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SimilarityIndexTest.java 5.8KB

Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
Implement similarity based rename detection Content similarity based rename detection is performed only after a linear time detection is performed using exact content match on the ObjectIds. Any names which were paired up during that exact match phase are excluded from the inexact similarity based rename, which reduces the space that must be considered. During rename detection two entries cannot be marked as a rename if they are different types of files. This prevents a symlink from being renamed to a regular file, even if their blob content appears to be similar, or is identical. Efficiently comparing two files is performed by building up two hash indexes and hashing lines or short blocks from each file, counting the number of bytes that each line or block represents. Instead of using a standard java.util.HashMap, we use a custom open hashing scheme similiar to what we use in ObjecIdSubclassMap. This permits us to have a very light-weight hash, with very little memory overhead per cell stored. As we only need two ints per record in the map (line/block key and number of bytes), we collapse them into a single long inside of a long array, making very efficient use of available memory when we create the index table. We only need object headers for the index structure itself, and the index table, but not per-cell. This offers a massive space savings over using java.util.HashMap. The score calculation is done by approximating how many bytes are the same between the two inputs (which for a delta would be how much is copied from the base into the result). The score is derived by dividing the approximate number of bytes in common into the length of the larger of the two input files. Right now the SimilarityIndex table should average about 1/2 full, which means we waste about 50% of our memory on empty entries after we are done indexing a file and sort the table's contents. If memory becomes an issue we could discard the table and copy all records over to a new array that is properly sized. Building the index requires O(M + N log N) time, where M is the size of the input file in bytes, and N is the number of unique lines/blocks in the file. The N log N time constraint comes from the sort of the index table that is necessary to perform linear time matching against another SimilarityIndex created for a different file. To actually perform the rename detection, a SxD matrix is created, placing the sources (aka deletions) along one dimension and the destinations (aka additions) along the other. A simple O(S x D) loop examines every cell in this matrix. A SimilarityIndex is built along the row and reused for each column compare along that row, avoiding the costly index rebuild at the row level. A future improvement would be to load a smaller square matrix into SimilarityIndexes and process everything in that sub-matrix before discarding the column dimension and moving down to the next sub-matrix block along that same grid of rows. An optional ProgressMonitor is permitted to be passed in, allowing applications to see the progress of the detector as it works through the matrix cells. This provides some indication of current status for very long running renames. The default line/block hash function used by the SimilarityIndex may not be optimal, and may produce too many collisions. It is borrowed from RawText's hash, which is used to quickly skip out of a longer equality test if two lines have different hash functions. We may need to refine this hash in the future, in order to minimize the number of collisions we get on common source files. Based on a handful of test commits in JGit (especially my own recent rename repository refactoring series), this rename detector produces output that is very close to C Git. The content similarity scores are sometimes off by 1%, which is most probably caused by our SimilarityIndex type using a different hash function than C Git uses when it computes the delta size between any two objects in the rename matrix. Bug: 318504 Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago
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  1. /*
  2. * Copyright (C) 2010, Google Inc.
  3. * and other copyright owners as documented in the project's IP log.
  4. *
  5. * This program and the accompanying materials are made available
  6. * under the terms of the Eclipse Distribution License v1.0 which
  7. * accompanies this distribution, is reproduced below, and is
  8. * available at http://www.eclipse.org/org/documents/edl-v10.php
  9. *
  10. * All rights reserved.
  11. *
  12. * Redistribution and use in source and binary forms, with or
  13. * without modification, are permitted provided that the following
  14. * conditions are met:
  15. *
  16. * - Redistributions of source code must retain the above copyright
  17. * notice, this list of conditions and the following disclaimer.
  18. *
  19. * - Redistributions in binary form must reproduce the above
  20. * copyright notice, this list of conditions and the following
  21. * disclaimer in the documentation and/or other materials provided
  22. * with the distribution.
  23. *
  24. * - Neither the name of the Eclipse Foundation, Inc. nor the
  25. * names of its contributors may be used to endorse or promote
  26. * products derived from this software without specific prior
  27. * written permission.
  28. *
  29. * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
  30. * CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
  31. * INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
  32. * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
  33. * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
  34. * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
  35. * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
  36. * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
  37. * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
  38. * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
  39. * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
  40. * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
  41. * ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  42. */
  43. package org.eclipse.jgit.diff;
  44. import static java.nio.charset.StandardCharsets.UTF_8;
  45. import static org.junit.Assert.assertEquals;
  46. import static org.junit.Assert.assertTrue;
  47. import java.io.ByteArrayInputStream;
  48. import java.io.IOException;
  49. import org.eclipse.jgit.diff.SimilarityIndex.TableFullException;
  50. import org.eclipse.jgit.lib.Constants;
  51. import org.junit.Test;
  52. public class SimilarityIndexTest {
  53. @Test
  54. public void testIndexingSmallObject() throws TableFullException {
  55. SimilarityIndex si = hash("" //
  56. + "A\n" //
  57. + "B\n" //
  58. + "D\n" //
  59. + "B\n" //
  60. );
  61. int key_A = keyFor("A\n");
  62. int key_B = keyFor("B\n");
  63. int key_D = keyFor("D\n");
  64. assertTrue(key_A != key_B && key_A != key_D && key_B != key_D);
  65. assertEquals(3, si.size());
  66. assertEquals(2, si.count(si.findIndex(key_A)));
  67. assertEquals(4, si.count(si.findIndex(key_B)));
  68. assertEquals(2, si.count(si.findIndex(key_D)));
  69. }
  70. @Test
  71. public void testIndexingLargeObject() throws IOException,
  72. TableFullException {
  73. byte[] in = ("" //
  74. + "A\n" //
  75. + "B\n" //
  76. + "B\n" //
  77. + "B\n").getBytes(UTF_8);
  78. SimilarityIndex si = new SimilarityIndex();
  79. si.hash(new ByteArrayInputStream(in), in.length, false);
  80. assertEquals(2, si.size());
  81. }
  82. @Test
  83. public void testCommonScore_SameFiles() throws TableFullException {
  84. String text = "" //
  85. + "A\n" //
  86. + "B\n" //
  87. + "D\n" //
  88. + "B\n";
  89. SimilarityIndex src = hash(text);
  90. SimilarityIndex dst = hash(text);
  91. assertEquals(8, src.common(dst));
  92. assertEquals(8, dst.common(src));
  93. assertEquals(100, src.score(dst, 100));
  94. assertEquals(100, dst.score(src, 100));
  95. }
  96. @Test
  97. public void testCommonScore_SameFiles_CR_canonicalization()
  98. throws TableFullException {
  99. String text = "" //
  100. + "A\r\n" //
  101. + "B\r\n" //
  102. + "D\r\n" //
  103. + "B\r\n";
  104. SimilarityIndex src = hash(text);
  105. SimilarityIndex dst = hash(text.replace("\r", ""));
  106. assertEquals(8, src.common(dst));
  107. assertEquals(8, dst.common(src));
  108. assertEquals(100, src.score(dst, 100));
  109. assertEquals(100, dst.score(src, 100));
  110. }
  111. @Test
  112. public void testCommonScoreLargeObject_SameFiles_CR_canonicalization()
  113. throws TableFullException, IOException {
  114. String text = "" //
  115. + "A\r\n" //
  116. + "B\r\n" //
  117. + "D\r\n" //
  118. + "B\r\n";
  119. SimilarityIndex src = new SimilarityIndex();
  120. byte[] bytes1 = text.getBytes(UTF_8);
  121. src.hash(new ByteArrayInputStream(bytes1), bytes1.length, true);
  122. src.sort();
  123. SimilarityIndex dst = new SimilarityIndex();
  124. byte[] bytes2 = text.replace("\r", "").getBytes(UTF_8);
  125. dst.hash(new ByteArrayInputStream(bytes2), bytes2.length, true);
  126. dst.sort();
  127. assertEquals(8, src.common(dst));
  128. assertEquals(8, dst.common(src));
  129. assertEquals(100, src.score(dst, 100));
  130. assertEquals(100, dst.score(src, 100));
  131. }
  132. @Test
  133. public void testCommonScore_EmptyFiles() throws TableFullException {
  134. SimilarityIndex src = hash("");
  135. SimilarityIndex dst = hash("");
  136. assertEquals(0, src.common(dst));
  137. assertEquals(0, dst.common(src));
  138. }
  139. @Test
  140. public void testCommonScore_TotallyDifferentFiles()
  141. throws TableFullException {
  142. SimilarityIndex src = hash("A\n");
  143. SimilarityIndex dst = hash("D\n");
  144. assertEquals(0, src.common(dst));
  145. assertEquals(0, dst.common(src));
  146. }
  147. @Test
  148. public void testCommonScore_SimiliarBy75() throws TableFullException {
  149. SimilarityIndex src = hash("A\nB\nC\nD\n");
  150. SimilarityIndex dst = hash("A\nB\nC\nQ\n");
  151. assertEquals(6, src.common(dst));
  152. assertEquals(6, dst.common(src));
  153. assertEquals(75, src.score(dst, 100));
  154. assertEquals(75, dst.score(src, 100));
  155. }
  156. private static SimilarityIndex hash(String text) throws TableFullException {
  157. SimilarityIndex src = new SimilarityIndex();
  158. byte[] raw = Constants.encode(text);
  159. src.hash(raw, 0, raw.length);
  160. src.sort();
  161. return src;
  162. }
  163. private static int keyFor(String line) throws TableFullException {
  164. SimilarityIndex si = hash(line);
  165. assertEquals("single line scored", 1, si.size());
  166. return si.key(0);
  167. }
  168. }