Resolving Stale Data in Multi-Client SQLite WAL Mode on Linux

Database Connection Lifecycle and Transaction Isolation in Multi-Client WAL Mode

The core challenge involves a SQLite database operating in Write-Ahead Logging (WAL) mode with concurrent access from a C-based backend service and multiple Java-based UI clients via JNI. The UI fails to display new data written by the backend after initial load, specifically on Linux systems. This behavior suggests a breakdown in synchronization between readers (UI) and writers (backend) despite SQLite’s concurrency guarantees in WAL mode. The problem is rooted in the interplay between connection management, transaction isolation, filesystem semantics, and WAL checkpointing strategies. Below, we dissect the issue through three lenses: Connection and Transaction Isolation Boundaries, WAL Checkpointing and Filesystem Semantics, and Cross-Language Concurrency Patterns.

Connection and Transaction Isolation Boundaries

SQLite connections operate with snapshot isolation in WAL mode. When a connection initiates a read transaction, it observes the database state as of the first SELECT query or implicit read operation. Subsequent writes from other connections are not visible to this reader until the transaction concludes. For write transactions, changes become visible to new readers after a COMMIT, but existing readers retain their snapshots until their transactions end. This isolation mechanism is foundational to the observed behavior.

In the described architecture, the Java UI clients open connections via JNI. If these connections initiate long-running read transactions (explicit or implicit), they will not see updates from the backend until they restart transactions. For example, a UI rendering loop that reuses a single connection without finalizing transactions will perpetually reference the initial snapshot. This is exacerbated when multiple UI instances share a single connection (e.g., via connection pooling misconfiguration) or hold transactions open indefinitely.

Key Checks:

1. Transaction Scope: Verify whether the UI’s JNI layer wraps read operations in explicit transactions (BEGIN/COMMIT) or relies on SQLite’s autocommit. Long-lived explicit transactions will block visibility of backend writes.
2. Connection Per Client: Confirm that each UI instance opens a dedicated connection. Shared connections risk transaction state collisions.
3. Cursor Management: In Java, ResultSet objects tied to a connection may implicitly hold read transactions open until closed. Ensure all cursors are explicitly closed after data retrieval.

Diagnostic Queries:

• Use PRAGMA data_version; to detect changes. If the version increments (backend writes) but the UI’s reported version remains static, the UI connection is not refreshing its snapshot.
• Execute SELECT * FROM sqlite_master; to force a schema read, which can reset snapshots in some cases.

WAL Checkpointing and Filesystem Semantics

WAL mode decouples writes from reads by appending changes to a separate log file. Readers access the main database file and the WAL simultaneously. The WAL is integrated into the main database via checkpointing, which can be automatic or manual. Checkpointing frequency and filesystem behavior directly impact read visibility.

Automatic Checkpointing: By default, SQLite triggers a checkpoint when the WAL reaches 1000 pages. However, if the backend service performs frequent small writes without triggering this threshold, the WAL may not be checkpointed promptly. Readers using older connections might bypass the WAL if filesystem metadata (e.g., WAL index) is not updated atomically.

Linux-Specific Filesystem Behavior:

• Inode Locking: On Linux, SQLite uses fcntl() for file locking. Network filesystems (NFS, CIFS) or virtualized storage layers may not honor these locks reliably, leading to stale cache reads. However, the user confirmed local disk usage, so this is less likely.
• Overlay Filesystems: If the application runs in containers (Docker, Snap) with overlay mounts, copy-on-write semantics can delay visibility of WAL updates across mount layers.

Checkpointing Strategies:

1. Explicit Checkpoint Calls: Modify the backend to invoke PRAGMA wal_checkpoint(RESTART); after critical writes to force WAL integration. This ensures new readers see the latest data.
2. Checkpoint Threading: Use PRAGMA wal_autocheckpoint; to reduce the page threshold, but balance this against write performance.
3. WAL Size Monitoring: Log WAL file size (stat <database>-wal) to correlate UI staleness with unchecked WAL growth.

Diagnostic Steps:

• Execute PRAGMA wal_checkpoint(TRUNCATE); during UI staleness. If data appears afterward, checkpointing is the culprit.
• Monitor sqlite3_wal_checkpoint_stats() in the C backend to track checkpoint activity.

Cross-Language Concurrency Patterns

The hybrid C/Java architecture introduces subtle concurrency risks. JNI interactions and RMI calls add layers where connection handles or transactions might leak.

JNI Connection Lifetime:

• Global vs. Local References: In JNI, database connections wrapped as Java objects require careful reference management. Premature garbage collection of a JNI proxy object could close the underlying SQLite handle mid-transaction, but improper global references might retain connections indefinitely.
• JNI Exception Handling: Unchecked C exceptions in JNI methods (e.g., sqlite3_step() errors) may leave transactions open. Ensure all JNI entry points use try...catch blocks to finalize statements and reset transactions.

RMI-Induced Connection Sharing:
If UI components access the database via RMI to a central backend (contrary to the initial description), this serializes all UI database operations through a single process. This creates a bottleneck but avoids true concurrency. However, if RMI is used for non-database operations (e.g., business logic), while JNI handles direct database access, race conditions emerge between the backend’s writes and the UI’s local reads.

Concurrency Validation:

1. Trace Connection Handles: Log SQLite’s sqlite3_open_v2() and sqlite3_close() calls in both C and JNI layers. Count active connections during UI staleness events.
2. Thread Affinity: SQLite connections are not thread-safe by default unless SQLITE_THREADSAFE=1 (serialized mode). Confirm that JNI connections are not shared across Java threads without mutex guards.

Mitigations:

• Explicit Transaction Boundaries: In Java, use try-with-resources for Statement and ResultSet objects to enforce prompt closure.
• Connection Timeouts: Configure sqlite3_busy_timeout() to handle contention gracefully instead of blocking indefinitely.

Resolution Workflow

1. Audit Transaction Scope:

   • In the Java UI, replace all implicit transactions with explicit BEGIN IMMEDIATE and COMMIT blocks. Measure if data visibility improves.
   • Use sqlite3_trace_v2() in the C backend to log transaction start/end times and correlate with UI staleness.

2. Force Checkpointing After Writes:

   • Modify the C backend to execute PRAGMA wal_checkpoint(RESTART); after every write operation. Observe if UI updates immediately.
   • If performance degrades, switch to incremental checkpoints (PRAGMA wal_checkpoint(PASSIVE);) during idle periods.

3. Isolate Filesystem Artifacts:

   • Mount the database directory on a tmpfs RAM disk to eliminate filesystem caching delays. If the issue resolves, investigate disk I/O configuration (e.g., noatime, sync mount options).
   • Compare strace outputs from Linux and Process Monitor traces from Windows to identify divergent locking/read behaviors.

4. JNI and RMI Decoupling:

   • Temporarily disable RMI-mediated database access in the UI. Force all reads through JNI-local connections. If staleness disappears, RMI serialization is introducing delays.
   • Instrument JNI methods to log connection handles and thread IDs. Detect reused connections across UI instances.

5. Snapshot Refresh Heuristics:

   • Implement a UI-side timer to periodically reopen database connections or issue PRAGMA optimize; to reset internal query planners and snapshots.
   • Use sqlite3_snapshot_open() (if available in the custom build) to explicitly advance read snapshots after backend writes.

By methodically addressing connection lifecycle management, checkpointing policies, and cross-language concurrency primitives, the staleness issue can be isolated to a specific component. The Linux-specific manifestation likely stems from subtle differences in filesystem metadata synchronization or threading model enforcement compared to Windows.