Database mongodb

MongoDB Best Practices

Priority: P0 (CRITICAL)

Guidelines

• Schema Design:

  • Embed vs Reference:
    • Embed (1:Few): Addresses, Phone Numbers. Optimization: Read locality.
    • Reference (1:Many/Infinity): Logs, Activity History. Optimization: Document size limits (16MB).
  • Bucket Pattern: For time-series or high-cardinality "One-to-Many", bucket items into documents (e.g., DailyLog).

• Indexing:

  • ESR Rule: Equality, Sort, Range. Order your index keys (status, date, price) if you query status='A', sort by date, filter price > 10.
  • Text Search: Use $text search instead of $regex for keywords. $regex is slow (linear scan) unless anchored (^prefix).
  • Covered Queries: Project only indexed fields to avoid fetching the document (PROJECTION is key).
  • Explain Plan: Target nReturned / keysExamined ratio of ~1. If docsExamined >> nReturned, index is inefficient.

• Sharding (Horizontal Scale):

  • Shard Key: Avoid monotonically increasing keys (e.g., Timestamp, ObjectId) for high-write workloads (creates "Hot Shards"). Use Hashed Sharding or high-cardinality natural keys.

• Performance:

  • Avoid skip(): Use _id or sort-key based pagination (bucket_id > last_id). skip(10000) scans 10000 docs.
  • Aggregation: Prefer Aggregation Framework ($match, $group) over bringing data to client (JS).

• Operations:

  • Write Concern: Understand w:1 (Ack) vs w:majority (Safe).
  • Transactions: Use only when ACID across multiple documents is stricter than performance needs.

Anti-Patterns

• No unbounded arrays: Use $push with $slice or redesign using the Bucket Pattern.
• No client-side filtering: Project only needed fields; never fetch full docs to filter in memory.
• No deep nesting: Keep nesting ≤4 levels; flatten paths that are frequently queried.

References

• Best Practices Guide
• Anti-Patterns
• Postgres vs Mongo Comparison