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n8n Queue Mode separates coordination from execution: the main process receives webhook calls and handles timers, Redis queues execution jobs, and worker processes run them using shared database state. To set it up, configure every participating process to use the same Redis service, production database, encryption key, and queue execution mode, then start workers and tune concurrency to what your database can support.
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What Queue Mode changes
In a single-process deployment, the process that handles an incoming trigger also runs the resulting workflow. Queue Mode distributes those responsibilities across processes. The main process coordinates work; Redis holds queued execution jobs; workers take jobs from Redis, read workflow data from the shared database, execute workflows, write results, and report completion through Redis.
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A typical request path is:
Client or webhook → load balancer → webhook processor or main → Redis queue → worker pool → shared database and storage → completion notification
Queue Mode is a distributed deployment, not simply a setting that makes one n8n process run faster. Every participating process must be able to reach the same state services and use compatible configuration. n8n describes Queue Mode as its most scalable execution mode.
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Which process does what?
| Component | Responsibility | When to add or scale it |
|---|---|---|
| Main process | Coordinates n8n, handles timers and webhook calls unless ingress is separated, and enqueues execution IDs. | Keep it responsive for the editor and API; separate webhook ingress when inbound traffic is the bottleneck. |
| Redis | Provides the queue used to hand jobs from coordinating processes to workers. | Ensure it is reachable and has capacity for queued jobs and webhook response relay data. |
| Worker | Dequeues jobs, runs workflow executions, accesses the shared database, and reports completion. | Add workers to increase execution capacity, while accounting for concurrency and database connection limits. |
| Webhook processor | Receives production webhook traffic and puts work into the queue. | Add processors behind a load balancer if inbound webhook volume strains the main process. |
| Shared database and binary storage | Persist workflow state and execution data; shared object storage can hold binary data used across instances. | Make services available to all relevant processes; do not depend on each instance’s local filesystem for shared binary data. |
Before you configure it
Choose shared state services
Use a production database that every n8n process can reach, and a Redis instance reachable by the main process, every worker, and any webhook processors. SQLite is not recommended for Queue Mode in n8n’s documentation. Treat Redis and the database as essential parts of the deployment: if a process cannot reach them, it cannot participate normally in queuing or execution.
Prepare one encryption key for all processes
Workers and webhook processors need to decrypt credentials stored in the database. Set the same N8N_ENCRYPTION_KEY on the main instance, all workers, and all webhook processors. Use the key generated or chosen for the main instance; do not let each separately started process generate or use a different key.
Plan binary data and large responses
Queue Mode does not support filesystem-only binary storage across independently running instances: a worker may not have access to a file stored on another process’s local disk. Use shared storage, such as S3 or Azure, for persisted binary data that must be available across instances.
Webhook responses also have a size consideration. n8n documents a default N8N_WEBHOOK_RESPONSE_RELAY_SIZE_MAX of 64 MiB. Because Redis may hold several copies of a response while it is in flight, plan for roughly 1.5 times that limit per in-flight response rather than budgeting only for one copy. If larger response bodies need to be offloaded, workers can use N8N_WEBHOOK_RESPONSE_RELAY_OFFLOAD_ENABLED=true when every main and webhook instance is on n8n 2.34.0 or later and all instances can read the selected storage.
Set up Queue Mode step by step
- Provision the shared database and Redis. Choose production services reachable from the main process, workers, and webhook processors. Verify network access and credentials from each environment before starting the distributed deployment.
- Set the encryption key consistently. Configure
N8N_ENCRYPTION_KEYto the same value on the main process, every worker, and every webhook processor. Keep the value protected as a secret rather than maintaining separate keys for individual processes. - Enable queue execution mode on every process. Set
EXECUTIONS_MODE=queuefor the main process and workers; webhook processors also need this setting. A process left in a different execution mode is not configured as part of the same Queue Mode deployment. - Point each process at Redis. Set
QUEUE_BULL_REDIS_HOSTandQUEUE_BULL_REDIS_PORTon all participating processes. The documented defaults arelocalhostand6379, respectively; those defaults only make sense when they identify the Redis service from the process’s own network context. Configure Redis username, password, TLS, or cluster-node settings as required by your Redis deployment. - Start the main process. Run the main n8n instance with the shared database, Redis, encryption key, and queue settings. It coordinates the deployment and enqueues work; it does not replace the worker pool.
- Start one or more workers with the same shared environment. The documented command is
./packages/cli/bin/n8n worker. In Docker, run the n8n image with theworkercommand and provide the same shared configuration. Workers must connect to the same Redis and database as the main process and use the same encryption key. - Set worker concurrency deliberately. The documented form is
n8n worker --concurrency=5. The worker default is 10, and n8n recommends a concurrency of 5 or higher per worker. UseN8N_CONCURRENCY_PRODUCTION_LIMITto impose a production limit across Queue Mode workers when you need to cap production concurrency. - Move webhook ingress to processors only if needed. Webhook processors are optional. If inbound webhook volume is the pressure point, run processor processes behind a load balancer. Keep the main process out of that pool when possible so the editor and API remain responsive. Set
N8N_DISABLE_PRODUCTION_MAIN_PROCESS=trueif production webhooks should never execute on the main process. - Choose shared binary storage before relying on multiple instances. Configure shared storage such as S3 or Azure for persisted binary data that workers and other processes need to access. For response offloading, enable
N8N_WEBHOOK_RESPONSE_RELAY_OFFLOAD_ENABLED=trueonly when all main and webhook instances use n8n 2.34.0 or later and can read the chosen storage. - Validate the end-to-end path. Trigger a workflow and check that the main process can enqueue it, a worker can execute it, and the execution completes with results persisted in the shared database. Also verify that each process can reach Redis and that workflows requiring binary data can access the shared storage.
How to size workers and concurrency
Worker count and per-worker concurrency jointly determine how many executions can run in parallel. Adding workers can raise throughput, but only while the database, Redis, and the rest of the workload can sustain the added activity. The documentation does not provide a universal worker-count formula: the right setting depends on the execution load and database connection capacity.
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Start from the bottleneck
- Executions are waiting in the queue while workers are healthy: additional workers or increased concurrency may raise execution throughput, provided the database can support it.
- Inbound webhooks are overwhelming the main process: add webhook processors behind a load balancer. Processors address ingress pressure; they do not perform the workers’ execution role.
- Database connection pools are under pressure: avoid creating many workers with low concurrency. n8n warns that many low-concurrency workers can exhaust database connection pools. Revisit the worker and concurrency arrangement rather than assuming that adding processes is always beneficial.
- Redis or database connectivity is unreliable: adding workers will not fix a shared-state service that the processes cannot reach. Resolve service reachability and availability first.
Use the documented values as guidance, not a capacity promise
The worker default concurrency is 10. n8n recommends 5 or higher per worker and documents --concurrency=5 as an example of setting it explicitly. These are configuration recommendations, not a guarantee that a particular deployment can handle a given number of executions. Set concurrency with the database connection pool in mind, then observe queue depth, worker health, and database pressure as load changes.
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A practical capacity model
Use worker count multiplied by per-worker concurrency as a first planning metric, then test it against real workflows. For example, three workers configured at concurrency five provide 15 nominal execution slots, not a promise of 15 completed workflows per second. Long-running nodes, external API limits, binary transfers, and database queries can keep slots busy for very different lengths of time. Increase one variable at a time and compare queue depth, execution latency, Redis memory, and database pool utilization before making the next change.
Deployment choices and trade-offs
| Deployment | Throughput and ingress | Operational trade-off |
|---|---|---|
| Single main process | Lowest operational complexity, but the same process handles coordination, ingress, and execution. | Simple to run, yet a busy workflow can affect editor, API, and webhook responsiveness. |
| Main plus workers | Execution throughput scales with worker count and concurrency. | Requires shared Redis, database access, identical keys, and worker health monitoring. |
| Main, workers, and webhook processors | Separates inbound webhook capacity from execution capacity; processors sit behind a load balancer. | Adds processes and routing rules. Processors do not replace workers. |
| Multi-main with workers | Adds coordinating-process availability in addition to worker scaling. | Documented as a self-hosted Enterprise feature and requires leader coordination settings. |
Budget for the services that make the deployment distributed: managed Redis, a production PostgreSQL-compatible database, container or Kubernetes hosting, shared object storage, load-balancer capacity, and observability. Costs depend on provider, region, retention, traffic, and instance size, so there is no responsible universal price. A lower infrastructure bill can be offset by queue delays or database saturation if workers are added without enough shared-service capacity. Choose vendors based on reachability, authentication, TLS, backup, and monitoring requirements rather than assuming n8n endorses a particular provider.
Security and configuration checklist
- Store
N8N_ENCRYPTION_KEY, Redis credentials, and database credentials in a secret manager or equivalent protected configuration. - Use Redis authentication and TLS when required by the deployment, and restrict Redis and database network access to the n8n processes that need them.
- Verify the same key and queue-mode setting in every container, pod, or virtual machine; an overlooked replica can fail only when it receives work.
- Give all main, worker, and webhook instances access to the selected binary and response-offload storage without relying on local disk.
- Keep versions aligned. When external storage is enabled, upgrade main, workers, and runners together to avoid protocol incompatibilities.
When to add webhook processors
Workers and webhook processors solve different scaling problems. Workers increase execution capacity after a job is queued. Webhook processors increase the capacity to accept inbound webhook traffic and place that work into the queue. If executions are backing up because the workers cannot keep up, more ingress processors alone will not clear the execution backlog. If webhook traffic is making the main process less responsive, processors can separate ingress from the editor and API.
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Put optional webhook processor processes behind a load balancer. When production webhook traffic should be handled only by processors, set N8N_DISABLE_PRODUCTION_MAIN_PROCESS=true on the main process. This keeps production webhooks from executing there; it does not remove the main process’s coordination role.
High availability and licensing
Multi-main Queue Mode is documented as a self-hosted Enterprise feature. To enable it, set N8N_MULTI_MAIN_SETUP_ENABLED=true; leader-key time-to-live and check-interval settings are also available. This is distinct from adding workers: workers expand execution capacity, while multi-main setup addresses availability of the coordinating main role. Confirm the relevant Enterprise entitlement and configuration requirements for your deployment before relying on multi-main behavior.
Troubleshoot backlogs and failed executions
A growing queue means work is arriving faster than it is being completed, or workers are unable to claim or finish it. Follow the request path from the entry point through Redis, workers, and shared storage rather than changing concurrency without identifying the constrained component.
Webhook calls are delayed or the main process is unresponsive
- Check whether inbound webhook volume is putting pressure on the main process.
- If ingress is the bottleneck, run webhook processors behind a load balancer and keep the main process out of that pool when possible.
- Use
N8N_DISABLE_PRODUCTION_MAIN_PROCESS=truewhen production webhooks must never execute on the main process.
The queue grows while workers appear idle
- Check Redis reachability and configuration from the main process and every worker, including host, port, authentication, TLS, and cluster settings where applicable.
- Confirm that each process has
EXECUTIONS_MODE=queue, and that workers are started with the worker command. - Check worker health and confirm the worker processes use the same database and encryption key as the main process.
Workers execute slowly or the database is strained
- Inspect database connection-pool pressure and worker concurrency together.
- Do not assume that many low-concurrency workers are safer; n8n warns that this pattern can exhaust database connection pools.
- Adjust worker count or concurrency in light of observed database capacity rather than treating the documented recommendation as a guaranteed workload target.
Credentials cannot be decrypted
Check that N8N_ENCRYPTION_KEY is identical on the main process, workers, and webhook processors. The key allows those processes to access credentials stored in the shared database; a mismatch prevents the relevant process from decrypting them.
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Check whether the workflow relies on filesystem-only binary storage. Local files on one independently running instance are not a shared storage strategy. Use shared storage such as S3 or Azure for persisted binary data that must be available to the worker handling an execution.
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Large webhook responses fail or put pressure on Redis
Check response size against the documented 64 MiB default for N8N_WEBHOOK_RESPONSE_RELAY_SIZE_MAX and account for multiple in-flight copies in Redis memory planning. For offloading larger bodies, verify the n8n 2.34.0-or-later requirement for every main and webhook instance and confirm that all of them can read the selected storage.
Operate Queue Mode as one deployment
Monitor Redis reachability, queue depth, worker health, database connection-pool pressure, execution failures, and response-size errors. These signals distinguish an ingress bottleneck from a worker-capacity problem or a shared-service issue. When external storage is in use, upgrade the main process, workers, and runners together; mixed versions can create protocol incompatibilities.
Queue Mode is most useful when its shared services and process roles are managed together. Add workers for execution capacity, processors for webhook ingress, and a multi-main configuration only when the availability requirement and licensing support it. Each scaling change should be evaluated against the component it adds load to, especially the shared database and Redis.
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Do all n8n Queue Mode processes need the same encryption key?
Yes. Set the same N8N_ENCRYPTION_KEY on the main process, every worker, and every webhook processor so they can decrypt credentials stored in the shared database.
What is the minimum Redis configuration?
Set QUEUE_BULL_REDIS_HOST and QUEUE_BULL_REDIS_PORT on every participating process. The documented defaults are localhost and 6379; add username, password, TLS, or cluster-node settings when your Redis service requires them.
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How many workers should I run?
There is no universal number. Start with the queue depth and database capacity you observe, then increase worker count or concurrency gradually. n8n recommends concurrency of 5 or higher per worker, while warning that many low-concurrency workers can exhaust database connection pools.
Should I add webhook processors or workers first?
Add workers when executions are queued faster than they finish. Add webhook processors when inbound webhook traffic is overloading the main process. Processors improve ingress capacity but do not execute workflows.
Can Queue Mode use SQLite?
n8n’s documentation does not recommend SQLite for Queue Mode. Use a production database reachable by the main process, workers, and webhook processors.
Can I keep binary data on each worker’s local filesystem?
Not reliably across independently scaled instances. Use shared storage such as S3 or Azure for binary data that another process must read.
What is the webhook response size limit?
The documented default for N8N_WEBHOOK_RESPONSE_RELAY_SIZE_MAX is 64 MiB. Redis can hold several copies while a response is in flight, so budget about 1.5 times that limit per in-flight response. Larger bodies can be offloaded only when all main and webhook instances meet the n8n 2.34.0-or-later requirement and share access to the selected storage.
Is multi-main Queue Mode included in the community edition?
Multi-main Queue Mode is documented as a self-hosted Enterprise feature. It uses N8N_MULTI_MAIN_SETUP_ENABLED=true plus leader-key timing settings, so confirm licensing and configuration requirements before deploying it.
The Bottom Line
Queue Mode scales n8n by separating coordination, queuing, ingress, and execution. Share a production database, Redis service, encryption key, queue-mode setting, and binary storage strategy across every process; add workers for execution throughput and webhook processors for ingress pressure. Tune concurrency against database capacity, monitor queue and service health, plan Redis memory for large responses, and keep versions aligned as the deployment grows.
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