Serialize row writes on a workspace data frame - #896
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WalkthroughThe core crate adds a process-global registry of per-data-frame write mutexes. Workspace row mutations use normalized DuckDB paths to serialize writes. Tests cover lock behavior, registry reclamation, panic recovery, and concurrent appends during connection-cache eviction. ChangesData-frame write serialization
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to The PR adds per-data-frame serialization for row writes without any supplied merge-blocking concern; no actionable merge risk remains beyond normal checks and review. Sequence Diagram(s)sequenceDiagram
participant RowMutation as Workspace row mutation
participant LockRegistry as with_data_frame_write
participant DataFrameMutex as Data-frame mutex
participant DuckDB as DuckDB row operation
RowMutation->>LockRegistry: submit normalized DuckDB path and closure
LockRegistry->>DataFrameMutex: acquire path-specific mutex
DataFrameMutex-->>LockRegistry: grant exclusive access
LockRegistry->>DuckDB: execute add, update, batch_update, or delete
DuckDB-->>LockRegistry: return operation result
LockRegistry-->>RowMutation: return result
🚥 Pre-merge checks | ✅ 4✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Inline comments:
In `@crates/liboxen/src/core/data_frame_locks.rs`:
- Around line 56-57: Bound the process-global registry near REGISTRY so entries
for unused db_path values can be evicted instead of retaining every PathBuf
indefinitely. Replace strong lock storage with an eviction-safe weak-entry
design, remove expired keys during lookup, and preserve atomic lock
lookup/creation under the registry mutex while ensuring active locks remain
valid.
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crates/liboxen/src/core.rscrates/liboxen/src/core/data_frame_locks.rscrates/liboxen/src/repositories/workspaces/data_frames.rscrates/liboxen/src/repositories/workspaces/data_frames/rows.rs
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Pushed a follow-up to the lock module: the registry now reclaims an entry once its last user is done, so it holds one mutex per data frame currently being written rather than one per data frame ever written. Memory is proportional to concurrency instead of to history. The reclaim is a refcount check under the registry lock: let mut registry = REGISTRY.lock();
if Arc::strong_count(&lock) == 2 {
registry.remove(db_path);
}A count of two is the map's reference plus this one. Observing it while holding the registry lock is what makes removal safe: every clone is taken under that same lock, so a caller blocked on the data frame's mutex, or one that has cloned but not yet locked, is already counted. The next caller for a reclaimed path just creates a fresh entry. The registry lock is never held at the same time as a data frame's lock, so the ordering discipline documented in the module is unchanged. A panic inside the guarded work skips the reclaim and leaks one entry. That is bounded and harmless, and adding an unwind guard to recover it did not seem worth the machinery.
#897 and #898 have been rebased onto this and force-pushed; the only change to them is this commit underneath. |
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Inline comments:
In `@crates/liboxen/src/core/data_frame_locks.rs`:
- Around line 71-74: Update the lock handling around work() to use an RAII
cleanup guard whose Drop runs after the data-frame mutex guard is released,
ensuring registry reclamation also occurs when work() unwinds due to a caught
panic. Preserve the existing cleanup behavior for successful execution and
associate the guard with the relevant db_path registry entry.
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crates/liboxen/src/core/data_frame_locks.rs
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Opening a DuckDB database file that this process already has open yields a second, independent database rather than joining the first: DuckDB's single-writer protection is a file lock that excludes other processes. The two then diverge, and whichever folds its state into the file last is the version that survives, so the other's rows are gone while both callers were told they succeeded. What normally keeps one database per file is the `df_db` connection cache, whose entry is a connection behind a mutex. That serializes writers only while they share the entry, and the entry is not guaranteed to be there: the cache evicts under LRU pressure, and `rename`, workspace delete, and repo delete evict by hand. An eviction while one writer is still inside its operation hands the next writer its own database over the same file. `core::data_frame_locks` adds one lock per data frame, keyed by the path of its staged DuckDB file, so exclusion no longer depends on what the connection cache happens to be holding. The four row operations in `repositories::workspaces::data_frames::rows` take it, which covers every caller: the server's row endpoints, the CLI, and the Python bindings. The repository's shared write reservation is unchanged. This lock only orders writers against each other. `test_concurrent_row_appends_all_land` fires three rounds of 16 concurrent appends at one data frame while a task evicts its cached connection, asserting the exact row count after each round. It fails 10 out of 10 runs without the lock, first failing in round 0 with 20 rows where 22 were expected, and passes 10 out of 10 with it. Reaching the hazard needs the eviction to land inside another writer's critical section, so the test amplifies rather than forces it.
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| /// Run `work` with exclusive access to the data frame whose staged DuckDB table lives at | ||
| /// `db_path`, blocking until any other row write on that same data frame finishes. | ||
| pub fn with_data_frame_write<T>(db_path: &Path, work: impl FnOnce() -> T) -> T { |
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The lock is a mutex, if one thread tries to grab it twice then it will get stuck. (e.g. grab a lock for something, then call a function that tries to grab the lock again). Might be worth a small comment here in case someone (claude) runs into it.
…callers (#921) Folds the per-data-frame write lock from #896 into the DuckDB connection cache. The hazard is two DuckDB instances over one file, an identity property the cache already owns; it only broke because eviction could drop an entry a caller still held. The cache now never evicts a held entry, `with_db_closed` covers `rename`'s filesystem work, and `core::data_frame_locks` is deleted along with the wrappers around the row writes. Readers never took the lock, so this also closes that gap. `test_concurrent_row_appends_all_land` now uses LRU pressure with back-to-back writers. Reverting the holder check fails it with a DuckDB checkpoint error. Rust and Python suites green. #897 and #898 will be rebased onto this and drop their wrappers.
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First of three stacked PRs: #896 (this one) → #897 → #898.
Opening a DuckDB file the process already has open yields a second, independent database rather than joining the first, since DuckDB's single-writer protection is a file lock that excludes other processes. The two diverge, and whichever writes its state to the file last wins, so the other's rows are gone while both callers were told they succeeded. A lone
INSERTis exposed to this too, so SQL-level atomicity would not fix it.What normally keeps one database per file is the
df_dbconnection cache, whose entry is a connection behind a mutex. That only serializes writers who share the entry, and the entry can vanish: LRU eviction, plusrename, workspace delete, and repo delete evicting by hand. An eviction while one writer is mid-operation hands the next writer its own database over the same file.core::data_frame_locksadds one lock per data frame, keyed by its staged DuckDB path, so exclusion no longer depends on the cache. The four row operations inrepositories::workspaces::data_frames::rowstake it, covering every caller: the server's row endpoints, the CLI, and the Python bindings. Different data frames never contend, and the repo's shared write reservation is unchanged.Deadlock-free structurally: always taken outside the connection lock, and never held across an
.awaitbecausewith_data_frame_writetakes a synchronous closure.Tests.
test_concurrent_row_appends_all_landfires 3 rounds of 16 concurrent appends while a task evicts the cached connection, asserting exact row counts. Fails 10/10 runs without the lock (round 0: 20 rows, expected 22), passes 10/10 with it. It amplifies the hazard rather than forcing it, since the eviction has to land inside another writer's critical section. Two unit tests cover the primitive. Rust and Python suites green.Follow-ups, not here. #897 extends the lock to the other staged-table writers. Separately, this path runs synchronous DuckDB IO on async runtime threads and the lock puts a reliable queue in front of it; that is pre-existing and spans the row handlers, so it gets its own PR.