What is Batch Queue?
The Batch Queue is Transloadit’s shared processing lane for Encoding Jobs that do not need real-time handling. Jobs land there when an import Robot ingests multiple files in one Assembly, or when a Step opts in by setting "queue": "batch" in its Assembly Instructions.
How Batch Queue works
The Batch Queue separates throughput-oriented media work from latency-sensitive processing. Import Steps that fetch multiple files are flagged as batch work automatically, while single-file imports stay in the Live Queue, and the per-Step queue setting only permits downgrades from live to batch, never upgrades. The processing operations themselves do not change; only when resources are assigned differs. Work exceeding a plan’s batch job slots trickles further into a Backup Queue and is re-enqueued as slots free, which is overflow handling rather than deliberate batching.
Key facts
- 1Queue placement changes scheduling priority rather than the meaning of an Assembly’s Instructions, so the same workflow can yield equivalent outputs after a longer wait.
- 2Backlog depth and job cost both affect completion time; counting queued Assemblies alone is a weak estimate when their file sizes and processing graphs differ.
- 3Downstream systems must use completion events or status checks instead of fixed delays, because capacity contention can make batch start and finish times variable.
When Batch Queue matters
Send delay-tolerant bulk work to the Batch Queue so it does not consume the Priority Job Slots that urgent jobs rely on. Because batch jobs are only taken when the shared Live Queue is empty, consumers must tolerate longer and less predictable completion times when scheduling downstream steps.
Common use cases for platform workflows
These examples cover platform workflows broadly, not specifically Batch Queue.
- Running repeatable upload, import, processing, AI, storage, and notification pipelines.
- Tracking long-running media work independently from an application request.
- Referencing centrally stored credentials by name instead of sending storage secrets with each request.
Working with platform workflows
This guidance covers platform workflows broadly, not just Batch Queue.
A client authenticates and submits files or references together with workflow instructions. The platform validates the request, schedules dependent operations, records state transitions, and exposes results through a response, polling endpoint, or notification.
Platform concepts become reliable only when their lifecycle is explicit. Authentication, idempotency, retries, timeouts, observability, quotas, and terminal states should be designed together rather than added after failures occur.
What you gain
- Reusable workflows separate application intent from processing infrastructure.
- Stable job identifiers and lifecycle events improve observability and recovery.
- Managed queues and workers let products scale without embedding every media tool.
What it costs
- Synchronous responses are simple but keep connections open while long work executes.
- Aggressive retries improve recovery from transient faults but can duplicate work or overload a dependency.
- Higher concurrency reduces queue time until resource contention or a downstream limit becomes the bottleneck.
Before production
- 1Define authentication, authorization, idempotency, retries, and terminal error behavior.
- 2Observe queue time, execution time, callbacks, and partial results with stable identifiers.
- 3Exercise malformed, duplicate, interrupted, and unauthorized requests before launch.