Conveyor lets an app hand long-running work to a background worker instead of keeping the HTTP request open. In Dennis kinuthia’s Deno Desktop example, a request queues repository-enrichment jobs; a SQLite-backed queue retains pending work, while PGlite stores the resulting embeddings. Progress is tracked separately in memory, so it resets when the app restarts.
Why move work into a queue?
Fetching repository data and creating embeddings can take long enough that tying the process to the initiating request is inconvenient. A user might navigate away or close the app while the work is still running. A queue separates the request that starts the work from the worker that performs it: the endpoint adds jobs, then the worker processes them independently.
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This is a single-machine desktop design, not evidence of measured performance or a guarantee that every queue deployment can recover from every failure. Its practical value is architectural: work can remain pending in persistent storage even when the initiating request has ended.
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- Start with an HTTP request. The endpoint paginates through the user’s starred GitHub repositories.
- Enqueue one job per repository. The GitHub node ID is used to deduplicate repository jobs, avoiding duplicate work for the same repository.
- Process jobs in batches. The example reports batches of 10. A failure in one job does not automatically fail the other jobs in its batch.
- Build a compact document. For each repository, the worker fetches the README and takes only its first 20 lines.
- Embed and store the result. The selected text is embedded with EmbeddingGemma, then the embedding is upserted into PGlite.
The design uses Conveyor’s Queue and Worker APIs with @conveyor/store-sqlite-node. The author describes the queue database as a local file, avoiding a need to operate Redis for this single-machine setup.
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What survives an app restart?
The example uses different storage boundaries for queued work, search data, and visible progress. Treating them as one kind of state would give an inaccurate picture of restart recovery.
| State | Where it is stored | Reported restart behavior |
|---|---|---|
| Pending or retrying jobs | Conveyor’s SQLite file | Remain in the queue across restarts, according to the author’s account. |
| Repository embeddings and records | PGlite | Persist and remain searchable after restart. |
| Progress status | In memory | Resets to idle after restart; it is not persisted with the queue jobs. |
This distinction matters to the UI. Persisted jobs and search results do not imply persisted progress indicators. An app that needs to show the same detailed progress after reopening would need to store that status durably as well.
Retries and GitHub rate limits
The author reports configuring five job attempts with exponential backoff. The example also pauses and resumes processing to handle GitHub rate limits, including a reported 60-second pause. These are settings in this implementation, not general performance benchmarks or universal recommendations; appropriate retry limits and wait times depend on the service and workload.
Conveyor’s current @conveyor/core package documentation lists retries with backoff, pause/resume, deduplication, batching, concurrency controls, scheduling, and parent-child job flows among its capabilities. The documentation describes what the package supports; it does not mean the example uses every feature.
The README cutoff: compact documents, shallower coverage
Indexing only the first 20 README lines keeps each repository’s embedding document short and makes a simple one-vector-per-repository design possible. It also creates a clear retrieval tradeoff: details that appear deeper in a README are not included and therefore cannot be matched through that indexed text. The author points to chunking as a possible improvement when deeper technical details matter.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What Conveyor contributes
Conveyor provides software APIs for defining queues and workers. Its package documentation identifies Queue, Worker, Job, FlowProducer, and JobObservable classes, and lists Node.js, Deno, and Bun support. The package page accessed for this article shows version 1.5.0 and an MIT license; package metadata can change, so check the current JSR listing when choosing a version.
For this desktop workflow, the central decision is not simply whether to use a queue. It is whether the persistence and operating model fit the application: this example keeps queue storage in a local SQLite file and embeddings in PGlite, without requiring a separately operated Redis service. A deployment with multiple machines or distributed workers has different infrastructure requirements and is not established by this example.
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