高性能MongoDB查询的高级索引策略

复合索引字段顺序至关重要，需遵循前缀匹配规则并优先放置等值查询字段；2. 覆盖查询可避免文档读取，提升速度，需确保查询和投影字段均在索引中；3. 部分索引仅索引必要数据，节省空间且加速查询，适用于固定子集访问模式；4. TTL索引自动清理过期数据，减少冗余提升性能；5. 索引交集可用但非最优，关键路径应使用单一复合索引。

When you're working with large datasets in MongoDB, query performance can make or break your application. While MongoDB handles indexing well out of the box, advanced indexing strategies are essential for high-performance queries—especially as your data scales or your access patterns become more complex.

Here’s what actually matters in practice:

1. Compound Indexes: Order Matters

A compound index combines multiple fields. The order of fields in the index definition directly affects which queries can use it efficiently.

• Prefix Matching Rule: MongoDB can only use the beginning part of a compound index.
  For example, if you have an index on { status: 1, createdAt: -1, userId: 1 }:
  • ✅ Can optimize: { status: "active" }
  • ✅ Can optimize: { status: "active", createdAt: { $gt: ... } }
  • ❌ Cannot optimize: { createdAt: ..., userId: ... } (skips status)

? Tip: Put the most selective (high-cardinality) field first if you’re filtering broadly, but prioritize fields used in equality matches before range or sort operations.

2. Covered Queries: Skip Document Fetches Entirely

If all the data your query needs is already in the index, MongoDB won’t hit the actual documents—this is a covered query and is lightning fast.

Example:

db.orders.find(
  { status: "shipped" },
  { orderId: 1, status: 1, _id: 0 }
)

With index: { status: 1, orderId: 1 } → ✅ Covered
Without it → ❌ Must fetch full documents

? Key Insight: Always include only indexed fields in projection when possible. Use explain("executionStats") to confirm "totalDocsExamined": 0.

3. Partial Indexes: Save Space, Boost Speed

Only index documents that matter. If 90% of your queries target active users, don’t waste space/indexing inactive ones.

db.users.createIndex(
  { email: 1 },
  { partialFilterExpression: { isActive: true } }
)

Benefits:

• Smaller index → faster seeks
• Lower RAM usage
• Faster writes (fewer index entries to update)

? Use when query patterns are predictable and target a subset of data.

4. TTL Indexes for Auto-Cleanup

For time-sensitive data like logs, sessions, or caches—use TTL indexes to auto-expire documents.

db.sessions.createIndex({ createdAt: 1 }, { expireAfterSeconds: 3600 })

No cron jobs needed. MongoDB’s background thread handles cleanup automatically.

? Great for reducing clutter and keeping working set small—critical for performance.

5. Use Index Intersection Wisely

MongoDB can combine multiple indexes to satisfy a single query (index intersection). But it’s not always optimal—it can be slower than a single compound index.

Check explain() output:

• If you see "stage": "FETCH" after "IXSCAN" on multiple indexes → intersection in play
• Often better to create one well-designed compound index instead

? Don’t rely on intersection for critical paths—optimize with intentional indexing.

Bonus: Avoid Common Indexing Traps

• Too many indexes? Each one slows down inserts/updates. Monitor with db.collection.totalIndexSize().
• Unused indexes? Use db.currentOp() or Atlas Performance Advisor to find them.
• Regex queries? Only efficient if they’re prefix-based (/^John/)—otherwise, they can’t use indexes effectively.

Bottom line:
Indexing isn’t just about adding more—it’s about adding smarter. Profile real queries, understand your access patterns, and test with explain().
These strategies aren’t just theoretical—they’re what separate a sluggish app from one that scales smoothly.

Basically, that’s it.

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