Elasticsearch & Kibana

Elasticsearch is a distributed search and analytics engine built on Apache Lucene.

Kibana is a visualization and management UI for Elasticsearch data.

Elasticsearch stores/searches data; Kibana explores/visualizes/manages it.

Logical namespace containing related documents.

JSON object stored and indexed in Elasticsearch.

Key in a document with a typed value used for search/aggregation.

Schema definition describing field types and indexing behavior.

They control correctness, query behavior, and storage/performance characteristics.

Automatic field type inference when new fields appear.

Mapping explosion and incorrect inferred types.

Partition of an index (Lucene index unit).

Original shard holding authoritative indexed data segment.

Copy of primary shard for high availability and read scaling.

Fault tolerance and increased search throughput.

Group of nodes working together as one logical system.

Single Elasticsearch server instance in a cluster.

Green/yellow/red indicating shard allocation state.

Green: all shards allocated; yellow: replicas unassigned; red: primaries unassigned.

Data structure mapping terms to documents for fast full-text search.

Pipeline that transforms text into searchable tokens.

Character filters, tokenizer, token filters.

Splitting text into terms/tokens during analysis.

Exact value field (not analyzed) for filtering/sorting/aggregations.

text for full-text search; keyword for exact matching and aggregations.

Numeric ranking value indicating document match quality.

JSON-based query language for Elasticsearch searches.

Full-text query using analyzer semantics.

Exact term lookup (commonly for keyword/numeric fields).

Combines must/should/filter/must_not clauses.

Filter is non-scoring and cache-friendly; must affects scoring.

Analytics computation over documents (counts, stats, buckets).

Terms aggregation for top values by frequency.

Find docs where field values fall within bounds.

Pattern-based term matching (can be expensive).

Returning result subsets via from/size or search_after.

Deep pagination cost grows significantly.

Interface for ad-hoc document exploration and filtering.

Collection of visualizations for monitoring/analysis.

Chart/table/metric built from Elasticsearch queries/aggregations.

Kibana definition selecting indices/fields for analysis.

Global time range constraint for time-based data exploration.

Narrows search scope for speed and relevance.

Preprocessing documents before indexing (enrich/transform/parse).

Pipeline tool often used to ingest/transform data into Elasticsearch.

Lightweight shippers (Filebeat/Metricbeat/etc.) for data collection.

Using default mappings for all fields without planning.

Storing extremely high-cardinality fields without purpose.

Replicas enabled and basic snapshot strategy.

Right field types and controlled shard count.

Authentication, TLS, and role-based access enabled.

Backup mechanism storing index data/metadata in repository.

Primary disaster recovery method for clusters.

Copy/transform documents from one index to another.

Schema evolution and index migration tasks.

Logical name pointing to one or more indices.

Decouple application index name from physical index versions.

Create new write index when size/age/doc thresholds reached.

Manage index growth and retention efficiently.

Design mappings and lifecycle before scaling ingestion.

Treat search schema as a product contract.

Index same field in multiple ways (e.g., text + keyword).

Support both full-text and exact-match aggregations on same data.

Language-specific stemming, synonyms, edge n-grams, normalization.

Analyzer-like processing for keyword fields (no tokenization).

Expands equivalent terms during analysis or query time.

Incorrect expansion can reduce precision and relevance quality.

Reducing words to root forms for broader matching.

Tolerance for edit distance typos.

Better recall vs potentially higher query cost/noise.

Controls required should-clause match proportion/count.

Increasing influence of fields/clauses on relevance ranking.

Custom scoring based on numeric signals/functions.

Model arrays of objects preserving per-object field relationships.

Avoid cross-object matching errors in arrays of objects.

Relationship model across documents in same index (specialized use).

Flexibility vs query/indexing complexity and overhead.

Field computed at query time without full reindex.

Flexibility vs higher query-time cost.

Columnar storage for sorting/aggregations/script access.

Efficient aggregations and sorting on disk-backed structures.

Heap-based data structure for text aggregations/sorts (costly).

High memory usage; prefer keyword multi-fields.

Frequency making indexed docs searchable (near real-time behavior).

Faster visibility vs higher indexing overhead.

Write-ahead transaction log for durability/recovery.

Immutable index file unit created during indexing.

Background compaction of segments for efficiency.

I/O and CPU spikes affecting query/index performance.

Reusable settings/mappings/aliases for new indices or data streams.

Composable reusable part of index template.

Index Lifecycle Management automating rollover, warm/cold/delete phases.

Cost control and retention governance at scale.

Time-series abstraction managing backing indices automatically.

Optimized write-once append-heavy time-series lifecycle workflows.

Built-in transform step (grok, date, geoip, set, remove, etc.).

Pattern-based text parsing into structured fields.

Elasticsearch scripting language for updates, scoring, transforms.

Performance and security implications if overused/unbounded.

Protective limit preventing out-of-memory from expensive operations.

Cache for filter context results to accelerate repeated queries.

Cache for aggregation-heavy identical requests (conditions apply).

Efficient deep pagination using sort values from previous page.

search_after for user pagination; scroll for large batch extraction/reprocessing.

Consistent search snapshot context for paginated queries.

Logs slow indexing/search operations for tuning diagnostics.

Tiered node roles/storage classes by data age/access frequency.

Optimize cost-performance across retention lifecycle.

Too many tiny shards causing overhead and poor performance.

Right-size shard count based on data volume and query patterns.

Redistributing shards across nodes for balance/resilience.

Shard placement respecting zones/racks for fault tolerance.

User-friendly drag-and-drop visualization builder.

Rule engine for threshold/query/anomaly notifications.

Send notifications/webhooks/tickets on conditions.

Role-based permissions for indices, features, spaces.

Logical UI separation for dashboards/saved objects/access control.

Track query latency, indexing throughput, JVM heap, shard states.

TLS everywhere, least-privilege roles, audit logging.

Snapshot automation and restore drills.

Template/mapping standards and lifecycle policies.

Team can evolve mappings with minimal downtime.

Benchmark before and after major schema/query changes.

Use ILM tiers and prune unused fields aggressively.

Separate ingest-heavy and query-heavy workloads when needed.

Design for predictable performance under realistic load.

Manage cluster state updates and shard allocation decisions.

Improve cluster stability by isolating coordination workload.

Cluster partition leading to conflicting masters/state (mitigated by modern coordination).

Majority decisions protect consistency during failures.

Large mappings/indices can slow state publication and operations.

Template discipline, field limits, index count control.

Excessive unique fields causing memory/state/performance problems.

dynamic: strict/false, field caps, ingestion normalization.

Pre-sorting segments by fields to accelerate certain queries.

Faster query patterns vs indexing overhead and rigidity.

Approximate nearest-neighbor search over dense vector embeddings.

Combine lexical BM25 and vector semantic retrieval.

Balances precision of keyword with semantic recall.

Offline judgments + online metrics + iterative analyzer/query adjustments.

NDCG, precision@k, recall@k (evaluation-dependent).

Query multiple clusters from one coordinating endpoint.

Replicate indices between clusters for DR/locality.

Disaster recovery, geo-distributed read locality.

Automates snapshot scheduling and retention.

Coordinate backup and retention across data lifecycle.

Create new index version, reindex, swap alias atomically.

Instant cutover with rollback option.

Stable logical write target pointing to current active index.

Input rate exceeds indexing capacity causing lag/failures.

Queue buffering, bulk tuning, scaling ingest nodes, pipeline optimization.

Efficient batched indexing/update/delete request format.

Too small wastes overhead; too large increases memory/latency risk.

Temporarily reduce refresh frequency/replicas for faster ingestion (with risk controls).

Low-selectivity/high-churn queries reduce cache effectiveness.

Detailed breakdown of query execution phases for optimization.

mTLS, fine-grained roles, encrypted snapshots, network segmentation.

Restrict access to specific fields/docs by roles/policies.

PII retention, right-to-delete, auditability, regional regulations.

Index only required fields and redact sensitive content at ingest.

One tenant’s heavy indexing/query load degrades others.

Separate clusters, quotas, index-level controls, workload isolation.

Lifecycle tiers, frozen/cold storage, shard optimization, query budgets.

Dashboard/query sprawl causing backend load spikes.

Review dashboards, enforce ownership, archive unused assets.

Use logs/metrics/traces correlation and saved investigations/runbooks.

Combine Elasticsearch logs with metrics/traces for triage context.

Treating Elasticsearch as primary OLTP database for heavy transactions.

Use Elasticsearch for search/analytics; keep source of truth in transactional store.

Snapshots, tested restore, CCR where needed, infra-as-code rebuild paths.

Backup confidence requires proven recovery execution.

Plan shard/layout/lifecycle for failure scenarios, not ideal conditions.

Tune mappings, queries, and shard strategy with production-like benchmarks.

Enforce least privilege and encryption across ingest, storage, and access.

Schema and dashboard changes must be reviewed/versioned like code.

Retention and tiering should reflect actual query value.

Scale horizontally with clear node roles and workload separation.

Continuously evaluate relevance with real user behavior and test sets.

Instrument cluster health and automate runbooks for common failures.

Platform, data, and app teams co-own schema and search outcomes.

Elasticsearch/Kibana excellence is secure, relevant, and operationally disciplined search at scale.