RavenDB Architecture Overview

Key Components

• Document Store: JSON document persistence engine
• Indexing Engine: Lucene-based, auto-indexing and custom indexes
• Cluster Coordination: Raft-based consensus for node coordination
• Subscriptions: Event-driven change listeners for ETL and real-time processing

Deployment Models

RavenDB supports single-node, replicated, and sharded topologies. In enterprise systems, clusters span multiple regions and nodes, requiring precise tuning for consistency, performance, and fault tolerance.

Common Issues in Enterprise RavenDB Deployments

1. Stale Reads and Indexing Lag

By default, queries may hit stale indexes to ensure low latency, leading to eventual consistency surprises.

// Potentially stale read
var results = session.Query<Order>()
  .Where(x => x.Status == "Pending")
  .ToList();

ToList() doesn't wait for the index to update unless explicitly specified.

Fix

var results = session.Query<Order>()
  .Customize(x => x.WaitForNonStaleResults())
  .Where(x => x.Status == "Pending")
  .ToList();

2. High Memory Usage from Indexing

Custom indexes that load related documents or use heavy projections can consume significant memory and CPU.

from order in docs.Orders
select new {
  order.CustomerName,
  Items = order.Lines.Select(l => LoadDocument(l.ProductId).Name)
}

This causes memory pressure during background indexing and can starve system resources.

Fix

• Reduce document loads in indexes
• Precompute and store projections in documents
• Split large indexes into smaller, targeted ones

3. Cluster Replication Delays

In multi-node setups, replication lag can cause data mismatch across nodes, especially under high write load.

Symptoms:

• Subscriptions missing documents
• Reads returning outdated data
• Replication retries in logs

Resolution

• Monitor /admin/replication dashboard for node lag
• Increase write assurance settings (session.Advanced.UseOptimisticConcurrency)
• Tune Replication.MaxItemsCount and batch size

Diagnostics and Observability

Enable Detailed Logging

Set RavenDB log mode to Information or Verbose in production for:

• Index build durations
• Replication retries and errors
• Subscription acknowledgements

Profiling Indexes

Use the Studio's Index Performance view to track index cost and duration. Watch for:

• High map time
• Frequent re-indexing
• Excessive document loads

Query Timing Metrics

Use session.Advanced.ShowTimings() to profile query cost breakdowns:

var results = session.Advanced.DocumentQuery<User>()
  .AddOrder("Name", descending: false)
  .ToList();

var timings = session.Advanced.GetTimings();

Architectural Anti-Patterns

Using RavenDB as a Relational Store

Overuse of LoadDocument() in indexes or queries can replicate JOIN-heavy behavior, hurting performance.

Large Documents and Attachments

Storing large BLOBs directly in documents can inflate I/O latency and cache pressure. Use attachments or external file stores (e.g., S3) for binary assets.

Unbounded Subscriptions

Subscriptions that don't acknowledge or fail frequently can lead to retry storms or dropped events.

Remediation Strategy

1. Audit all custom indexes and disable unused ones
2. Enable index side-by-side build to avoid downtime during updates
3. Use WaitForNonStaleResults() selectively for consistency-critical reads
4. Avoid heavy LoadDocument() usage in index definitions
5. Split large collections with different access patterns

Best Practices

• Model documents with denormalized, aggregate-friendly structures
• Use aggressive caching with change vector awareness
• Configure health checks using RavenDB's node status endpoints
• Implement back-pressure logic in subscriptions
• Scale out writes with optimized batch size and index priority tuning

Conclusion

RavenDB provides powerful NoSQL capabilities suitable for complex, distributed systems, but hidden performance and consistency pitfalls must be addressed proactively. Understanding its indexing model, replication mechanics, and session behavior is critical in diagnosing production issues. With proper observability, cautious use of features like LoadDocument, and architectural alignment to its document-centric nature, teams can ensure that RavenDB deployments remain robust, performant, and scalable across environments.

FAQs

1. Why are my queries returning outdated data?

RavenDB returns from indexes which may be stale. Use WaitForNonStaleResults() if you require up-to-date reads at the cost of latency.

2. Can I JOIN documents like in SQL?

No. RavenDB supports denormalization and related document loading, but JOINs should be modeled via aggregation or projection patterns.

3. How do I handle large files in RavenDB?

Use the attachment feature rather than embedding BLOBs directly into documents to maintain efficient storage and retrieval.

4. What causes high memory usage in RavenDB?

Memory is often consumed by indexing operations, large documents, or high concurrent queries. Monitor memory dashboards and profile index activity.

5. How can I ensure consistent reads in a cluster?

Use session customization with WaitForNonStaleResults(), enable optimistic concurrency, and configure replication assurance levels across nodes.