MongoDB Google cloud run error - Connection pool cleared for cluster<cluster_name> because another operation failed with: "operation was interrupted"

MongoDB Atlas
1

I am getting below error from MongoDb Atlas while running an API in google cloud run.

Connection pool cleared for cluster<cluster_name> because another operation failed with: “operation was interrupted”

Can someone please help what could be the issue is?

2

The error message “Connection pool cleared for cluster <cluster_name> because another operation failed with: ‘operation was interrupted’” indicates that MongoDB is experiencing connection pool disruptions. This could happen for several reasons when running MongoDB with Google Cloud Run. Here are some potential causes and troubleshooting steps:

Potential Causes:

  1. Timeouts or Long-Running Queries:
  • Cloud Run has execution time limits (depending on how you configured your service), and if MongoDB queries take too long, the connection might be interrupted.
  1. Resource Constraints (Memory/CPU):
  • Google Cloud Run instances could run out of memory or CPU resources, causing the connections to be reset.
  1. Network Issues:
  • Connection between Cloud Run and MongoDB (especially if hosted externally) might face intermittent network issues.
  1. Connection Pool Limits:
  • If you’re using many connections from Cloud Run, MongoDB’s connection pool might hit its limits, causing issues when new operations are initiated.
  1. MongoDB Driver Issues:
  • Your MongoDB client or driver might be outdated or configured improperly, causing issues during operations like retries or connection pooling.
  1. MongoDB Cluster:
  • There might be some cluster-related issues like node unavailability, failovers, or maintenance operations happening on your MongoDB deployment.

Troubleshooting Steps:

  1. Increase Timeout:
  • Try increasing the timeout for operations in your MongoDB client configuration.
const client = new MongoClient(uri, {
  serverSelectionTimeoutMS: 10000,  // Adjust the timeout value
});
  1. Check Cloud Run Resources:
  • Ensure that the Cloud Run instance has sufficient memory and CPU allocated to avoid resource exhaustion. You can scale the instance size or increase the available memory and CPU.
  1. Monitor Connections:
  • Monitor the number of connections in your MongoDB cluster using the MongoDB Atlas dashboard (or any self-hosted monitoring if not using Atlas). If you’re reaching connection limits, consider increasing the connection pool size.
  1. Retry Logic:
  • Implement retry logic in your MongoDB client connection, especially for transient network failures.
const client = new MongoClient(uri, {
  retryWrites: true,
  w: 'majority',
});
  1. Update MongoDB Driver:
  • Ensure that you are using the latest version of the MongoDB driver that is compatible with the version of MongoDB you are running.
  1. Check MongoDB Logs:
  • Look at the MongoDB logs for any additional information related to cluster issues, timeouts, or operations getting interrupted.
  1. Network Configuration:
  • If MongoDB is hosted externally (e.g., MongoDB Atlas), ensure that the Cloud Run service can connect to it reliably. This may include setting up a VPC, VPN, or allowing IP ranges from Cloud Run.
3

@Mayank_Anand2, for this one too, please update your post with the mention that you use some kind of Generative AI to generate your response? Just like you did in Performance Impact of 955 indexes in one database - #2 by Mayank_Anand2.

I think it is important for people to know. To me it is obvious it comes from AI since your 4 replies appears at the same time and uses the same nice formatting.

4

@Mayank_Anand2 Thanks for your help