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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten 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 vuotta sitten Implemented file path based tie breaking to exact rename detection
During the exact rename detection phase in RenameDetector, ties were
resolved on a first-found basis. I added support for file path based
tie breaking during that phase. Basically, there are four situations
that have to be handled:
One add matching one delete:
In this simple case, we pair them as a rename.
One add matching many deletes:
Find the delete whos path matches the add the closest, and
pair them as a rename.
Many adds matching one delete:
Similar to the above case, we find the add that matches the
delete the closest, and pair them as a rename. The other adds
are marked as copies of the delete.
Many adds matching many deletes:
Build a scoring matrix similar to the one used for content-
based matching, scoring instead by file path. Some of the
utility functions in SimilarityRenameDetector are used in
this case, as we use the same encoding scheme. Once the
matrix is built, scan it for the best matches, marking them
as renames. The rest are marked as copies.
I don't particularly like the idea of using utility functions right
out of SimilarityRenameDetector, but it works for the moment. A later
commit will likely refactor this into a common utility class, as well
as bringing exact rename detection out of RenameDetector and into a
separate class, much like SimilarityRenameDetector.
Change-Id: I1fb08390aebdcbf20d049aecf402a36506e55611
14 vuotta sitten Implemented file path based tie breaking to exact rename detection
During the exact rename detection phase in RenameDetector, ties were
resolved on a first-found basis. I added support for file path based
tie breaking during that phase. Basically, there are four situations
that have to be handled:
One add matching one delete:
In this simple case, we pair them as a rename.
One add matching many deletes:
Find the delete whos path matches the add the closest, and
pair them as a rename.
Many adds matching one delete:
Similar to the above case, we find the add that matches the
delete the closest, and pair them as a rename. The other adds
are marked as copies of the delete.
Many adds matching many deletes:
Build a scoring matrix similar to the one used for content-
based matching, scoring instead by file path. Some of the
utility functions in SimilarityRenameDetector are used in
this case, as we use the same encoding scheme. Once the
matrix is built, scan it for the best matches, marking them
as renames. The rest are marked as copies.
I don't particularly like the idea of using utility functions right
out of SimilarityRenameDetector, but it works for the moment. A later
commit will likely refactor this into a common utility class, as well
as bringing exact rename detection out of RenameDetector and into a
separate class, much like SimilarityRenameDetector.
Change-Id: I1fb08390aebdcbf20d049aecf402a36506e55611
14 vuotta sitten Implemented file path based tie breaking to exact rename detection
During the exact rename detection phase in RenameDetector, ties were
resolved on a first-found basis. I added support for file path based
tie breaking during that phase. Basically, there are four situations
that have to be handled:
One add matching one delete:
In this simple case, we pair them as a rename.
One add matching many deletes:
Find the delete whos path matches the add the closest, and
pair them as a rename.
Many adds matching one delete:
Similar to the above case, we find the add that matches the
delete the closest, and pair them as a rename. The other adds
are marked as copies of the delete.
Many adds matching many deletes:
Build a scoring matrix similar to the one used for content-
based matching, scoring instead by file path. Some of the
utility functions in SimilarityRenameDetector are used in
this case, as we use the same encoding scheme. Once the
matrix is built, scan it for the best matches, marking them
as renames. The rest are marked as copies.
I don't particularly like the idea of using utility functions right
out of SimilarityRenameDetector, but it works for the moment. A later
commit will likely refactor this into a common utility class, as well
as bringing exact rename detection out of RenameDetector and into a
separate class, much like SimilarityRenameDetector.
Change-Id: I1fb08390aebdcbf20d049aecf402a36506e55611
14 vuotta sitten Implemented file path based tie breaking to exact rename detection
During the exact rename detection phase in RenameDetector, ties were
resolved on a first-found basis. I added support for file path based
tie breaking during that phase. Basically, there are four situations
that have to be handled:
One add matching one delete:
In this simple case, we pair them as a rename.
One add matching many deletes:
Find the delete whos path matches the add the closest, and
pair them as a rename.
Many adds matching one delete:
Similar to the above case, we find the add that matches the
delete the closest, and pair them as a rename. The other adds
are marked as copies of the delete.
Many adds matching many deletes:
Build a scoring matrix similar to the one used for content-
based matching, scoring instead by file path. Some of the
utility functions in SimilarityRenameDetector are used in
this case, as we use the same encoding scheme. Once the
matrix is built, scan it for the best matches, marking them
as renames. The rest are marked as copies.
I don't particularly like the idea of using utility functions right
out of SimilarityRenameDetector, but it works for the moment. A later
commit will likely refactor this into a common utility class, as well
as bringing exact rename detection out of RenameDetector and into a
separate class, much like SimilarityRenameDetector.
Change-Id: I1fb08390aebdcbf20d049aecf402a36506e55611
14 vuotta sitten Implemented file path based tie breaking to exact rename detection
During the exact rename detection phase in RenameDetector, ties were
resolved on a first-found basis. I added support for file path based
tie breaking during that phase. Basically, there are four situations
that have to be handled:
One add matching one delete:
In this simple case, we pair them as a rename.
One add matching many deletes:
Find the delete whos path matches the add the closest, and
pair them as a rename.
Many adds matching one delete:
Similar to the above case, we find the add that matches the
delete the closest, and pair them as a rename. The other adds
are marked as copies of the delete.
Many adds matching many deletes:
Build a scoring matrix similar to the one used for content-
based matching, scoring instead by file path. Some of the
utility functions in SimilarityRenameDetector are used in
this case, as we use the same encoding scheme. Once the
matrix is built, scan it for the best matches, marking them
as renames. The rest are marked as copies.
I don't particularly like the idea of using utility functions right
out of SimilarityRenameDetector, but it works for the moment. A later
commit will likely refactor this into a common utility class, as well
as bringing exact rename detection out of RenameDetector and into a
separate class, much like SimilarityRenameDetector.
Change-Id: I1fb08390aebdcbf20d049aecf402a36506e55611
14 vuotta sitten Implemented file path based tie breaking to exact rename detection
During the exact rename detection phase in RenameDetector, ties were
resolved on a first-found basis. I added support for file path based
tie breaking during that phase. Basically, there are four situations
that have to be handled:
One add matching one delete:
In this simple case, we pair them as a rename.
One add matching many deletes:
Find the delete whos path matches the add the closest, and
pair them as a rename.
Many adds matching one delete:
Similar to the above case, we find the add that matches the
delete the closest, and pair them as a rename. The other adds
are marked as copies of the delete.
Many adds matching many deletes:
Build a scoring matrix similar to the one used for content-
based matching, scoring instead by file path. Some of the
utility functions in SimilarityRenameDetector are used in
this case, as we use the same encoding scheme. Once the
matrix is built, scan it for the best matches, marking them
as renames. The rest are marked as copies.
I don't particularly like the idea of using utility functions right
out of SimilarityRenameDetector, but it works for the moment. A later
commit will likely refactor this into a common utility class, as well
as bringing exact rename detection out of RenameDetector and into a
separate class, much like SimilarityRenameDetector.
Change-Id: I1fb08390aebdcbf20d049aecf402a36506e55611
14 vuotta sitten Implemented file path based tie breaking to exact rename detection
During the exact rename detection phase in RenameDetector, ties were
resolved on a first-found basis. I added support for file path based
tie breaking during that phase. Basically, there are four situations
that have to be handled:
One add matching one delete:
In this simple case, we pair them as a rename.
One add matching many deletes:
Find the delete whos path matches the add the closest, and
pair them as a rename.
Many adds matching one delete:
Similar to the above case, we find the add that matches the
delete the closest, and pair them as a rename. The other adds
are marked as copies of the delete.
Many adds matching many deletes:
Build a scoring matrix similar to the one used for content-
based matching, scoring instead by file path. Some of the
utility functions in SimilarityRenameDetector are used in
this case, as we use the same encoding scheme. Once the
matrix is built, scan it for the best matches, marking them
as renames. The rest are marked as copies.
I don't particularly like the idea of using utility functions right
out of SimilarityRenameDetector, but it works for the moment. A later
commit will likely refactor this into a common utility class, as well
as bringing exact rename detection out of RenameDetector and into a
separate class, much like SimilarityRenameDetector.
Change-Id: I1fb08390aebdcbf20d049aecf402a36506e55611
14 vuotta sitten |
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- /*
- * Copyright (C) 2008-2013, Google Inc.
- * and other copyright owners as documented in the project's IP log.
- *
- * This program and the accompanying materials are made available
- * under the terms of the Eclipse Distribution License v1.0 which
- * accompanies this distribution, is reproduced below, and is
- * available at http://www.eclipse.org/org/documents/edl-v10.php
- *
- * All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or
- * without modification, are permitted provided that the following
- * conditions are met:
- *
- * - Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- *
- * - Redistributions in binary form must reproduce the above
- * copyright notice, this list of conditions and the following
- * disclaimer in the documentation and/or other materials provided
- * with the distribution.
- *
- * - Neither the name of the Eclipse Foundation, Inc. nor the
- * names of its contributors may be used to endorse or promote
- * products derived from this software without specific prior
- * written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
- * CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
- * INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
- * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
- * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
- * ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- */
-
- package org.eclipse.jgit.diff;
-
- import java.io.IOException;
- import java.util.ArrayList;
- import java.util.Arrays;
- import java.util.List;
-
- import org.eclipse.jgit.internal.JGitText;
- import org.eclipse.jgit.lib.AbbreviatedObjectId;
- import org.eclipse.jgit.lib.AnyObjectId;
- import org.eclipse.jgit.lib.FileMode;
- import org.eclipse.jgit.lib.MutableObjectId;
- import org.eclipse.jgit.lib.ObjectId;
- import org.eclipse.jgit.treewalk.TreeWalk;
- import org.eclipse.jgit.treewalk.filter.TreeFilter;
- import org.eclipse.jgit.treewalk.filter.TreeFilterMarker;
-
- /** A value class representing a change to a file */
- public class DiffEntry {
- /** Magical SHA1 used for file adds or deletes */
- static final AbbreviatedObjectId A_ZERO = AbbreviatedObjectId
- .fromObjectId(ObjectId.zeroId());
-
- /** Magical file name used for file adds or deletes. */
- public static final String DEV_NULL = "/dev/null"; //$NON-NLS-1$
-
- /** General type of change a single file-level patch describes. */
- public static enum ChangeType {
- /** Add a new file to the project */
- ADD,
-
- /** Modify an existing file in the project (content and/or mode) */
- MODIFY,
-
- /** Delete an existing file from the project */
- DELETE,
-
- /** Rename an existing file to a new location */
- RENAME,
-
- /** Copy an existing file to a new location, keeping the original */
- COPY;
- }
-
- /** Specify the old or new side for more generalized access. */
- public static enum Side {
- /** The old side of a DiffEntry. */
- OLD,
-
- /** The new side of a DiffEntry. */
- NEW;
- }
-
- /**
- * Create an empty DiffEntry
- */
- protected DiffEntry(){
- // reduce the visibility of the default constructor
- }
-
- /**
- * Convert the TreeWalk into DiffEntry headers.
- *
- * @param walk
- * the TreeWalk to walk through. Must have exactly two trees.
- * @return headers describing the changed files.
- * @throws IOException
- * the repository cannot be accessed.
- * @throws IllegalArgumentException
- * When given TreeWalk doesn't have exactly two trees.
- */
- public static List<DiffEntry> scan(TreeWalk walk) throws IOException {
- return scan(walk, false);
- }
-
- /**
- * Convert the TreeWalk into DiffEntry headers, depending on
- * {@code includeTrees} it will add tree objects into result or not.
- *
- * @param walk
- * the TreeWalk to walk through. Must have exactly two trees and
- * when {@code includeTrees} parameter is {@code true} it can't
- * be recursive.
- * @param includeTrees
- * include tree objects.
- * @return headers describing the changed files.
- * @throws IOException
- * the repository cannot be accessed.
- * @throws IllegalArgumentException
- * when {@code includeTrees} is true and given TreeWalk is
- * recursive. Or when given TreeWalk doesn't have exactly two
- * trees
- */
- public static List<DiffEntry> scan(TreeWalk walk, boolean includeTrees)
- throws IOException {
- return scan(walk, includeTrees, null);
- }
-
- /**
- * Convert the TreeWalk into DiffEntry headers, depending on
- * {@code includeTrees} it will add tree objects into result or not.
- *
- * @param walk
- * the TreeWalk to walk through. Must have exactly two trees and
- * when {@code includeTrees} parameter is {@code true} it can't
- * be recursive.
- * @param includeTrees
- * include tree objects.
- * @param markTreeFilters
- * array of tree filters which will be tested for each entry. If
- * an entry matches, the entry will later return true when
- * queried through {{@link #isMarked(int)} (with the index from
- * this passed array).
- * @return headers describing the changed files.
- * @throws IOException
- * the repository cannot be accessed.
- * @throws IllegalArgumentException
- * when {@code includeTrees} is true and given TreeWalk is
- * recursive. Or when given TreeWalk doesn't have exactly two
- * trees
- * @since 2.3
- */
- public static List<DiffEntry> scan(TreeWalk walk, boolean includeTrees,
- TreeFilter[] markTreeFilters)
- throws IOException {
- if (walk.getTreeCount() != 2)
- throw new IllegalArgumentException(
- JGitText.get().treeWalkMustHaveExactlyTwoTrees);
- if (includeTrees && walk.isRecursive())
- throw new IllegalArgumentException(
- JGitText.get().cannotBeRecursiveWhenTreesAreIncluded);
-
- TreeFilterMarker treeFilterMarker;
- if (markTreeFilters != null && markTreeFilters.length > 0)
- treeFilterMarker = new TreeFilterMarker(markTreeFilters);
- else
- treeFilterMarker = null;
-
- List<DiffEntry> r = new ArrayList<DiffEntry>();
- MutableObjectId idBuf = new MutableObjectId();
- while (walk.next()) {
- DiffEntry entry = new DiffEntry();
-
- walk.getObjectId(idBuf, 0);
- entry.oldId = AbbreviatedObjectId.fromObjectId(idBuf);
-
- walk.getObjectId(idBuf, 1);
- entry.newId = AbbreviatedObjectId.fromObjectId(idBuf);
-
- entry.oldMode = walk.getFileMode(0);
- entry.newMode = walk.getFileMode(1);
- entry.newPath = entry.oldPath = walk.getPathString();
-
- if (treeFilterMarker != null)
- entry.treeFilterMarks = treeFilterMarker.getMarks(walk);
-
- if (entry.oldMode == FileMode.MISSING) {
- entry.oldPath = DiffEntry.DEV_NULL;
- entry.changeType = ChangeType.ADD;
- r.add(entry);
-
- } else if (entry.newMode == FileMode.MISSING) {
- entry.newPath = DiffEntry.DEV_NULL;
- entry.changeType = ChangeType.DELETE;
- r.add(entry);
-
- } else if (!entry.oldId.equals(entry.newId)) {
- entry.changeType = ChangeType.MODIFY;
- if (RenameDetector.sameType(entry.oldMode, entry.newMode))
- r.add(entry);
- else
- r.addAll(breakModify(entry));
- } else if (entry.oldMode != entry.newMode) {
- entry.changeType = ChangeType.MODIFY;
- r.add(entry);
- }
-
- if (includeTrees && walk.isSubtree())
- walk.enterSubtree();
- }
- return r;
- }
-
- static DiffEntry add(String path, AnyObjectId id) {
- DiffEntry e = new DiffEntry();
- e.oldId = A_ZERO;
- e.oldMode = FileMode.MISSING;
- e.oldPath = DEV_NULL;
-
- e.newId = AbbreviatedObjectId.fromObjectId(id);
- e.newMode = FileMode.REGULAR_FILE;
- e.newPath = path;
- e.changeType = ChangeType.ADD;
- return e;
- }
-
- static DiffEntry delete(String path, AnyObjectId id) {
- DiffEntry e = new DiffEntry();
- e.oldId = AbbreviatedObjectId.fromObjectId(id);
- e.oldMode = FileMode.REGULAR_FILE;
- e.oldPath = path;
-
- e.newId = A_ZERO;
- e.newMode = FileMode.MISSING;
- e.newPath = DEV_NULL;
- e.changeType = ChangeType.DELETE;
- return e;
- }
-
- static DiffEntry modify(String path) {
- DiffEntry e = new DiffEntry();
- e.oldMode = FileMode.REGULAR_FILE;
- e.oldPath = path;
-
- e.newMode = FileMode.REGULAR_FILE;
- e.newPath = path;
- e.changeType = ChangeType.MODIFY;
- return e;
- }
-
- /**
- * Breaks apart a DiffEntry into two entries, one DELETE and one ADD.
- *
- * @param entry
- * the DiffEntry to break apart.
- * @return a list containing two entries. Calling {@link #getChangeType()}
- * on the first entry will return ChangeType.DELETE. Calling it on
- * the second entry will return ChangeType.ADD.
- */
- static List<DiffEntry> breakModify(DiffEntry entry) {
- DiffEntry del = new DiffEntry();
- del.oldId = entry.getOldId();
- del.oldMode = entry.getOldMode();
- del.oldPath = entry.getOldPath();
-
- del.newId = A_ZERO;
- del.newMode = FileMode.MISSING;
- del.newPath = DiffEntry.DEV_NULL;
- del.changeType = ChangeType.DELETE;
-
- DiffEntry add = new DiffEntry();
- add.oldId = A_ZERO;
- add.oldMode = FileMode.MISSING;
- add.oldPath = DiffEntry.DEV_NULL;
-
- add.newId = entry.getNewId();
- add.newMode = entry.getNewMode();
- add.newPath = entry.getNewPath();
- add.changeType = ChangeType.ADD;
- return Arrays.asList(del, add);
- }
-
- static DiffEntry pair(ChangeType changeType, DiffEntry src, DiffEntry dst,
- int score) {
- DiffEntry r = new DiffEntry();
-
- r.oldId = src.oldId;
- r.oldMode = src.oldMode;
- r.oldPath = src.oldPath;
-
- r.newId = dst.newId;
- r.newMode = dst.newMode;
- r.newPath = dst.newPath;
-
- r.changeType = changeType;
- r.score = score;
-
- r.treeFilterMarks = src.treeFilterMarks | dst.treeFilterMarks;
-
- return r;
- }
-
- /** File name of the old (pre-image). */
- protected String oldPath;
-
- /** File name of the new (post-image). */
- protected String newPath;
-
- /** Old mode of the file, if described by the patch, else null. */
- protected FileMode oldMode;
-
- /** New mode of the file, if described by the patch, else null. */
- protected FileMode newMode;
-
- /** General type of change indicated by the patch. */
- protected ChangeType changeType;
-
- /** Similarity score if {@link #changeType} is a copy or rename. */
- protected int score;
-
- /** ObjectId listed on the index line for the old (pre-image) */
- protected AbbreviatedObjectId oldId;
-
- /** ObjectId listed on the index line for the new (post-image) */
- protected AbbreviatedObjectId newId;
-
- /**
- * Bitset for marked flags of tree filters passed to
- * {@link #scan(TreeWalk, boolean, TreeFilter...)}
- */
- private int treeFilterMarks = 0;
-
- /**
- * Get the old name associated with this file.
- * <p>
- * The meaning of the old name can differ depending on the semantic meaning
- * of this patch:
- * <ul>
- * <li><i>file add</i>: always <code>/dev/null</code></li>
- * <li><i>file modify</i>: always {@link #getNewPath()}</li>
- * <li><i>file delete</i>: always the file being deleted</li>
- * <li><i>file copy</i>: source file the copy originates from</li>
- * <li><i>file rename</i>: source file the rename originates from</li>
- * </ul>
- *
- * @return old name for this file.
- */
- public String getOldPath() {
- return oldPath;
- }
-
- /**
- * Get the new name associated with this file.
- * <p>
- * The meaning of the new name can differ depending on the semantic meaning
- * of this patch:
- * <ul>
- * <li><i>file add</i>: always the file being created</li>
- * <li><i>file modify</i>: always {@link #getOldPath()}</li>
- * <li><i>file delete</i>: always <code>/dev/null</code></li>
- * <li><i>file copy</i>: destination file the copy ends up at</li>
- * <li><i>file rename</i>: destination file the rename ends up at</li>
- * </ul>
- *
- * @return new name for this file.
- */
- public String getNewPath() {
- return newPath;
- }
-
- /**
- * Get the path associated with this file.
- *
- * @param side
- * which path to obtain.
- * @return name for this file.
- */
- public String getPath(Side side) {
- return side == Side.OLD ? getOldPath() : getNewPath();
- }
-
- /** @return the old file mode, if described in the patch */
- public FileMode getOldMode() {
- return oldMode;
- }
-
- /** @return the new file mode, if described in the patch */
- public FileMode getNewMode() {
- return newMode;
- }
-
- /**
- * Get the mode associated with this file.
- *
- * @param side
- * which mode to obtain.
- * @return the mode.
- */
- public FileMode getMode(Side side) {
- return side == Side.OLD ? getOldMode() : getNewMode();
- }
-
- /** @return the type of change this patch makes on {@link #getNewPath()} */
- public ChangeType getChangeType() {
- return changeType;
- }
-
- /**
- * @return similarity score between {@link #getOldPath()} and
- * {@link #getNewPath()} if {@link #getChangeType()} is
- * {@link ChangeType#COPY} or {@link ChangeType#RENAME}.
- */
- public int getScore() {
- return score;
- }
-
- /**
- * Get the old object id from the <code>index</code>.
- *
- * @return the object id; null if there is no index line
- */
- public AbbreviatedObjectId getOldId() {
- return oldId;
- }
-
- /**
- * Get the new object id from the <code>index</code>.
- *
- * @return the object id; null if there is no index line
- */
- public AbbreviatedObjectId getNewId() {
- return newId;
- }
-
- /**
- * Whether the mark tree filter with the specified index matched during scan
- * or not, see {@link #scan(TreeWalk, boolean, TreeFilter...)}. Example:
- * <p>
- *
- * <pre>
- * TreeFilter filterA = ...;
- * TreeFilter filterB = ...;
- * List<DiffEntry> entries = DiffEntry.scan(walk, false, filterA, filterB);
- * DiffEntry entry = entries.get(0);
- * boolean filterAMatched = entry.isMarked(0);
- * boolean filterBMatched = entry.isMarked(1);
- * </pre>
- * <p>
- * Note that 0 corresponds to filterA because it was the first filter that
- * was passed to scan.
- * <p>
- * To query more than one flag at once, see {@link #getTreeFilterMarks()}.
- *
- * @param index
- * the index of the tree filter to check for (must be between 0
- * and {@link Integer#SIZE}).
- *
- * @return true, if the tree filter matched; false if not
- * @since 2.3
- */
- public boolean isMarked(int index) {
- return (treeFilterMarks & (1L << index)) != 0;
- }
-
- /**
- * Get the raw tree filter marks, as set during
- * {@link #scan(TreeWalk, boolean, TreeFilter...)}. See
- * {@link #isMarked(int)} to query each mark individually.
- *
- * @return the bitset of tree filter marks
- * @since 2.3
- */
- public int getTreeFilterMarks() {
- return treeFilterMarks;
- }
-
- /**
- * Get the object id.
- *
- * @param side
- * the side of the id to get.
- * @return the object id; null if there is no index line
- */
- public AbbreviatedObjectId getId(Side side) {
- return side == Side.OLD ? getOldId() : getNewId();
- }
-
- @SuppressWarnings("nls")
- @Override
- public String toString() {
- StringBuilder buf = new StringBuilder();
- buf.append("DiffEntry[");
- buf.append(changeType);
- buf.append(" ");
- switch (changeType) {
- case ADD:
- buf.append(newPath);
- break;
- case COPY:
- buf.append(oldPath + "->" + newPath);
- break;
- case DELETE:
- buf.append(oldPath);
- break;
- case MODIFY:
- buf.append(oldPath);
- break;
- case RENAME:
- buf.append(oldPath + "->" + newPath);
- break;
- }
- buf.append("]");
- return buf.toString();
- }
- }
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