Aem query optimization

AEM Query Optimization

Purpose

Write, analyze, and optimize JCR queries (SQL2, XPath, QueryBuilder) for maximum performance including proper index utilization, result set management, and query pattern best practices.

When to Use (Triggers)

• User mentions "query," "QueryBuilder," "SQL2," "XPath," or "search"
• References to slow queries, traversal warnings, or query explain plans
• Questions about search predicates, result pagination, or faceted search
• Requests involving content queries for components, servlets, or workflows
• Discussion of query performance, index usage, or query limits

Core Capabilities

• Write optimized queries using QueryBuilder API, JCR-SQL2, and XPath
• Analyze query explain plans and optimize for index utilization
• Implement efficient pagination, sorting, and result limiting
• Design search experiences with facets, suggestions, and fulltext
• Convert between query languages and select the most efficient approach

Domain Knowledge Required

Technical Foundation

• JCR query languages: SQL2, XPath, and their capabilities
• Query optimizer behavior: cost estimation, index selection, traversal
• Pagination patterns: offset vs. keyset, cursor-based pagination
• Full-text search concepts: analyzers, stemming, boost, relevance

AEM-Specific Context

• QueryBuilder API predicates and parameter syntax
• QueryBuilder PredicateEvaluator extension mechanism
• AEM Query Debugger tool (query explain, timing)
• Oak query engine traversal limits and configuration
• Index cost estimation and query plan selection
• Common OOTB indexes and their covered query patterns

Implementation Approach

Step 1: Query Requirements

Define what data needs to be retrieved and how.

• Identify the content type and properties to query
• Determine filtering conditions (path, type, property values, date ranges)
• Define sorting requirements and sort index availability
• Establish result size expectations and pagination needs

Step 2: Query Writing

Craft the query using the appropriate language.

• Use QueryBuilder for dynamic, programmatic query construction
• Use JCR-SQL2 for complex joins or subqueries
• Apply path restrictions to narrow query scope
• Include nodetype restrictions for Oak index optimization
• Limit results explicitly (p.limit in QueryBuilder)

Step 3: Explain Plan Analysis

Validate query uses indexes effectively.

• Run query through AEM Query Debugger (Tools → Query Performance)
• Check explain plan for "traverse" vs. "index" execution
• Verify estimated cost and actual node traversal count
• Identify missing indexes for covered properties

Step 4: Performance Optimization

Apply optimization techniques to problematic queries.

• Add path restrictions to reduce search scope
• Ensure property used in WHERE clause exists in an index
• Use native full-text constraints for text search (not LIKE '%x%')
• Implement keyset pagination instead of offset for large result sets
• Cache query results when data freshness allows

Step 5: Production Safeguards

Prevent queries from impacting system performance.

• Set query traversal limits appropriate for the use case
• Implement query timeout handling in application code
• Add fallback behavior for query limit exceeded scenarios
• Monitor production query performance and traversal counts

Quality Checklist

• Query uses Oak index (explain plan shows no traversal)
• Path restriction applied and as narrow as possible
• Results limited — no unbounded queries in production
• Pagination implemented efficiently (keyset for large sets)
• Query tested with production-scale data volume
• No LIKE '%value%' patterns (use full-text instead)
• Sort clause covered by index ordering
• Fallback behavior handles query timeout/limit exceeded

Related Skills

• aem-oak-repository-optimization (index management)
• aem-performance-tuning-profiling (query in performance context)
• aem-debugging-log-analysis (query debugging)

Example Use Cases

1. Content Listing Component: Implement a dynamic content listing with QueryBuilder using path, tag, and date range filters with pagination, sorting by publish date, and result count display.
2. Asset Search Service: Build a DAM search service supporting metadata search, file type filtering, size ranges, and faceted results using custom Lucene index with analyzed fields for partial matching.
3. Workflow Query Optimization: Fix a content approval workflow querying all pending items across the repository by adding path/type restrictions, proper indexing, and result limiting to prevent traversal on 1M+ node trees.

Notes

• QueryBuilder adds overhead compared to raw JCR-SQL2 — for performance-critical code, use SQL2 directly
• Offset-based pagination degrades linearly — keyset pagination maintains constant performance
• AEM Cloud Service enforces traversal limits strictly — queries must use indexes or they will fail
• LIKE predicates with leading wildcards ('LIKE %term%') cannot use indexes — use full-text contains()