backfill: add --sparse option
One way to significantly reduce the cost of a Git clone and later fetches is to use a blobless partial clone and combine that with a sparse-checkout that reduces the paths that need to be populated in the working directory. Not only does this reduce the cost of clones and fetches, the sparse-checkout reduces the number of objects needed to download from a promisor remote. However, history investigations can be expensive as computing blob diffs will trigger promisor remote requests for one object at a time. This can be avoided by downloading the blobs needed for the given sparse-checkout using 'git backfill' and its new '--sparse' mode, at a time that the user is willing to pay that extra cost. Note that this is distinctly different from the '--filter=sparse:<oid>' option, as this assumes that the partial clone has all reachable trees and we are using client-side logic to avoid downloading blobs outside of the sparse-checkout cone. This avoids the server-side cost of walking trees while also achieving a similar goal. It also downloads in batches based on similar path names, presenting a resumable download if things are interrupted. This augments the path-walk API to have a possibly-NULL 'pl' member that may point to a 'struct pattern_list'. This could be more general than the sparse-checkout definition at HEAD, but 'git backfill --sparse' is currently the only consumer. Be sure to test this in both cone mode and not cone mode. Cone mode has the benefit that the path-walk can skip certain paths once they would expand beyond the sparse-checkout. Non-cone mode can describe the included files using both positive and negative patterns, which changes the possible return values of path_matches_pattern_list(). Test both kinds of matches for increased coverage. To test this, we can create a blobless sparse clone, expand the sparse-checkout slightly, and then run 'git backfill --sparse' to see how much data is downloaded. The general steps are 1. git clone --filter=blob:none --sparse <url> 2. git sparse-checkout set <dir1> ... <dirN> 3. git backfill --sparse For the Git repository with the 'builtin' directory in the sparse-checkout, we get these results for various batch sizes: | Batch Size | Pack Count | Pack Size | Time | |-----------------|------------|-----------|-------| | (Initial clone) | 3 | 110 MB | | | 10K | 12 | 192 MB | 17.2s | | 15K | 9 | 192 MB | 15.5s | | 20K | 8 | 192 MB | 15.5s | | 25K | 7 | 192 MB | 14.7s | This case matters less because a full clone of the Git repository from GitHub is currently at 277 MB. Using a copy of the Linux repository with the 'kernel/' directory in the sparse-checkout, we get these results: | Batch Size | Pack Count | Pack Size | Time | |-----------------|------------|-----------|------| | (Initial clone) | 2 | 1,876 MB | | | 10K | 11 | 2,187 MB | 46s | | 25K | 7 | 2,188 MB | 43s | | 50K | 5 | 2,194 MB | 44s | | 100K | 4 | 2,194 MB | 48s | This case is more meaningful because a full clone of the Linux repository is currently over 6 GB, so this is a valuable way to download a fraction of the repository and no longer need network access for all reachable objects within the sparse-checkout. Choosing a batch size will depend on a lot of factors, including the user's network speed or reliability, the repository's file structure, and how many versions there are of the file within the sparse-checkout scope. There will not be a one-size-fits-all solution. Signed-off-by: Derrick Stolee <stolee@gmail.com> Signed-off-by: Junio C Hamano <gitster@pobox.com>
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Junio C Hamano

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@ -56,6 +56,14 @@ better off using the revision walk API instead.
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the revision walk so that the walk emits commits marked with the
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`UNINTERESTING` flag.
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`pl`::
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This pattern list pointer allows focusing the path-walk search to
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a set of patterns, only emitting paths that match the given
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patterns. See linkgit:gitignore[5] or
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linkgit:git-sparse-checkout[1] for details about pattern lists.
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When the pattern list uses cone-mode patterns, then the path-walk
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API can prune the set of paths it walks to improve performance.
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Examples
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--------
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