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‎docs/MongoDB/Advanced MongoDB/_category.json

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{
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"label": "Advanced MongoDB",
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"position": 25,
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"link": {
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"type": "generated-index",
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"description": "Dive deeper into advanced MongoDB concepts and techniques."
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}
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}

‎docs/MongoDB/Advanced MongoDB/mongodb-advanced-indexing.md

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---
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id: mongodb-advanced-indexing
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title: MongoDB - Advanced Indexing
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sidebar_label: Advanced Indexing
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sidebar_position: 6
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tags: [mongodb, indexing, array indexing, sub-document indexing]
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description: Learn how to create advanced indexes in MongoDB, including indexing array fields and sub-document fields for optimized query performance.
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---
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## Indexing Array Fields
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We have inserted the following document in the collection named `users`:
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```javascript
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db.users.insert(
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{
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"address": {
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"city": "Los Angeles",
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"state": "California",
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"pincode": "123"
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},
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"tags": [
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"music",
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"cricket",
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"blogs"
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],
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"name": "Tom Benzamin"
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}
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)
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```
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The above document contains an `address` sub-document and a `tags` array.
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### Creating an Index on Array Fields
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Suppose we want to search user documents based on the user’s tags. For this, we will create an index on the `tags` array in the collection.
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Creating an index on an array in turn creates separate index entries for each of its fields. So in our case, when we create an index on the `tags` array, separate indexes will be created for its values `music`, `cricket`, and `blogs`.
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To create an index on the `tags` array, use the following code:
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```javascript
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db.users.createIndex({"tags":1})
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```
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```json
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{
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"createdCollectionAutomatically" : false,
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"numIndexesBefore" : 2,
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"numIndexesAfter" : 3,
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"ok" : 1
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}
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```
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After creating the index, we can search on the `tags` field of the collection like this:
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```javascript
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db.users.find({tags:"cricket"}).pretty()
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```
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```json
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{
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"_id" : ObjectId("5dd7c927f1dd4583e7103fdf"),
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"address" : {
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"city" : "Los Angeles",
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"state" : "California",
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"pincode" : "123"
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},
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"tags" : [
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"music",
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"cricket",
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"blogs"
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],
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"name" : "Tom Benzamin"
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}
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```
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To verify that proper indexing is used, use the following `explain` command:
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```javascript
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db.users.find({tags:"cricket"}).explain()
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```
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This gives you the following result:
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```json
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{
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"queryPlanner" : {
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"plannerVersion" : 1,
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"namespace" : "mydb.users",
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"indexFilterSet" : false,
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"parsedQuery" : {
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"tags" : {
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"$eq" : "cricket"
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}
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},
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"queryHash" : "9D3B61A7",
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"planCacheKey" : "04C9997B",
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"winningPlan" : {
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"stage" : "FETCH",
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"inputStage" : {
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"stage" : "IXSCAN",
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"keyPattern" : {
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"tags" : 1
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},
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"indexName" : "tags_1",
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"isMultiKey" : false,
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"multiKeyPaths" : {
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"tags" : [ ]
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},
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"isUnique" : false,
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"isSparse" : false,
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"isPartial" : false,
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"indexVersion" : 2,
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"direction" : "forward",
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"indexBounds" : {
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"tags" : [
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"[\"cricket\", \"cricket\"]"
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]
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}
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}
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},
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"rejectedPlans" : [ ]
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},
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"serverInfo" : {
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"host" : "Krishna",
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"port" : 27017,
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"version" : "4.2.1",
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"gitVersion" : "edf6d45851c0b9ee15548f0f847df141764a317e"
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},
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"ok" : 1
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}
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```
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The above command resulted in `"stage" : "IXSCAN", "indexName" : "tags_1"` which confirms that proper indexing is used.
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## Indexing Sub-Document Fields
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Suppose that we want to search documents based on `city`, `state`, and `pincode` fields. Since all these fields are part of the `address` sub-document field, we will create an index on all the fields of the sub-document.
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For creating an index on all the three fields of the sub-document, use the following code:
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```javascript
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db.users.createIndex({"address.city":1,"address.state":1,"address.pincode":1})
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```
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```json
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{
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"numIndexesBefore" : 4,
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"numIndexesAfter" : 4,
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"note" : "all indexes already exist",
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"ok" : 1
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}
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```
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Once the index is created, we can search for any of the sub-document fields utilizing this index as follows:
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```javascript
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db.users.find({"address.city":"Los Angeles"}).pretty()
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```
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```json
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{
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"_id" : ObjectId("5dd7c927f1dd4583e7103fdf"),
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"address" : {
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"city" : "Los Angeles",
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"state" : "California",
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"pincode" : "123"
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},
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"tags" : [
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"music",
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"cricket",
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"blogs"
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],
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"name" : "Tom Benzamin"
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}
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```
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Remember that the query expression has to follow the order of the index specified. So the index created above would support the following queries:
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```javascript
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db.users.find({"address.city":"Los Angeles","address.state":"California"}).pretty()
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```
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```json
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{
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"_id" : ObjectId("5dd7c927f1dd4583e7103fdf"),
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"address" : {
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"city" : "Los Angeles",
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"state" : "California",
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"pincode" : "123"
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},
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"tags" : [
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"music",
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"cricket",
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"blogs"
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],
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"name" : "Tom Benzamin"
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}
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```
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### Diagram (Mermaid)
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Here is a visual representation of the document structure and indexing process:
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```mermaid
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graph TD;
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A[Document: users]
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A --> B[Sub-document: address]
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A --> C[Array: tags]
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B --> D[city]
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B --> E[state]
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B --> F[pincode]
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C --> G[music]
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C --> H[cricket]
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C --> I[blogs]
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```
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### Notes
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- Indexing array fields in MongoDB creates separate index entries for each value in the array.
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- Indexing sub-document fields allows efficient queries on nested fields.
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- Ensure the query order matches the index order to fully utilize the indexes.
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### Table
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| Field | Type | Indexed | Description |
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|-----------------|------------------|---------|------------------------------------------|
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| `address` | Sub-document | Yes | Contains nested fields for address info. |
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| `address.city` | String | Yes | City name in the address. |
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| `address.state` | String | Yes | State name in the address. |
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| `address.pincode`| String | Yes | Pincode in the address. |
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| `tags` | Array of Strings | Yes | User's tags for indexing. |

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