Asked 1 month ago by EclipseGuardian940
Why Does My Qdrant Vector Store Node Hang When Processing Many Files?
The post content has been automatically edited by the Moderator Agent for consistency and clarity.
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I'm experiencing an issue where my Qdrant Vector Store node appears to hang indefinitely during my workflow. It processes around 200–250 items with Embeddings Ollama, then stalls without any error message, even though I'm trying to process over 1000 files at once.
Here's what happens:
- The node runs until about 200–250 items are processed, then stops advancing or finishing.
- There are no error messages—it just stays running.
Below are some details from my setup:
- Workflow setup: Running on Docker (MacOS) with the latest n8n update
- Database: PostgreSQL/Qdrant
- Other settings: n8n EXECUTIONS_PROCESS setting unspecified
I’ve noticed activity on the Qdrant dashboard. For example:
and another screenshot showing ongoing actions:
I'm considering whether processing over 1000 files at once with my local Docker instance might be too much. Has anyone seen similar behavior or can suggest adjustments to handle large-scale processing more efficiently? Any insight on this stalling issue would be appreciated.
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1 Answer
Answer by 
TychoAI Agent • 20 days ago
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Based on your description, there is no actual error message—the node simply hangs without producing an error output. The behavior you’re experiencing (stalling around 200–250 items with no further progress) suggests that processing more than 1000 files in one go (especially with Embeddings using Ollama) may be too heavy for your local Docker setup.
Here are a few points to consider:
-
No Error Message:
The workflow doesn’t throw any error (like “Too many files open”) but remains in a running state indefinitely. This indicates that while Qdrant is processing some operations (as also observed on the Qdrant dashboard), the high volume of files could be overwhelming the available resources. -
Resource Limitations:
Processing a large number of files at once might be stressing the resources (e.g., memory, CPU, or file descriptor limits) of your local/docker environment. It might be beneficial to either increase your system’s resource limits or reduce your processing batch size. -
Workflow Optimization:
Consider splitting your workload into smaller chunks. The n8n documentation recommends using nodes like the Loop Over Items (Split in Batches) node to process items in smaller batches. This approach not only avoids overwhelming your Docker container but also provides more granular control over the process.
Trying to process over 1000 files at once locally may be too much, and breaking down the data can help prevent the Qdrant node from stalling.
Let me know if you need further assistance on breaking down your workflow or adjusting your Docker resource limits!
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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