Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesTo make a pipeline resumable, persist its run and step state in SQLite as work progresses; do not treat log lines or SQLite’s write-ahead log (WAL) as a record of completed work. An application-level checkpoint says what the pipeline has safely finished and what it can retry. A SQLite WAL checkpoint is a separate database operation that copies committed database pages from the WAL into the main database file.
What a pipeline checkpoint records
Logs are useful for diagnosing what happened, but they are a poor sole source of recovery state: they may be short-lived, difficult to query, or ambiguous about whether an operation completed. A structured checkpoint gives the application a queryable record of progress that survives process restarts.
SQLite does not provide a pipeline schema or infer completed steps from its WAL. The application must decide what constitutes a safely completed step, write that state, and use it when a run restarts. A practical record can include a stable run ID, a step ID, a status, an attempt count, timestamps, and references to the relevant inputs and outputs. Store enough information to identify the work without unnecessarily copying large payloads into the database.
Design state around safe restart points
Persist each meaningful progress transition at the boundary where the application can safely resume. For example, a step might move from pending to running, then to completed only after its output is available and the application has recorded the result. If a process stops before the completion transaction commits, a later run should treat that step as unfinished and follow its retry policy.
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The exact schema depends on the pipeline. This illustrative design separates runs from steps and makes attempts and output references explicit; SQLite does not require or supply these tables:
CREATE TABLE pipeline_runs (
run_id TEXT PRIMARY KEY,
status TEXT NOT NULL,
started_at TEXT NOT NULL,
updated_at TEXT NOT NULL
);
CREATE TABLE pipeline_steps (
run_id TEXT NOT NULL,
step_id TEXT NOT NULL,
status TEXT NOT NULL,
attempt_count INTEGER NOT NULL DEFAULT 0,
started_at TEXT,
completed_at TEXT,
input_ref TEXT,
output_ref TEXT,
error_summary TEXT,
PRIMARY KEY (run_id, step_id),
FOREIGN KEY (run_id) REFERENCES pipeline_runs(run_id)
);Use a transaction for related database changes—for example, marking a step complete while updating its run’s progress—so they are applied together or not at all. The SQLite project documents transactions as atomic even if interrupted by a program crash, operating-system crash, or power failure. That guarantee applies to changes in the SQLite transaction, not to side effects outside the database.
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Make retries safe
A SQLite transaction cannot atomically commit a remote API request, message publication, or external file write. A crash can occur after an external action succeeds but before the database records completion. Design those boundaries deliberately: use an idempotency key where the external service supports one, make outputs safe to overwrite, or reconcile the external result before retrying. Otherwise, a restart may repeat an effect even though the database correctly reports that the step was not committed.
On startup, query persisted state to find unfinished steps, apply the pipeline’s retry and reconciliation rules, and record each new transition transactionally. Track attempts and useful timestamps so operators can distinguish a fresh step from repeated failures. Keep error details proportionate: a short summary in the state table and fuller diagnostics in retained logs can be more manageable than treating every log line as checkpoint data.
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WAL mode is a database mechanism, not a resume feature
In WAL mode, SQLite records commits in the WAL file. A checkpoint later transfers WAL content into the main database file. That database checkpoint does not inspect pipeline state, identify completed steps, or decide what to rerun; the application’s own persisted run and step records do that.
SQLite’s documented default is to attempt an automatic WAL checkpoint when a commit causes the WAL to reach about 1,000 pages, and when the last database connection closes. Applications can configure this behavior, so 1,000 pages is not a universal size limit. In the documentation’s example/default context, the WAL is normally about 4 MB at 1,000 pages; this is an approximation, not a performance benchmark.
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Reader duration affects WAL growth
A checkpoint may be unable to finish while active readers still need older WAL content. Long-lived or overlapping read transactions can therefore delay checkpoint completion and let the WAL grow. If WAL size matters operationally, monitor checkpoint behavior and keep read transactions no longer than necessary.
Keep the database files together
The WAL is part of the database’s persistent state. When copying or moving a live WAL-mode database, do not separate the main database from its -wal file: committed transactions may be lost or the database may be corrupted. Use a consistent backup or copy strategy that accounts for the active database files rather than copying only the main file while writes continue.
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After an unclean shutdown, SQLite can rebuild the WAL index from valid frames when the database is reopened. The first connection may hold locks during recovery, which can block other connections. Plan startup and health checks with that recovery behavior in mind.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose durability and deployment settings deliberately
WAL is not suitable for every deployment. SQLite’s WAL documentation says participating processes must share a host; WAL does not work over a network filesystem. If writers or readers need to operate across hosts through a network-mounted database file, choose a different deployment design rather than assuming WAL makes that arrangement safe.
Synchronization settings also change the failure trade-off. In WAL mode, synchronous=NORMAL avoids syncs during most transactions and can roll back after a power failure or hard reset. synchronous=FULL adds a WAL sync for each commit. Select a policy based on the failures the pipeline must survive and validate it against the actual filesystem and SQLite VFS in use.
| Decision | What to weigh |
|---|---|
| Failure model | For process crashes, transactional state provides atomic updates. For OS crashes or power loss, decide whether the additional commit sync under FULL is necessary for the pipeline’s recovery requirements. |
| Topology | WAL is for processes sharing a host, not a database file shared over a network filesystem. |
| Read behavior | Long-lived readers can hold back checkpoints and contribute to WAL growth. |
| Backup and recovery | Backups of an active WAL-mode database must preserve a consistent view that includes the WAL state. |
| Audit and retention | Checkpoint tables can preserve structured progress history, but retention rules and any separate audit trail are application decisions. |
A practical implementation sequence
- Define the recovery boundary. For each step, specify what must be true before it can be marked complete and what the application will do if it is interrupted earlier.
- Assign stable identifiers. Create run and step IDs that let a restart find the same work and connect outputs to the step that produced them.
- Write transitions transactionally. Persist status changes and related database state together, using a transaction where they must succeed or fail as one unit.
- Plan external effects. Decide how retries avoid duplicating API calls, files, or messages, or how the application reconciles outcomes that may have happened before a crash.
- Select SQLite behavior for the deployment. Choose WAL only when the host topology supports it; select synchronization behavior and backup procedures for the required failure model.
- Exercise recovery paths. Test interruption before and after state commits, during external effects, and during restart. Confirm that the application resumes from persisted state rather than inferring progress from log output.
These choices are workload-specific: SQLite’s documented mechanisms establish transaction and WAL behavior, but they do not establish performance or suitability for a particular pipeline’s size, write concurrency, filesystem, or recovery target.
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