Transloadit

What is the Live Queue?

The Live Queue is Transloadit’s shared fast lane for Encoding Jobs that require near-real-time handling; direct file uploads place jobs there by default. Jobs exceeding a Workspace’s regional Priority Job Slots wait in a Backup Queue and rejoin the Live Queue as slots free up.

Request + files
Processing platform
Results + status
A processing platform accepts an authenticated request, executes a workflow, and returns observable results. This diagram shows platform workflows broadly, not specifically the Live Queue.

How the Live Queue works

This processing lane serves direct-upload work ahead of nonurgent imported or explicitly batched jobs. It is shared across customers, while each Workspace’s regional Priority Job Slots bound how much of its work can occupy the lane at once. Excess live jobs wait in a Backup Queue and return as their Workspace releases slots. Operationally, queue residence, Robot execution time, and end-to-end Assembly duration are separate measurements and should be monitored independently.

Key facts

  1. Direct uploads normally create Live Queue jobs, while import Steps that fetch multiple files in one Assembly are placed in the Batch Queue; a Step can also opt into batch treatment with its queue setting.
  2. Live work is prioritized over shared batch work, but priority does not mean unbounded parallelism: each concurrently running job consumes slots according to its Robot’s slot claim.
  3. Queue wait estimates are exposed by processing region and measured in seconds, which lets admission logic distinguish platform backlog from a slow individual encode.

When the Live Queue matters

Reserve Live Queue capacity for jobs with meaningful latency requirements. Bursts beyond the plan’s Priority Job Slots increase waiting time because excess work is parked in the Backup Queue until running live jobs complete, and nonurgent Steps can instead be routed deliberately to the Batch Queue.

Common use cases for platform workflows

These examples cover platform workflows broadly, not specifically the Live 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 the Live 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

  1. Define authentication, authorization, idempotency, retries, and terminal error behavior.
  2. Observe queue time, execution time, callbacks, and partial results with stable identifiers.
  3. Exercise malformed, duplicate, interrupted, and unauthorized requests before launch.

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