[PATCH 15/34] commit-graph-format.txt: document the modified path Bloom filter chunks
During a pathspec-limited revision walk, e.g. 'git log -- dir/file', Git spends a significant part of its runtime in the tree-diff machinery, checking whether the given path was modified between subsequent commits. By using Bloom filters to store the paths modified by each commit we can quickly tell that a commit didn't modify the given path without invoking tree-diff, thus considerably reduce this diffing overhead, along with the runtime and memory usage. This patch extends the commit-graph file format with additional chunks to store those modified path Bloom filters.
The rest of this log message takes a closer look at the problem and explains why these new chunks look like they do.
In the following the terms "file" and "path" are not used interchangeably. If a commit modified 'dir/subdir/foo.c', then it modified that one file, but it modified three paths (namely 'dir', 'dir/subdir', and 'dir/subdir/foo.c').
Furthermore, unless otherwise noted, "a lot of paths" means 5000 randomly selected paths that have existed at one time or another during the history of the corresponding repository's default branch [1], except for git it means all 5089 paths that have ever existed during the history of v2.25.0, and for homebrew-cask and homebrew-core all 7307 and 6535 paths, respectively, that have ever existed in the history of their default branches some time ago.
About pathspec-limited revision walks -------------------------------------
So, during a pathspec-limited revision walk Git spends a significant part of its runtime in the tree-diff machinery, checking whether the given path was modified between subsequent commits. The table below shows the average runtime of 'git rev-list HEAD -- $path' for "a lot of paths" in a couple of repositories, and how much of that runtime is spent in diff_tree_oid(). It also shows the potential average speedup, should we be able to reduce the tree-diff overhead to zero without introducing some other overhead (spoiler alert: we won't be able to achieve that, of course, but still will achieve even higher average speedup in several cases).
Average Average Potential
runtime diff time speedup
-------------------------------------------------------
android-base 0.8780s 0.7320s 83.37% 6.14x
cmssw 0.3143s 0.2800s 88.95% 9.19x
cpython 0.7453s 0.6602s 88.58% 8.76x
elasticsearch 0.1492s 0.1351s 90.55% 10.58x
gcc 7.1852s 6.9432s 96.63% 29.69x
gecko-dev 4.6113s 4.0964s 88.83% 8.96x
git 0.6180s 0.5911s 95.65% 22.97x
glibc 0.5618s 0.5313s 94.57% 18.42x
go 0.4913s 0.4469s 90.96% 11.07x
jdk 0.0482s 0.0431s 89.42% 9.45x
linux 0.7043s 0.6163s 87.50% 8.00x
llvm-project 2.6844s 2.2607s 84.22% 6.34x
rails 0.2784s 0.2372s 85.20% 6.76x
rust 0.7757s 0.7349s 94.74% 19.01x
tensorflow 0.6258s 0.5735s 91.64% 11.96x
webkit 1.9137s 1.6580s 86.64% 7.48xNotice that the average time spent diffing in the Linux and Git repositories is quite close (0.6163s vs 0.5911s), although the Linux repository contains about 15x more commits and 25x more files; more on this later.
Instrumenting the tree-diff machinery and gathering some stats about the number of diff calls, tree object comparisons and decoded tree entries revealed some interesting, perhaps even counterintuitive things:
- The number of scanned tree entries can be more important than the
number of tree comparisons. Here are four paths from two repositories, two each in the same
frequently-modified directory, with one near the beginning and one
at the end of that directory. When listing the commits modifying
these paths, i.e. 'git rev-list HEAD -- $path', the number of tree
comparisons is (nearly) the same, but the number of scanned tree
entries differs by over two orders of magnitude. Average Nr of scanned Nr of tree
Repository $path diff time tree entries comparisons
--------------------------------------------------------------------
git .clang-format 0.188s 40115 18055
git zlib.c 0.729s 10705983 17120
homebrew-core Formula/a2ps.rb 8.390s 2122937 326758
homebrew-core Formula/zzz.rb 80.495s 1235547836 326758 This is also noticeable when looking at the average number of
scanned tree entries and tree comparisons when running 'git
rev-list HEAD -- $path' for "a lot of paths": Average nr Average Average
Average of scanned nr of tree nr of diff
diff time tree entries comparisons calls
----------------------------------------------------------------
jdk 0.0431s 204466 5520 3702
elasticsearch 0.1351s 720393 17212 13457
rails 0.2372s 1299221 43369 33670
cmssw 0.2800s 1938067 24798 23739
go 0.4469s 3207155 76432 42346
glibc 0.5313s 6555231 40442 29208
tensorflow 0.5735s 3914851 97227 47262
git 0.5911s 7480371 21789 18168
linux 0.6163s 4004067 79514 58145
cpython 0.6602s 4695754 88408 70439
android-base 0.7320s 4312043 122280 96376
rust 0.7349s 5084683 68110 29847
webkit 1.6580s 9395349 303105 218906
llvm-project 2.2607s 11773792 469801 330250
gecko-dev 4.0964s 42396246 415843 387162
gcc 6.9432s 84853083 286904 174218 Note that although we have a lot less tree comparisons in the git
than in the linux repository, it has almost double the amount of
scanned tree entries, because the git repository has some
frequently modified biggish directories (notably its root dir, 't'
or 'Documentation'). As a result the average time spent diffing
in the two repositories is roughly the same, although the linux
repository is much larger. Or that we spend the most time diffing in the gcc repository,
because it has by far the most scanned tree entries, even though
it has less tree comparisons than any of the next three slowest
repositories. - The number of path components in the pathspec, i.e. its depth in
the directory tree seems to be irrelevant. When checking whether a path somewhere deep in the directory tree
has been modified between a commit and its parent the tree-diff
machinery can short-circuit, and it returns as soon as it finds
the first leading directory that hasn't been modified. And more
often than not it can short-circuit already while comparing the
root trees, as all the <1.5 values in the "Average tree
comparisons per diff call" column show. Average Average tree Average Average
pathspec comparisons nr of diff nr of tree
depth per diff call calls comparisons
----------------------------------------------------------------
android-base 5.78 1.26 96376 122280
cmssw 4.28 1.04 23739 24798
cpython 3.72 1.25 70439 88408
elasticsearch 9.13 1.28 13457 17212
gcc 5.13 1.64 174218 286904
gecko-dev 6.25 1.07 387162 415843
git 2.30 1.19 18168 21789
glibc 4.17 1.38 29208 40442
go 4.60 1.80 42346 76432
jdk 8.45 1.49 3702 5520
linux 4.49 1.36 58145 79514
llvm-project 5.45 1.42 330250 469801
rails 5.43 1.28 33670 43369
rust 4.70 2.28 29847 68110
tensorflow 5.56 2.06 47262 97227
webkit 6.33 1.38 218906 303105 Note the 2.xy average tree comparisons per diff call in the rust
and tensorflow repositories. In the rust repository over 98% of
the paths are in the 'src' directory and over 93% of commits
modify a file under that directory, while in the tensorflow
repository over 92% of paths are in the 'tensorflow' directory and
over 90% of commits modify a file under that directory.
Consequently, the tree-diff machinery can only rarely
short-circuit while comparing the root trees of two subsequent
commits, but has to dive down and compare the contents of the
'src' or 'tensorflow' directories most of the time as well. I
suspect that we would get a similar ~2 value in the homebrew-core
repository as well, because over 99.7% of all commits modify the
'Formula' directory, which contains over 90% of paths in the
repository.Bloom filters intro -------------------
Quoting the (quite good) Wikipedia article, "A Bloom filter is a space-efficient probabilistic data structure [...] that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not – in other words, a query returns either 'possibly in set' or 'definitely not in set'".
A Bloom filter is a bit array, initially all bits set to 0. To add an element to a Bloom filter, the element is hashed using 'k' independent hash functions, the resulting hashes are turned into 'k' array positions, and the bits at those positions in the filter are set to 1. To query for an element, the element is hashed using the same 'k' hash functions, the resulting hashes are turned into 'k' array positions, and the values of the bits at those positions are checked. If all those bits are set, then the element is 'possibly in set'. If even one of those bits is unset, then the element is 'definitely not in set'.
Some of the for us relevant properties of Bloom filters are:
- A Bloom filter doesn't store the elements themselves, it only sets
a couple of bits based on the elements' hashes. Consequently, a
Bloom filter can't tell which elements it contains; it can't even
tell the number of elements in there. - A bit in the Bloom filter's bit array might be set by more than
one element. This is where the probabilistic nature and the
possibility of false positives come from: it is possible that some
elements in the set happen to have set all 'k' bits that would
indicate the presence of a particular element, even though that
element itself is not part of the set. - Elements can't be removed from a Bloom filter: the 'k' bits
indicating the presence of an element can't simply be unset,
because that might unset bits that were set by other elements as
well.
There are enhanced Bloom filter variants that allow removal of
elements, like a counting Bloom filter, but they come with
additional complexity and require considerably more space. - Bloom filters can't be resized, because turning the hashes into
positions in the bit array critically depends on the size of the
bit array (usually pos[i] = hash[i] % bit_array_size). - Bloom filters of the same size can be bitwise OR-ed to create the
union of the sets of elements in the filters. - When playing long enough with probabilities and making some
reasonable assumptions and approximations it can be shown (see the
Wikipedia article) that for a desired false positive probability
'p' there is an optimal number of 'k' hash functions: k = -log₂(p) (that's a base 2 logarithm there)
For 1% false positive probability k = 6.64, but of course there
can only be an integral number of hash functions, so 7 it is. Note that this value is independent both from the size of the
Bloom filter and from the number of elements in there. Inversely, when using a properly sized bit array, then the false
positive probability falls exponentially as 'k' increases. - To store 'n' elements in a Bloom filter with a desired false
positive probability using the optimal number of 'k' bits/hash
functions, we need a bit array with approximate size 'm': m ≈ n * k / ln(2) ≈ n * k * 10 / 7
When using 7 hash functions to aim for < 1% false positive
probability this simplifies down to: m ≈ n * 7 * 10 / 7 = 10 * n
i.e. approx. 10 bits per element.
- In general the more elements, IOW the more set bits there are in a
Bloom filter of certain size, the higher the probability of false
positives. Similarly, the larger the size of a Bloom filter's bit
array for a certain number of elements, the lower the probability
of false positives. - A Bloom filter with all bits set appears to contain "everything",
because all queries will return 'possibly in set'.Modified Path Bloom Filters ---------------------------
We'll use Bloom filters to store modified paths and will store them in the Modified Path Bloom Filters chunk. Yes, plural: we'll use a lot of small Bloom filters, each one storing all paths modified by one commit compared to one of its parents (see the Alternatives section near the end for reasons for not using one big Bloom filter). Then as the pathspec-limited revision walk iterates over all commits it will query each modified path Bloom filter to see whether the given pathspec is in there, and if it is 'definitely not in set', then we can spare a tree-diff call and skip the commit right away, but if it's 'possibly in set', then we must do the tree-diff to make sure.
Each modified path Bloom filter consists of:
- 4 bytes specifying the number of bits in the Bloom filter's bit
array. For practical purposes 32 bit is more than sufficient to store the
number of bits in the Bloom filter's array. When using k = 7
hashes, i.e. 10 bits per path, then we could store over 400
million paths in a single Bloom filter; with k = 32 hashes we'd
use 46 bit per path, which is still over 93 million paths. - The actual bit array. See the next and the Hashing scheme
sections for details about how this bit array is used.All modified path Bloom filters will use the same k number of hashes per path, so we won't have to store that in each Bloom filter. I suspect that having modified path Bloom filters with different k number of hashes wouldn't bring enough benefit to justify storing that value in each and every filter.
The order of modified path Bloom filters in this chunk is unspecified on purpose, so implementations can experiment with writing them in history or topological order, which may bring performance benefits through better locality.
[Somewhere in the middle of this section the length of this commit message surpassed the length of 72441af7c4 (tree-diff: rework diff_tree() to generate diffs for multiparent cases as well, 2014-04-07), the mother of all commit messages from the great Kirill Smelkov, yay! :)]
Modified Path Bloom Filter Index --------------------------------
Since modified path Bloom filters vary in size, we can't have an array of modified path Bloom filters indexed by the position of the commit OID in the OID Lookup chunk, but we'll need a level of indirection between the commit OID position and the position of the associated modified path Bloom filter.
So the Modified Path Bloom Filter Index chunk will contain an array of offsets to quickly map commit OID positions to offsets in the Modified Path Bloom Filters chunk, i.e. its Nth entry contains the offset for the commit whose OID is Nth in the OID Lookup chunk.
Since the commit-graph file can store information about 2^31-1 commits, a merely 4 byte offset per commit is clearly insufficient. However, surely noone want to have lots of gigabyte-sized modified path Bloom filters, so a standard 8 byte file offset will be underutilized. This allows us to use a few bits from those 8 bytes for special purposes. For now we'll have two special purpose bits:
- The most significant bit indicates that the entry is not an offset
into the Modified Path Bloom Filters chunk, but an "embedded"
modified path Bloom filter containing all paths modified by the
commit compared to its first parent; see the next section. - The second most significant bit indicates that modified path Bloom
filters are stored not only for the first parent of a merge
commit, but for all its parents, and the entry is neither an
offset nor an "embedded" modified path Bloom filter, but an array
index into the Modified Path Bloom Filter Merge Index chunk; see
the "Merges" section below. - [TODO: perhaps we might want to reserve one or two more bits for
special purposes?]Make these offsets relative to the start of the Modified Path Bloom Filters chunk, so they will depend neither on the number of chunks nor on the combined size of any other chunks that are written before the Modified Path Bloom Filters chunk, thus allowing implementations to calculate all these offsets without knowing anything about those other chunks. Furthermore, if the offsets were relative to the beginning of the file, then some huge chunks could make the offsets grow too large, and would mess up those special purpose bits (though, arguably, an exabyte sized commit-graph file is just too unlikely to be worried about...).
An "all bits set" index entry can be used to indicate that there is no modified path Bloom filter stored for the corresponding commit. A reader implementation can either special-case such an entry, or interpret it as an embedded modified path Bloom filter that replies 'possibly in set' to all queries, the result is the same: it has to resort to running tree-diff for that commit.
These embedded modified path Bloom filters have implications on the low-level Bloom filter format in the Modified Path Bloom Filters chunk as well, namely:
- Their array contains only 63 bits, not 64, i.e. not 8 full bytes.
Therefore, for simplicity, we'll store the size of the bit array
as the number of bits, not bytes. - Assigning special purpose to the most significant bits of the
index entries is convenient when the entry is an offset into the
Modified Path Bloom Filters chunk. OTOH, it makes indexing into
the Bloom filter's array awkward if we try to treat it like an
ordinary array, i.e. whose 0th element comes first, because we'd
have to account for the special purpose bits. Therefore, we'll
store the bit array kind of in big endian byte order, i.e. the
most significant byte first and the 0th byte last.In addition, this chunk will start with a small header storing a bit of metadata that applies to all modified path Bloom filters in all related chunks. The reason for storing this header in the Modified Path Bloom Filter Index chunk is that only this chunk is essential to use modified path Bloom filters (if all commits modify so few files that their modified path Bloom filters can all be embedded into the index chunk, then the Modified Path Bloom Filters chunk will contain nothing and thus can be omitted; e.g. the two homebrew repositories come quite close to this, as shown below).
This header contains:
- One byte storing the number of 'k' hashes per path.
Using a single byte is more than sufficient: as 'k' increases the
false positive probability falls exponentially, and quickly
becomes unnecessarily low (i.e. with k = 31 even a full
commit-graph containing 2^31-1 commits is expected to have only a
single false positive). - [TODO: What else? We might want to have a version byte, though if
a format change becomes necessary, then we could simply rename the
chunks just as well...]Embedded Modified Path Bloom Filters ------------------------------------
The ideal size of a Bloom filter to store a set of 6 or 7 elements with 7 hash functions is approximately 60 or 70 bits, respectively. So in the space of a 8 byte Modified Path Bloom Filter Index entry we could comfortably store 6 entries by using one bit to indicate that the entry is not a file offset but an "embedded" modified path Bloom filter and the remaining 63 bits as the Bloom filter's bit array.
As the table below shows, a significant portion or even the vast majority of commits modify no more than 6 paths, so we can embed a lot of modified path Bloom filters into the Modified Path Bloom Filter Index chunk. (Also note the unusually large percentage of empty diffs in the android-base and cpython repositories.)
Percentage of commits modifying <=N (or =N) paths compared to their first parents
0 <=1 <=2 <=3 <=4 <=5 <=6 <=7 <=8
(=1) (=2) (=3) (=4) (=5) (=6) (=7) (=8)
--------------------------------------------------------------------------------------------
elasticsearch 0.70% 4.00% 5.67% 8.50% 13.17% 16.55% 18.32% 21.18% 27.47%
(3.30%) (1.67%) (2.83%) (4.67%) (3.37%) (1.77%) (2.86%) (6.29%)
jdk 0.26% 3.47% 10.60% 13.99% 15.62% 19.02% 26.62% 34.30% 40.57%
(3.20%) (7.14%) (3.39%) (1.63%) (3.40%) (7.60%) (7.68%) (6.27%)
webkit 0.05% 0.07% 0.77% 2.14% 9.15% 26.66% 38.42% 47.34% 52.83%
(0.02%) (0.71%) (1.37%) (7.01%) (17.51%) (11.76%) (8.92%) (5.49%)
android-base 13.20% 13.62% 14.23% 18.55% 20.91% 35.18% 42.32% 50.82% 62.05%
(0.42%) (0.62%) (4.32%) (2.36%) (14.28%) (7.14%) (8.49%) (11.23%)
llvm-project 0.12% 0.12% 0.94% 6.45% 25.24% 46.68% 53.60% 60.97% 67.33%
(0.00%) (0.81%) (5.51%) (18.79%) (21.44%) (6.92%) (7.37%) (6.36%)
gecko-dev 0.14% 0.96% 1.88% 15.44% 32.42% 46.12% 54.54% 61.37% 66.65%
(0.82%) (0.92%) (13.56%) (16.98%) (13.70%) (8.42%) (6.82%) (5.28%)
tensorflow 0.09% 1.26% 2.72% 5.00% 26.30% 42.36% 55.17% 63.11% 69.70%
(1.17%) (1.46%) (2.28%) (21.27%) (16.07%) (12.81%) (7.94%) (6.59%)
rails 0.10% 2.09% 5.79% 16.03% 35.57% 51.47% 58.71% 65.15% 72.96%
(1.99%) (3.70%) (10.23%) (19.54%) (15.90%) (7.24%) (6.44%) (7.82%)
rust 0.07% 2.20% 5.11% 22.81% 42.35% 52.50% 59.29% 65.33% 70.02%
(2.13%) (2.91%) (17.70%) (19.54%) (10.15%) (6.79%) (6.04%) (4.69%)
glibc 0.02% 7.33% 14.03% 30.86% 42.22% 52.53% 61.59% 68.50% 73.68%
(7.31%) (6.70%) (16.83%) (11.36%) (10.32%) (9.06%) (6.91%) (5.19%)
gcc 0.00% 0.24% 10.92% 26.61% 39.85% 54.97% 63.80% 69.37% 76.52%
(0.24%) (10.68%) (15.69%) (13.24%) (15.13%) (8.82%) (5.57%) (7.14%)
go 0.00% 0.96% 9.09% 19.35% 39.97% 53.16% 65.31% 72.10% 77.36%
(0.95%) (8.13%) (10.26%) (20.63%) (13.19%) (12.15%) (6.79%) (5.26%)
cmssw 0.15% 0.19% 0.20% 2.43% 45.35% 56.49% 67.58% 73.17% 77.99%
(0.03%) (0.01%) (2.23%) (42.92%) (11.15%) (11.08%) (5.59%) (4.83%)
linux 0.01% 0.66% 3.97% 23.49% 46.15% 62.97% 72.79% 79.16% 83.57%
(0.65%) (3.30%) (19.52%) (22.66%) (16.82%) (9.82%) (6.37%) (4.41%)
cpython 3.07% 4.97% 27.73% 59.54% 70.34% 77.48% 81.91% 86.76% 89.34%
(1.91%) (22.75%) (31.82%) (10.80%) (7.13%) (4.43%) (4.85%) (2.59%)
git 0.11% 27.54% 55.92% 73.79% 81.90% 87.05% 90.28% 92.29% 93.82%
(27.43%) (28.38%) (17.87%) (8.11%) (5.15%) (3.23%) (2.01%) (1.53%)
homebrew-cask 0.40% 0.94% 95.41% 97.42% 98.11% 98.40% 98.61% 98.79% 98.93%
(0.54%) (94.46%) (2.01%) (0.70%) (0.29%) (0.21%) (0.18%) (0.14%)
homebrew-core 0.01% 0.07% 98.81% 99.35% 99.56% 99.75% 99.81% 99.84% 99.86%
(0.07%) (98.74%) (0.53%) (0.22%) (0.19%) (0.06%) (0.03%) (0.02%)This saves space, because the Modified Path Bloom Filters chunk will contain a lot less Bloom filters (albeit those would be rather small filters).
It reduces the probability of false positives, because all commits modifying 1-6 paths will have larger than strictly necessary modified path Bloom filters.
Finally, it makes the Bloom filter query ever so slightly faster, partly because there is no redirection into the Modified Path Bloom Filters chunk, and partly because we can check all bit positions in a 63 bit modified path Bloom filter using a 64 bit mask at once, instead of checking those bit positions one by one, and we can use the same mask to check all embedded Bloom filters.
The number of paths that can be stored in a 63 bit Bloom filter depending on the number of hashes per path:
k | 3 | 4 | 5 | 6 | 7 | 8 | 9 - 11 | 12 - 14 | 15 - 22 | 23 - 44
------+------+------+-----+-----+-----+-----+--------+---------+---------+--------
paths | <=14 | <=11 | <=8 | <=7 | <=6 | <=5 | <=4 | <=3 | <=2 | <=1
Hashing scheme --------------
We need to map each modified path to 'k' independent bit positions in the Bloom filters bit array. We want the hash function and hashing scheme that results in the lowest false positive rate and has the lowest probability of "colliding" paths (see below), but it should still be fast to compute, and should be widely available for alternative Git implementations.
At this point we don't care about the runtime of pathspec-limited revision walks here. Lower false positive rate inherently leads to lower runtime, though we are reaching diminishing returns on common setups as the false positive rate gets lower and lower... and e.g. in the webkit repository we'll reach an average false positive rate of ~0.001% with only k = 7 hashes per path. However, on unusual setups/configurations accessing tree objects might be considerably more expensive than accessing commit objects, especially when using only commit info stored in the commit-graph. E.g. consider a future where we can distribute commit-graphs with modified path Bloom filters to partial clones containing only commit objects for most of the history: any tree-diff will be really expensive, because the tree objects must be fetched from the promisor. In such a setup every avoidable tree-diff call counts, and low false positive rate is king.
For now I went with 32 bit MurmurHash3 used in enhanced double hashing scheme with 32 bit unsigned integer arithmetic, though as I will show below it seems that this is not the best option.
So, to map each modified path to 'k' bit positions in the Bloom filter's array we first need 'k' independent hashes. In general, hashing a path 'k' times with the same hash function but using 'k' different seeds produces hashes that are independent enough. In practice, to reduce the overhead of hashing, especially for larger 'k' values, some variant of double hashing is often used to generate the 'k' independent-ish hashes from the results of only two hash function calls with different seeds. In general:
h1 = h(seed1, path)
h2 = h(seed2, path)
for (i = 0; i < k; i++)
pos[i] = (h1 + i * h2 + f(i)) % nr_bitsDepending on how the f(i) term is defined there are a few named variants:
- Double hashing: f(1) = 0:
pos[i] = (h1 + i * h2) % nr_bits
- Improved double hashing: f(i) adds a simple quadratic term:
pos[i] = (h1 + i * h2 + i^2) % nr_bits
- Enhanced double hashing: f(i) adds a not-so-simple cubic term:
pos[i] = (h1 + i * h2 + (i^3 - i) / 6) % nr_bits
This cubic term is equal to the following sequence of numbers,
starting from i = 0: 0, 0, 1, 4, 10, 20, 35, 56, 84, 120, etc.
These are almost the same as tetrahedral numbers, except that the
mathematical definition starts with 1, 4, 10..., while the OEIS
A000292 sequence starts with 0, 1, 4, 10... I'm puzzled by this term being 0 not only for i = 0, but for i = 1
as well, because this means that if (h2 % nr_bits == 0) (see
below), then both pos[0] = h1 and pos[1] = h1, IOW in that case
the path has one less bit set. We'll take a look at whether
starting the sequence with a single 0 makes a difference (it does)
and how that affects the false positive rate (slightly increases
it overall), and this will be labeled "enhanced double hashing
variant #1" below. This sequence is supposed to be just as good as a simple i^3 term,
but this can easily be calculated incrementally without
multiplication, using only addition. We'll take a look at how a
simple cubic term affects the false positive rate (it increases
it), and this will be labeled "enhanced double hashing variant
#2" below.These hashing schemes usually work fairly well in practice, but in practice Bloom filters are primarily used to check whether an element is part of _one_ _big_ set, and, consequently, most of the wisdom and experience out there applies to big Bloom filters.
We, however, will repeatedly check whether a particular element (i.e. path) is part of a large number of mostly very small sets, and our use case does challenge those best practices.
In our use case it is important when two paths "collide", i.e. when one path in itself sets all the bit positions that (falsely) indicate the presence of another path in a modified path Bloom filter, so let's have a look at that. And let's hope against hope that I don't mess up the math...
So, if we have k truly independent hashes for each path, then:
(1) The probability of two paths mapping to the same k separate bit
positions in a Bloom filter of size nr_bits is nr_bits! /
((nr_bits - k)! * k!). (2) The probability of a path setting only a single bit position is
1 / nr_bits^(k-1), and the probability of a path setting one
particular bit position is k / nr_bits (assuming that it does
set k separate bits), so the probability of a path setting a bit
position that happens to be the only bit position set by an
other path is k / nr_bits^k.In case of our 63 bit embedded modified path Bloom filters and k = 7 the probabilities of these two cases are about 1.81 * 10^(-9) and 1.77 * 10^(-12), respectively. I think that these are low enough not to worry about.
However, the hashes in the various double hashing schemes are far from being independent. When starting out with unsigned 32 bit hashes (like we'll do) and using 64 bit arithmetic, then there is no integer overflow, because neither i nor f(i) are large enough for that. Then, due to the distributivity of the modulo operation (and because i < nr_bits), all double hashing schemes are equivalent to:
pos[i] = (h1 % nr_bits + i * (h2 % nr_bits) + f(i) % nr_bits) % nr_bits
For the above mentioned two colliding cases this means:
(1) if (h(seed1, "foo") % nr_bits == h(seed1, "bar") % nr_bits &&
h(seed2, "foo") % nr_bits == h(seed2, "bar") % nr_bits) then all double hashing variants will work as if both paths were
hashed to the same h1 and h2 values, and, consequently, both
paths will be mapped to the same bit positions. This has the
probability of 1 / nr_bits^2. (2) if (h2 % nr_bits == 0)
then the i * h2 term will basically amount to nothing and this
term can be simplified away, and all bit positions will depend
only on h1; this has the probability of 1 / nr_bits. In case of double hashing the f(i) term is 0 as well, meaning
that the path maps to a single bit position. The probability of
a path setting a bit position that happens to be the only bit
position set by an other path is k / nr_bits^2. The quadratic or cubic f(i) terms in improved or enhanced double
hashing ensure that a path is mapped to multiple bit positions
even in this case, though those bit positions are not nearly as
random as one would like.In case of 63 bit Bloom filters and k = 7, the probabilities of these two cases are 1 / 3969 and 1 / 567, respectively.
When using 32 bit unsigned integer arithmetic, then an integer overflow is definitely possible, so the double hashing formula becomes:
pos[i] = (h1 + i * h2 + f(i)) % 2^32 % nr_bits
If nr_bits is a power of two, then the "% 2^32" term can be simplified away, and we end up with the same formula as with 64 bit arithmetic, and the probabilities of cases (1) and (2) above remain the same.
If nr_bits is not a power of two, then... well, I don't offhand know how to approach that formally :) Anyway:
(1) This type of collision seems to occur if that two-liner
condition above is true and for both paths there is an overflow
for the same values of i, which has the approximate probability
of 1 / ((k - 1) * nr_bits^2). (2) This type of collision seems to occur if (h2 % nr_bits == 0) and
there is either no integer overflow for any values of i or if
there is an overflow for all values of i, which has the
approximate probability of k / ((k - 1) * nr_bits^2).In case of 63 bit Bloom filters and k = 7 the probabilities of these two cases are 1 / 23814 and 1 / 3402, respectively.
There are several other colliding cases, e.g. with double hashing variants its more probable that a path maps only to 2, 3, etc. bit positions instead of 'k' than with 'k' truly independent hashes, though I haven't looked into how the probabilities work out.
Anyway, all the above already shows that:
- These colliding cases are several orders of magnitude more likely
with any double hashing variant than with k truly independent hash
functions. - When using some sort of double hashing, then these colliding cases
can happen with a high enough probability that we can't just
ignore them. - These colliding cases can happen much more frequently with double
hashing than with improved or enhanced double hashing. - Using 64 vs 32 bit arithmetic while calculating various double
hashing schemes makes a difference, and suggests that 32 bit
arithmetic has lower false positives probability. - Bloom filters whose size is a power of two might have higher false
positive probability.All this is important, because there are repositories out there that modify the same path in the majority of commits, e.g.:
- homebrew-core: contains 6535 paths, and over 99.5% of commits
modify the 'Formula' directory and have an embedded modified path
Bloom filter. - homebrew-cask: contains 7307 paths, and over 95.5% of commits
modify the 'Casks' directory and have an embedded modified path
Bloom filter. - rust: contains 58k+ paths, and almost 93% of commits modify the
'src' directory, though only over 54% of commits modify that
directory and have an embedded modified path Bloom filter. - tensorflow: contains 47k+ paths, and almost 91% of commits modify
the 'tensorflow' directory, though only about 51% of commits
modify that directory and have an embedded modified path Bloom
filter. - go: contains 22k+ paths, and almost 84% of commits modify the
'src' directory, though only over 50% of commits modify that
directory and have an embedded modified path Bloom filter.So e.g. if we were to look for commits modifying a path in the homebrew-core repository, which happens to map to the same bit positions in a 63 bit Bloom filter as the 'Formula' directory, then Boom! we would get over 99.5% false positive rate, effectively rendering modified path Bloom filters useless for that particular path.
The table below shows the number of paths that happen to collide with the repository's frequently modified directory in embedded modified path Bloom filters using different hash functions and hashing schemes with 32 bit unsigned integer arithmetic and 64 bit arithmetic (the latter in parentheses):
| Double hashing | Enhanced | 7 seeds
| | double hashing |
| Murmur3 | xxHash | Murmur3 | xxHash | Murmur3 | xxHash
--------------+----------+----------+---------+---------+---------+--------
homebrew-core | 4 (15) | 5 (17) | 0 (0) | 0 (2) | 0 | 0
homebrew-cask | 2 (21) | 6 (14) | 0 (2) | 1 (1) | 0 | 0
rust | 18 (143) | 38 (144) | 1 (15) | 6 (24) | 0 | 0
tensorflow | 20 (110) | 18 (105) | 4 (15) | 0 (12) | 0 | 0
go | 9 (66) | 12 (62) | 0 (1) | 3 (8) | 0 | 0
--------------+----------+----------+---------+---------+---------+--------
all | 53 (355) | 79 (342) | 5 (33) | 10 (47) | 0 0The effect of embedded modified path Bloom filters on these colliding cases can be both beneficial and harmful:
- We use a larger than necessary Bloom filter to hold 1-6 paths,
which lowers the probability of these cases considerably. This is especially important for the homebrew repositories. E.g.
in homebrew-core over 98.6% of commits modify a single file in the
'Formula' directory, i.e. two paths in total. To store 2 paths
using 7 hashes per path we would need a Bloom filter with a 20 bit
array, which we would round up to 24 bits to use full bytes, i.e.
those 98.6% of commits would have merely 24 bit Bloom filters.
This makes those colliding cases all the more probable: 1 / 3456
for case (1) and 1 / 493.7 for case (2) with 32 bit arithmetic). - We use Bloom filters of the same size to hold 1-6 paths, so if a
path were to run into these cases, then more Bloom filter queries
would return false positives than when using appropriately sized
Bloom filters.Now, there is a simple trick that can, to some extent, alleviate these collision issues: check all leading directories of the pathspecs, i.e. while looking for commits modifying 'dir/subdir/file', then query the modified path Bloom filters not only for the full path, but for all its leading directories 'dir/subdir' and 'dir' as well. This way we would only get a false positive if all bit positions of all leading directories were set as well, which can significantly reduce the probability of a pathspec running afoul of these colliding cases, and, in general, can reduce the false positive rate by an order of magnitude or three as well (see later in this patch series).
Checking all leading directories is not a silver bullet, though, because it can only help if the pathspec does actually have leading directories, and the deeper the pathspec in the directory tree, i.e. the more leading directories it has, the lower the false positive rate becomes.
- So this doesn't help pathspecs in the root tree, because they
obviously don't have any leading directories. - Furthermore, this doesn't help pathspecs that are immediately
below such a frequently modified directory, because their only
leading directory is modified in the majority of commits. This means that it can't help in the homebrew repositories,
because 85% or 90% of their paths are directly under their
frequently modified directories.The only thing that can help even in these cases is hashing the paths k times using k different seeds.
Phew. Let's see some actual benchmarks, shall we?
The following tables in this section show the average false positive rate of various hash functions and hashing schemes while listing the histories of "a lot of paths", i.e. 'git rev-list HEAD -- $path'. A '*' denotes the lowest value in each row. All cases use k = 7 hashes per path, use the same basically random seeds of immense historical significance, store 6 path in embedded modified path Bloom filters, and check all leading directories of the given pathspec.
The table below compares the average false positive rate of 64 bit and 32 bit unsigned integer arithmetic using double hashing and enhanced double hashing with the MurmurHash3 hash function:
Double hashing | Enhanced double hashing
32 bit 64 bit | 32 bit 64 bit
-------------------------------------+------------------------
android-base 0.008539% 0.015709% | *0.004155% 0.004961%
cmssw 0.006022% 0.013334% | *0.003953% 0.004109%
cpython 0.036840% 0.079816% | *0.016607% 0.019330%
elasticsearch 0.004069% 0.005297% | 0.003249% *0.003101%
gcc 0.016982% 0.034096% | *0.008919% 0.011152%
gecko-dev 0.001058% 0.002691% | *0.000725% 0.000829%
git 0.144256% 0.331346% | *0.069921% 0.079405%
glibc 0.026480% 0.053838% | *0.016389% 0.017902%
go 0.021930% 0.050348% | *0.012616% 0.014178%
homebrew-cask 0.097523% 0.508175% | *0.009096% 0.042034%
homebrew-core 0.120860% 0.556014% | *0.005360% 0.026810%
jdk 0.006085% 0.007526% | 0.006431% *0.005911%
linux 0.010908% 0.019081% | *0.007494% 0.007896%
llvm-project 0.006417% 0.009327% | *0.003913% 0.004050%
rails 0.024997% 0.046829% | 0.013134% *0.012361%
rust 0.038579% 0.056852% | *0.025509% 0.027068%
tensorflow 0.013732% 0.023307% | *0.008243% 0.008848%
webkit 0.002212% 0.002950% | *0.001007% 0.001065%
-------------------------------------+------------------------
all 0.028395% 0.110075% | *0.006085% 0.006675%
w/o homebrew 0.010940% 0.021286% | *0.005968% 0.011023%The 64 bit unsigned arithmetic does indeed fare worse in almost every case, and significantly worse in the two homebrew repositories.
So it seems that if using some form of double hashing, then 32 bit unsigned integer arithmetic is the way to go, even though several programming languages lack support for unsigned types (though thanks to the distributivity of the modulo operation, they can simply and cheaply implement it using 64 bit arithmetic and a '& (2^32-1)' mask).
The table below compares the average false positive rate of various hashing schemes using 32 bit unsigned integer arithmetic and the MurmurHash3 hash function:
Enhanced Enhanced Enhanced
double double double Improved
hashing hashing hashing double Double
7 seeds (original) variant 1 variant 2 hashing hashing
---------------------------------------------------------------------------------
android-base 0.004214% *0.004155% 0.004853% 0.005193% 0.008252% 0.008539%
cmssw *0.003344% 0.003953% 0.003546% 0.004732% 0.004226% 0.006022%
cpython 0.016120% 0.016607% *0.015896% 0.020259% 0.020311% 0.036840%
elasticsearch *0.003167% 0.003249% *0.003164% 0.003531% 0.003553% 0.004069%
gcc 0.010281% *0.008919% 0.010359% 0.010642% 0.012166% 0.016982%
gecko-dev 0.000804% 0.000725% *0.000646% *0.000648% 0.000822% 0.001058%
git *0.063025% 0.069921% 0.067643% 0.080744% 0.091423% 0.144256%
glibc *0.016278% 0.016389% 0.018660% 0.021094% 0.025596% 0.026480%
go 0.013009% *0.012616% 0.013301% 0.014316% 0.016345% 0.021930%
homebrew-cask *0.007199% 0.009096% 0.009070% 0.010722% 0.016617% 0.097523%
homebrew-core *0.003041% 0.005360% 0.005277% 0.005148% 0.016150% 0.120860%
jdk 0.005873% 0.006431% *0.005326% 0.005955% 0.006984% 0.006085%
linux 0.007764% *0.007494% 0.007714% 0.008709% 0.009429% 0.010908%
llvm-project *0.003367% 0.003913% 0.003730% 0.004064% 0.004809% 0.006417%
rails *0.012708% 0.013134% 0.013929% 0.016316% 0.014358% 0.024997%
rust *0.024245% 0.025509% 0.025045% 0.028204% 0.032491% 0.038579%
tensorflow *0.007907% 0.008243% 0.008670% 0.009913% 0.010115% 0.013732%
webkit *0.000999% *0.001007% 0.001142% 0.001113% 0.001274% 0.002212%
---------------------------------------------------------------------------------
all *0.005646% 0.006085% 0.006226% 0.006928% 0.009264% 0.028395%
w/o homebrew *0.005888% 0.005968% 0.006152% 0.006899% 0.007807% 0.010940%Double hashing and improved double hashing have higher average false positive rates than enhanced double hashing; note in particular the significantly higher false positive rate with double hashing in the two homebrew repositories. Enhanced double hashing is only slightly worse than 7 different seeds, at least in this particular case (i.e. MurmurHash3 and these specific seeds).
Comparing these hashing schemes using a different hash function (xxHash or FNV1a) shows a similar trend; for brevity I won't include those tables here.
The table below compares the average false positive rate of different 32 bit hash functions when used in enhanced double hashing scheme with k = 7 and 32 bit unsigned int arithmetic, or with 7 different seeds:
Enhanced double hashing | 7 seeds | 7 uint32
Murmur3 xxHash FNV1a | Murmur3 xxHash FNV1a | SHA256
-----------------------------------------------+---------------------------------+-----------
android-base 0.004155% 0.005556% 0.006113% | 0.004214% *0.004101% 0.005918% | 0.004168%
cmssw 0.003953% 0.003775% 0.005087% | 0.003344% 0.003677% 0.004353% |*0.003322%
cpython 0.016607% 0.015919% 0.021957% | 0.016120% *0.015238% 0.023649% | 0.017632%
elasticsearch 0.003249% 0.003589% 0.004616% | 0.003167% 0.003360% 0.003586% |*0.003027%
gcc *0.008919% 0.009376% 0.010555% | 0.010281% 0.009157% 0.011882% | 0.010338%
gecko-dev 0.000725% 0.000721% 0.000932% | 0.000804% *0.000611% 0.000911% | 0.000737%
git 0.069921% 0.063449% 0.083097% |*0.063025% 0.069137% 0.087132% | 0.063763%
glibc 0.016389% 0.017477% 0.024321% | 0.016278% *0.016062% 0.022641% | 0.017241%
go 0.012616% 0.012449% 0.016728% | 0.013009% *0.011692% 0.017214% | 0.012489%
homebrew-cask 0.009096% 0.025104% 0.011073% | 0.007199% 0.007343% 0.009037% |*0.007050%
homebrew-core 0.005360% 0.007940% 0.005450% |*0.003041% 0.003832% 0.004888% | 0.003884%
jdk 0.006431% *0.005451% 0.007428% | 0.005873% 0.005738% 0.006654% | 0.005749%
linux *0.007494% 0.008266% 0.009917% | 0.007764% 0.007723% 0.009256% | 0.007939%
llvm-project 0.003913% 0.003727% 0.004584% | 0.003367% *0.003247% 0.005115% | 0.003664%
rails 0.013134% 0.011277% 0.014103% | 0.012708% *0.010389% 0.015928% | 0.012506%
rust 0.025509% 0.024596% 0.032983% |*0.024245% 0.025697% 0.031593% | 0.024794%
tensorflow 0.008243% 0.008743% 0.011499% |*0.007907% 0.008089% 0.011922% | 0.008033%
webkit 0.001007% 0.001155% 0.001401% | 0.000999% *0.000962% 0.001291% | 0.001054%
-----------------------------------------------+---------------------------------+----------
all 0.006085% 0.007327% 0.007518% | 0.005646% *0.005554% 0.007591% | 0.005857%
w/o homebrew 0.005968% 0.005979% 0.007545% | 0.005888% *0.005660% 0.007855% | 0.006039%MurmurHash3 and xxHash are neck and neck, be it enhanced double hashing or 7 different seeds, at least when we ignore the unusually high false positive rate of enhanced double hashing with xxHash in the homebrew-cask repository (it stumbles upon one of those colliding cases discussed above).
FNV1a has a decidedly higher average false positive rate than any of the others.
I was curious to see whether using 7 unsigned integers from SHA256 offers any benefits (being a cryptographic hash function, it should provide some high quality hash values), but apparently it doesn't fare any better than MurmurHash3 and xxHash. This leads me to believe that both MurmurHash3 and xxHash are as good as it gets, and I would not expect that any other hash function could achieve notably lower false positive rates.
Now let's see how these hash functions and hashing schemes fare when writing commit-graph files with '--reachable' and with modified path Bloom filters from scratch at the end of this patch series. Hash functions tend to praise themselves about how fast they can process huge chunks of data, but we'll use them to hash many tiny strings...
Total runtime of writing a commit-graph file
with modified path Bloom filters from scratch
(Time spent hashing) Enhanced double hashing | 7 seeds | 7 uint32
Murmur3 xxHash FNV1a | Murmur3 xxHash FNV1a | SHA256
-----------------------------------------------+---------------------------------+----------
android-base 40.880s 40.368s 40.569s | 41.375s 40.557s 41.843s | 42.706s
(0.627s) (0.484s) (0.777s) | (1.467s) (0.886s) (2.138s) | (3.682s)
cmssw 25.691s 25.224s 25.645s | 26.715s 25.998s 27.762s | 29.527s
(0.894s) (0.650s) (1.179s) | (2.083s) (1.235s) (3.259s) | (5.077s)
cpython 8.951s 8.929s 9.067s | 9.072s 9.015s 9.008s | 9.275s
(0.057s) (0.042s) (0.045s) | (0.112s) (0.067s) (0.102s) | (0.324s)
elasticsearch 14.470s 14.320s 14.703s | 14.983s 14.588s 15.948s | 16.720s
(0.407s) (0.300s) (0.622s) | (0.991s) (0.594s) (1.760s) | (2.332s)
gcc 36.917s 36.724s 37.251s | 37.971s 37.449s 38.178s | 38.332s
(0.313s) (0.230s) (0.346s) | (0.694s) (0.418s) (0.919s) | (1.796s)
gecko-dev 97.729s 96.791s 97.233s | 99.215s 97.158s 101.403s | 105.332s
(1.730s) (1.267s) (2.099s) | (3.902s) (2.344s) (5.773s) | (9.553s)
git 5.245s 5.401s 5.412s | 5.518s 5.457s 5.474s | 5.494s
(0.022s) (0.017s) (0.018s) | (0.045s) (0.027s) (0.040s) | (0.124s)
glibc 4.146s 4.156s 4.187s | 4.267s 4.201s 4.278s | 4.495s
(0.060s) (0.045s) (0.057s) | (0.128s) (0.079s) (0.144s) | (0.331s)
go 3.565s 3.563s 3.564s | 3.631s 3.582s 3.607s | 3.727s
(0.040s) (0.030s) (0.035s) | (0.084s) (0.051s) (0.084s) | (0.221s)
homebrew-cask 29.818s 29.936s 30.279s | 30.316s 30.435s 29.942s | 29.939s
(0.025s) (0.020s) (0.019s) | (0.049s) (0.032s) (0.038s) | (0.153s)
homebrew-core 55.478s 55.534s 56.248s | 56.551s 56.100s 56.445s | 55.641s
(0.031s) (0.024s) (0.023s) | (0.062s) (0.038s) (0.047s) | (0.195s)
jdk 19.418s 19.151s 20.246s | 21.270s 20.037s 24.073s | 25.916s
(1.260s) (0.878s) (2.105s) | (3.154s) (1.833s) (6.133s) | (7.732s)
linux 100.837s 100.130s 101.244s | 103.775s 101.645s 103.856s | 109.365s
(2.027s) (1.498s) (2.030s) | (4.319s) (2.682s) (5.316s) | (11.075s)
llvm-project 31.188s 31.392s 31.442s | 31.895s 31.479s 31.984s | 32.863s
(0.334s) (0.251s) (0.345s) | (0.724s) (0.437s) (0.895s) | (1.794s)
rails 5.607s 5.639s 5.694s | 5.742s 5.720s 5.865s | 6.087s
(0.084s) (0.063s) (0.095s) | (0.189s) (0.116s) (0.252s) | (0.467s)
rust 13.250s 13.206s 13.422s | 13.701s 13.426s 13.680s | 13.949s
(0.163s) (0.122s) (0.169s) | (0.359s) (0.217s) (0.440s) | (0.880s)
tensorflow 11.808s 11.608s 11.915s | 12.379s 11.966s 12.860s | 13.588s
(0.368s) (0.267s) (0.497s) | (0.860s) (0.517s) (1.369s) | (2.081s)
webkit 30.469s 30.735s 30.945s | 32.005s 31.004s 32.401s | 33.303s
(0.501s) (0.376s) (0.733s) | (1.212s) (0.725s) (2.084s) | (3.044s)So xxHash is indeed the fastest, even in our use case, and MurmurHash3 comes in second. The time spent hashing with 7 seeds tends to be around twice as much as with enhanced double hashing when using the same hash function. However, the time spent hashing is only a fraction of the total runtime, and while its effect on total runtime is measurable both in best-of-five and average, it tends to be smaller than run-to-run noise.
As for availability of hash functions:
- MurmurHash3 is widely available; both the reference implementation
and a streaming-capable ANSI C implementation is in the public
domain. - xxHash is allegedly available in a variety of programming
languages, as shown on www.xxhash.com (supposedly, but I haven't
been able to load that page since months... some cloudflare host
error persists) - SHA256 is widely available, and it must be part of every Git
implementation in the near future anyway, but it's slower than the
others, and, more importantly, it doesn't scale for k > 8. - FNV1a is so simple that anyone can implement a variant that
incrementally computes two hashes up to the next directory
separator in one go in about 20 lines of code (though note that
the above benchmarks didn't use such an implementation). Alas,
because of its higher false positive rate it's out anyway.Conclusion: we should seriously consider using MurmurHash3 (or xxHash)
and hashing each path k times with k different seeds instead of any
double hashing scheme.
Merges ------
It's not clear whether it's worth computing, storing and using modified path Bloom filters for all parents of merge commits.
- The number of paths modified between a merge commit and its
second..nth parents is in general considerably larger than between
any commit and its first parent. E.g. a lot of files are modified
in Git's master branch while a topic cooks for a few weeks, and
much more in Linux when a branch started from the previous release
is merged near the end of the next merge window. Average number of
modified paths
Percentage compared to:
of merge first second
commits parent parent
--------------------------------------------
android-base 73.6% 14.1 1553.6
cmssw 11.0% 16.1 977.4
cpython 11.7% 5.1 933.7
elasticsearch 8.4% 40.1 246.5
gecko-dev 3.5% 23.5 602.0
git 25.3% 4.0 394.8
homebrew-cask 9.6% 2.7 42.8
jdk 25.0% 184.1 408.2
linux 7.4% 26.1 2268.0
rails 22.2% 9.6 101.0
rust 27.0% 15.7 397.3
tensorflow 9.1% 39.2 1057.4 Consequently:
- The tree-diff machinery has to work that much more to gather
modified paths for all parents of merge commits, significantly
increasing the runtime of writing the commit-graph file. - Storing modified path Bloom filters for all parents of merge
commits significantly increases the size of the Modified Path
Bloom Filters chunk, though depending on the percentage of
merge commits and on the size of the other chunks the relative
size increase of the whole commit-graph file might not be all
that much. - [TODO: A few old test results suggest that pathspec-limited
revision walks with default history simplification using a
commit-graph file storing modified path Bloom filters for all
merge parents are a few percents slower than when storing
Bloom filters only for first parents. Even fewer old test
results suggest that writing all Bloom filters for first
parents first, and then all for second..nth parents might
eliminate much of that runtime difference.
Definitely need more benchmarking.] - During a pathspec-limited revision walk Git's default history
simplification only checks whether the given path was modified
between a merge commit and its second..nth parents when the path
was modified between that merge commit and its first parent. This
usually happens rarely, though these second..nth parent diffs tend
to be more expensive than first parent diffs (because of the
considerably more modified paths the tree-diff machinery can't
short-circuit that early). Anyway, the potential speedup is low. - However, with '--full-history', i.e. without any history
simplification, all merge commits are compared to all their
parents, and typical additional speedups are in the range of
2x-3x, while in some cases over 7x or 11x can be achieved by using
modified path Bloom filters for all parents of merge commits.Does it worth it? For me personally it doesn't, but I don't know how often others use '--full-history' and what trade-offs they might be willing to make.
So the file format described here adds _optional_ support for storing modified path Bloom filters for all parents of merge commits, and the users can make this decision themselves.
[TODO: describe it!]
Deduplication -------------
Some commits have identical modified path Bloom filters, because they modify the same set of paths (or because they modify different set of paths but happen to end up setting the same bit positions in the Bloom filter). By omitting duplicates from the Modified Path Bloom Filters chunk its size can be reduced typically by around 5-20%, and in case of the android-base repository by over 69%.
Explicitly allow that multiple entries of the Modified Path Bloom Filter Index chunk can refer to the same offset in the Modified Path Bloom Filters chunk. This is important, because even if an implementation doesn't perform this deduplication while writing the commit-graph file, it must be prepared for multiple index entries referring to the same offset in commit-graph file written by a different implementation.
Modified Path Bloom Filter Excludes -----------------------------------
Some repositories contain leading directories that are modified in the great majority of commits, e.g. the homebrew-core repository's 'Formula' directory is modified in over 99.7% of commits, while homebrew-cask's 'Casks' and rust's 'src' are modified in over 93% of commits. And there is 'src/main/java/com/company/division/project' in convention-following Java projects...
Modified path Bloom filters can't speed up revision walks when the pathspec is such a frequently modified leading directory, because because of potential false positives we'll have to run tree-diff for the majority of commits anyway. And it doesn't really make sense to query the history of such a leading directory in practice, because it will list the majority of commits, so one might as well look straight at the output of a pathspec-less 'git log'.
However, adding those frequently modified leading directories to the modified path Bloom filters requires more space and increases the probability of false positives.
So the file format described here adds support for excluding specific paths from modified path Bloom filters by listing them in the Modified Path Bloom Filter Excludes chunk.
[TODO: Figure out the details!]
Limitations -----------
Because of the possibility of false positives, if a modified path Bloom filter query returns 'possibly in set', then we have to invoke tree-diff to make sure that the path in question was indeed modified by the given commit. Consequently, Bloom filters can't improve performance that much when looking for the history of a frequently modified path, because a lot of tree-diff invocations can't be eliminated. In the extreme case when looking for the history of a path modified in every commit, then using Bloom filters will only add extra overhead.
A modified path Bloom filter doesn't store the names of modified paths, it only sets a couple of bits based on those paths' hashes. Consequently, it can only be used when looking for the history of a concrete path, and:
- It can't be used with a pathspec containing wildcards like 'git
log -- "*.h"'. However, it could still be used when the pathspec contains leading
directories without wildcards, e.g. 'git log --
"arch/x86/include/*.h", by limiting tree-diff only to commits
modifying the 'arch/x86/include/' directory. - It can't tell which paths were modified by a given commit; for
that we would still have to run tree-diff for the full tree.Submodules [TODO] -----------------
No modified path Bloom filters should be stored for commits modifying submodules.
This is questionable, but is necessary to produce the same output with and without modified path Bloom filters. If 'dir/submod' is a gitlink file, then currently 'git rev-list HEAD -- dir/submod/whatever' lists all commits touching 'dir/submod', even when that 'whatever' has never existed. And that 'whatever' can be basically anything, so we will not find them in any of our modified path Bloom filters, therefore in a Bloom-filter-assisted revision walk we won't list any commits.
The only way around this is to not not write any modified path Bloom filters for commits modifying submodules.
Note, however, that once upon a time that command wouldn't list anything, either, but the behavior changed with commit 74b4f7f277 (tree-walk.c: ignore trailing slash on submodule in tree_entry_interesting(), 2014-01-23) to what we have now. As 74b4f7f277's log message only talks about handling 'dir/submod/' and 'dir/submod' (i.e. with and without trailing slash) consistently, I suspect that this behavior change with 'dir/submod/anything' is an unintended and undesired side effect. However, as it involves submodules I would rather not have an opinion :)
In any case, someone with more clues about submodules should take a closer look and decide whether this is a bug or not, before this modified path Bloom filter thing goes much further. If it is a bug indeed, then it should be fixed and the remark about submodules should be removed from the modified path Bloom filter specs. If the current behavior is desired, then the remark about submodules should be updated to use proper English (IMO it must be part of the spec, because this is a subtle issue that developers of other implementations (JGit, libgit2, etc.) might easily overlook).
Threats to validity -------------------
- Random paths are... random. Picking random paths can
over-represent rarely modified files. Since modified path Bloom
filters bring more benefits to rarely modified paths, the reported
speedups later in the series might be higher than what the users
will usually see. (I suppose that users more often check the logs
of frequently modified files than of rarely modified ones.) Though some of these random paths made me stumble upon the issue
with submodules discussed above, so... - Bugs :) It's not that hard to make subtle bugs that don't affect
correctness, because the probabilistic nature of Bloom filters
cover them up. However, bugs like incorrectly calculating the
size of a Bloom filter or having an off-by-one error in the
filter's array handling affect the false positive rate and in turn
the runtime of pathspec-limited revision walks.Alternatives considered -----------------------
Here are some alternatives that I've considered but discarded and ideas that I haven't (yet) followed through:
- One Bloom filter to rule them all? No.
While the first proof of concept implementation [2] demonstrated
that by combining hashes of modified pathnames with commit and
parent object ids it is possible to use a single big Bloom filter
and achieve notable speedups, that approach has several drawbacks: - Poor locality: The bit positions that are set for any element
are supposed to be random, so we need to access random parts
of the big Bloom filter array for each checked commit and
path, which is bad for the caches. Furthermore, we would
likely need to read the whole big Bloom filter array even when
looking for only a handful of commits, e.g. for 'git log -3 --
dir/file'. - Need to grow: As the repository grows, more and more paths
will be added to the modified path Bloom filter, thus lowering
its false positive rate. Eventually we'll reach a point where
we would need to increase the size of the Bloom filter, but a
Bloom filter can't be resized, so we'll have to create a
larger Bloom filter and re-fill it from scratch. This is a
considerably more expensive operation than creating a
modified-path-Bloom-filter-less commit-graph from scratch. - Expired commits: Unreachable commits are eventually pruned by
'git gc', but elements can't be removed from a Bloom filter.
Consequently, all the bits set for paths modified by expired
commits will remain set, slightly increasing the probability
of false positives until the Bloom filter is eventually
resized/rewritten. There are enhanced Bloom filter variants
that allow removal of elements, like a counting Bloom filter,
but they come with additional complexity and require
considerably more space. - Excluded commits: Sometimes we can't or just don't want to
record the paths modified by a particular commit, so we need a
way to record which commits don't have entries in the big
Bloom filter. - There are plans for split commit-graph files, because
rewriting the whole commit-graph file in big repositories to
add only relatively few new commits seems to impose
considerable overhead. We would need a big modified path
Bloom filter in each of the split commit-graph files that
we'll combine together when the split commit-graph files are
combined. Bloom filters of the same size can be quickly
combined by bitwise OR-ing their arrays, but this implies that
the split commit-graph files holding only a few commits would
have to contain as large a Bloom filter as is used in the
shared commit-graph file holding most of the commits in the
repository, wasting valuable storage space. (Though the false
positive rate for commits stored in the split commit-graph
should be spectacularly low). Using a small Bloom filter for each commit avoids these issues,
though, as discussed earlier, not without introducing issues of
its own. - Xor filters "are a faster and more concise alternative to Bloom
filters", but they turned out to be too large in our use case. Each Xor filter consists of a header and an array: the header
contains a 64 bit seed and a 64 bit blocklength, while the array
contains 3 * blocklength bytes. Consequently, the space advantage
of Xor filters compared to Bloom filters is only true for large
filters, where the header's size is negligible compared to the
array's size. We, however, have a lot of very small filters, and
all those extra headers cause an unacceptable size increase
(partly because the Xor filter's header is 4 times the size of the
header of our modified path Bloom filters, and partly because
their headers make Xor filters too large to be embedded in the
index chunk). MPBF chunk Total
size Xor Filter
(no dedup) size
------------------------------------------------
android-base 8620198 28840220 +334%
cmssw 10347018 21538283 +208%
cpython 526381 6393731 +1214%
elasticsearch 4733274 9101088 +192%
gcc 3724544 14637474 +393%
gecko-dev 20591010 56887518 +276%
git 160718 3115827 +1938%
glibc 759132 3162898 +416%
go 458375 2676306 +584%
homebrew-cask 94383 5272317 +5586%
homebrew-core 27823 8051585 +28938%
jdk 13213208 15574498 +117%
linux 26575278 69577374 +261%
llvm-project 3988133 20235803 +507%
rails 958691 5172643 +539%
rust 1985782 7082949 +356%
tensorflow 4173570 8333062 +200%
webkit 5891751 15927504 +270% https://github.com/FastFilter/xor_singleheader
- fastrange is "a fast alternative to the modulo reduction", but it
turned out to significantly increase the average false positive
rate when using (enhanced) double hashing. The traditional way to fairly map an integer value to the range
[0,m) is the modulo reduction, i.e. 'h % m'. Alas, integer
division is a costly instruction, and we have to do it a lot while
checking modified path Bloom filters for every commit. Fastrange
proposes to use the combination of an integer multiplication and a
shift as: '((uint64_t)h * (uint64_t)m) >> 32', as it is supposed
to be faster while being just as fair. In my benchmarks fastrange did indeed reduce the time spent
checking modified path Bloom filters by up to ~10% (no table about
this), though that's usually only a fraction of the total runtime
of a pathspec-limited revision walk. Unfortunately, as the table
below shows, it also increased the average false positive rate by
about 5x when using enhanced double hashing. I haven't yet
investigated why this happens. However, fastrange is a viable
option when using the same hash function with 'k' different seeds,
as in that case it's on par with the good old % operator, and in
fact its false positive rate happens to be slightly lower. Enhanced |
double hashing | 7 seeds
|
% operator fastrange | % operator fastrange
-------------------------------------+----------------------
android-base *0.004155% 0.013987% | 0.004214% 0.004873%
cmssw 0.003953% 0.006129% | *0.003344% 0.005513%
cpython 0.016607% 0.033979% | *0.016120% 0.018069%
elasticsearch 0.003249% *0.003149% | 0.003167% 0.003333%
gcc 0.008919% 0.013098% | 0.010281% *0.007324%
gecko-dev 0.000725% 0.000923% | 0.000804% *0.000631%
git 0.069921% 0.106424% | *0.063025% 0.065347%
glibc 0.016389% 0.025323% | *0.016278% 0.016796%
go *0.012616% 0.020914% | 0.013009% 0.012915%
homebrew-cask 0.009096% 0.182016% | 0.007199% *0.006937%
homebrew-core 0.005360% 0.092957% | *0.003041% 0.004251%
jdk 0.006431% 0.005732% | 0.005873% *0.005673%
linux 0.007494% 0.013862% | 0.007764% *0.007127%
llvm-project 0.003913% 0.004829% | 0.003367% *0.002943%
rails 0.013134% 0.019524% | *0.012708% 0.012979%
rust 0.025509% 0.029824% | *0.024245% 0.025174%
tensorflow 0.008243% 0.012485% | *0.007907% 0.008120%
webkit *0.001007% 0.001044% | *0.000999% 0.001220%
-------------------------------------+----------------------
all 0.006085% 0.029034% | 0.005646% *0.005579%
w/o homebrew 0.005968% 0.009478% | 0.005888% *0.005662% https://github.com/lemire/fastrange
- fastmod "is a header file for fast 32-bit division remainders on
64-bit hardware", promising faster computation when the same
divisor is (re)used for multiple remainder calculations. This is
exactly what we are doing when checking a modified path Bloom
filter, as we have to map a bunch of unsigned 32 bit hash values
to bit positions with pos = hash % nr_bits, using the same nr_bits
for all hash values. However, benchmarking has not shown conclusive improvements: while
fastmod slightly reduces the time spent checking modified path
Bloom filters in some repositories, it slightly increases in
others, and any effect on the runtime of the whole
pathspec-limited revision walk is below the noise. Perhaps we
have too many "embedded" modified path Bloom filters that we can
check with a mask. Or perhaps my CPU is too old and its integer
multiplication instruction is not that much faster compared to
integer division. https://github.com/lemire/fastmod
- Varints. Using 4 bytes for the size of each Bloom filter in the
Modified Path Bloom Filters chunk is a lot more than necessary.
How much space can be saved by using varints? How much is the
runtime overhead of decoding those varints? How can we ensure
that varint decoding doesn't read past the end of the mmapped
memory region? - Path-depth-dependent number of hash functions.
As shown later in this patch series, we can drastically reduce the
average false positive rate by checking the presence of the
pathspec's leading directories as well. With that change the
false positive rate will depend on the depth of the path in the
directory tree as well: the deeper the path (i.e. the more leading
directories can be checked), the lower the false positive rate,
sometimes maybe even unnecessarily low (0% is not an
approximation, but there was not a single false positive, though
the number of checked paths at those depths is quite low): linux.git webkit.git
Number of Number of
Path checked Average false checked Average false
depth paths at positive rate paths at positive rate
depth that depth at that depth that depth at that depth
--------------------------------------------------------------
1 2 0.31974050% 0 n/a
2 97 0.06539501% 16 0.04927891%
3 1052 0.01782569% 176 0.00472128%
4 1674 0.00396338% 586 0.00399830%
5 1315 0.00350913% 775 0.00089653%
6 548 0.00149110% 1396 0.00025000%
7 113 0.00057555% 1004 0.00004777%
8 124 0.00085726% 397 0.00001380%
9 34 0.00025182% 311 0.00001468%
10 14 0% 250 0.00000182%
11 8 0% 45 0%
12 19 0% 39 0%
13 0 n/a 3 0%
14 0 n/a 1 0%
15 0 n/a 1 0%
--------------------------------------------------------------
all 5000 0.007607% 5000 0.001012% So we could use fewer hashes when adding deep paths to modified
path Bloom filters, saving same space while still achieving quite
low false positive rate. Or when aiming for consistently lower
false positive rate, then we could use more hashes only for
shallower paths.[1] The repositories used in the above benchmarks are:
android-base:
https://android.googlesource.com/platform/frameworks/base
cmssw:
https://github.com/cms-sw/cmssw
cpython:
https://github.com/python/cpython
elasticsearch:
https://github.com/elastic/elasticsearch
gcc:
git://gcc.gnu.org/git/gcc.git
gecko-dev:
https://github.com/mozilla/gecko-dev
git:
https://git.kernel.org/pub/scm/git/git.git
glibc:
git://sourceware.org/git/glibc.git
go:
https://github.com/golang/go
homebrew-cask:
https://github.com/homebrew/homebrew-cask
homebrew-core:
https://github.com/homebrew/homebrew-core
jdk:
https://github.com/openjdk/jdk
linux:
https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git
llvm-project:
https://github.com/llvm/llvm-project.git
rails:
https://github.com/rails/rails
rust:
https://github.com/rust-lang/rust
tensorflow:
https://github.com/tensorflow/tensorflow
webkit:
git://git.webkit.org/WebKit.git[2] First PoC Bloom filter experiment:
https://public-inbox.org/git/20181009193445.21908-1-szeder.dev@gmail.com/Signed-off-by: SZEDER Gábor <szeder.dev@gmail.com>
---
.../technical/commit-graph-format.txt | 125 ++++++++++++++++++
1 file changed, 125 insertions(+)
SZEDER Gábor
