Understanding the Meteor Stack

Architecture Overview

Meteor uses a pub/sub model with a distributed data protocol (DDP) over WebSockets. It syncs MongoDB data reactively to clients using live queries. Each client's subscription is tracked on the server, consuming memory and CPU.

Common Symptoms of Reactive Overload

• High server memory usage per connected client
• MongoDB CPU spikes with no write-intensive workload
• Excessive CPU usage in Node processes during idle periods
• Laggy or frozen clients during updates to unrelated data

Root Causes of Meteor Performance Degradation

1. Over-Subscribing to Data

Clients subscribing to multiple publications with overlapping or unbounded queries cause excessive reactive invalidation tracking.

2. Inefficient MongoDB Queries in Publications

Using large fetches or non-indexed fields in publications results in full collection scans. Every document update triggers reactivity on all matching clients.

3. Memory Bloat from LiveQuery Observers

Each subscription creates a LiveQuery observer on the server. Poorly scoped queries (e.g., {}) can spawn observers that hold thousands of documents in memory per user.

4. DDP Message Flooding

Frequent database writes or user-generated updates flood the DDP channel with update messages, overwhelming the client and the network transport layer.

Diagnostic Strategies

Step-by-Step Troubleshooting Flow

1. Enable Meteor APM: Use tools like Kadira, Monti APM, or Meteor APM to track publication performance.
2. Monitor MongoDB queries: Enable profilingLevel: 2 on MongoDB and inspect slow queries related to publications.
3. Profile subscriptions: Log active subscriptions and their associated query selectors. Look for overbroad queries.
4. Check LiveQuery handles: Use Meteor.server._publishHandlers and custom logging to track active observers.
5. Use heap snapshots: Attach Chrome DevTools or Node inspector to analyze memory growth in the server process.

Example: Logging Subscription Selectors

Meteor.publish('orders', function() {
  console.log('Subscription from:', this.userId);
  return Orders.find({ status: 'active' });
});

Common Pitfalls

• Subscribing to entire collections without pagination
• Not cleaning up subscriptions on client-side route changes
• Failing to use fields projection in publications
• Overusing observeChanges in server-side methods for live dashboards

Remediation and Long-Term Fixes

1. Optimize Publications

Scope queries tightly. Always project with fields to reduce payload size. Paginate results using limit and skip patterns.

2. Switch to Methods for Non-Reactive Data

Use Meteor methods to fetch static data that does not require real-time updates. This avoids LiveQuery overhead entirely.

3. Throttle Writes to Reduce DDP Overhead

Batch writes or debounce client actions that trigger database updates. Consider using meteorhacks:unblock to free fibers for high-throughput methods.

4. Use Redis Oplog and Scaling Tools

Use cultofcoders:redis-oplog to offload LiveQuery invalidation logic to Redis, improving pub/sub performance significantly at scale.

Best Practices

• Always use query indexes in publications
• Limit use of reactive joins; pre-process data where possible
• Use onStop hooks to clean up long-lived observers or background tasks
• Test with realistic load using tools like meteor-down or Artillery

Conclusion

Meteor's reactive architecture is powerful but demands discipline at scale. Without carefully scoped publications and subscription hygiene, applications may experience memory leaks, CPU overload, or client instability. By auditing subscriptions, optimizing publication queries, and adopting non-reactive patterns for static content, teams can build resilient Meteor applications that maintain performance under high concurrency. Integrating Redis oplog and proper monitoring completes a production-grade Meteor deployment.

FAQs

1. How can I detect if my app has too many LiveQuery observers?

Use APM tools or custom logging to track active publication observers. If each user session opens multiple long-lived subscriptions, memory will grow rapidly.

2. Why is MongoDB CPU usage high in my Meteor app?

Unindexed queries or reactive re-fetching from publications can cause MongoDB to scan large collections frequently. Ensure proper indexes and avoid over-broad selectors.

3. Can I disable reactivity for certain data?

Yes. Use Meteor methods for one-time fetches or static content. This bypasses DDP and LiveQuery completely.

4. Is Redis oplog suitable for all Meteor apps?

Redis oplog is ideal for apps with high read and write volumes. It significantly improves scalability but adds Redis as an infrastructure dependency.

5. How do I simulate production load in Meteor?

Use tools like meteor-down or Artillery to simulate concurrent connections and subscription behavior. Combine with APM metrics to profile performance.