Learning path
Elasticsearch learning path: everything to study, topic by topic
Comprehensive Elasticsearch assessment covering core search concepts, indexing and mappings, full-text and structured querying, aggregations, cluster operations, performance tuning, ingest pipelines, index lifecycle management, security, and the ELK stack.
Short answer
To learn Elasticsearch, work through 6 modules and 22 topics in the order they build on each other. It starts with Fundamentals, then moves on to Indexing & Mappings and Querying. Use the map as a checklist, or measure your level and study only what is missing.
What you will study
Topics are grouped by module. Open one to see its objectives and key concepts.
1 · Fundamentals3 topics
Core Concepts
Inverted index, document-oriented storage, near real-time search, indices vs databases, nodes and clusters, Lucene foundation
Objectives
- Explain how an inverted index enables fast full-text search
- Describe the relationship between Elasticsearch and Apache Lucene
Key concepts
- inverted index
- document-oriented storage
- near real-time search
- index vs database
- node and cluster
- Lucene segments
- JSON documents
- schema-on-write
Documents & CRUD
Indexing, retrieving, updating, and deleting documents; bulk API, _source field, document versioning, optimistic concurrency control
Objectives
- Perform CRUD operations on Elasticsearch documents
- Use the bulk API for efficient batch operations
Key concepts
- index API
- get API
- update API
- delete API
- bulk API
- _source field
- document versioning
- optimistic concurrency control
REST API
HTTP methods for Elasticsearch, _cat APIs, cluster health endpoint, index management APIs, search API basics, query parameters
Objectives
- Navigate the Elasticsearch REST API confidently
- Use _cat APIs for quick cluster inspection
Key concepts
- HTTP methods (GET, PUT, POST, DELETE)
- _cat APIs
- _cluster/health
- index management APIs
- _search endpoint
- query string parameters
- request body search
- pretty printing
2 · Indexing & Mappings4 topics
Mappings
Explicit vs dynamic mappings, field data types (text, keyword, integer, date, geo_point, nested, object), multi-fields, mapping parameters, mapping updates and limitations
Objectives
- Design appropriate mappings for different data types
- Understand the difference between text and keyword fields
Key concepts
- explicit mapping
- dynamic mapping
- text vs keyword
- field data types
- multi-fields
- mapping parameters (index, doc_values, store)
- nested vs object
- mapping immutability
Analyzers
Analysis chain (character filters, tokenizer, token filters), built-in analyzers (standard, simple, whitespace, keyword), custom analyzers, testing with _analyze API
Objectives
- Build custom analyzers for specific search requirements
- Debug analysis results using the _analyze API
Key concepts
- analysis chain
- character filters
- tokenizer
- token filters
- standard analyzer
- custom analyzer
- _analyze API
- search_analyzer vs index analyzer
Index Settings
Static vs dynamic settings, number_of_shards, number_of_replicas, refresh_interval, codec settings, max_result_window, translog settings
Objectives
- Configure index settings for optimal performance
- Distinguish between static and dynamic settings
Key concepts
- static vs dynamic settings
- number_of_shards
- number_of_replicas
- refresh_interval
- codec settings
- max_result_window
- translog settings
- index.blocks
Index Templates
Index templates, component templates, template priority and composition, dynamic templates, index patterns, template versioning
Objectives
- Design reusable index templates with component templates
- Apply dynamic templates for flexible field mapping
Key concepts
- index templates
- component templates
- template priority
- template composition
- dynamic templates
- index patterns
- template versioning
- _template API
3 · Querying4 topics
Full-Text Search
match query, multi_match, match_phrase, match_phrase_prefix, query_string, simple_query_string, fuzziness, highlighting
Objectives
- Choose the right full-text query for different search scenarios
- Configure fuzziness and highlighting for user-friendly search
Key concepts
- match query
- multi_match query
- match_phrase query
- match_phrase_prefix
- query_string
- fuzziness
- highlighting
- operator (AND/OR)
Term-Level Queries
term, terms, range, exists, prefix, wildcard, regexp, ids queries; filtering vs scoring, keyword fields for exact match
Objectives
- Select appropriate term-level queries for structured data
- Understand when to use filter context for performance
Key concepts
- term query
- terms query
- range query
- exists query
- prefix query
- wildcard query
- filter context vs query context
- keyword fields
Compound Queries
bool query (must, should, must_not, filter), boosting query, constant_score, dis_max, function_score, nested query, has_child/has_parent
Objectives
- Compose complex queries using bool query clauses
- Apply function_score for custom relevance tuning
Key concepts
- bool query
- must clause
- should clause
- must_not clause
- filter clause
- boosting query
- function_score
- dis_max query
Relevance Scoring
TF-IDF, BM25, explain API, score boosting, field-level boosting, index-level boosting, custom scoring with scripts, rescore queries
Objectives
- Explain how BM25 computes relevance scores
- Use the explain API to debug scoring behavior
Key concepts
- BM25 algorithm
- TF-IDF
- explain API
- field boosting
- index boosting
- script scoring
- rescore queries
- score normalization
4 · Aggregations3 topics
Metric Aggregations
avg, sum, min, max, stats, extended_stats, cardinality, percentiles, percentile_ranks, value_count, geo_bounds, geo_centroid
Objectives
- Compute statistical summaries over document fields
- Understand cardinality estimation with HyperLogLog++
Key concepts
- avg aggregation
- stats aggregation
- cardinality aggregation
- percentiles aggregation
- value_count
- extended_stats
- geo_bounds
- scripted metrics
Bucket Aggregations
terms, date_histogram, histogram, range, filters, nested, significant_terms, composite, sampler, diversified_sampler
Objectives
- Group documents into meaningful buckets for analysis
- Use composite aggregation for paginated aggregation results
Key concepts
- terms aggregation
- date_histogram
- histogram aggregation
- range aggregation
- filters aggregation
- nested aggregation
- composite aggregation
- significant_terms
Pipeline Aggregations
Parent pipeline (derivative, cumulative_sum, moving_avg, serial_diff) and sibling pipeline (avg_bucket, max_bucket, min_bucket, sum_bucket, stats_bucket, percentiles_bucket)
Objectives
- Build multi-level aggregation pipelines for time-series analysis
- Understand the difference between parent and sibling pipeline aggregations
Key concepts
- parent pipeline aggregations
- sibling pipeline aggregations
- derivative aggregation
- cumulative_sum
- moving_avg / moving_fn
- avg_bucket
- stats_bucket
- buckets_path syntax
5 · Cluster Operations4 topics
Cluster Architecture
Node roles (master, data, coordinating, ingest, ml), cluster formation, discovery and quorum, split-brain prevention, dedicated master nodes
Objectives
- Design cluster topologies for different workload patterns
- Explain node roles and their responsibilities
Key concepts
- master node
- data node
- coordinating node
- ingest node
- cluster formation
- discovery module
- quorum-based consensus
- split-brain prevention
Sharding & Replication
Primary and replica shards, shard allocation, shard sizing strategies, routing, rebalancing, forced awareness, allocation filtering
Objectives
- Plan shard sizing strategies for indices of varying sizes
- Configure allocation awareness for high availability
Key concepts
- primary shard
- replica shard
- shard allocation
- shard sizing
- custom routing
- allocation awareness
- shard rebalancing
- allocation filtering
Monitoring & Diagnostics
Cluster health, node stats, index stats, hot threads, task management, slow logs, circuit breakers, pending tasks
Objectives
- Diagnose cluster health issues using monitoring APIs
- Configure slow logs for query performance analysis
Key concepts
- cluster health (green/yellow/red)
- node stats API
- index stats API
- hot threads API
- slow logs
- circuit breakers
- task management API
- pending cluster tasks
Performance Tuning
Indexing throughput optimization, search latency reduction, heap sizing, fielddata vs doc_values, caching strategies, merge policies, forcemerge
Objectives
- Optimize indexing throughput for high-volume data pipelines
- Tune search performance through caching and query optimization
Key concepts
- indexing throughput
- search latency
- JVM heap sizing
- fielddata vs doc_values
- query cache
- request cache
- merge policy
- forcemerge API
6 · Advanced Topics4 topics
Ingest Pipelines
Ingest node processing, processor types (grok, date, geoip, set, rename, script), pipeline chaining, conditional processing, failure handling, simulate API
Objectives
- Design ingest pipelines for data transformation at index time
- Handle processor failures gracefully with on_failure
Key concepts
- ingest pipeline
- processors (grok, date, geoip, set, rename)
- script processor
- pipeline chaining
- conditional processing (if)
- on_failure handling
- simulate API
- enrich processor
Index Lifecycle Management
ILM policies, hot-warm-cold-frozen-delete architecture, rollover, shrink, forcemerge actions, data streams, searchable snapshots
Objectives
- Design ILM policies for time-series data management
- Implement hot-warm-cold architecture for cost optimization
Key concepts
- ILM policy
- hot-warm-cold architecture
- rollover action
- shrink action
- forcemerge action
- data streams
- searchable snapshots
- frozen tier
Security
Authentication (native, LDAP, SAML, OIDC), role-based access control, field-level and document-level security, encryption (TLS), audit logging, API keys
Objectives
- Configure role-based access control for multi-tenant clusters
- Implement field-level and document-level security policies
Key concepts
- authentication realms
- role-based access control
- field-level security
- document-level security
- TLS/SSL encryption
- audit logging
- API keys
- service tokens
ELK Stack Integration
Logstash pipelines, Kibana dashboards and visualizations, Beats agents (Filebeat, Metricbeat, Packetbeat), Elastic Agent, Fleet, data views
Objectives
- Design end-to-end data pipelines with the Elastic stack
- Choose between Logstash, Beats, and Elastic Agent for data ingestion
Key concepts
- Logstash pipeline (input/filter/output)
- Kibana dashboards
- Kibana data views
- Filebeat
- Metricbeat
- Elastic Agent
- Fleet management
- Beats modules
Levels: Junior, Mid, and Senior
Explain it in your own words
Graded by AI · no account · 1 questionNo options to guess from: you write, the AI grades against the topic's rubric and tells you what level your answer reads at.
Already studying Elasticsearch?
The assessment measures topic by topic, and your plan skips what you already know.
Open to read. An account is only needed to be measured.
Frequently asked questions
Where should I start studying Elasticsearch?
Start with Fundamentals: Core Concepts, Documents & CRUD, and REST API. The map orders topics by what they depend on, so each one prepares the next.
How long does a full Elasticsearch assessment take?
A full assessment of this path takes about 45 minutes. Study time depends on where you start; a diagnostic shows which topics you can skip.
Do I need an account to use this page?
No. The map, objectives and key concepts are open. You only need an account to be measured, get your gap by topic and receive a plan.