Index optimization strategies and common issues

Index Optimization Strategies and Common Issues

Indexes are the core means of optimizing query performance in MongoDB. Proper index design can significantly improve query efficiency, while improper indexes may lead to performance degradation or even system crashes. MongoDB supports various types of indexes, including single-field indexes, compound indexes, multikey indexes, text indexes, etc., each with its applicable scenarios and optimization techniques.

Index Types and Selection Strategies

Single-field indexes are the most basic type of index and are suitable for frequently queried individual fields. For example, creating an index on the username field in a users collection:

db.users.createIndex({ username: 1 })  

Compound indexes are suitable for multi-condition query scenarios, where the order of index fields is critical. MongoDB follows the leftmost prefix principle, meaning queries must include the leftmost field of the index to utilize it. For example:

db.orders.createIndex({ customerId: 1, createdAt: -1 })  

This index can optimize the following queries:

db.orders.find({ customerId: "123", createdAt: { $lt: ISODate() } })  
db.orders.find({ customerId: "123" }).sort({ createdAt: -1 })  

Index Optimization Techniques

Covered queries are the most efficient query method. When all fields in a query are included in the index, MongoDB can retrieve data directly from the index without accessing the documents. For example:

db.products.createIndex({ category: 1, price: 1 })  
db.products.find({ category: "electronics" }, { _id: 0, category: 1, price: 1 })  

Index selectivity refers to the concept of how distinct the values of an indexed field are. Fields with high selectivity are more suitable for indexing. For example, the email field is generally more suitable for indexing than the gender field because its values are more unique.

Common Index Issues and Solutions

Overusing indexes can degrade write performance because each write operation requires updating all related indexes. A collection should typically not exceed 5-6 indexes.

Unused indexes are another common issue. The explain() method can be used to analyze query execution plans:

db.orders.find({ status: "shipped" }).explain("executionStats")  

The winningPlan field in the output will show whether an index was used and which one.

Index size is also a concern, as excessively large indexes consume significant memory. The following command can be used to check index size:

db.collection.totalIndexSize()  

Index Optimization for Special Scenarios

For array fields, MongoDB creates index entries for each array element, which can lead to index bloat. Multikey indexes are suitable for array fields but should be used with caution:

db.blog.createIndex({ tags: 1 })  

TTL indexes are a special type of index used to automatically delete expired documents:

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

Text indexes support full-text search but significantly increase index size:

db.articles.createIndex({ content: "text" })  

Index Monitoring and Maintenance

Regularly monitoring index usage is essential. The $indexStats aggregation operator can be used to obtain index usage statistics:

db.collection.aggregate([ { $indexStats: {} } ])  

Unused indexes should be promptly removed to reduce storage and maintenance overhead:

db.collection.dropIndex("index_name")  

When creating indexes on large collections, consider doing so during off-peak hours. The background build option can be used:

db.collection.createIndex({ field: 1 }, { background: true })  

Indexes and Sharded Clusters

In a sharded cluster environment, the choice of shard key is particularly important because it determines data distribution. An inappropriate shard key may lead to uneven data distribution (hotspot issues). For example:

sh.shardCollection("db.orders", { customerId: 1, orderId: 1 })  

Queries on sharded collections should ideally include the shard key; otherwise, they may result in scatter-gather operations (executing the query across all shards), severely impacting performance.