Performance Optimization in Azure AI Search

Azure AI Search enables developers to build high-performance search applications. However, as data grows, ensuring optimal speed and efficiency becomes challenging. This guide explores advanced techniques to optimize query performance and indexing efficiency with real-world examples and code snippets.

Optimizing Index Configurations for Faster Queries

Choose the Right Field Types

Selecting the correct field types reduces storage overhead and improves query performance.

{
  "name": "productName",
  "type": "Edm.String",
  "searchable": true,
  "filterable": false,
  "sortable": true
}

• Use Edm.String for text fields.
• Use Edm.Int32 or Edm.Double for numerical data.
• Set fields as searchable, filterable, or sortable based on query needs.

Optimize Index Size

• Avoid excessive filterable or sortable fields.
• Use facetable fields only where necessary.
• Remove unused fields to minimize index size.

Enhancing Query Performance

Implement Efficient Query Filtering

• Use $filter to refine queries and reduce dataset size.

GET https://your-search-service.search.windows.net/indexes/products/docs?
api-version=2023-07-01-preview&$filter=price ge 100 and price le 500

• Filter fields should be indexed as filterable for better efficiency.

Optimize Query Execution with $select

Reduce payload size by selecting only required fields.

GET https://your-search-service.search.windows.net/indexes/products/docs?
api-version=2023-07-01-preview&$select=name,price,category

Improve Scoring Profiles

Enhance relevance ranking with custom scoring profiles.

{
  "name": "customScoring",
  "functionAggregation": "sum",
  "functions": [
    {
      "type": "freshness",
      "fieldName": "createdAt",
      "boost": 2
    }
  ]
}

• Boost recent products with higher relevance.
• Adjust boost values based on user search intent.

Caching for Faster Search Results

Caching helps reduce query latency and improves response times by storing frequently accessed data.

Enable Azure Front Door or Azure CDN for Caching

• Use Azure Front Door or Azure CDN to cache search responses closer to users.
• Reduces repeated queries to Azure AI Search, improving performance.

{
  "caching": 
   { 
     "enabled" : true, 
     "ttl" : 300
   }
  }

Leverage Application-Level Caching

• Use Redis Cache or Azure Cache for Redis to store frequent queries.
• Implement a TTL (Time-to-Live) strategy to refresh stale data.
• Use Sliding Expiration to extend cache lifetime when frequently accessed.
• Retrieves results from Redis if available; otherwise, fetches from Azure AI Search and caches them.

Scaling Azure AI Search for Large Datasets

Choosing the Right Service Tier

• Basic & Standard – Suitable for small to medium datasets.
• Standard 3 & Storage Optimized – Best for high-volume queries.

Managing Replicas and Partitions

• Increase Replicas – Enhances query throughput.
• Increase Partitions – Improves index storage capacity.

Monitoring and Troubleshooting Performance Issues

Using Azure Monitor and Logs

Enable diagnostic logs to track query performance.

az monitor diagnostic-settings create \
--name "SearchMetrics" \
--resource "your-search-service" \
--metrics "AllMetrics" \
--logs "AllLogs"

Analyzing High-Latency Queries

• Use Azure Metrics Explorer to track query duration.
• Identify slow queries and optimize filters and indexes.

Improving Indexing Performance

Use Bulk Indexing for Faster Data Ingestion

• Use batch uploads for better performance.

POST https://your-search-service.search.windows.net/indexes/products/docs/index?api-version=2023-07-01-preview
Content-Type: application/json
{
  "value": [
    { "@search.action": "upload", "id": "1", "name": "Laptop", "price": 1000 },
    { "@search.action": "upload", "id": "2", "name": "Phone", "price": 500 }
  ]
}

• Avoid sending single document updates frequently.
• Batch documents in chunks of 1,000 for optimal speed.

Implement Incremental Updates

Reduce unnecessary re-indexing with partial updates.

PATCH https://your-search-service.search.windows.net/indexes/products/docs/index?api-version=2023-07-01-preview
Content-Type: application/json
{
  "value": [
    { "@search.action": "merge", "id": "1", "price": 900 }
  ]
}

• Only update changed fields instead of reindexing entire documents.

Tools and Resources for Optimization

• Azure Metrics Explorer – Monitor query latency and indexing speed.
• Azure Cognitive Search REST API – Automate search configurations.
• Application Insights – Identify performance bottlenecks.

Conclusion

Optimizing Azure AI Search ensures faster query execution, efficient indexing, and scalable performance. Implement these strategies to improve search relevance and user experience.

Need Expert Guidance?

Ansi ByteCode LLP specializes in Azure AI Search optimization. Contact us for tailored solutions to enhance your search performance.

Do feel free to Contact Us or Schedule a Call to discuss any of your projects

Author : Ms. Hinal Kaneriya