AEM and Elasticsearch for custom full-text search
Adobe Experience Manager excels at authoring and delivering structured content, but its built-in queries are not a substitute for a dedicated full-text retrieval engine. Organisations needing ranking, phrase matching, faceting and typo tolerance across thousands of pages can use AEM and Elasticsearch as a separated authoring and search pair. The central pattern is AEM and Elasticsearch: building a custom search index for full-text queries, a fitting topic for the AEM specialists who gather at CIRCUIT.
AEM should remain the source of truth. Editors use familiar authoring tools, content is approved and published there, and permissions stay attached to it. Elasticsearch receives a shaped copy for discovery; it does not become another content repository or expose unpublished material.
A workable design connects publication events to an indexing pipeline, stores stable identifiers and normalised text, then exposes search through an AEM-owned API. The API applies authorisation, locale rules and result formatting before returning data to websites, apps or internal tools, keeping front ends independent of Elasticsearch infrastructure.
Australian teams must also consider cloud regions, latency and market vocabulary. A Sydney publisher may run its primary search service in Australia with a secondary deployment in Melbourne, while a Brisbane team serving Perth and Darwin balances responsiveness with operational simplicity. Decisions should follow measured traffic, data classification and recovery requirements.
Choose the content boundary
Start with a content inventory, not a request to “index everything”. Select the content types users need, identify authoritative fields and define extraction from rich text, XHTML or JSON. Give each record a stable identifier, retain its AEM path and locale, and capture a source checksum.
AEM models shape both schema and updates. The content fragment comparison helps teams distinguish structured, reusable content from delivery-oriented experience fragments. An index may combine fragment fields with related page text, while metadata retains the originating path, model type and publication state.
Keep local content identifiable. An Australian retailer might index buying guides alongside Australian Customs tariff classifications, but the records have different owners and lifecycles. Give tariff feeds, supplier documents and campaign pages separate retention rules and search weights. This keeps updates precise when customs data changes without redrawing the editorial catalogue.
Build a repeatable indexing pipeline
Use AEM publication events as triggers, not the only consistency mechanism. A listener can enqueue a job with the content path, operation and source version; a Sling Job Consumer extracts the document and performs an idempotent Elasticsearch upsert. Pass deletions and unpublish operations through the same queue as tombstones, keeping network calls out of authoring requests.
Events can be delayed, duplicated or reordered, so the consumer must be defensive. Compare incoming versions with indexed checksums, use the AEM identifier as the document ID and make repeated jobs harmless. A reconciliation pass should compare published content with indexed records, repair omissions and retire missing documents. Full reindexing should use a planned alias switch.
Measure document size, update rate and acceptable delay before choosing queue capacity. Large launches may need a dedicated worker pool and back-pressure; smaller sites can use limited concurrency. Keep transformation code versioned and testable, because a change to HTML extraction or tokenisation can alter every result even without authoring edits.
Tune relevance for real Australian queries
Map exact-match fields separately from analysed text. Titles, product codes, domains and phrases such as “Experience Manager” need keyword precision, while descriptions benefit from an English analyser with sensible stemming. Store unmodified values for highlighting and test Australian spellings, abbreviations and internal terminology.
Local behaviour can be subtle. A shopper seeking “colour”, “organisation” or “favourites” should not lose results through incorrect regional rules. Stores also need relevant treatment for .com.au domains, AU$ prices, GST-inclusive totals and names shared across Australian and New Zealand catalogues. Use synonyms only for actual customer language; avoid lists that make “AU” and “NZ” interchangeable. Evaluate zero-result searches with site support staff.
Relevance needs version control and feedback. Keep labelled queries with expected documents, record the Elasticsearch version, analyser configuration and tuning changes, then run the set before release. Track clicks, refinements and successful searches as aggregate signals. Where personalisation is involved, keep identifiers out of the text index and separate behavioural data from content access.
Serve search through AEM
Front-end code should call an AEM servlet, Sling Model or equivalent endpoint rather than connect directly to Elasticsearch. The server component can validate queries, impose result limits, apply access filters and return a stable contract. This keeps cluster details, credentials and scoring logic out of browser bundles while providing a reusable component for AEM templates and apps.
Construct requests with multi_match or match_phrase across weighted fields, then express locale, content type, date and taxonomy as filters. Use fuzziness narrowly: it can rescue a misspelt product name but also distort an ambiguous acronym. Limit clauses and pagination, and escape highlighted snippets. Distinguish an empty result from an unavailable service so pages can offer a fallback.
The endpoint also needs caching and observability. Cache only results that respect the caller’s permissions, and use a correlation ID across AEM, search and analytics. For broader query-pattern examples, the WCSQ reference library offers a useful checkpoint before choosing a DSL shape. Sample requests for latency, errors and zero-result trends without retaining sensitive queries indiscriminately.
Secure updates and multi-region operations
Use least privilege at every boundary. The indexing worker should read only required content paths, while the query service has read-only access to active indexes. Keep service credentials in the approved secrets system, rotate them and record administrative changes. Never place indexing credentials in an AEM bundle or expose them through client-side configuration.
Map AEM access controls to a compact search representation, then test against real group structures. Indexing an entire audience field does not guarantee that downstream systems interpret it correctly. For sensitive content, consider indexing a reference or redacted body and fetching authorised content from AEM after the match. Australian organisations should assess the Australian Privacy Principles, data minimisation and cross-border processing against their policy commitments.
Multi-region search needs explicit consistency and recovery choices. A primary Elasticsearch cluster in Sydney and a warm standby in Melbourne can reduce regional outage exposure, but replication lag means new content may be missing briefly elsewhere. If the site accepts that trade-off, configure and test a read preference or failover. Infrastructure material on AEM Terraform patterns can help define deployment steps, health checks, secrets and rollback order.
Measure quality and operational health
Measure the search journey, not just the cluster dashboard. Useful indicators include indexed-versus-published latency, indexing failures, 95th- and 99th-percentile search latency, result coverage, zero-result rate and meaningful engagement. Break figures down by locale, device and content type, and avoid exposing individual queries without consent. Alert on symptoms users feel, including empty results after deployment.
Run load tests with representative Australian conditions. Combine normal traffic with campaign bursts and heavier mobile use from regional areas. A Brisbane service can look healthy locally while Perth users experience poor interaction latency if assets and APIs cross several network boundaries. Test multiple locations, cache-hit and cache-miss states, and degraded Elasticsearch behaviour to confirm that AEM can serve a fallback.
Treat the index as a versioned product. Publish schema, analyser and synonym changes through staged deployment, retain the previous index for rollback and reconcile after restoration. Record which AEM models invalidate a document and who approves emergency reindexing. Restoration drills should prove the team can rebuild from source, not merely restore a snapshot that depends on a missing pipeline artefact.
Start the first implementation with one high-value content type and a representative query set. Map its fields, publication events and permissions, then build an indexer that can be rerun safely. An internal pilot can expose poor tokenisation, irrelevant matches and operational gaps before public release.
Treat the release as a managed service. Agree ownership for content, relevance, infrastructure and privacy; set latency and freshness targets; schedule reconciliation and quality reviews. With those controls, AEM retains editorial authority, Elasticsearch delivers flexible discovery, and Australian users receive results that are faster, clearer and securely matched to their needs.