创建单字段索引和复合索引
您将创建单字段索引和复合索引，并通过 explain('executionStats') 观察查询计划的变化。
创建单字段索引和复合索引 是 CoddyKit 上的免费 MongoDB Academy 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 MongoDB Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MongoDB Academy 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
Single-Field Index Basics
A single-field index is the simplest index type: it tracks the values of exactly one document field in a sorted B-tree. You create one with db.collection.createIndex({ field: 1 }), where 1 means ascending order and -1 means descending. Single-field indexes are ideal for queries that filter or sort on just one field.
// Create a single-field ascending index on 'email'
db.users.createIndex({ email: 1 });
// This query now hits the index instead of scanning every document
db.users.find({ email: 'alice@example.com' });Naming Your Indexes
MongoDB auto-generates an index name like email_1 or age_-1 from the field name and direction. You can override this with the name option to give meaningful labels to your indexes—especially helpful when managing many indexes in production or when the auto-generated name would exceed the 127-character limit imposed on compound indexes with many fields.
db.users.createIndex(
{ email: 1 },
{ name: 'idx_users_email' }
);
// List all indexes with their names
db.users.getIndexes();What Is a Compound Index?
A compound index tracks multiple fields together in a single B-tree. The entries are sorted first by the first field, then by the second field within each group of the first, and so on. This makes compound indexes far more selective and versatile than multiple single-field indexes for queries that filter or sort on several fields at once.
// Compound index on lastName (asc) then firstName (asc)
db.users.createIndex({ lastName: 1, firstName: 1 });
// This single index satisfies all three queries efficiently:
db.users.find({ lastName: 'Smith' });
db.users.find({ lastName: 'Smith', firstName: 'John' });
db.users.find({}).sort({ lastName: 1, firstName: 1 });The Prefix Rule for Compound Indexes
A compound index on { a, b, c } can serve queries on { a }, { a, b }, and { a, b, c }—these are called index prefixes. It cannot serve a query on just { b } or { c } alone because the B-tree is ordered by the first field first. Understanding the prefix rule helps you avoid creating redundant single-field indexes when a compound index already covers them.
db.orders.createIndex({ userId: 1, status: 1, createdAt: -1 });
// Supported by the compound index (prefixes):
db.orders.find({ userId: 'u1' });
db.orders.find({ userId: 'u1', status: 'pending' });
db.orders.find({ userId: 'u1', status: 'pending' }).sort({ createdAt: -1 });
// NOT supported - skips the first key
db.orders.find({ status: 'pending' }); // still does COLLSCANCreating a Compound Index
You create a compound index by passing an object with multiple fields to createIndex. Field order matters: put equality fields first (fields filtered with $eq or exact values), then range fields, then sort fields. This arrangement ensures the index is used for both filtering and sorting in one traversal, following the ESR (Equality, Sort, Range) principle.
// Orders queried by userId (equality), sorted by date (sort),
// then filtered by amount (range)
// ESR order: userId -> createdAt -> amount
db.orders.createIndex({ userId: 1, createdAt: -1, amount: 1 });
// Perfectly served by this index:
db.orders
.find({ userId: 'u123', amount: { $gt: 100 } })
.sort({ createdAt: -1 });Using explain() to See Index Usage
Always verify that your new index is actually being used with .explain('executionStats'). Look for winningPlan.stage: 'IXSCAN' to confirm index use, and check totalDocsExamined vs nReturned—a well-indexed query should examine roughly the same number of documents it returns.
const result = db.orders.find(
{ userId: 'u123' }
).explain('executionStats');
// Key fields to check:
// result.executionStats.executionStages.stage === 'IXSCAN'
// result.executionStats.totalDocsExamined
// result.executionStats.nReturnedBackground Index Builds
In MongoDB 4.2+, all index builds are non-blocking by default: they hold an exclusive lock only briefly at the start and end of the build, allowing reads and writes to continue during the lengthy build phase. On older versions you had to specify { background: true } explicitly. Building a large index can still consume significant CPU and I/O resources, so schedule builds during low-traffic periods in production.
// MongoDB 4.2+: non-blocking by default
db.bigCollection.createIndex({ category: 1 });
// Check ongoing index builds
db.currentOp({ 'command.createIndexes': { $exists: true } });Dropping Indexes
You can remove an index with db.collection.dropIndex(), passing either the index name or the key specification. Dropping unused indexes reduces write overhead and frees memory. The _id index cannot be dropped. Use dropIndexes() (plural) to drop all non-_id indexes at once—useful when rebuilding an index strategy from scratch.
// Drop by name
db.users.dropIndex('idx_users_email');
// Drop by key pattern
db.users.dropIndex({ email: 1 });
// Drop all except _id
db.users.dropIndexes();
// List remaining indexes
db.users.getIndexes();Index Statistics With $indexStats
$indexStats is an aggregation stage that shows how many times each index has been used since the mongod process last started. An index with zero accesses is a prime candidate for removal. Combine this data with the index size from db.collection.stats() to build a complete cost/benefit picture for each index in your collection.
db.orders.aggregate([
{ $indexStats: {} },
{ $project: {
name: 1,
'accesses.ops': 1,
'accesses.since': 1
}}
]);Compound vs Multiple Single Indexes
Multiple single-field indexes can sometimes be combined via index intersection, but MongoDB's planner prefers compound indexes when they exist. A single well-designed compound index is almost always faster and more predictable than relying on the planner to intersect two separate indexes. Create compound indexes for your most frequent multi-field queries rather than hoping intersection will save you.
// Less ideal: two separate indexes
db.products.createIndex({ category: 1 });
db.products.createIndex({ price: 1 });
// Better: one compound index for the common query pattern
db.products.createIndex({ category: 1, price: 1 });The 64 Indexes Per Collection Limit
MongoDB allows a maximum of 64 indexes per collection. This is more than enough for well-designed schemas, but if you approach this limit, it is a strong signal that something is wrong—perhaps you have many redundant indexes or a table-like design that doesn't fit the document model. Audit your index usage regularly and prune indexes that have zero $indexStats accesses.
// See how many indexes your collection has
const indexes = db.myCollection.getIndexes();
console.log('Index count:', indexes.length);
// Hard limit: 64 indexes per collection
// Approaching it? Audit with $indexStats firstQuick Check
Test your understanding of single-field and compound indexes in MongoDB.
Lesson Recap
In this lesson you learned: single-field indexes track one field in a sorted B-tree and support equality, range, and sort queries, compound indexes track multiple fields and must be queried using their prefixes, and ESR ordering (Equality, Sort, Range) maximises compound index effectiveness. Next up we explore special index properties like unique, sparse, partial, and TTL.
常见问题解答
「创建单字段索引和复合索引」课时是免费的吗？
是的 — 「创建单字段索引和复合索引」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 MongoDB Academy 课程的其余内容，请升级到 CoddyKit PRO。 MongoDB Academy 课程共包含 4 节课。
「创建单字段索引和复合索引」这节课中我会学到什么？
您将创建单字段索引和复合索引，并通过 explain('executionStats') 观察查询计划的变化。 你通过在浏览器中直接运行的动手代码来练习 MongoDB Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 MongoDB Academy 需要有经验吗？
无需任何先前经验。CoddyKit 上的 MongoDB Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。
「创建单字段索引和复合索引」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 MongoDB Academy 课中编写并运行代码吗？
能。每节 MongoDB Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。