Summary

Audit MongoDB adapter data patterns, indexes, and query patterns to identify performance bottlenecks and create a remediation plan for production readiness.

Problem

The MongoDB adapter works functionally but uses patterns that may not scale to production workloads:

• JSON payloads stored as strings instead of native BSON
• Custom id fields instead of _id
• Unknown index coverage for real query patterns
• No transactions around multi-step operations
• No performance benchmarks or capacity planning guidance

Before fixing these issues, we need to understand:
1. Which patterns are actually problematic vs theoretical concerns
2. What the real query patterns are (not just what we assume)
3. Where indexes are missing or suboptimal
4. Performance impact at realistic data volumes
5. What "production ready" means quantitatively (latency targets, throughput, data size)

Spike Goals

This spike will produce a technical design document that answers:

Data Model Analysis

• Review all MongoDB schemas across memory, RAG, vectors, observability, scores
• Document use of stringified JSON vs native BSON
• Identify foreign key relationships and how they're modeled
• Flag any schema anti-patterns

Index Coverage Analysis

• List all existing indexes in the MongoDB adapter
• For each top query pattern, determine if an index exists
• Identify missing indexes that would improve performance
• Flag redundant or unused indexes
• Check for covered queries vs index + collection scans

Transaction & Consistency Analysis

• List all multi-step operations (e.g., saveMessages + thread update)
• Document current concurrency/error handling
• Identify consistency risks (race conditions, partial failures)
• Recommend where transactions are needed vs nice-to-have

Out of Scope

This spike does NOT implement fixes — only research and planning. Implementation will be separate tickets based on this spike's findings.

Related Links

• MongoDB adapter source
• Postgres adapter benchmarks
• Mastra storage constants