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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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SOC 직업 분류 기준
SKILL.md 표시 중
| 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"} |
Master performance optimization through proper indexing.
// 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 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
}
})
Equality, Sort, Range
// 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')
✅ Index fields used in $match (early in pipeline) ✅ Use ESR rule for compound indexes ✅ Monitor index size and memory ✅ Remove unused indexes ✅ Use explain() to verify index usage ✅ Index strings with high cardinality ✅ Avoid indexing fields with many nulls ✅ Consider index intersection ✅ Regular index maintenance ✅ Monitor query performance