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# 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:

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.

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.

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:

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:

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.

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

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

View Source

      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
     13from itertools import chain
     14
     15from sqlglot import Dialect, expressions as exp
     16from sqlglot.helper import seq_get
     17
     18if t.TYPE_CHECKING:
     19    from sqlglot.dialects.dialect import DialectType
     20
     21
     22@dataclass(frozen=True)
     23class Insert:
     24    """Indicates that a new node has been inserted"""
     25
     26    expression: exp.Expression
     27
     28
     29@dataclass(frozen=True)
     30class Remove:
     31    """Indicates that an existing node has been removed"""
     32
     33    expression: exp.Expression
     34
     35
     36@dataclass(frozen=True)
     37class Move:
     38    """Indicates that an existing node's position within the tree has changed"""
     39
     40    source: exp.Expression
     41    target: exp.Expression
     42
     43
     44@dataclass(frozen=True)
     45class Update:
     46    """Indicates that an existing node has been updated"""
     47
     48    source: exp.Expression
     49    target: exp.Expression
     50
     51
     52@dataclass(frozen=True)
     53class Keep:
     54    """Indicates that an existing node hasn't been changed"""
     55
     56    source: exp.Expression
     57    target: exp.Expression
     58
     59
     60if t.TYPE_CHECKING:
     61    from sqlglot._typing import T
     62
     63    Edit = t.Union[Insert, Remove, Move, Update, Keep]
     64
     65
     66def diff(
     67    source: exp.Expression,
     68    target: exp.Expression,
     69    matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None,
     70    delta_only: bool = False,
     71    copy: bool = True,
     72    **kwargs: t.Any,
     73) -> t.List[Edit]:
     74    """
     75    Returns the list of changes between the source and the target expressions.
     76
     77    Examples:
     78        >>> diff(parse_one("a + b"), parse_one("a + c"))
     79        [
     80            Remove(expression=(COLUMN this: (IDENTIFIER this: b, quoted: False))),
     81            Insert(expression=(COLUMN this: (IDENTIFIER this: c, quoted: False))),
     82            Keep(
     83                source=(ADD this: ...),
     84                target=(ADD this: ...)
     85            ),
     86            Keep(
     87                source=(COLUMN this: (IDENTIFIER this: a, quoted: False)),
     88                target=(COLUMN this: (IDENTIFIER this: a, quoted: False))
     89            ),
     90        ]
     91
     92    Args:
     93        source: the source expression.
     94        target: the target expression against which the diff should be calculated.
     95        matchings: the list of pre-matched node pairs which is used to help the algorithm's
     96            heuristics produce better results for subtrees that are known by a caller to be matching.
     97            Note: expression references in this list must refer to the same node objects that are
     98            referenced in the source / target trees.
     99        delta_only: excludes all `Keep` nodes from the diff.
    100        copy: whether to copy the input expressions.
    101            Note: if this is set to false, the caller must ensure that there are no shared references
    102            in the two trees, otherwise the diffing algorithm may produce unexpected behavior.
    103        kwargs: additional arguments to pass to the ChangeDistiller instance.
    104
    105    Returns:
    106        the list of Insert, Remove, Move, Update and Keep objects for each node in the source and the
    107        target expression trees. This list represents a sequence of steps needed to transform the source
    108        expression tree into the target one.
    109    """
    110    matchings = matchings or []
    111    matching_ids = {id(n) for pair in matchings for n in pair}
    112
    113    def compute_node_mappings(
    114        original: exp.Expression, copy: exp.Expression
    115    ) -> t.Dict[int, exp.Expression]:
    116        node_mapping = {}
    117        for old_node, new_node in zip(
    118            reversed(tuple(original.walk())), reversed(tuple(copy.walk()))
    119        ):
    120            # We cache the hash of each new node here to speed up equality comparisons. If the input
    121            # trees aren't copied, these hashes will be evicted before returning the edit script.
    122            new_node._hash = hash(new_node)
    123
    124            old_node_id = id(old_node)
    125            if old_node_id in matching_ids:
    126                node_mapping[old_node_id] = new_node
    127
    128        return node_mapping
    129
    130    source_copy = source.copy() if copy else source
    131    target_copy = target.copy() if copy else target
    132
    133    node_mappings = {
    134        **compute_node_mappings(source, source_copy),
    135        **compute_node_mappings(target, target_copy),
    136    }
    137    matchings_copy = [(node_mappings[id(s)], node_mappings[id(t)]) for s, t in matchings]
    138
    139    edit_script = ChangeDistiller(**kwargs).diff(
    140        source_copy,
    141        target_copy,
    142        matchings=matchings_copy,
    143        delta_only=delta_only,
    144    )
    145
    146    if not copy:
    147        for node in chain(source.walk(), target.walk()):
    148            node._hash = None
    149
    150    return edit_script
    151
    152
    153# The expression types for which Update edits are allowed.
    154UPDATABLE_EXPRESSION_TYPES = (
    155    exp.Alias,
    156    exp.Boolean,
    157    exp.Column,
    158    exp.DataType,
    159    exp.Lambda,
    160    exp.Literal,
    161    exp.Table,
    162    exp.Window,
    163)
    164
    165IGNORED_LEAF_EXPRESSION_TYPES = (exp.Identifier,)
    166
    167
    168class ChangeDistiller:
    169    """
    170    The implementation of the Change Distiller algorithm described by Beat Fluri and Martin Pinzger in
    171    their paper https://ieeexplore.ieee.org/document/4339230, which in turn is based on the algorithm by
    172    Chawathe et al. described in http://ilpubs.stanford.edu:8090/115/1/1995-46.pdf.
    173    """
    174
    175    def __init__(self, f: float = 0.6, t: float = 0.6, dialect: DialectType = None) -> None:
    176        self.f = f
    177        self.t = t
    178        self._sql_generator = Dialect.get_or_raise(dialect).generator()
    179
    180    def diff(
    181        self,
    182        source: exp.Expression,
    183        target: exp.Expression,
    184        matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None,
    185        delta_only: bool = False,
    186    ) -> t.List[Edit]:
    187        matchings = matchings or []
    188        pre_matched_nodes = {id(s): id(t) for s, t in matchings}
    189        if len({n for pair in pre_matched_nodes.items() for n in pair}) != 2 * len(matchings):
    190            raise ValueError("Each node can be referenced at most once in the list of matchings")
    191
    192        self._source = source
    193        self._target = target
    194        self._source_index = {
    195            id(n): n for n in self._source.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
    196        }
    197        self._target_index = {
    198            id(n): n for n in self._target.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
    199        }
    200        self._unmatched_source_nodes = set(self._source_index) - set(pre_matched_nodes)
    201        self._unmatched_target_nodes = set(self._target_index) - set(pre_matched_nodes.values())
    202        self._bigram_histo_cache: t.Dict[int, t.DefaultDict[str, int]] = {}
    203
    204        matching_set = self._compute_matching_set() | set(pre_matched_nodes.items())
    205        return self._generate_edit_script(dict(matching_set), delta_only)
    206
    207    def _generate_edit_script(self, matchings: t.Dict[int, int], delta_only: bool) -> t.List[Edit]:
    208        edit_script: t.List[Edit] = []
    209        for removed_node_id in self._unmatched_source_nodes:
    210            edit_script.append(Remove(self._source_index[removed_node_id]))
    211        for inserted_node_id in self._unmatched_target_nodes:
    212            edit_script.append(Insert(self._target_index[inserted_node_id]))
    213        for kept_source_node_id, kept_target_node_id in matchings.items():
    214            source_node = self._source_index[kept_source_node_id]
    215            target_node = self._target_index[kept_target_node_id]
    216
    217            identical_nodes = source_node == target_node
    218
    219            if not isinstance(source_node, UPDATABLE_EXPRESSION_TYPES) or identical_nodes:
    220                if identical_nodes:
    221                    source_parent = source_node.parent
    222                    target_parent = target_node.parent
    223
    224                    if (
    225                        (source_parent and not target_parent)
    226                        or (not source_parent and target_parent)
    227                        or (
    228                            source_parent
    229                            and target_parent
    230                            and matchings.get(id(source_parent)) != id(target_parent)
    231                        )
    232                    ):
    233                        edit_script.append(Move(source=source_node, target=target_node))
    234                else:
    235                    edit_script.extend(
    236                        self._generate_move_edits(source_node, target_node, matchings)
    237                    )
    238
    239                source_non_expression_leaves = dict(_get_non_expression_leaves(source_node))
    240                target_non_expression_leaves = dict(_get_non_expression_leaves(target_node))
    241
    242                if source_non_expression_leaves != target_non_expression_leaves:
    243                    edit_script.append(Update(source_node, target_node))
    244                elif not delta_only:
    245                    edit_script.append(Keep(source_node, target_node))
    246            else:
    247                edit_script.append(Update(source_node, target_node))
    248
    249        return edit_script
    250
    251    def _generate_move_edits(
    252        self, source: exp.Expression, target: exp.Expression, matchings: t.Dict[int, int]
    253    ) -> t.List[Move]:
    254        source_args = [id(e) for e in _expression_only_args(source)]
    255        target_args = [id(e) for e in _expression_only_args(target)]
    256
    257        args_lcs = set(
    258            _lcs(source_args, target_args, lambda l, r: matchings.get(t.cast(int, l)) == r)
    259        )
    260
    261        move_edits = []
    262        for a in source_args:
    263            if a not in args_lcs and a not in self._unmatched_source_nodes:
    264                move_edits.append(
    265                    Move(source=self._source_index[a], target=self._target_index[matchings[a]])
    266                )
    267
    268        return move_edits
    269
    270    def _compute_matching_set(self) -> t.Set[t.Tuple[int, int]]:
    271        leaves_matching_set = self._compute_leaf_matching_set()
    272        matching_set = leaves_matching_set.copy()
    273
    274        ordered_unmatched_source_nodes = {
    275            id(n): None for n in self._source.bfs() if id(n) in self._unmatched_source_nodes
    276        }
    277        ordered_unmatched_target_nodes = {
    278            id(n): None for n in self._target.bfs() if id(n) in self._unmatched_target_nodes
    279        }
    280
    281        for source_node_id in ordered_unmatched_source_nodes:
    282            for target_node_id in ordered_unmatched_target_nodes:
    283                source_node = self._source_index[source_node_id]
    284                target_node = self._target_index[target_node_id]
    285                if _is_same_type(source_node, target_node):
    286                    source_leaf_ids = {id(l) for l in _get_expression_leaves(source_node)}
    287                    target_leaf_ids = {id(l) for l in _get_expression_leaves(target_node)}
    288
    289                    max_leaves_num = max(len(source_leaf_ids), len(target_leaf_ids))
    290                    if max_leaves_num:
    291                        common_leaves_num = sum(
    292                            1 if s in source_leaf_ids and t in target_leaf_ids else 0
    293                            for s, t in leaves_matching_set
    294                        )
    295                        leaf_similarity_score = common_leaves_num / max_leaves_num
    296                    else:
    297                        leaf_similarity_score = 0.0
    298
    299                    adjusted_t = (
    300                        self.t if min(len(source_leaf_ids), len(target_leaf_ids)) > 4 else 0.4
    301                    )
    302
    303                    if leaf_similarity_score >= 0.8 or (
    304                        leaf_similarity_score >= adjusted_t
    305                        and self._dice_coefficient(source_node, target_node) >= self.f
    306                    ):
    307                        matching_set.add((source_node_id, target_node_id))
    308                        self._unmatched_source_nodes.remove(source_node_id)
    309                        self._unmatched_target_nodes.remove(target_node_id)
    310                        ordered_unmatched_target_nodes.pop(target_node_id, None)
    311                        break
    312
    313        return matching_set
    314
    315    def _compute_leaf_matching_set(self) -> t.Set[t.Tuple[int, int]]:
    316        candidate_matchings: t.List[t.Tuple[float, int, int, exp.Expression, exp.Expression]] = []
    317        source_expression_leaves = list(_get_expression_leaves(self._source))
    318        target_expression_leaves = list(_get_expression_leaves(self._target))
    319        for source_leaf in source_expression_leaves:
    320            for target_leaf in target_expression_leaves:
    321                if _is_same_type(source_leaf, target_leaf):
    322                    similarity_score = self._dice_coefficient(source_leaf, target_leaf)
    323                    if similarity_score >= self.f:
    324                        heappush(
    325                            candidate_matchings,
    326                            (
    327                                -similarity_score,
    328                                -_parent_similarity_score(source_leaf, target_leaf),
    329                                len(candidate_matchings),
    330                                source_leaf,
    331                                target_leaf,
    332                            ),
    333                        )
    334
    335        # Pick best matchings based on the highest score
    336        matching_set = set()
    337        while candidate_matchings:
    338            _, _, _, source_leaf, target_leaf = heappop(candidate_matchings)
    339            if (
    340                id(source_leaf) in self._unmatched_source_nodes
    341                and id(target_leaf) in self._unmatched_target_nodes
    342            ):
    343                matching_set.add((id(source_leaf), id(target_leaf)))
    344                self._unmatched_source_nodes.remove(id(source_leaf))
    345                self._unmatched_target_nodes.remove(id(target_leaf))
    346
    347        return matching_set
    348
    349    def _dice_coefficient(self, source: exp.Expression, target: exp.Expression) -> float:
    350        source_histo = self._bigram_histo(source)
    351        target_histo = self._bigram_histo(target)
    352
    353        total_grams = sum(source_histo.values()) + sum(target_histo.values())
    354        if not total_grams:
    355            return 1.0 if source == target else 0.0
    356
    357        overlap_len = 0
    358        overlapping_grams = set(source_histo) & set(target_histo)
    359        for g in overlapping_grams:
    360            overlap_len += min(source_histo[g], target_histo[g])
    361
    362        return 2 * overlap_len / total_grams
    363
    364    def _bigram_histo(self, expression: exp.Expression) -> t.DefaultDict[str, int]:
    365        if id(expression) in self._bigram_histo_cache:
    366            return self._bigram_histo_cache[id(expression)]
    367
    368        expression_str = self._sql_generator.generate(expression)
    369        count = max(0, len(expression_str) - 1)
    370        bigram_histo: t.DefaultDict[str, int] = defaultdict(int)
    371        for i in range(count):
    372            bigram_histo[expression_str[i : i + 2]] += 1
    373
    374        self._bigram_histo_cache[id(expression)] = bigram_histo
    375        return bigram_histo
    376
    377
    378def _get_expression_leaves(expression: exp.Expression) -> t.Iterator[exp.Expression]:
    379    has_child_exprs = False
    380
    381    for node in expression.iter_expressions():
    382        if not isinstance(node, IGNORED_LEAF_EXPRESSION_TYPES):
    383            has_child_exprs = True
    384            yield from _get_expression_leaves(node)
    385
    386    if not has_child_exprs:
    387        yield expression
    388
    389
    390def _get_non_expression_leaves(expression: exp.Expression) -> t.Iterator[t.Tuple[str, t.Any]]:
    391    for arg, value in expression.args.items():
    392        if isinstance(value, exp.Expression) or (
    393            isinstance(value, list) and isinstance(seq_get(value, 0), exp.Expression)
    394        ):
    395            continue
    396
    397        yield (arg, value)
    398
    399
    400def _is_same_type(source: exp.Expression, target: exp.Expression) -> bool:
    401    if type(source) is type(target):
    402        if isinstance(source, exp.Join):
    403            return source.args.get("side") == target.args.get("side")
    404
    405        if isinstance(source, exp.Anonymous):
    406            return source.this == target.this
    407
    408        return True
    409
    410    return False
    411
    412
    413def _parent_similarity_score(
    414    source: t.Optional[exp.Expression], target: t.Optional[exp.Expression]
    415) -> int:
    416    if source is None or target is None or type(source) is not type(target):
    417        return 0
    418
    419    return 1 + _parent_similarity_score(source.parent, target.parent)
    420
    421
    422def _expression_only_args(expression: exp.Expression) -> t.Iterator[exp.Expression]:
    423    yield from (
    424        arg
    425        for arg in expression.iter_expressions()
    426        if not isinstance(arg, IGNORED_LEAF_EXPRESSION_TYPES)
    427    )
    428
    429
    430def _lcs(
    431    seq_a: t.Sequence[T], seq_b: t.Sequence[T], equal: t.Callable[[T, T], bool]
    432) -> t.Sequence[t.Optional[T]]:
    433    """Calculates the longest common subsequence"""
    434
    435    len_a = len(seq_a)
    436    len_b = len(seq_b)
    437    lcs_result = [[None] * (len_b + 1) for i in range(len_a + 1)]
    438
    439    for i in range(len_a + 1):
    440        for j in range(len_b + 1):
    441            if i == 0 or j == 0:
    442                lcs_result[i][j] = []  # type: ignore
    443            elif equal(seq_a[i - 1], seq_b[j - 1]):
    444                lcs_result[i][j] = lcs_result[i - 1][j - 1] + [seq_a[i - 1]]  # type: ignore
    445            else:
    446                lcs_result[i][j] = (
    447                    lcs_result[i - 1][j]
    448                    if len(lcs_result[i - 1][j]) > len(lcs_result[i][j - 1])  # type: ignore
    449                    else lcs_result[i][j - 1]
    450                )
    451
    452    return lcs_result[len_a][len_b]  # type: ignore

@dataclass(frozen=True)
class Insert: View Source

    23@dataclass(frozen=True)
    24class Insert:
    25    """Indicates that a new node has been inserted"""
    26
    27    expression: exp.Expression

Indicates that a new node has been inserted

Insert(expression: sqlglot.expressions.Expression)
expression: sqlglot.expressions.Expression
@dataclass(frozen=True)
class Remove: View Source

    30@dataclass(frozen=True)
    31class Remove:
    32    """Indicates that an existing node has been removed"""
    33
    34    expression: exp.Expression

Indicates that an existing node has been removed

Remove(expression: sqlglot.expressions.Expression)
expression: sqlglot.expressions.Expression
@dataclass(frozen=True)
class Move: View Source

    37@dataclass(frozen=True)
    38class Move:
    39    """Indicates that an existing node's position within the tree has changed"""
    40
    41    source: exp.Expression
    42    target: exp.Expression

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

Move( source: sqlglot.expressions.Expression, target: sqlglot.expressions.Expression)
source: sqlglot.expressions.Expression
target: sqlglot.expressions.Expression
@dataclass(frozen=True)
class Update: View Source

    45@dataclass(frozen=True)
    46class Update:
    47    """Indicates that an existing node has been updated"""
    48
    49    source: exp.Expression
    50    target: exp.Expression

Indicates that an existing node has been updated

Update( source: sqlglot.expressions.Expression, target: sqlglot.expressions.Expression)
source: sqlglot.expressions.Expression
target: sqlglot.expressions.Expression
@dataclass(frozen=True)
class Keep: View Source

    53@dataclass(frozen=True)
    54class Keep:
    55    """Indicates that an existing node hasn't been changed"""
    56
    57    source: exp.Expression
    58    target: exp.Expression

Indicates that an existing node hasn't been changed

Keep( source: sqlglot.expressions.Expression, target: sqlglot.expressions.Expression)
source: sqlglot.expressions.Expression
target: sqlglot.expressions.Expression
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, copy: bool = True, **kwargs: Any) -> List[Union[Insert, Remove, Move, Update, Keep]]: View Source

     67def diff(
     68    source: exp.Expression,
     69    target: exp.Expression,
     70    matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None,
     71    delta_only: bool = False,
     72    copy: bool = True,
     73    **kwargs: t.Any,
     74) -> t.List[Edit]:
     75    """
     76    Returns the list of changes between the source and the target expressions.
     77
     78    Examples:
     79        >>> diff(parse_one("a + b"), parse_one("a + c"))
     80        [
     81            Remove(expression=(COLUMN this: (IDENTIFIER this: b, quoted: False))),
     82            Insert(expression=(COLUMN this: (IDENTIFIER this: c, quoted: False))),
     83            Keep(
     84                source=(ADD this: ...),
     85                target=(ADD this: ...)
     86            ),
     87            Keep(
     88                source=(COLUMN this: (IDENTIFIER this: a, quoted: False)),
     89                target=(COLUMN this: (IDENTIFIER this: a, quoted: False))
     90            ),
     91        ]
     92
     93    Args:
     94        source: the source expression.
     95        target: the target expression against which the diff should be calculated.
     96        matchings: the list of pre-matched node pairs which is used to help the algorithm's
     97            heuristics produce better results for subtrees that are known by a caller to be matching.
     98            Note: expression references in this list must refer to the same node objects that are
     99            referenced in the source / target trees.
    100        delta_only: excludes all `Keep` nodes from the diff.
    101        copy: whether to copy the input expressions.
    102            Note: if this is set to false, the caller must ensure that there are no shared references
    103            in the two trees, otherwise the diffing algorithm may produce unexpected behavior.
    104        kwargs: additional arguments to pass to the ChangeDistiller instance.
    105
    106    Returns:
    107        the list of Insert, Remove, Move, Update and Keep objects for each node in the source and the
    108        target expression trees. This list represents a sequence of steps needed to transform the source
    109        expression tree into the target one.
    110    """
    111    matchings = matchings or []
    112    matching_ids = {id(n) for pair in matchings for n in pair}
    113
    114    def compute_node_mappings(
    115        original: exp.Expression, copy: exp.Expression
    116    ) -> t.Dict[int, exp.Expression]:
    117        node_mapping = {}
    118        for old_node, new_node in zip(
    119            reversed(tuple(original.walk())), reversed(tuple(copy.walk()))
    120        ):
    121            # We cache the hash of each new node here to speed up equality comparisons. If the input
    122            # trees aren't copied, these hashes will be evicted before returning the edit script.
    123            new_node._hash = hash(new_node)
    124
    125            old_node_id = id(old_node)
    126            if old_node_id in matching_ids:
    127                node_mapping[old_node_id] = new_node
    128
    129        return node_mapping
    130
    131    source_copy = source.copy() if copy else source
    132    target_copy = target.copy() if copy else target
    133
    134    node_mappings = {
    135        **compute_node_mappings(source, source_copy),
    136        **compute_node_mappings(target, target_copy),
    137    }
    138    matchings_copy = [(node_mappings[id(s)], node_mappings[id(t)]) for s, t in matchings]
    139
    140    edit_script = ChangeDistiller(**kwargs).diff(
    141        source_copy,
    142        target_copy,
    143        matchings=matchings_copy,
    144        delta_only=delta_only,
    145    )
    146
    147    if not copy:
    148        for node in chain(source.walk(), target.walk()):
    149            node._hash = None
    150
    151    return edit_script

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 the source / target trees.
- delta_only: excludes all `Keep` nodes from the diff.
- copy: whether to copy the input expressions. Note: if this is set to false, the caller must ensure that there are no shared references in the two trees, otherwise the diffing algorithm may produce unexpected behavior.
- 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.

UPDATABLE_EXPRESSION_TYPES = (<class 'sqlglot.expressions.Alias'>, <class 'sqlglot.expressions.Boolean'>, <class 'sqlglot.expressions.Column'>, <class 'sqlglot.expressions.DataType'>, <class 'sqlglot.expressions.Lambda'>, <class 'sqlglot.expressions.Literal'>, <class 'sqlglot.expressions.Table'>, <class 'sqlglot.expressions.Window'>)
IGNORED_LEAF_EXPRESSION_TYPES = (<class 'sqlglot.expressions.Identifier'>,)
class ChangeDistiller: View Source

    169class ChangeDistiller:
    170    """
    171    The implementation of the Change Distiller algorithm described by Beat Fluri and Martin Pinzger in
    172    their paper https://ieeexplore.ieee.org/document/4339230, which in turn is based on the algorithm by
    173    Chawathe et al. described in http://ilpubs.stanford.edu:8090/115/1/1995-46.pdf.
    174    """
    175
    176    def __init__(self, f: float = 0.6, t: float = 0.6, dialect: DialectType = None) -> None:
    177        self.f = f
    178        self.t = t
    179        self._sql_generator = Dialect.get_or_raise(dialect).generator()
    180
    181    def diff(
    182        self,
    183        source: exp.Expression,
    184        target: exp.Expression,
    185        matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None,
    186        delta_only: bool = False,
    187    ) -> t.List[Edit]:
    188        matchings = matchings or []
    189        pre_matched_nodes = {id(s): id(t) for s, t in matchings}
    190        if len({n for pair in pre_matched_nodes.items() for n in pair}) != 2 * len(matchings):
    191            raise ValueError("Each node can be referenced at most once in the list of matchings")
    192
    193        self._source = source
    194        self._target = target
    195        self._source_index = {
    196            id(n): n for n in self._source.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
    197        }
    198        self._target_index = {
    199            id(n): n for n in self._target.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
    200        }
    201        self._unmatched_source_nodes = set(self._source_index) - set(pre_matched_nodes)
    202        self._unmatched_target_nodes = set(self._target_index) - set(pre_matched_nodes.values())
    203        self._bigram_histo_cache: t.Dict[int, t.DefaultDict[str, int]] = {}
    204
    205        matching_set = self._compute_matching_set() | set(pre_matched_nodes.items())
    206        return self._generate_edit_script(dict(matching_set), delta_only)
    207
    208    def _generate_edit_script(self, matchings: t.Dict[int, int], delta_only: bool) -> t.List[Edit]:
    209        edit_script: t.List[Edit] = []
    210        for removed_node_id in self._unmatched_source_nodes:
    211            edit_script.append(Remove(self._source_index[removed_node_id]))
    212        for inserted_node_id in self._unmatched_target_nodes:
    213            edit_script.append(Insert(self._target_index[inserted_node_id]))
    214        for kept_source_node_id, kept_target_node_id in matchings.items():
    215            source_node = self._source_index[kept_source_node_id]
    216            target_node = self._target_index[kept_target_node_id]
    217
    218            identical_nodes = source_node == target_node
    219
    220            if not isinstance(source_node, UPDATABLE_EXPRESSION_TYPES) or identical_nodes:
    221                if identical_nodes:
    222                    source_parent = source_node.parent
    223                    target_parent = target_node.parent
    224
    225                    if (
    226                        (source_parent and not target_parent)
    227                        or (not source_parent and target_parent)
    228                        or (
    229                            source_parent
    230                            and target_parent
    231                            and matchings.get(id(source_parent)) != id(target_parent)
    232                        )
    233                    ):
    234                        edit_script.append(Move(source=source_node, target=target_node))
    235                else:
    236                    edit_script.extend(
    237                        self._generate_move_edits(source_node, target_node, matchings)
    238                    )
    239
    240                source_non_expression_leaves = dict(_get_non_expression_leaves(source_node))
    241                target_non_expression_leaves = dict(_get_non_expression_leaves(target_node))
    242
    243                if source_non_expression_leaves != target_non_expression_leaves:
    244                    edit_script.append(Update(source_node, target_node))
    245                elif not delta_only:
    246                    edit_script.append(Keep(source_node, target_node))
    247            else:
    248                edit_script.append(Update(source_node, target_node))
    249
    250        return edit_script
    251
    252    def _generate_move_edits(
    253        self, source: exp.Expression, target: exp.Expression, matchings: t.Dict[int, int]
    254    ) -> t.List[Move]:
    255        source_args = [id(e) for e in _expression_only_args(source)]
    256        target_args = [id(e) for e in _expression_only_args(target)]
    257
    258        args_lcs = set(
    259            _lcs(source_args, target_args, lambda l, r: matchings.get(t.cast(int, l)) == r)
    260        )
    261
    262        move_edits = []
    263        for a in source_args:
    264            if a not in args_lcs and a not in self._unmatched_source_nodes:
    265                move_edits.append(
    266                    Move(source=self._source_index[a], target=self._target_index[matchings[a]])
    267                )
    268
    269        return move_edits
    270
    271    def _compute_matching_set(self) -> t.Set[t.Tuple[int, int]]:
    272        leaves_matching_set = self._compute_leaf_matching_set()
    273        matching_set = leaves_matching_set.copy()
    274
    275        ordered_unmatched_source_nodes = {
    276            id(n): None for n in self._source.bfs() if id(n) in self._unmatched_source_nodes
    277        }
    278        ordered_unmatched_target_nodes = {
    279            id(n): None for n in self._target.bfs() if id(n) in self._unmatched_target_nodes
    280        }
    281
    282        for source_node_id in ordered_unmatched_source_nodes:
    283            for target_node_id in ordered_unmatched_target_nodes:
    284                source_node = self._source_index[source_node_id]
    285                target_node = self._target_index[target_node_id]
    286                if _is_same_type(source_node, target_node):
    287                    source_leaf_ids = {id(l) for l in _get_expression_leaves(source_node)}
    288                    target_leaf_ids = {id(l) for l in _get_expression_leaves(target_node)}
    289
    290                    max_leaves_num = max(len(source_leaf_ids), len(target_leaf_ids))
    291                    if max_leaves_num:
    292                        common_leaves_num = sum(
    293                            1 if s in source_leaf_ids and t in target_leaf_ids else 0
    294                            for s, t in leaves_matching_set
    295                        )
    296                        leaf_similarity_score = common_leaves_num / max_leaves_num
    297                    else:
    298                        leaf_similarity_score = 0.0
    299
    300                    adjusted_t = (
    301                        self.t if min(len(source_leaf_ids), len(target_leaf_ids)) > 4 else 0.4
    302                    )
    303
    304                    if leaf_similarity_score >= 0.8 or (
    305                        leaf_similarity_score >= adjusted_t
    306                        and self._dice_coefficient(source_node, target_node) >= self.f
    307                    ):
    308                        matching_set.add((source_node_id, target_node_id))
    309                        self._unmatched_source_nodes.remove(source_node_id)
    310                        self._unmatched_target_nodes.remove(target_node_id)
    311                        ordered_unmatched_target_nodes.pop(target_node_id, None)
    312                        break
    313
    314        return matching_set
    315
    316    def _compute_leaf_matching_set(self) -> t.Set[t.Tuple[int, int]]:
    317        candidate_matchings: t.List[t.Tuple[float, int, int, exp.Expression, exp.Expression]] = []
    318        source_expression_leaves = list(_get_expression_leaves(self._source))
    319        target_expression_leaves = list(_get_expression_leaves(self._target))
    320        for source_leaf in source_expression_leaves:
    321            for target_leaf in target_expression_leaves:
    322                if _is_same_type(source_leaf, target_leaf):
    323                    similarity_score = self._dice_coefficient(source_leaf, target_leaf)
    324                    if similarity_score >= self.f:
    325                        heappush(
    326                            candidate_matchings,
    327                            (
    328                                -similarity_score,
    329                                -_parent_similarity_score(source_leaf, target_leaf),
    330                                len(candidate_matchings),
    331                                source_leaf,
    332                                target_leaf,
    333                            ),
    334                        )
    335
    336        # Pick best matchings based on the highest score
    337        matching_set = set()
    338        while candidate_matchings:
    339            _, _, _, source_leaf, target_leaf = heappop(candidate_matchings)
    340            if (
    341                id(source_leaf) in self._unmatched_source_nodes
    342                and id(target_leaf) in self._unmatched_target_nodes
    343            ):
    344                matching_set.add((id(source_leaf), id(target_leaf)))
    345                self._unmatched_source_nodes.remove(id(source_leaf))
    346                self._unmatched_target_nodes.remove(id(target_leaf))
    347
    348        return matching_set
    349
    350    def _dice_coefficient(self, source: exp.Expression, target: exp.Expression) -> float:
    351        source_histo = self._bigram_histo(source)
    352        target_histo = self._bigram_histo(target)
    353
    354        total_grams = sum(source_histo.values()) + sum(target_histo.values())
    355        if not total_grams:
    356            return 1.0 if source == target else 0.0
    357
    358        overlap_len = 0
    359        overlapping_grams = set(source_histo) & set(target_histo)
    360        for g in overlapping_grams:
    361            overlap_len += min(source_histo[g], target_histo[g])
    362
    363        return 2 * overlap_len / total_grams
    364
    365    def _bigram_histo(self, expression: exp.Expression) -> t.DefaultDict[str, int]:
    366        if id(expression) in self._bigram_histo_cache:
    367            return self._bigram_histo_cache[id(expression)]
    368
    369        expression_str = self._sql_generator.generate(expression)
    370        count = max(0, len(expression_str) - 1)
    371        bigram_histo: t.DefaultDict[str, int] = defaultdict(int)
    372        for i in range(count):
    373            bigram_histo[expression_str[i : i + 2]] += 1
    374
    375        self._bigram_histo_cache[id(expression)] = bigram_histo
    376        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, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None) View Source

    176    def __init__(self, f: float = 0.6, t: float = 0.6, dialect: DialectType = None) -> None:
    177        self.f = f
    178        self.t = t
    179        self._sql_generator = Dialect.get_or_raise(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]]: View Source

    181    def diff(
    182        self,
    183        source: exp.Expression,
    184        target: exp.Expression,
    185        matchings: t.List[t.Tuple[exp.Expression, exp.Expression]] | None = None,
    186        delta_only: bool = False,
    187    ) -> t.List[Edit]:
    188        matchings = matchings or []
    189        pre_matched_nodes = {id(s): id(t) for s, t in matchings}
    190        if len({n for pair in pre_matched_nodes.items() for n in pair}) != 2 * len(matchings):
    191            raise ValueError("Each node can be referenced at most once in the list of matchings")
    192
    193        self._source = source
    194        self._target = target
    195        self._source_index = {
    196            id(n): n for n in self._source.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
    197        }
    198        self._target_index = {
    199            id(n): n for n in self._target.bfs() if not isinstance(n, IGNORED_LEAF_EXPRESSION_TYPES)
    200        }
    201        self._unmatched_source_nodes = set(self._source_index) - set(pre_matched_nodes)
    202        self._unmatched_target_nodes = set(self._target_index) - set(pre_matched_nodes.values())
    203        self._bigram_histo_cache: t.Dict[int, t.DefaultDict[str, int]] = {}
    204
    205        matching_set = self._compute_matching_set() | set(pre_matched_nodes.items())
    206        return self._generate_edit_script(dict(matching_set), delta_only)