Search indexer

Security impact is direct because an indexer reads source data and writes copied content into the search index. Credentials, managed identity assignments, connection strings, private endpoints, firewall rules, and Key Vault references must be reviewed together. The indexer may access data that application users cannot access directly, so field mappings and filters must preserve security context. Avoid broad shared secrets when managed identity works, and do not let enrichment outputs expose sensitive fields unnecessarily. Audit who can create, run, reset, or modify indexers, because that control affects data exposure. Reviewers should test source access after every credential, firewall, or identity change.

An indexer has no standalone bill, but it drives cost through source reads, search service load, enrichment skills, vectorization, storage growth, and operations effort. Aggressive schedules can create unnecessary churn, while slow schedules create support costs from stale results. Skillsets and projections can multiply records or run billable AI enrichment on many documents. FinOps review should track run frequency, documents processed, failed retries, average enrichment cost, index storage growth, and whether the freshness requirement justifies the schedule. The cheapest indexer is one that runs exactly as often as the business needs. That evidence prevents expensive reprocessing from becoming an unnoticed daily habit.

Reliability depends on indexer run success, source availability, retry behavior, and detection of changed or deleted records. If an indexer fails silently, users experience stale search, missing answers, or duplicated documents. Schedules should match business freshness needs, not arbitrary hourly defaults. Operators need alerts for failed runs, high warning counts, long durations, and unexpected document changes. A reliable indexer design includes small test sources, known sample records, run history review, and reset procedures. During migrations, verify that the indexer can recover after source throttling or schema drift. Freshness probes should alert before users discover missing records themselves. Operators should record baseline behavior and expected recovery evidence.

Performance impact appears during ingestion and later query behavior. Long indexer runs can delay freshness, consume service resources, and hide source access problems. Field mappings, skillsets, projections, vectorization, and large blobs can all increase processing time. On the query side, a well-run indexer improves perceived performance by ensuring users search current, correctly shaped documents. Monitor run duration, items processed per minute, failures, warnings, source throttling, and query freshness. If indexing cannot keep up with source changes, the workload has a performance problem even if queries remain fast. Capacity review should include source throttling and enrichment time, not query latency alone.

Operators work with indexers by checking execution history, last result status, error messages, warning counts, item failures, document counts, and schedule settings. They inspect data-source credentials, field mappings, skillset outputs, target index schema, and diagnostic logs. Routine tasks include running an indexer on demand, resetting it after mapping changes, comparing source and index counts, and documenting the expected freshness window. Azure CLI and az rest are practical for repeatable status checks, while detailed editing usually happens through REST, SDKs, or infrastructure templates. Runbooks should include expected duration, known warnings, and escalation contacts. The support path should include owner, evidence, and next action.