Efficient Querying - CosmosDB Indexing and Querying

Syntax

To query efficiently in Cosmos DB, we need to use indexing on the properties that we frequently use in the queries. Indexing is used to store the value of a specific property in a separate data structure that is optimized for querying. Indexing in Cosmos DB is defined by creating index policies on the containers and indexing paths on the properties.

"indexes": [
  {
    "kind": "Range",
    "dataType": "String",
    "precision": -1,
    "path": "/propertyName/?"
  }
]

Example

Suppose we have a container "users" in Cosmos DB, which contains the following documents:

[
  {
    "id": "1",
    "name": "Alice",
    "age": 25,
    "gender": "female"
  },
  {
    "id": "2",
    "name": "Bob",
    "age": 30,
    "gender": "male"
  },
  {
    "id": "3",
    "name": "Charlie",
    "age": 20,
    "gender": "male"
  },
  {
    "id": "4",
    "name": "David",
    "age": 35,
    "gender": "male"
  }
]

Suppose we want to query for all the documents with age greater than 25. We can create an index policy on the "users" container to index the "age" property. We can also create a composite index on the "age" and "gender" properties to optimize the query performance even further.

"indexes": [
  {
    "kind": "Range",
    "dataType": "Number",
    "precision": -1,
    "path": "/age/?"
  },
  {
    "kind": "Range",
    "dataType": "String",
    "precision": -1,
    "path": "/gender/?"
  }
]

We can then use the following query to get the desired results:

SELECT * FROM c WHERE c.age > 25

Output

The query returns the following output:

[
  {
    "id": "2",
    "name": "Bob",
    "age": 30,
    "gender": "male"
  },
  {
    "id": "4",
    "name": "David",
    "age": 35,
    "gender": "male"
  }
]

Explanation

Creating indexes on frequently used properties can greatly improve the query performance in Cosmos DB. By using indexes, the query engine can avoid scanning the entire container and directly access the required documents. Composite indexes can further optimize the query performance by combining multiple properties in a single index.

Use

We should use indexing in Cosmos DB for the following:

• To improve query performance by avoiding full scans of containers
• To reduce RU consumption by minimizing query execution time
• To enforce uniqueness constraints on properties
• To optimize queries that combine multiple properties

Important Points

• We can create index policies on containers to apply indexes to all properties or specific indexing paths.
• Indexing paths can be defined using dot-separated syntax for nested properties.
• There are different types of indexes such as Range, Hash, and Spatial indexes, each optimized for a particular data type and query pattern.
• Indexing can result in higher storage costs due to maintaining separate index structures.
• Indexing can affect write performance due to the additional overhead of maintaining indexes.
• We can monitor index usage and optimize indexes periodically to improve query performance.

Summary

Creating indexes on frequently used properties is essential for efficient querying in Cosmos DB. By using indexing, we can optimize query performance and reduce RU consumption. We should carefully choose the right indexing paths and types of indexes based on our specific use cases. Monitoring index usage and optimizing indexes periodically can further improve query performance in Cosmos DB.