_generator = Dialect().generator()
155
156    def diff(
157        self,
158        source: exp.Expression,
159        target: exp.Expression,
160        matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None,
161        delta_only: bool = False,
162    ) -> t.List[Edit]:
163        matchings = matchings or []
164        pre_matched_nodes = {id(s): id(t) for s, t in matchings}
165        if len({n for pair in pre_matched_nodes.items() for n in pair}) != 2 * len(matchings):
166            raise ValueError("Each node can be referenced at most once in the list of matchings")
167
168        self._source = source
169        self._target = target
170        self._source_index = {
171            id(n): n for n in self._source.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
172        }
173        self._target_index = {
174            id(n): n for n in self._target.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
175        }
176        self._unmatched_source_nodes = set(self._source_index) - set(pre_matched_nodes)
177        self._unmatched_target_nodes = set(self._target_index) - set(pre_matched_nodes.values())
178        self._bigram_histo_cache: t.Dict[int, t.DefaultDict[str, int]] = {}
179
180        matching_set = self._compute_matching_set() | {(s, t) for s, t in pre_matched_nodes.items()}
181        return self._generate_edit_script(matching_set, delta_only)
182
183    def _generate_edit_script(
184        self,
185        matching_set: t.Set[t.Tuple[int, int]],
186        delta_only: bool,
187    ) -> t.List[Edit]:
188        edit_script: t.List[Edit] = []
189        for removed_node_id in self._unmatched_source_nodes:
190            edit_script.append(Remove(self._source_index[removed_node_id]))
191        for inserted_node_id in self._unmatched_target_nodes:
192            edit_script.append(Insert(self._target_index[inserted_node_id]))
193        for kept_source_node_id, kept_target_node_id in matching_set:
194            source_node = self._source_index[kept_source_node_id]
195            target_node = self._target_index[kept_target_node_id]
196            if (
197                not isinstance(source_node, UPDATABLE_EXPRESSION_TYPES)
198                or source_node == target_node
199            ):
200                edit_script.extend(
201                    self._generate_move_edits(source_node, target_node, matching_set)
202                )
203                if not delta_only:
204                    edit_script.append(Keep(source_node, target_node))
205            else:
206                edit_script.append(Update(source_node, target_node))
207
208        return edit_script
209
210    def _generate_move_edits(
211        self, source: exp.Expression, target: exp.Expression, matching_set: t.Set[t.Tuple[int, int]]
212    ) -> t.List[Move]:
213        source_args = [id(e) for e in _expression_only_args(source)]
214        target_args = [id(e) for e in _expression_only_args(target)]
215
216        args_lcs = set(_lcs(source_args, target_args, lambda l, r: (l, r) in matching_set))
217
218        move_edits = []
219        for a in source_args:
220            if a not in args_lcs and a not in self._unmatched_source_nodes:
221                move_edits.append(Move(self._source_index[a]))
222
223        return move_edits
224
225    def _compute_matching_set(self) -> t.Set[t.Tuple[int, int]]:
226        leaves_matching_set = self._compute_leaf_matching_set()
227        matching_set = leaves_matching_set.copy()
228
229        ordered_unmatched_source_nodes = {
230            id(n): None for n in self._source.bfs() if id(n) in self._unmatched_source_nodes
231        }
232        ordered_unmatched_target_nodes = {
233            id(n): None for n in self._target.bfs() if id(n) in self._unmatched_target_nodes
234        }
235
236        for source_node_id in ordered_unmatched_source_nodes:
237            for target_node_id in ordered_unmatched_target_nodes:
238                source_node = self._source_index[source_node_id]
239                target_node = self._target_index[target_node_id]
240                if _is_same_type(source_node, target_node):
241                    source_leaf_ids = {id(l) for l in _get_leaves(source_node)}
242                    target_leaf_ids = {id(l) for l in _get_leaves(target_node)}
243
244                    max_leaves_num = max(len(source_leaf_ids), len(target_leaf_ids))
245                    if max_leaves_num:
246                        common_leaves_num = sum(
247                            1 if s in source_leaf_ids and t in target_leaf_ids else 0
248                            for s, t in leaves_matching_set
249                        )
250                        leaf_similarity_score = common_leaves_num / max_leaves_num
251                    else:
252                        leaf_similarity_score = 0.0
253
254                    adjusted_t = (
255                        self.t if min(len(source_leaf_ids), len(target_leaf_ids)) > 4 else 0.4
256                    )
257
258                    if leaf_similarity_score >= 0.8 or (
259                        leaf_similarity_score >= adjusted_t
260                        and self._dice_coefficient(source_node, target_node) >= self.f
261                    ):
262                        matching_set.add((source_node_id, target_node_id))
263                        self._unmatched_source_nodes.remove(source_node_id)
264                        self._unmatched_target_nodes.remove(target_node_id)
265                        ordered_unmatched_target_nodes.pop(target_node_id, None)
266                        break
267
268        return matching_set
269
270    def _compute_leaf_matching_set(self) -> t.Set[t.Tuple[int, int]]:
271        candidate_matchings: t.List[t.Tuple[float, int, int, exp.Expression, exp.Expression]] = []
272        source_leaves = list(_get_leaves(self._source))
273        target_leaves = list(_get_leaves(self._target))
274        for source_leaf in source_leaves:
275            for target_leaf in target_leaves:
276                if _is_same_type(source_leaf, target_leaf):
277                    similarity_score = self._dice_coefficient(source_leaf, target_leaf)
278                    if similarity_score >= self.f:
279                        heappush(
280                            candidate_matchings,
281                            (
282                                -similarity_score,
283                                -_parent_similarity_score(source_leaf, target_leaf),
284                                len(candidate_matchings),
285                                source_leaf,
286                                target_leaf,
287                            ),
288                        )
289
290        # Pick best matchings based on the highest score
291        matching_set = set()
292        while candidate_matchings:
293            _, _, _, source_leaf, target_leaf = heappop(candidate_matchings)
294            if (
295                id(source_leaf) in self._unmatched_source_nodes
296                and id(target_leaf) in self._unmatched_target_nodes
297            ):
298                matching_set.add((id(source_leaf), id(target_leaf)))
299                self._unmatched_source_nodes.remove(id(source_leaf))
300                self._unmatched_target_nodes.remove(id(target_leaf))
301
302        return matching_set
303
304    def _dice_coefficient(self, source: exp.Expression, target: exp.Expression) -> float:
305        source_histo = self._bigram_histo(source)
306        target_histo = self._bigram_histo(target)
307
308        total_grams = sum(source_histo.values()) + sum(target_histo.values())
309        if not total_grams:
310            return 1.0 if source == target else 0.0
311
312        overlap_len = 0
313        overlapping_grams = set(source_histo) & set(target_histo)
314        for g in overlapping_grams:
315            overlap_len += min(source_histo[g], target_histo[g])
316
317        return 2 * overlap_len / total_grams
318
319    def _bigram_histo(self, expression: exp.Expression) -> t.DefaultDict[str, int]:
320        if id(expression) in self._bigram_histo_cache:
321            return self._bigram_histo_cache[id(expression)]
322
323        expression_str = self._sql_generator.generate(expression)
324        count = max(0, len(expression_str) - 1)
325        bigram_histo: t.DefaultDict[str, int] = defaultdict(int)
326        for i in range(count):
327            bigram_histo[expression_str[i : i + 2]] += 1
328
329        self._bigram_histo_cache[id(expression)] = bigram_histo
330        return bigram_histo

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.

ChangeDistiller(f: float = 0.6, t: float = 0.6)
151    def __init__(self, f: float = 0.6, t: float = 0.6) -> None:
152        self.f = f
153        self.t = t
154        self._sql_generator = Dialect().generator()
f
t
def diff( self, source: sqlglot.expressions.Expression, target: sqlglot.expressions.Expression, matchings: Optional[List[Tuple[sqlglot.expressions.Expression, sqlglot.expressions.Expression]]] = None, delta_only: bool = False) -> List[Union[Insert, Remove, Move, Update, Keep]]:
156    def diff(
157        self,
158        source: exp.Expression,
159        target: exp.Expression,
160        matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None,
161        delta_only: bool = False,
162    ) -> t.List[Edit]:
163        matchings = matchings or []
164        pre_matched_nodes = {id(s): id(t) for s, t in matchings}
165        if len({n for pair in pre_matched_nodes.items() for n in pair}) != 2 * len(matchings):
166            raise ValueError("Each node can be referenced at most once in the list of matchings")
167
168        self._source = source
169        self._target = target
170        self._source_index = {
171            id(n): n for n in self._source.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
172        }
173        self._target_index = {
174            id(n): n for n in self._target.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
175        }
176        self._unmatched_source_nodes = set(self._source_index) - set(pre_matched_nodes)
177        self._unmatched_target_nodes = set(self._target_index) - set(pre_matched_nodes.values())
178        self._bigram_histo_cache: t.Dict[int, t.DefaultDict[str, int]] = {}
179
180        matching_set = self._compute_matching_set() | {(s, t) for s, t in pre_matched_nodes.items()}
181        return self._generate_edit_script(matching_set, delta_only)