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Database / Azure Cosmos DB interview questions

How does indexing work in Azure Cosmos DB?

By default, Cosmos DB automatically indexes every property in every JSON item you insert — no schema definition, no CREATE INDEX statement required. This is called the automatic indexing policy. When you write a document, the indexing engine traverses the JSON tree and adds every field path, array element, and nested object to a set of inverted indexes maintained alongside the data. This is why write costs in Cosmos DB are higher than read costs per RU — every write triggers index updates.

The indexing policy is configured per container in JSON and controls three things:

  1. indexingModeconsistent (default, synchronous), lazy (asynchronous, lower write cost, not recommended), or none (disables indexing entirely).
  2. includedPaths — Explicit paths to index (e.g., /address/city/?). Wildcard /* indexes everything.
  3. excludedPaths — Paths to exclude from indexing (e.g., large text fields you never filter on). Excluding high-cardinality or rarely-queried large fields significantly reduces RU cost per write and storage overhead.

Three index types exist:

  • Range index — Default for scalar values. Supports =, <, >, BETWEEN, and ORDER BY.
  • Spatial index — For GeoJSON geometry types. Supports ST_DISTANCE, ST_INTERSECTS, and other geo queries.
  • Composite index — Required when an ORDER BY clause references two or more properties, or when a filter and ORDER BY target different properties. You must explicitly define composite indexes.

A common optimization: exclude the /_etag path and any large blob-like string fields from indexing by default to reduce write RU consumption on write-heavy containers.

What type of Cosmos DB index is required to support ORDER BY on two different properties in the same query?
What is the best way to reduce the RU cost per write on a container with many rarely-queried large text fields?

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