mongodb-indexing-optimization

Master MongoDB indexing and query optimization. Learn index types, explain plans, performance tuning, and query analysis. Use when optimizing slow queries, analyzing performance, or designing indexes.

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Repository: pluginagentmarketplace/custom-plugin-mongodb
Letzte Quellaktivität: 30. Dezember 2025 um 11:45
Erkannte Sprache von SKILL.md: Englisch
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Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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name	mongodb-indexing-optimization
version	2.1.0
description	Master MongoDB indexing and query optimization. Learn index types, explain plans, performance tuning, and query analysis. Use when optimizing slow queries, analyzing performance, or designing indexes.
sasmp_version	1.3.0
bonded_agent	04-mongodb-performance-indexing
bond_type	PRIMARY_BOND
capabilities	["index-design","explain-analysis","query-optimization","performance-profiling","esr-rule-application"]
input_validation	{"required_context":["query_pattern","collection_info"],"optional_context":["explain_output","current_indexes","collection_size"]}
output_format	{"index_recommendation":"object","explain_interpretation":"string","performance_impact":"string","trade_offs":"array"}
error_handling	{"common_errors":[{"code":"IDX001","condition":"COLLSCAN in explain output","recovery":"Create index on filter fields following ESR rule"},{"code":"IDX002","condition":"Index not being used","recovery":"Check query shape, hint usage, or index prefix"},{"code":"IDX003","condition":"Too many indexes","recovery":"Review index usage stats, remove unused indexes"}]}
prerequisites	{"mongodb_version":"4.4+","required_knowledge":["basic-queries","explain-command"],"performance_baseline":["Know current query latencies before optimization"]}
testing	{"unit_test_template":"// Verify index is used\nconst explain = await collection.find(query).explain('executionStats')\nexpect(explain.executionStats.executionStages.stage).toBe('IXSCAN')\nexpect(explain.executionStats.totalDocsExamined).toBeLessThanOrEqual(limit)\n"}

MongoDB Indexing & Optimization

Master performance optimization through proper indexing.

Quick Start

Create Indexes

// Single field index
await collection.createIndex({ email: 1 });

// Compound index
await collection.createIndex({ status: 1, createdAt: -1 });

// Unique index
await collection.createIndex({ email: 1 }, { unique: true });

// Sparse index (skip null values)
await collection.createIndex({ phone: 1 }, { sparse: true });

// Text index (full-text search)
await collection.createIndex({ title: 'text', description: 'text' });

// TTL index (auto-delete documents)
await collection.createIndex({ createdAt: 1 }, { expireAfterSeconds: 3600 });

List and Analyze Indexes

// List all indexes
const indexes = await collection.indexes();
console.log(indexes);


 collection.();


 collection.();

// Analyze query execution
const explain = await collection.find({ email: 'test@example.com' }).explain('executionStats');

console.log(explain.executionStats);
// Shows: executionStages, nReturned, totalDocsExamined, executionTimeMillis

// Index on one field
db.collection.createIndex({ age: 1 })

// Query uses index if searching on age
db.collection.find({ age: { $gte: 18 } })

// Index on multiple fields - order matters!
db.collection.createIndex({ status: 1, createdAt: -1 })

// Queries that benefit:
// 1. { status: 'active', createdAt: { $gt: date } }
// 2. { status: 'active' }
// But NOT: { createdAt: { $gt: date } } alone

// Automatically created for arrays
db.collection.createIndex({ tags: 1 })

// Matches documents where tags contains value
db.collection.find({ tags: 'mongodb' })

// Full-text search
db.collection.createIndex({ title: 'text', body: 'text' })

// Query
db.collection.find({ $text: { $search: 'mongodb database' } })

// 2D spherical for lat/long
db.collection.createIndex({ location: '2dsphere' })

// Find nearby
db.collection.find({
  location: {
    $near: { type: 'Point', coordinates: [-73.97, 40.77] },
    $maxDistance: 5000
  }
})

// Query: find active users, sort by created date, limit age
db.users.find({
  status: 'active',
  age: { $gte: 18 }
}).sort({ createdAt: -1 })

// Optimal index:
db.users.createIndex({
  status: 1,      // Equality
  createdAt: -1,  // Sort
  age: 1          // Range
})

const explain = await collection.find({ email: 'test@example.com' }).explain('executionStats');

// IXSCAN = Good (index scan)
// COLLSCAN = Bad (collection scan)
console.log(explain.executionStats.executionStages.stage);

// Query results entirely from index
db.users.createIndex({ email: 1, name: 1, _id: 1 })

// This query is "covered" - no need to fetch documents
db.users.find({ email: 'test@example.com' }, { email: 1, name: 1, _id: 0 })

from pymongo import ASCENDING, DESCENDING

# Create index
collection.create_index([('email', ASCENDING)], unique=True)

# Compound index
collection.create_index([('status', ASCENDING), ('createdAt', DESCENDING)])

# Explain plan
explain = collection.find({'email': 'test@example.com'}).explain()
print(explain['executionStats'])

# Drop index
collection.drop_index('email_1')