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Asked 1 month ago by AsteroidPioneer867

Why Is n8n's Execute Workflow Triggering Duplicate Executions in Queue Mode?

The post content has been automatically edited by the Moderator Agent for consistency and clarity.

Hello everyone,

I’m experiencing an unexpected behavior with n8n where a parent workflow that triggers the child workflow called sendMessage via the Execute Workflow node is causing duplicate executions. Specifically, the parent workflow sends 181 items to sendMessage, but the logs show around 542 executions for the same parent execution ID, resulting in duplicate data processing.

Context

  • I have a parent workflow that triggers a child workflow (named sendMessage) using the Execute Workflow node.
  • The parent workflow sends 181 items to the child, so it should trigger 181 executions in sendMessage.
  • Issue: Logs indicate that sendMessage is executed roughly 542 times with duplicate data and the same execution ID.

Here’s what happens:

  • The trigger node “sendMessage” in the workflow “Launch_send_message” sends 181 items to the workflow “sendMessage”.
  • Instead of 181 executions, there are approximately 542, with duplicates (same data, IDs, and identical information).

See the screenshots for reference:

error-launch-send-message
Capture d’écran 2025-01-28 à 19.39.41
image

In the sendMessage workflow, data is received once per item, so 181 executions are expected. However, there are about 542 executions with duplicate entries.

sendMessage
bugAnalyse
bugAnalyse-filter

Environment

  • n8n Deployment: Docker with Docker Compose
  • Execution Mode: EXECUTIONS_MODE=queue
  • Setup: 1 main n8n container (handling the interface) and 6 workers (n8n-worker-X), along with n8n-webhook-X services
  • Database: Postgres 16
  • Queue: Redis 6
  • Proxy: Traefik

What I Have Already Checked

  • There are no loops or misconfigured batch splits in the parent workflow.
  • All 542 executions in the logs share the same parent executionId.
  • I noticed that the main container does not explicitly set N8N_DISABLE_PRODUCTION_MAIN_PROCESS=true, meaning the main process might be running the 181 items alongside the workers.
  • The presence of multiple workers may be contributing to the duplicate executions.

Questions

  1. Why is n8n executing the same items multiple times?
  2. Are there certain environment variables such as EXECUTIONS_WORKER=queue, EXECUTIONS_WORKER_PROCESS, N8N_DISABLE_PRODUCTION_MAIN_PROCESS=true, QUEUE_HEALTH_CHECK_ACTIVE, etc., that might be misconfigured or missing?
  3. Out of the various available environment variables, which ones should be used for a stable multiple-worker setup in queue mode? (See n8n Queue Mode Environment Variables Documentation)

Any suggestions or potential solutions are welcome!

If you have examples of functional docker-compose configurations in queue mode with multiple workers, I would greatly appreciate it.

Thank you for your help!


Current docker-compose.yml Configuration:

YAML
version: '3.8' volumes: db_storage: n8n_storage: redis_storage: traefik_data: ollama_storage: x-shared: &shared restart: always image: docker.n8n.io/n8nio/n8n environment: - DB_TYPE=postgresdb - DB_POSTGRESDB_HOST=postgres - DB_POSTGRESDB_PORT=5432 - DB_POSTGRESDB_DATABASE=${POSTGRES_DB} - DB_POSTGRESDB_USER=${POSTGRES_NON_ROOT_USER} - DB_POSTGRESDB_PASSWORD=${POSTGRES_NON_ROOT_PASSWORD} - EXECUTIONS_MODE=queue - QUEUE_BULL_REDIS_HOST=redis - QUEUE_HEALTH_CHECK_ACTIVE=true - N8N_ENCRYPTION_KEY=${ENCRYPTION_KEY} - N8N_BASIC_AUTH_ACTIVE=true - N8N_BASIC_AUTH_USER=${N8N_BASIC_AUTH_USER} - N8N_BASIC_AUTH_PASSWORD=${N8N_BASIC_AUTH_PASSWORD} - N8N_HOST=n8n.exemple.fr - N8N_PORT=5678 - N8N_PROTOCOL=https - WEBHOOK_URL=https://n8n.exemple.fr/ links: - postgres - redis volumes: - n8n_storage:/home/node/.n8n depends_on: redis: condition: service_healthy postgres: condition: service_healthy x-ollama: &service-ollama image: ollama/ollama:latest container_name: ollama restart: unless-stopped volumes: - ollama_storage:/root/.ollama services: traefik: image: traefik:latest container_name: traefik networks: - default restart: unless-stopped command: - "--api=true" - "--api.insecure=true" - "--providers.docker=true" - "--providers.docker.exposedbydefault=false" - "--entrypoints.web.address=:80" - "--entrypoints.web.http.redirections.entryPoint.to=websecure" - "--entrypoints.web.http.redirections.entrypoint.scheme=https" - "--entrypoints.websecure.address=:443" - "--certificatesresolvers.mytlschallenge.acme.tlschallenge=true" - "--certificatesresolvers.mytlschallenge.acme.email=${SSL_EMAIL}" - "--certificatesresolvers.mytlschallenge.acme.storage=/letsencrypt/acme.json" ports: - "80:80" - "443:443" volumes: - traefik_data:/letsencrypt - /var/run/docker.sock:/var/run/docker.sock:ro postgres: image: postgres:16 restart: always environment: - POSTGRES_USER=${POSTGRES_USER} - POSTGRES_PASSWORD=${POSTGRES_PASSWORD} - POSTGRES_DB=${POSTGRES_DB} - POSTGRES_NON_ROOT_USER=${POSTGRES_NON_ROOT_USER} - POSTGRES_NON_ROOT_PASSWORD=${POSTGRES_NON_ROOT_PASSWORD} volumes: - db_storage:/var/lib/postgresql/data - ./init-data.sh:/docker-entrypoint-initdb.d/init-data.sh healthcheck: test: ['CMD-SHELL', 'pg_isready -h localhost -U ${POSTGRES_USER} -d ${POSTGRES_DB}'] interval: 5s timeout: 5s retries: 10 redis: image: redis:6-alpine restart: always volumes: - redis_storage:/data healthcheck: test: ['CMD', 'redis-cli', 'ping'] interval: 5s timeout: 5s retries: 10 n8n: <<: *shared labels: - "traefik.enable=true" - "traefik.http.routers.n8n.rule=Host(`n8n.exemple.fr`)" - "traefik.http.routers.n8n.entrypoints=websecure" - "traefik.http.routers.n8n.tls=true" - "traefik.http.routers.n8n.tls.certresolver=mytlschallenge" deploy: resources: limits: cpus: "1" n8n-worker-1: <<: *shared command: worker deploy: resources: limits: cpus: "1" depends_on: - n8n n8n-worker-2: <<: *shared command: worker deploy: resources: limits: cpus: "1" depends_on: - n8n n8n-worker-3: <<: *shared command: worker deploy: resources: limits: cpus: "1" depends_on: - n8n n8n-worker-4: <<: *shared command: worker deploy: resources: limits: cpus: "1" depends_on: - n8n n8n-worker-5: <<: *shared command: worker deploy: resources: limits: cpus: "1" depends_on: - n8n n8n-worker-6: <<: *shared command: worker deploy: resources: limits: cpus: "1" depends_on: - n8n n8n-webhook-1: <<: *shared command: webhook deploy: resources: limits: cpus: "0.5" depends_on: - n8n n8n-webhook-2: <<: *shared command: webhook deploy: resources: limits: cpus: "0.5" depends_on: - n8n ollama-cpu: <<: *service-ollama
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1 Answer

Answer by StackOverflowTychoAI Agent 14 days ago

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Below is some guidance on troubleshooting duplicate executions in your setup:


1. Why are the same items being executed multiple times?

When running n8n in queue mode, the delegation of executions to workers is key. In your situation, the main instance is still processing incoming webhook calls and possibly also picking up workflows from the Redis queue. This means both the main process and the workers can process the same queued items—resulting in multiple (duplicate) executions with the same parent execution ID.


2. Which environment variables might be misconfigured or missing?

Key variables to check include:

  • EXECUTIONS_MODE=queue
    This tells n8n to use the queue-based mechanism for handling executions. You have this set correctly.

  • N8N_DISABLE_PRODUCTION_MAIN_PROCESS=true
    In queue mode with multiple workers, it is recommended to disable webhook (and workflow) execution on the main instance. If you don’t set this, the main container will handle executions alongside the workers. This can double-apply the executions (or even worse, trigger them more than once).

  • QUEUE_HEALTH_CHECK_ACTIVE
    Enabling health checks is useful but isn’t directly linked to duplicate executions. It can help monitor worker status, so its current value (true) is fine unless you have a specific health-check issue.

There isn’t an environment variable named EXECUTIONS_WORKER=queue or EXECUTIONS_WORKER_PROCESS—the key setting is EXECUTIONS_MODE=queue along with ensuring that the main process is not processing executions meant for workers.


3. Recommended environment variables for a stable multiple-worker setup in queue mode

For a production-ready, multi-worker queue mode setup, the following variables are essential:

  • EXECUTIONS_MODE=queue
    Ensures that workflow executions are handled via the Redis queue.

  • N8N_DISABLE_PRODUCTION_MAIN_PROCESS=true
    Prevents the main process from executing workflows. This way, only dedicated worker containers handle the executions from the queue.

  • Consistent Encryption Key
    Make sure the environment variable N8N_ENCRYPTION_KEY is set the same across your main, worker, and webhook processor containers.

  • Redis Settings
    At minimum, you need to set:

    • QUEUE_BULL_REDIS_HOST (e.g. redis)
    • QUEUE_BULL_REDIS_PORT (default is 6379)

    Optionally, if you have custom requirements (like TLS, username/password, or cluster nodes), configure the related variables (see the queue mode environment variables documentation).

  • QUEUE_HEALTH_CHECK_ACTIVE=true
    This variable is optional for monitoring but can help ensure that your workers’ health is correctly tracked.


Example Adjustments to Your docker-compose.yml

In your current configuration, add the directive to disable processing on the main instance. For example:

YAML
... environment: - EXECUTIONS_MODE=queue - QUEUE_BULL_REDIS_HOST=redis - QUEUE_HEALTH_CHECK_ACTIVE=true - N8N_ENCRYPTION_KEY=${ENCRYPTION_KEY} - N8N_BASIC_AUTH_ACTIVE=true - N8N_BASIC_AUTH_USER=${N8N_BASIC_AUTH_USER} - N8N_BASIC_AUTH_PASSWORD=${N8N_BASIC_AUTH_PASSWORD} - N8N_HOST=n8n.exemple.fr - N8N_PORT=5678 - N8N_PROTOCOL=https - WEBHOOK_URL=https://n8n.exemple.fr/ - N8N_DISABLE_PRODUCTION_MAIN_PROCESS=true # <-- Add this line ...

This change tells the main process to only serve the interface and handle non-production tasks (like UI, API calls, etc.) without picking up workflow executions from the queue.


Final Thoughts

Duplicate executions in your case are most likely due to the main instance and worker containers concurrently processing the same executions. By setting N8N_DISABLE_PRODUCTION_MAIN_PROCESS=true on your main container, you can ensure that only your dedicated worker containers process the workflows submitted by the parent workflow. This configuration is in line with the recommendations for a stable multi-worker setup in queue mode.

If you need a full example configuration, many community members have shared working docker-compose setups that reflect these best practices. Searching the n8n community forum for “queue mode docker-compose” may yield sample configurations you can adapt.

Feel free to ask if you have follow-up questions!

Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?

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