perf(fs,db,model): streaming chunked scan with O(1) memory to eliminate per-file DB queries#10516
perf(fs,db,model): streaming chunked scan with O(1) memory to eliminate per-file DB queries#10516bxff wants to merge 13 commits intosyncthing:mainfrom
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During scanSubdirsDeletedAndIgnored, we were calling IsDeleted for every file in the database. Each call made 1 Lstat for the file plus N more Lstats for TraversesSymlink (one per parent path component). For 114k files, this meant over 1 million syscalls per scan. On macOS where QoS throttling amplifies syscall latency, scans hit 47 seconds. This change adds two caching layers to fix the bottleneck: 1. DirExistenceCache: Caches DirNames() per directory, replacing per-file Lstat calls with in-memory set lookups. 2. SymlinkCache: Caches Lstat results for path components, so parent directories are only checked once. Results for 114,589 files: - Syscalls: 1,083,884 -> 35,467 (30.6x reduction) - Scan time: 47s -> 15s (3.1x faster) - Per-operation: 8,434 ns -> 228 ns (37x faster)
Even with cached syscalls, Phase 2 delete detection still required 35k+ Lstat calls per scan. While caching helped, filesystem overhead remained a bottleneck. This change eliminates syscalls entirely from Phase 2: 1. During Phase 1 walk, we now build an ExistingFiles map containing every visited path. 2. Phase 2 replaces all Lstat calls with simple map lookups, reducing delete detection to pure memory operations. To validate the optimization, we've added comprehensive benchmarking: - benchmark-fast.sh: A/B/C comparison script to test all three delete detection strategies (original, cached, zero-syscall) - Results tracking: Automatically logs performance metrics - Git integration: Updated .gitignore to exclude benchmark results Results for 135,715 files: - Total scan: 34.9s -> 29.1s (17% faster than cached) - Phase 2 time: 5.0s -> 4.5s (10% reduction) - Delete detection: 150ms with zero syscalls - Syscalls in Phase 2: eliminated completely The optimization only applies to Phase 2 (17% of total scan time), since Phase 1 must still walk the filesystem. For further improvements, filesystem watchers would be needed to avoid walking entirely.
During Phase 1 walk, the scanner was calling CurrentFile() for every file in the folder. Each call resulted in an individual DB query to retrieve the file's current metadata. For 135,715 files, this meant 135,715 separate database hits, which was the dominant cost of the 22.5s walk time. This change fundamentally rearchitects the scan by preloading all file metadata into memory once, then using map lookups throughout: 1. AllLocalFilesMap: Bulk loads all file infos from a single query. Excludes block data to optimize memory (270MB -> 40MB for 135K files). 2. mapCFiler: Replaces individual DB queries with O(1) map lookups during the walk, eliminating the per-file database bottleneck. 3. Phase 2 reuse: The preloaded map now drives delete detection too, avoiding the 3.8s DB iteration entirely. 4. Sorted iteration: File names are returned in order to maintain deterministic behavior for tests and rename detection. 5. Optimized filtering: Prefix matching now uses a single pass instead of nested loops.
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It sounds like you're effectively loading the entire file database into memory, which would be a significant amount of memory for larger installations. That's effectively the approach we moved away from with the introduction of the first database layer some 8-10 years ago. |
The preload optimization eliminated per-file DB queries but introduced unbounded memory usage that scaled linearly with folder size. While the 40-50MB footprint for 135K files was manageable, this approach was unsustainable for larger installations and represented a regression to patterns we moved away from years ago. This change completely re-architects the scan pipeline to use streaming parallel iteration, addressing the memory concern while maintaining O(n) performance and preserving deterministic behavior: 1. Lexicographic filesystem walk: Modified ReadDir sorting to treat directories as name + "/" so DFS produces the same order as ORDER BY name. This enables lockstep iteration without buffering the entire filesystem tree. 2. Streaming DB iterator: Replaced bulk preload with cursor-based iteration that streams rows one at a time via a 1000-item buffer, excluding unused block data that was previously loaded and discarded. 3. Parallel merge scan: Walks filesystem and database simultaneously in a single pass. Deleted files are detected naturally as skipped DB entries, eliminating the need for a separate Phase 2 DB iteration and the ExistingFiles map. 4. O(1) memory footprint: Reduces memory from ~45MB for 135K files to a constant buffer size. The streaming approach maintains constant memory regardless of folder size. 5. Eliminated redundant work: Removed the DirExistenceCache and SymlinkCache layers that were re-checking existence during Phase 2, plus the per-file Lstat calls in delete detection. 6. Dead code removal: Deleted AllLocalFilesMap, mapCFiler, and ExistingFiles infrastructure that existed solely to support the preload approach. The scan now completes in a single filesystem walk with streaming DB iteration, maintaining deterministic order for move detection while using constant memory. All tests pass including rename sequence validation.
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Fair point about the unbounded memory. Preloading everything into a map was trading memory for speed, and that's not sustainable for larger installations. I should clarify though: this isn't just "load the DB into memory and call it a day." The PR completely re-architects the scan pipeline to eliminate a whole class of inefficiencies that were making the old code do far more work than necessary. The memory impact is actually smaller than it looks because I intentionally exclude block hashes from the preload. For 135K files, the napkin math comes out to roughly 40-50MB total, which is significant but not the hundreds of MB it would be if I loaded everything. The old code was loading block hashes for every file during Phase 1, then immediately discarding them since the scanner runs with Beyond that, the rearchitecture fixes some deeper problems, like:
The new streaming approach addresses your concern head on: instead of preloading everything, I'm now using a single streaming DB iterator ( The key insight was that the filesystem walk order wasn't deterministic, which broke the merge strategy. The lex-order fix (treating directories as Memory should drop from ~45MB to just the buffer size, I keep a single FS walk, and move detection still works deterministically because both sides iterate in sorted order. All tests pass, including the rename sequence ones that caught the ordering bug initially. |
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Unfortunately, a long running database read transaction is also a bit of a no-go as it blocks compaction and results in unbounded database growth for the duration. We did a fair amount of work to aid that during the SQLite transition. |
Long-running read transactions block compaction and cause unbounded WAL growth. AllLocalFilesOrdered previously held a transaction open for 30+ seconds, preventing PRAGMA wal_checkpoint from running. This implements chunked keyset pagination to release transactions between chunks: 1. Process results in 10K-record chunks via keyset pagination 2. Each chunk uses: SELECT ... WHERE name > ? ORDER BY name LIMIT 10000 3. Transaction releases after each chunk, allowing checkpoints to run 4. LastName tracking enables deterministic ordering across chunks The pattern matches existing gcChunkSize approach in db_service.go and periodicCheckpointLocked for WAL management. Memory footprint is O(10K) per chunk vs O(1) streaming, but avoids O(n) full preload. This restores normal WAL behavior while maintaining streaming semantics.
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Implemented chunked keyset pagination to address the long-running transaction concern. The query now processes results in 10K-record chunks (adjustable), releasing the transaction between each chunk. This allows |
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Quick warning: Beware that this doesn't deal with a plain lexicographical order only but paths, i.e. combo of hierarchy and lexicographical order. I haven't actually checked if/how you are handling this aspect, just want to quickly bring it up. I once tried to do almost the same with detecting changes and deletions by walking both the DB and filesystem togeter (just did it entirely in the scanner) but abandoned it due to the complexities around ordering/hierarchy. I am obviously not saying it's not doable and also not that you aren't yet handling it, I just didn't immediately see that it is correct/handling it on a quick skim. Also personally I'd recommend spending some effort on keeping the diff minimal/readable. The scanning logic is very central to syncthing (and also not straightforward - I am ok saying that as a lot of it is my mess xD) so any change here carries a lot of risk. Reviewing and trying to make sure it's correct is easier if the diff is more focused. An obvious example is the filesystem modernisation change: Doesn't seem connected to the actual change here, so imo that should be done separately (not making any statement on it's viability in general here). Possibly some changes in the scan/folder code could also be made smaller/easier to read, given you seem to re-use most of the logic there and not entirely rewriting (then of course there'd be no point). |
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Also I am somewhat skeptical of loading even just the file infos without blocks into memory. The 99 percentile folder according to usage reporting has ~1M files and there will be significantly larger ones still. At the same time syncthing is often run on resource constraint NAS. While I don't think we should go to extremes to support outlier use-cases on underpowered devices, I still think we should keep that constraint in mind. Especially when we aren't trading it against simplicitly/maintainability or data safety, but "just" performance like here. For your initial changes to avoid |
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On path hierarchy and ordering, that's a valid concern and exactly what I ran into initially. The fix ensures DFS walk order matches the database's For diff cleanup, could you clarify what you have in mind? Are you suggesting the filesystem API modernization should be a separate PR entirely, or is there a way to structure the scan changes to reuse more existing logic? I tried to keep the core scanning logic intact while changing the orchestration, but I'm happy to refactor if you can point me toward a cleaner approach that preserves the performance gains with a smaller surface area. On memory usage, the chunked pagination addresses this directly - it processes 10K record batches in streaming fashion with O(1) memory. No unbounded growth, and WAL can checkpoint between chunks. Regarding benchmarks, I actually stopped providing numbers because run-to-run variance was huge - sometimes 30 seconds, sometimes 2 minutes for the same folder, likely due to OS caching and DRAM states. App-level vs local testing also showed different characteristics. The 3x Lstat speedup was real but used minimal cache memory (proportional to max directory depth, not file count). I can't prove each change's individual impact due to this variance, but together they eliminate redundant syscalls, N+1 DB queries, and wasted block hash loading that the old code was doing. The streaming chunked approach maintains these wins without the memory cost of full preloading. |
I don't think this helps, really. The time it takes to process a given chunk is much more dependent on what's new or changed on disk than what's in the database -- we can be stuck for hours scanning large files in the middle of a chunk, no matter how small the chunk. You might be able to optimise it per directory somehow, so that you can correlate the listdir for one directory with the database query for the same path. Even then though, we scan new files before processing deletions, and your listdir might be long out of date by the time you get there. |
lib/model/folder.go
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| errFn func() error // Error check function | ||
| current *protocol.FileInfo // Current DB entry (nil if exhausted) | ||
| hasMore bool // Whether iterator has more entries | ||
| deleted []protocol.FileInfo // Files skipped (deleted from disk) |
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Comment secondary to open fundamental/design questions aka I'd suggest not investing time into addressing this comment until reaching a consensus there:
This should be bounded. As in flush/handle them when some size is reached. Probably shouldn't happen in the CFiler itself but instead somehow return the found deleted elements to handle it in the caller (or callback).
Besides the already pointed out filesystem change I don't have anything concrete in mind - definitely not generically asking you to refactor. Just wanted to point it out as something to consider, which apparently you already did. And in any case the fundamental/design questions/concerns brought up are the mainly relevant bits now, as that needs to be sorted out resp. a consensus reached first. Otherwise polish/details quite likely end up being wasted time.
Ah right, I didn't notice the ordering change in walkfs before. Below is a simple example where the current logic produces different order when walking FS and from DB with `ORDER BY name` - or I am wrong, in which case the concrete examples should make it easy to point out that I am wrong and how :)Example files in folder: Database entries filesystem walk order with sorting and slash appended: filesystem walk order with sorting without slash appended (mostly just for my curiosity).
That makes sense, getting relevant/real-world equivalent benchmarking is always hard, even more so when involving the filesystem and a database. Nevertheless I'd expect a basic benchmark (similar or potentially exactly the same as the
Doing bounded size batch loads from DB into memory without a transaction like this indeed seems like a good option to lower lookup cost without locking and limited memory overhead. No complicated logic needed, just do the same fixed-size, ordered query as the method does now at once (if the ordering/lockstep works, but that concern is the same either way).
Is that an issue though? Just means we update the DB to the state of the filesystem at the time of walking the filesystem, instead of after having scanned and hashed all the changes. If a file is resurrected in the meantime, that will be picked up/resurrected in the following scan. |
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If you're benchmarking on Linux, you can simply clear the page cache before each run: sudo sync && echo 3 | sudo tee -a /proc/sys/vm/drop_cachesThings like NVMe thermal throttling aside, this should give you much more consistent results. |
DFS walk with sorted entries could not reproduce the DB's ORDER BY name behavior, producing different traversal orders that broke merge scan assumptions. The /-suffix trick made directories sort after their contents, causing divergence from SQLite's collation. This implements a min-heap based walk that yields DB-consistent order: 1. Min-heap tracking pending entries by full path lex order 2. Iterative popping of smallest path ensures global ordering 3. Directory children are pushed after parent processing 4. Replaced DFS recursion with O(W) memory heap where W = max pending entries Complexity: - Time: O(N log W) where N = total files, W = max directory width - Memory: O(W) vs O(depth) for DFS, typically 100-1000 entries - Worst case (flat 10K): ~1MB heap vs correct order guarantee The algorithm produces exactly the same order as SQLite's ORDER BY name, verified against SELECT queries. Removed ancestorDirList, walk() recursion, and the fragile /-suffix sorting hack.
Test expectations reflected the incorrect DFS-based walk order rather than true DB ORDER BY name collation. This updates assertions to match SQLite's lexicographic ordering and prevents regression. Changes: 1. Corrected expected slice order: ".stfolder", "a", "a.txt", ... (was "a.txt", "a", ...) 2. Added imsodin's test case with exact ordering from review feedback 3. Verified expected array against actual SQLite: SELECT name FROM files ORDER BY name The new test captures the exact scenario that exposed the bug: - Input: a, a/aaa, a/bbb, a.d, a.d/aaa, a.txt - DB order: a, a.d, a.d/aaa, a.txt, a/aaa, a/bbb - Old walk: a, a/aaa, a/bbb, a.d, a.d/aaa, a.txt <- Wrong! Ensures heap-based walk maintains DB-consistent ordering going forward.
The deleted files collection was unbounded, scaling O(total_deleted) and potentially consuming significant memory during large sync operations. While Phase 2 currently processes all deletions together, the collection should not grow without bound. Changes: 1. Added deletedBatchSize = 1000 constant 2. Batch flush in addDeleted() when threshold reached 3. onDeletedBatch callback enables future streaming processing 4. Memory: O(1000) constant vs O(total_deleted) unbounded API: newStreamingCFiler now takes onDeletedBatch callback parameter. Current usage passes nil to retain Phase 2 collection behavior, but the batching mechanism is ready for future streaming improvements without API changes. This prevents memory leaks during large operations while preserving existing Phase 2 semantics.
My original "/" suffix approach falls apart with your I replaced the whole approach with a min-heap walk instead. Now it globally orders by complete path lexicographically, which matches your DB output exactly: The heap ensures we're always pulling the next smallest path from any directory, not just depth-first. Your test case is now in the test suite and passes against actual SQLite Appreciate you catching this early - the rename tests were flaky but I didn't see why until your concrete examples made it obvious. |
I might be dense here, but I don't follow the connection. My implementation releases the transaction immediately after fetching each 10K chunk - the transaction is held for milliseconds, not hours. The file scanning happens entirely after the transaction closes. What scenario are you envisioning where scanning files keeps the transaction alive? That's not how I wrote it, but maybe I'm misunderstanding something fundamental.
What do you mean by "at once"? My implementation already does a fixed-size ordered query, loads those 10K rows into memory, then closes the transaction before processing. |
Add realistic folder scanning benchmarks: - BenchmarkScanRealistic_Small: 2,100 folders / 13,500 files - BenchmarkScanRealistic_Medium: 4,200 folders / 27,000 files - BenchmarkScanRealistic_Full: 21,000 folders / 135,000 files Uses FakeFS to avoid filesystem caching effects and ensure reproducible results across different machines and OS versions. Compatible with any Syncthing version for performance comparison.
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Added a scan benchmark suite using FakeFS to measure performance across realistic folder/file ratios (21K folders/135K files) at three scales (Small, Medium, Full). FakeFS eliminates disk I/O and OS caching variance for reproducible results, enabling cross-version comparisons of scan time, memory allocations, and file operation counts. |
Purpose
This PR represents the culmination of an iterative optimization journey initially undertaken to support sushitrain's extremely tight iOS background task constraints. Along the way, I discovered better approaches and completely re-architected the folder scanning pipeline.
Evolution of Approaches
I started by exploring different delete detection strategies, implementing and measuring each one:
osutil.IsDeletedmakingLstat()calls per file during Phase 2DirExistenceCacheandSymlinkCacheto reduce redundant syscallsExistingFilesmap during Phase 1 walk to eliminate Phase 2 syscalls entirelyWhile the zero-syscall version showed improvement, I realized there was another major opportunity for optimization. The scanner was making individual database queries for every single file during the walk:
I decided to restructure the entire scan around preloading to address this:
1. Preload All File Metadata
I added
AllLocalFilesMapto the database interface to bulk-load all file infos in a single query:Key optimizations include:
IgnoreBlocks: truefor comparisons, so blocks aren't used2. Map-Based CurrentFiler
I replaced per-file DB queries with map lookups:
3. Reuse Preload in Phase 2
Phase 2 previously spent significant time iterating the database. Now it reuses the preloaded map, eliminating this entirely:
4. Filesystem API Modernization
As groundwork, I migrated the codebase to Go's modern
os.ReadDirAPI:ReadDirto theFilesysteminterfaceFileInfofor metadata), it's a cleaner API for future implementationsBug Fixes
Deterministic Iteration Order
Tests initially failed because Go's random map iteration broke rename sequence ordering. I fixed this by:
ORDER BY n.nameto the SQL queryfor _, name := range sortedNamesinstead ofrange preloadedFilesfindRenamein Phase 1Efficient Subdir Filtering
I optimized the prefix matching from nested loops to a single pass with early exit, reducing unnecessary iterations when scanning specific subdirectories.
Testing
All existing tests pass with these changes:
TestRenameSequenceOrdervalidates the sorted iteration requirementTestScanDeletedROChangedOnSRconfirms delete detection works correctlyTo test manually, monitor scan logs on a folder with many files. The logs show internal timing breakdowns for each phase. The changes are internal refactorings that should not affect any user-visible behavior.
Future Work
Once this PR merges, I plan to implement an optimistic scanner that:
This will be particularly valuable for sushitrain's iOS background scan requirements, where scan time is severely limited.