EXPERTISE / 05
AEM Search & Elasticsearch Engineering
Search engineering begins with the questions users ask and the content they are allowed to see. I connect AEM content, indexing and search APIs while keeping repository access, user-facing relevance and emerging AI retrieval as distinct responsibilities.
Established search experience: Elasticsearch integration, filters, query optimization and APIs. Vector search, hybrid retrieval and RAG remain architecture / PoC exploration. No Elasticsearch cluster-administration or production AI ownership is claimed.
ASSESSMENT FLOW
- Published AEM content
- Indexing contract
- Elasticsearch
- Search API
- Filters / results
SEARCH EXPERIENCE
Separate indexing from the user-facing query contract
A search API should expose the filters, pagination and result fields a consumer needs without turning arbitrary client input into unrestricted backend queries. Define locale handling, sort behavior, empty results and invalid filters as part of the interface.
The indexing contract specifies stable identifiers, searchable fields, facets and publication state. Search is a derived view of content, so updates, deletion and unpublication are as important as the first successful indexing run.
- Validate and bound query inputs; distinguish full-text relevance from exact-match filters.
- Use deterministic identifiers and retry-safe updates, with a way to reconcile missed events.
- Preserve permission boundaries through indexing and retrieval, not only in the interface.
REPOSITORY BOUNDARY
When is Oak the right query engine?
A repository query for application content and a search experience for visitors are different workloads. Oak index selection and query plans matter when extracting or locating AEM content; Elasticsearch does not eliminate that upstream responsibility.
Inspect the query, restrictions and actual plan before changing an Oak index. An external index introduces synchronization lag and another operating dependency. It is justified by retrieval requirements, not by the assumption that every repository query needs an external search engine.
VALIDATION
Measure retrieval quality before tuning for speed
Build a small, reviewed set of representative queries: ambiguous terms, rare terms, locale variants and combinations of facets. Record the expected relevant documents and investigate why unexpected results rank highly. Do not invent latency targets or relevance scores before establishing the baseline.
Exercise the complete path under a slow search dependency, an unavailable endpoint and a stale index. Bound timeouts, distinguish a dependency failure from no matches, and provide enough correlation data to follow a request across the integration.
- Check result consistency after publish, update and unpublish events.
- Compare payload cost and pagination behavior with realistic content volumes.
- Validate a reindex or schema-change procedure without assuming operational ownership of the cluster.
ARCHITECTURE / AI EXPLORATION
Extend retrieval only when the evaluation supports it
Semantic and vector retrieval introduce embedding choices, chunking and evaluation requirements. Hybrid retrieval adds a ranking-combination decision; RAG adds grounding and answer-generation failure modes. These are distinct from a conventional Elasticsearch integration.
My exploration starts with governed content and a lexical baseline, then asks whether an additional retrieval path helps specific queries. Permissions, provenance, deletion propagation and evaluation remain acceptance gates. A plausible architecture diagram is not evidence of a production AI system.
ENGINEERING NOTES