Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Reduce content hash function collisions
The hash code returned by RawTextComparator (or that is used
by the SimilarityIndex) play an important role in the speed of
any algorithm that is based upon them. The lower the number of
collisions produced by the hash function, the shorter the hash
chains within hash tables will be, and the less likely we are to
fall into O(N^2) runtime behaviors for algorithms like PatienceDiff.
Our prior hash function was absolutely horrid, so replace it with
the proper definition of the DJB hash that was originally published
by Professor Daniel J. Bernstein.
To support this assertion, below is a table listing the maximum
number of collisions that result when hashing the unique lines in
each source code file of 3 randomly chosen projects:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
sha1 6
string_hash31 11
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
sha1 8
string_hash31 32
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
sha1 6
string_hash31 13
We can clearly see that prior_hash performed very poorly, resulting
in 8,675 collisions (elements in the same hash bucket) for at least
one file in the Linux kernel repository. This leads to some very
bad O(N) style insertion and lookup performance, even though the
hash table was sized to be the next power-of-2 larger than the
total number of unique lines in the file.
The djb hash we are replacing prior_hash with performs closer to
SHA-1 in terms of having very few collisions. This indicates it
provides a reasonably distributed output for this type of input,
despite being a much simpler algorithm (and therefore will be much
faster to execute).
The string_hash31 function is provided just to compare results with,
it is the algorithm commonly used by java.lang.String hashCode().
However, life isn't quite this simple.
djb produces a 32 bit hash code, but our hash tables are always
smaller than 2^32 buckets. Mashing the 32 bit code into an array
index used to be done by simply taking the lower bits of the hash
code by a bitwise and operator. This unfortuntely still produces
many collisions, e.g. 32 on the linux-2.6 repository files.
From [1] we can apply a final "cleanup" step to the hash code to
mix the bits together a little better, and give priority to the
higher order bits as they include data from more bytes of input:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
djb + cleanup 6
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
djb + cleanup 7
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
djb + cleanup 7
This is a massive improvement, as the number of collisions for
common inputs drops to acceptable levels, and we haven't really
made the hash functions any more complex than they were before.
[1] http://lkml.org/lkml/2009/10/27/404
Change-Id: Ia753b695de9526a157ddba265824240bd05dead1
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
13 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Reduce content hash function collisions
The hash code returned by RawTextComparator (or that is used
by the SimilarityIndex) play an important role in the speed of
any algorithm that is based upon them. The lower the number of
collisions produced by the hash function, the shorter the hash
chains within hash tables will be, and the less likely we are to
fall into O(N^2) runtime behaviors for algorithms like PatienceDiff.
Our prior hash function was absolutely horrid, so replace it with
the proper definition of the DJB hash that was originally published
by Professor Daniel J. Bernstein.
To support this assertion, below is a table listing the maximum
number of collisions that result when hashing the unique lines in
each source code file of 3 randomly chosen projects:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
sha1 6
string_hash31 11
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
sha1 8
string_hash31 32
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
sha1 6
string_hash31 13
We can clearly see that prior_hash performed very poorly, resulting
in 8,675 collisions (elements in the same hash bucket) for at least
one file in the Linux kernel repository. This leads to some very
bad O(N) style insertion and lookup performance, even though the
hash table was sized to be the next power-of-2 larger than the
total number of unique lines in the file.
The djb hash we are replacing prior_hash with performs closer to
SHA-1 in terms of having very few collisions. This indicates it
provides a reasonably distributed output for this type of input,
despite being a much simpler algorithm (and therefore will be much
faster to execute).
The string_hash31 function is provided just to compare results with,
it is the algorithm commonly used by java.lang.String hashCode().
However, life isn't quite this simple.
djb produces a 32 bit hash code, but our hash tables are always
smaller than 2^32 buckets. Mashing the 32 bit code into an array
index used to be done by simply taking the lower bits of the hash
code by a bitwise and operator. This unfortuntely still produces
many collisions, e.g. 32 on the linux-2.6 repository files.
From [1] we can apply a final "cleanup" step to the hash code to
mix the bits together a little better, and give priority to the
higher order bits as they include data from more bytes of input:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
djb + cleanup 6
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
djb + cleanup 7
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
djb + cleanup 7
This is a massive improvement, as the number of collisions for
common inputs drops to acceptable levels, and we haven't really
made the hash functions any more complex than they were before.
[1] http://lkml.org/lkml/2009/10/27/404
Change-Id: Ia753b695de9526a157ddba265824240bd05dead1
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
13 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Reduce content hash function collisions
The hash code returned by RawTextComparator (or that is used
by the SimilarityIndex) play an important role in the speed of
any algorithm that is based upon them. The lower the number of
collisions produced by the hash function, the shorter the hash
chains within hash tables will be, and the less likely we are to
fall into O(N^2) runtime behaviors for algorithms like PatienceDiff.
Our prior hash function was absolutely horrid, so replace it with
the proper definition of the DJB hash that was originally published
by Professor Daniel J. Bernstein.
To support this assertion, below is a table listing the maximum
number of collisions that result when hashing the unique lines in
each source code file of 3 randomly chosen projects:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
sha1 6
string_hash31 11
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
sha1 8
string_hash31 32
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
sha1 6
string_hash31 13
We can clearly see that prior_hash performed very poorly, resulting
in 8,675 collisions (elements in the same hash bucket) for at least
one file in the Linux kernel repository. This leads to some very
bad O(N) style insertion and lookup performance, even though the
hash table was sized to be the next power-of-2 larger than the
total number of unique lines in the file.
The djb hash we are replacing prior_hash with performs closer to
SHA-1 in terms of having very few collisions. This indicates it
provides a reasonably distributed output for this type of input,
despite being a much simpler algorithm (and therefore will be much
faster to execute).
The string_hash31 function is provided just to compare results with,
it is the algorithm commonly used by java.lang.String hashCode().
However, life isn't quite this simple.
djb produces a 32 bit hash code, but our hash tables are always
smaller than 2^32 buckets. Mashing the 32 bit code into an array
index used to be done by simply taking the lower bits of the hash
code by a bitwise and operator. This unfortuntely still produces
many collisions, e.g. 32 on the linux-2.6 repository files.
From [1] we can apply a final "cleanup" step to the hash code to
mix the bits together a little better, and give priority to the
higher order bits as they include data from more bytes of input:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
djb + cleanup 6
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
djb + cleanup 7
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
djb + cleanup 7
This is a massive improvement, as the number of collisions for
common inputs drops to acceptable levels, and we haven't really
made the hash functions any more complex than they were before.
[1] http://lkml.org/lkml/2009/10/27/404
Change-Id: Ia753b695de9526a157ddba265824240bd05dead1
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
13 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Reduce content hash function collisions
The hash code returned by RawTextComparator (or that is used
by the SimilarityIndex) play an important role in the speed of
any algorithm that is based upon them. The lower the number of
collisions produced by the hash function, the shorter the hash
chains within hash tables will be, and the less likely we are to
fall into O(N^2) runtime behaviors for algorithms like PatienceDiff.
Our prior hash function was absolutely horrid, so replace it with
the proper definition of the DJB hash that was originally published
by Professor Daniel J. Bernstein.
To support this assertion, below is a table listing the maximum
number of collisions that result when hashing the unique lines in
each source code file of 3 randomly chosen projects:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
sha1 6
string_hash31 11
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
sha1 8
string_hash31 32
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
sha1 6
string_hash31 13
We can clearly see that prior_hash performed very poorly, resulting
in 8,675 collisions (elements in the same hash bucket) for at least
one file in the Linux kernel repository. This leads to some very
bad O(N) style insertion and lookup performance, even though the
hash table was sized to be the next power-of-2 larger than the
total number of unique lines in the file.
The djb hash we are replacing prior_hash with performs closer to
SHA-1 in terms of having very few collisions. This indicates it
provides a reasonably distributed output for this type of input,
despite being a much simpler algorithm (and therefore will be much
faster to execute).
The string_hash31 function is provided just to compare results with,
it is the algorithm commonly used by java.lang.String hashCode().
However, life isn't quite this simple.
djb produces a 32 bit hash code, but our hash tables are always
smaller than 2^32 buckets. Mashing the 32 bit code into an array
index used to be done by simply taking the lower bits of the hash
code by a bitwise and operator. This unfortuntely still produces
many collisions, e.g. 32 on the linux-2.6 repository files.
From [1] we can apply a final "cleanup" step to the hash code to
mix the bits together a little better, and give priority to the
higher order bits as they include data from more bytes of input:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
djb + cleanup 6
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
djb + cleanup 7
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
djb + cleanup 7
This is a massive improvement, as the number of collisions for
common inputs drops to acceptable levels, and we haven't really
made the hash functions any more complex than they were before.
[1] http://lkml.org/lkml/2009/10/27/404
Change-Id: Ia753b695de9526a157ddba265824240bd05dead1
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
13 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Reduce content hash function collisions
The hash code returned by RawTextComparator (or that is used
by the SimilarityIndex) play an important role in the speed of
any algorithm that is based upon them. The lower the number of
collisions produced by the hash function, the shorter the hash
chains within hash tables will be, and the less likely we are to
fall into O(N^2) runtime behaviors for algorithms like PatienceDiff.
Our prior hash function was absolutely horrid, so replace it with
the proper definition of the DJB hash that was originally published
by Professor Daniel J. Bernstein.
To support this assertion, below is a table listing the maximum
number of collisions that result when hashing the unique lines in
each source code file of 3 randomly chosen projects:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
sha1 6
string_hash31 11
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
sha1 8
string_hash31 32
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
sha1 6
string_hash31 13
We can clearly see that prior_hash performed very poorly, resulting
in 8,675 collisions (elements in the same hash bucket) for at least
one file in the Linux kernel repository. This leads to some very
bad O(N) style insertion and lookup performance, even though the
hash table was sized to be the next power-of-2 larger than the
total number of unique lines in the file.
The djb hash we are replacing prior_hash with performs closer to
SHA-1 in terms of having very few collisions. This indicates it
provides a reasonably distributed output for this type of input,
despite being a much simpler algorithm (and therefore will be much
faster to execute).
The string_hash31 function is provided just to compare results with,
it is the algorithm commonly used by java.lang.String hashCode().
However, life isn't quite this simple.
djb produces a 32 bit hash code, but our hash tables are always
smaller than 2^32 buckets. Mashing the 32 bit code into an array
index used to be done by simply taking the lower bits of the hash
code by a bitwise and operator. This unfortuntely still produces
many collisions, e.g. 32 on the linux-2.6 repository files.
From [1] we can apply a final "cleanup" step to the hash code to
mix the bits together a little better, and give priority to the
higher order bits as they include data from more bytes of input:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
djb + cleanup 6
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
djb + cleanup 7
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
djb + cleanup 7
This is a massive improvement, as the number of collisions for
common inputs drops to acceptable levels, and we haven't really
made the hash functions any more complex than they were before.
[1] http://lkml.org/lkml/2009/10/27/404
Change-Id: Ia753b695de9526a157ddba265824240bd05dead1
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
13 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Reduce content hash function collisions
The hash code returned by RawTextComparator (or that is used
by the SimilarityIndex) play an important role in the speed of
any algorithm that is based upon them. The lower the number of
collisions produced by the hash function, the shorter the hash
chains within hash tables will be, and the less likely we are to
fall into O(N^2) runtime behaviors for algorithms like PatienceDiff.
Our prior hash function was absolutely horrid, so replace it with
the proper definition of the DJB hash that was originally published
by Professor Daniel J. Bernstein.
To support this assertion, below is a table listing the maximum
number of collisions that result when hashing the unique lines in
each source code file of 3 randomly chosen projects:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
sha1 6
string_hash31 11
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
sha1 8
string_hash31 32
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
sha1 6
string_hash31 13
We can clearly see that prior_hash performed very poorly, resulting
in 8,675 collisions (elements in the same hash bucket) for at least
one file in the Linux kernel repository. This leads to some very
bad O(N) style insertion and lookup performance, even though the
hash table was sized to be the next power-of-2 larger than the
total number of unique lines in the file.
The djb hash we are replacing prior_hash with performs closer to
SHA-1 in terms of having very few collisions. This indicates it
provides a reasonably distributed output for this type of input,
despite being a much simpler algorithm (and therefore will be much
faster to execute).
The string_hash31 function is provided just to compare results with,
it is the algorithm commonly used by java.lang.String hashCode().
However, life isn't quite this simple.
djb produces a 32 bit hash code, but our hash tables are always
smaller than 2^32 buckets. Mashing the 32 bit code into an array
index used to be done by simply taking the lower bits of the hash
code by a bitwise and operator. This unfortuntely still produces
many collisions, e.g. 32 on the linux-2.6 repository files.
From [1] we can apply a final "cleanup" step to the hash code to
mix the bits together a little better, and give priority to the
higher order bits as they include data from more bytes of input:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
djb + cleanup 6
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
djb + cleanup 7
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
djb + cleanup 7
This is a massive improvement, as the number of collisions for
common inputs drops to acceptable levels, and we haven't really
made the hash functions any more complex than they were before.
[1] http://lkml.org/lkml/2009/10/27/404
Change-Id: Ia753b695de9526a157ddba265824240bd05dead1
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
13 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Reduce content hash function collisions
The hash code returned by RawTextComparator (or that is used
by the SimilarityIndex) play an important role in the speed of
any algorithm that is based upon them. The lower the number of
collisions produced by the hash function, the shorter the hash
chains within hash tables will be, and the less likely we are to
fall into O(N^2) runtime behaviors for algorithms like PatienceDiff.
Our prior hash function was absolutely horrid, so replace it with
the proper definition of the DJB hash that was originally published
by Professor Daniel J. Bernstein.
To support this assertion, below is a table listing the maximum
number of collisions that result when hashing the unique lines in
each source code file of 3 randomly chosen projects:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
sha1 6
string_hash31 11
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
sha1 8
string_hash31 32
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
sha1 6
string_hash31 13
We can clearly see that prior_hash performed very poorly, resulting
in 8,675 collisions (elements in the same hash bucket) for at least
one file in the Linux kernel repository. This leads to some very
bad O(N) style insertion and lookup performance, even though the
hash table was sized to be the next power-of-2 larger than the
total number of unique lines in the file.
The djb hash we are replacing prior_hash with performs closer to
SHA-1 in terms of having very few collisions. This indicates it
provides a reasonably distributed output for this type of input,
despite being a much simpler algorithm (and therefore will be much
faster to execute).
The string_hash31 function is provided just to compare results with,
it is the algorithm commonly used by java.lang.String hashCode().
However, life isn't quite this simple.
djb produces a 32 bit hash code, but our hash tables are always
smaller than 2^32 buckets. Mashing the 32 bit code into an array
index used to be done by simply taking the lower bits of the hash
code by a bitwise and operator. This unfortuntely still produces
many collisions, e.g. 32 on the linux-2.6 repository files.
From [1] we can apply a final "cleanup" step to the hash code to
mix the bits together a little better, and give priority to the
higher order bits as they include data from more bytes of input:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
djb + cleanup 6
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
djb + cleanup 7
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
djb + cleanup 7
This is a massive improvement, as the number of collisions for
common inputs drops to acceptable levels, and we haven't really
made the hash functions any more complex than they were before.
[1] http://lkml.org/lkml/2009/10/27/404
Change-Id: Ia753b695de9526a157ddba265824240bd05dead1
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
13 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Reduce content hash function collisions
The hash code returned by RawTextComparator (or that is used
by the SimilarityIndex) play an important role in the speed of
any algorithm that is based upon them. The lower the number of
collisions produced by the hash function, the shorter the hash
chains within hash tables will be, and the less likely we are to
fall into O(N^2) runtime behaviors for algorithms like PatienceDiff.
Our prior hash function was absolutely horrid, so replace it with
the proper definition of the DJB hash that was originally published
by Professor Daniel J. Bernstein.
To support this assertion, below is a table listing the maximum
number of collisions that result when hashing the unique lines in
each source code file of 3 randomly chosen projects:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
sha1 6
string_hash31 11
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
sha1 8
string_hash31 32
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
sha1 6
string_hash31 13
We can clearly see that prior_hash performed very poorly, resulting
in 8,675 collisions (elements in the same hash bucket) for at least
one file in the Linux kernel repository. This leads to some very
bad O(N) style insertion and lookup performance, even though the
hash table was sized to be the next power-of-2 larger than the
total number of unique lines in the file.
The djb hash we are replacing prior_hash with performs closer to
SHA-1 in terms of having very few collisions. This indicates it
provides a reasonably distributed output for this type of input,
despite being a much simpler algorithm (and therefore will be much
faster to execute).
The string_hash31 function is provided just to compare results with,
it is the algorithm commonly used by java.lang.String hashCode().
However, life isn't quite this simple.
djb produces a 32 bit hash code, but our hash tables are always
smaller than 2^32 buckets. Mashing the 32 bit code into an array
index used to be done by simply taking the lower bits of the hash
code by a bitwise and operator. This unfortuntely still produces
many collisions, e.g. 32 on the linux-2.6 repository files.
From [1] we can apply a final "cleanup" step to the hash code to
mix the bits together a little better, and give priority to the
higher order bits as they include data from more bytes of input:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
djb + cleanup 6
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
djb + cleanup 7
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
djb + cleanup 7
This is a massive improvement, as the number of collisions for
common inputs drops to acceptable levels, and we haven't really
made the hash functions any more complex than they were before.
[1] http://lkml.org/lkml/2009/10/27/404
Change-Id: Ia753b695de9526a157ddba265824240bd05dead1
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
13 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Reduce content hash function collisions
The hash code returned by RawTextComparator (or that is used
by the SimilarityIndex) play an important role in the speed of
any algorithm that is based upon them. The lower the number of
collisions produced by the hash function, the shorter the hash
chains within hash tables will be, and the less likely we are to
fall into O(N^2) runtime behaviors for algorithms like PatienceDiff.
Our prior hash function was absolutely horrid, so replace it with
the proper definition of the DJB hash that was originally published
by Professor Daniel J. Bernstein.
To support this assertion, below is a table listing the maximum
number of collisions that result when hashing the unique lines in
each source code file of 3 randomly chosen projects:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
sha1 6
string_hash31 11
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
sha1 8
string_hash31 32
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
sha1 6
string_hash31 13
We can clearly see that prior_hash performed very poorly, resulting
in 8,675 collisions (elements in the same hash bucket) for at least
one file in the Linux kernel repository. This leads to some very
bad O(N) style insertion and lookup performance, even though the
hash table was sized to be the next power-of-2 larger than the
total number of unique lines in the file.
The djb hash we are replacing prior_hash with performs closer to
SHA-1 in terms of having very few collisions. This indicates it
provides a reasonably distributed output for this type of input,
despite being a much simpler algorithm (and therefore will be much
faster to execute).
The string_hash31 function is provided just to compare results with,
it is the algorithm commonly used by java.lang.String hashCode().
However, life isn't quite this simple.
djb produces a 32 bit hash code, but our hash tables are always
smaller than 2^32 buckets. Mashing the 32 bit code into an array
index used to be done by simply taking the lower bits of the hash
code by a bitwise and operator. This unfortuntely still produces
many collisions, e.g. 32 on the linux-2.6 repository files.
From [1] we can apply a final "cleanup" step to the hash code to
mix the bits together a little better, and give priority to the
higher order bits as they include data from more bytes of input:
test_jgit: 931 files; 122 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 418
djb 5
djb + cleanup 6
test_linux26: 30198 files; 258 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 8675
djb 32
djb + cleanup 7
test_frameworks_base: 8381 files; 184 avg. unique lines/file
Algorithm | Collisions
-------------+-----------
prior_hash 4615
djb 10
djb + cleanup 7
This is a massive improvement, as the number of collisions for
common inputs drops to acceptable levels, and we haven't really
made the hash functions any more complex than they were before.
[1] http://lkml.org/lkml/2009/10/27/404
Change-Id: Ia753b695de9526a157ddba265824240bd05dead1
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
13 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago Implement similarity based rename detection
Content similarity based rename detection is performed only after
a linear time detection is performed using exact content match on
the ObjectIds. Any names which were paired up during that exact
match phase are excluded from the inexact similarity based rename,
which reduces the space that must be considered.
During rename detection two entries cannot be marked as a rename
if they are different types of files. This prevents a symlink from
being renamed to a regular file, even if their blob content appears
to be similar, or is identical.
Efficiently comparing two files is performed by building up two
hash indexes and hashing lines or short blocks from each file,
counting the number of bytes that each line or block represents.
Instead of using a standard java.util.HashMap, we use a custom
open hashing scheme similiar to what we use in ObjecIdSubclassMap.
This permits us to have a very light-weight hash, with very little
memory overhead per cell stored.
As we only need two ints per record in the map (line/block key and
number of bytes), we collapse them into a single long inside of
a long array, making very efficient use of available memory when
we create the index table. We only need object headers for the
index structure itself, and the index table, but not per-cell.
This offers a massive space savings over using java.util.HashMap.
The score calculation is done by approximating how many bytes are
the same between the two inputs (which for a delta would be how much
is copied from the base into the result). The score is derived by
dividing the approximate number of bytes in common into the length
of the larger of the two input files.
Right now the SimilarityIndex table should average about 1/2 full,
which means we waste about 50% of our memory on empty entries
after we are done indexing a file and sort the table's contents.
If memory becomes an issue we could discard the table and copy all
records over to a new array that is properly sized.
Building the index requires O(M + N log N) time, where M is the
size of the input file in bytes, and N is the number of unique
lines/blocks in the file. The N log N time constraint comes
from the sort of the index table that is necessary to perform
linear time matching against another SimilarityIndex created for
a different file.
To actually perform the rename detection, a SxD matrix is created,
placing the sources (aka deletions) along one dimension and the
destinations (aka additions) along the other. A simple O(S x D)
loop examines every cell in this matrix.
A SimilarityIndex is built along the row and reused for each
column compare along that row, avoiding the costly index rebuild
at the row level. A future improvement would be to load a smaller
square matrix into SimilarityIndexes and process everything in that
sub-matrix before discarding the column dimension and moving down
to the next sub-matrix block along that same grid of rows.
An optional ProgressMonitor is permitted to be passed in, allowing
applications to see the progress of the detector as it works through
the matrix cells. This provides some indication of current status
for very long running renames.
The default line/block hash function used by the SimilarityIndex
may not be optimal, and may produce too many collisions. It is
borrowed from RawText's hash, which is used to quickly skip out of
a longer equality test if two lines have different hash functions.
We may need to refine this hash in the future, in order to minimize
the number of collisions we get on common source files.
Based on a handful of test commits in JGit (especially my own
recent rename repository refactoring series), this rename detector
produces output that is very close to C Git. The content similarity
scores are sometimes off by 1%, which is most probably caused by
our SimilarityIndex type using a different hash function than C
Git uses when it computes the delta size between any two objects
in the rename matrix.
Bug: 318504
Change-Id: I11dff969e8a2e4cf252636d857d2113053bdd9dc
Signed-off-by: Shawn O. Pearce <spearce@spearce.org>
14 years ago |
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- /*
- * Copyright (C) 2010, Google Inc. and others
- *
- * This program and the accompanying materials are made available under the
- * terms of the Eclipse Distribution License v. 1.0 which is available at
- * https://www.eclipse.org/org/documents/edl-v10.php.
- *
- * SPDX-License-Identifier: BSD-3-Clause
- */
-
- package org.eclipse.jgit.diff;
-
- import java.io.EOFException;
- import java.io.IOException;
- import java.io.InputStream;
- import java.util.Arrays;
-
- import org.eclipse.jgit.errors.MissingObjectException;
- import org.eclipse.jgit.lib.ObjectLoader;
- import org.eclipse.jgit.lib.ObjectStream;
-
- /**
- * Index structure of lines/blocks in one file.
- * <p>
- * This structure can be used to compute an approximation of the similarity
- * between two files. The index is used by
- * {@link org.eclipse.jgit.diff.SimilarityRenameDetector} to compute scores
- * between files.
- * <p>
- * To save space in memory, this index uses a space efficient encoding which
- * will not exceed 1 MiB per instance. The index starts out at a smaller size
- * (closer to 2 KiB), but may grow as more distinct blocks within the scanned
- * file are discovered.
- *
- * @since 4.0
- */
- public class SimilarityIndex {
- /** A special {@link TableFullException} used in place of OutOfMemoryError. */
- public static final TableFullException
- TABLE_FULL_OUT_OF_MEMORY = new TableFullException();
-
- /**
- * Shift to apply before storing a key.
- * <p>
- * Within the 64 bit table record space, we leave the highest bit unset so
- * all values are positive. The lower 32 bits to count bytes.
- */
- private static final int KEY_SHIFT = 32;
-
- /** Maximum value of the count field, also mask to extract the count. */
- private static final long MAX_COUNT = (1L << KEY_SHIFT) - 1;
-
- /**
- * Total amount of bytes hashed into the structure, including \n. This is
- * usually the size of the file minus number of CRLF encounters.
- */
- private long hashedCnt;
-
- /** Number of non-zero entries in {@link #idHash}. */
- private int idSize;
-
- /** {@link #idSize} that triggers {@link #idHash} to double in size. */
- private int idGrowAt;
-
- /**
- * Pairings of content keys and counters.
- * <p>
- * Slots in the table are actually two ints wedged into a single long. The
- * upper 32 bits stores the content key, and the remaining lower bits stores
- * the number of bytes associated with that key. Empty slots are denoted by
- * 0, which cannot occur because the count cannot be 0. Values can only be
- * positive, which we enforce during key addition.
- */
- private long[] idHash;
-
- /** {@code idHash.length == 1 << idHashBits}. */
- private int idHashBits;
-
- /**
- * Create a new similarity index for the given object
- *
- * @param obj
- * the object to hash
- * @return similarity index for this object
- * @throws java.io.IOException
- * file contents cannot be read from the repository.
- * @throws org.eclipse.jgit.diff.SimilarityIndex.TableFullException
- * object hashing overflowed the storage capacity of the
- * SimilarityIndex.
- */
- public static SimilarityIndex create(ObjectLoader obj) throws IOException,
- TableFullException {
- SimilarityIndex idx = new SimilarityIndex();
- idx.hash(obj);
- idx.sort();
- return idx;
- }
-
- SimilarityIndex() {
- idHashBits = 8;
- idHash = new long[1 << idHashBits];
- idGrowAt = growAt(idHashBits);
- }
-
- static boolean isBinary(ObjectLoader obj) throws IOException {
- if (obj.isLarge()) {
- try (ObjectStream in1 = obj.openStream()) {
- return RawText.isBinary(in1);
- }
- }
- return RawText.isBinary(obj.getCachedBytes());
- }
-
- void hash(ObjectLoader obj) throws MissingObjectException, IOException,
- TableFullException {
- if (obj.isLarge()) {
- hashLargeObject(obj);
- } else {
- byte[] raw = obj.getCachedBytes();
- hash(raw, 0, raw.length);
- }
- }
-
- private void hashLargeObject(ObjectLoader obj) throws IOException,
- TableFullException {
- boolean text;
- text = !isBinary(obj);
-
- try (ObjectStream in2 = obj.openStream()) {
- hash(in2, in2.getSize(), text);
- }
- }
-
- void hash(byte[] raw, int ptr, int end) throws TableFullException {
- final boolean text = !RawText.isBinary(raw);
- hashedCnt = 0;
- while (ptr < end) {
- int hash = 5381;
- int blockHashedCnt = 0;
- int start = ptr;
-
- // Hash one line, or one block, whichever occurs first.
- do {
- int c = raw[ptr++] & 0xff;
- // Ignore CR in CRLF sequence if text
- if (text && c == '\r' && ptr < end && raw[ptr] == '\n')
- continue;
- blockHashedCnt++;
- if (c == '\n')
- break;
- hash = (hash << 5) + hash + c;
- } while (ptr < end && ptr - start < 64);
- hashedCnt += blockHashedCnt;
- add(hash, blockHashedCnt);
- }
- }
-
- void hash(InputStream in, long remaining, boolean text) throws IOException,
- TableFullException {
- byte[] buf = new byte[4096];
- int ptr = 0;
- int cnt = 0;
-
- while (0 < remaining) {
- int hash = 5381;
- int blockHashedCnt = 0;
-
- // Hash one line, or one block, whichever occurs first.
- int n = 0;
- do {
- if (ptr == cnt) {
- ptr = 0;
- cnt = in.read(buf, 0, buf.length);
- if (cnt <= 0)
- throw new EOFException();
- }
-
- n++;
- int c = buf[ptr++] & 0xff;
- // Ignore CR in CRLF sequence if text
- if (text && c == '\r' && ptr < cnt && buf[ptr] == '\n')
- continue;
- blockHashedCnt++;
- if (c == '\n')
- break;
- hash = (hash << 5) + hash + c;
- } while (n < 64 && n < remaining);
- hashedCnt += blockHashedCnt;
- add(hash, blockHashedCnt);
- remaining -= n;
- }
- }
-
- /**
- * Sort the internal table so it can be used for efficient scoring.
- * <p>
- * Once sorted, additional lines/blocks cannot be added to the index.
- */
- void sort() {
- // Sort the array. All of the empty space will wind up at the front,
- // because we forced all of the keys to always be positive. Later
- // we only work with the back half of the array.
- //
- Arrays.sort(idHash);
- }
-
- /**
- * Compute the similarity score between this index and another.
- * <p>
- * A region of a file is defined as a line in a text file or a fixed-size
- * block in a binary file. To prepare an index, each region in the file is
- * hashed; the values and counts of hashes are retained in a sorted table.
- * Define the similarity fraction F as the count of matching regions
- * between the two files divided between the maximum count of regions in
- * either file. The similarity score is F multiplied by the maxScore
- * constant, yielding a range [0, maxScore]. It is defined as maxScore for
- * the degenerate case of two empty files.
- * <p>
- * The similarity score is symmetrical; i.e. a.score(b) == b.score(a).
- *
- * @param dst
- * the other index
- * @param maxScore
- * the score representing a 100% match
- * @return the similarity score
- */
- public int score(SimilarityIndex dst, int maxScore) {
- long max = Math.max(hashedCnt, dst.hashedCnt);
- if (max == 0)
- return maxScore;
- return (int) ((common(dst) * maxScore) / max);
- }
-
- long common(SimilarityIndex dst) {
- return common(this, dst);
- }
-
- private static long common(SimilarityIndex src, SimilarityIndex dst) {
- int srcIdx = src.packedIndex(0);
- int dstIdx = dst.packedIndex(0);
- long[] srcHash = src.idHash;
- long[] dstHash = dst.idHash;
- return common(srcHash, srcIdx, dstHash, dstIdx);
- }
-
- private static long common(long[] srcHash, int srcIdx, //
- long[] dstHash, int dstIdx) {
- if (srcIdx == srcHash.length || dstIdx == dstHash.length)
- return 0;
-
- long common = 0;
- int srcKey = keyOf(srcHash[srcIdx]);
- int dstKey = keyOf(dstHash[dstIdx]);
-
- for (;;) {
- if (srcKey == dstKey) {
- common += Math.min(countOf(srcHash[srcIdx]),
- countOf(dstHash[dstIdx]));
-
- if (++srcIdx == srcHash.length)
- break;
- srcKey = keyOf(srcHash[srcIdx]);
-
- if (++dstIdx == dstHash.length)
- break;
- dstKey = keyOf(dstHash[dstIdx]);
-
- } else if (srcKey < dstKey) {
- // Regions of src which do not appear in dst.
- if (++srcIdx == srcHash.length)
- break;
- srcKey = keyOf(srcHash[srcIdx]);
-
- } else /* if (dstKey < srcKey) */{
- // Regions of dst which do not appear in src.
- if (++dstIdx == dstHash.length)
- break;
- dstKey = keyOf(dstHash[dstIdx]);
- }
- }
-
- return common;
- }
-
- // Testing only
- int size() {
- return idSize;
- }
-
- // Testing only
- int key(int idx) {
- return keyOf(idHash[packedIndex(idx)]);
- }
-
- // Testing only
- long count(int idx) {
- return countOf(idHash[packedIndex(idx)]);
- }
-
- // Brute force approach only for testing.
- int findIndex(int key) {
- for (int i = 0; i < idSize; i++)
- if (key(i) == key)
- return i;
- return -1;
- }
-
- private int packedIndex(int idx) {
- return (idHash.length - idSize) + idx;
- }
-
- void add(int key, int cnt) throws TableFullException {
- key = (key * 0x9e370001) >>> 1; // Mix bits and ensure not negative.
-
- int j = slot(key);
- for (;;) {
- long v = idHash[j];
- if (v == 0) {
- // Empty slot in the table, store here.
- if (idGrowAt <= idSize) {
- grow();
- j = slot(key);
- continue;
- }
- idHash[j] = pair(key, cnt);
- idSize++;
- return;
-
- } else if (keyOf(v) == key) {
- // Same key, increment the counter. If it overflows, fail
- // indexing to prevent the key from being impacted.
- //
- idHash[j] = pair(key, countOf(v) + cnt);
- return;
-
- } else if (++j >= idHash.length) {
- j = 0;
- }
- }
- }
-
- private static long pair(int key, long cnt) throws TableFullException {
- if (MAX_COUNT < cnt)
- throw new TableFullException();
- return (((long) key) << KEY_SHIFT) | cnt;
- }
-
- private int slot(int key) {
- // We use 31 - idHashBits because the upper bit was already forced
- // to be 0 and we want the remaining high bits to be used as the
- // table slot.
- //
- return key >>> (31 - idHashBits);
- }
-
- private static int growAt(int idHashBits) {
- return (1 << idHashBits) * (idHashBits - 3) / idHashBits;
- }
-
- @SuppressWarnings("UnusedException")
- private void grow() throws TableFullException {
- if (idHashBits == 30)
- throw new TableFullException();
-
- long[] oldHash = idHash;
- int oldSize = idHash.length;
-
- idHashBits++;
- idGrowAt = growAt(idHashBits);
-
- try {
- idHash = new long[1 << idHashBits];
- } catch (OutOfMemoryError noMemory) {
- throw TABLE_FULL_OUT_OF_MEMORY;
- }
-
- for (int i = 0; i < oldSize; i++) {
- long v = oldHash[i];
- if (v != 0) {
- int j = slot(keyOf(v));
- while (idHash[j] != 0)
- if (++j >= idHash.length)
- j = 0;
- idHash[j] = v;
- }
- }
- }
-
- private static int keyOf(long v) {
- return (int) (v >>> KEY_SHIFT);
- }
-
- private static long countOf(long v) {
- return v & MAX_COUNT;
- }
-
- /** Thrown by {@code create()} when file is too large. */
- public static class TableFullException extends Exception {
- private static final long serialVersionUID = 1L;
- }
- }
|