Mastering Cosmos DB Indexing Strategies
This article delves into the various indexing strategies available in Azure Cosmos DB and provides guidance on how to choose the most effective approach for your application's needs. Effective indexing is crucial for optimizing query performance and reducing request costs in Cosmos DB.
Understanding Indexing in Cosmos DB
Azure Cosmos DB automatically indexes all data by default. This indexing is part of the automatic data ingestion process. You can control how Cosmos DB indexes your data by defining indexing policies. The default indexing policy indexes every property of every document. For optimal performance and cost efficiency, it's often beneficial to customize this policy.
Indexing Modes
Cosmos DB supports two primary indexing modes:
- Consistent: The index is updated synchronously with the data. This is the default mode and ensures that queries always return the most up-to-date results. It incurs a slight overhead during write operations.
- Lazy: The index is updated asynchronously after the data is committed. This mode can improve write performance but might result in slightly stale data for a brief period immediately after a write. Queries might not reflect the latest changes until the index is updated.
Indexing Policies
An indexing policy is a JSON document that defines how Cosmos DB indexes data. Key components include:
- Indexing Mode: As described above (Consistent or Lazy).
- Automatic: A boolean value that, when true, ensures all paths are indexed.
- Included Paths: Specifies which paths within your documents should be indexed.
- Excluded Paths: Specifies which paths should be excluded from indexing.
- Composite Indexes: Allows you to define indexes on multiple properties for more efficient composite queries.
- Spatial Indexes: Indexes for geospatial data types like Point, Polygon, and LineString.
Common Indexing Strategies
Here are some common strategies and when to use them:
1. Indexing All Paths (Default)
When to use: Applications with unpredictable query patterns or when you need maximum flexibility for ad-hoc queries.
Pros: Simplest to implement, no need to anticipate query patterns.
Cons: Can lead to higher storage costs and potential performance degradation if many paths are indexed unnecessarily.
2. Selective Indexing (Include Paths)
When to use: When you have a clear understanding of your application's query patterns and can identify specific fields that are frequently queried.
Example: Indexing only the userId and orderDate fields if most queries filter or sort by these properties.
{
"indexingMode": "consistent",
"automatic": false,
"includedPaths": [
{ "path": "/userId/?" },
{ "path": "/orderDate/?" }
]
}Pros: Reduces storage footprint, improves write and query performance by avoiding indexing of irrelevant data.
Cons: Requires upfront analysis of query patterns. If new query patterns emerge, the indexing policy may need to be updated.
3. Excluding Unnecessary Paths
When to use: When specific fields are large or very numerous and are never queried, or when they are only used for display purposes after retrieval.
Example: Excluding a large description field or a generic auditLog array.
{
"indexingMode": "consistent",
"automatic": true,
"excludedPaths": [
{ "path": "/description/?" },
{ "path": "/auditLog/*" }
]
}Pros: Significantly reduces storage costs and can improve ingestion performance.
Cons: If you later need to query an excluded path, you'll need to modify the policy and potentially re-index.
4. Composite Indexes
When to use: For queries that filter or sort on multiple properties simultaneously. These indexes can dramatically speed up such queries.
Example: A query filtering by category and then sorting by price.
{
"indexingMode": "consistent",
"automatic": false,
"compositeIndexes": [
[
{ "path": "/category", "order": "ascending" },
{ "path": "/price", "order": "ascending" }
]
]
}Pros: Highly efficient for multi-property queries.
Cons: Can increase index size and have a minor impact on write performance. Each composite index is treated as a separate index.
5. Spatial Indexes
When to use: Applications that perform location-based queries, such as finding points within a radius or proximity searches.
Example: Indexing a location field containing GeoJSON Point data.
{
"indexingMode": "consistent",
"automatic": false,
"spatialIndexes": [
{ "path": "/location", "type": "Point" }
]
}Pros: Enables efficient geospatial queries.
Cons: Adds to index size.
Best Practices and Considerations
- Analyze Your Queries: The most critical step is to understand how your application accesses data. Use diagnostic tools and logs to identify frequently executed queries and their filters/sort orders.
- Start Simple, Then Optimize: Begin with a default or slightly optimized policy and monitor performance. Only introduce more complex indexing strategies if you encounter performance bottlenecks.
- Trade-offs: Remember that every indexed path adds to storage costs and write overhead. Every composite index adds further complexity.
- Index Transformations: When you change your indexing policy, Cosmos DB performs an index transformation. This can take time and consume Request Units (RUs), so plan for it, especially in production environments.
- Limit Composite Indexes: While powerful, avoid creating too many composite indexes, as each one is a separate index and can impact performance and cost.
- Use Wildcards Wisely: Wildcards like
/*in excluded paths can be powerful but ensure you don't accidentally exclude necessary data.
Conclusion
Choosing the right indexing strategy for Azure Cosmos DB is a key factor in achieving high performance and cost-effectiveness. By carefully analyzing your application's query patterns and understanding the different indexing modes and policy options, you can significantly optimize your database operations.