Elasticsearch 9 & Spring Boot: Search Engine Masterclass

Set up Elasticsearch and Kibana with docker compose, run a single-node Elasticsearch exposing 9200 and 9300, access Kibana on 5601, and perform CRUD via REST APIs for learning.

Learn to read documents in Elasticsearch by id or fetch all documents from an index using Kibana dev console. Understand how _source and hits appear in get and search responses.

Retrieve a document with a get request, then update using PUT or POST with upsert behavior, replacing the whole object or patching a single field like year.

Learn to delete a document in Elasticsearch by using the delete request with a book ID, verify not found, and remove the index if needed.

Elasticsearch defers newly indexed documents by default, making them searchable only after a short refresh; use the refresh API to see them, especially during bulk inserts in Spring Boot.

Explore how Elasticsearch uses Apache Lucene to index and tokenize documents, creating an inverted index, and learn crud operations for an index and its documents, including upserts and scripted updates.

Learn how Elasticsearch clusters use sharding and replication to achieve high availability and scalability, deploying multi-node setups with Docker Compose and understanding cluster coordination on port 9300.

Explain how primary shards distribute data and replica shards provide high availability by backing up data on separate nodes, and how replicas promote to primary and serve search requests.

Explore Elasticsearch node roles, including master election, data, coordinating, and ingest; learn how role assignment supports high availability, indexing, search, data replication, and hot, warm, cold tiers.

Understand how clustering, sharding, and replication keep Elasticsearch highly available and scalable, with primary and replica shards, indexing, and failover ensuring safe, high-throughput updates.

Explore the elasticsearch bulk api for performing multiple create, update, and delete operations in a single post to the /bulk endpoint using ndjson payloads with line-by-line parsing.

demonstrates optimistic concurrency control in bulk updates using sequence number and primary term to prevent race conditions across indices, with multi-index bulk actions and cross-index searches.

Copy documents from an old index to a new index with the reindex api, preserving the original data and applying shard settings. Customize with selected fields and multiple source indices.

Explore how the Elasticsearch analyze API simulates text analysis for indexing and searching, using character filters like HTML strip, mapping, and pattern replace to transform text and reveal tokens.

Master Elasticsearch tokenizers, including the standard tokenizer and uax url email, learn how HTML strip and character filters shape token output in Kibana.

Explore token filters in Elasticsearch, configuring lowercase and uppercase, length-based and unique token filters with character filters, tokenizers, and a Kibana workflow.

Explore stemming in Elasticsearch to reduce words to base forms with the Porter stemmer, improving the inverted index with examples like cooking to cook and ponies to pony.

Elasticsearch serves as a search engine, not the source of truth, with data flowing from the product service's Postgres via Kafka to Elasticsearch in bulk.

Discusses why custom java-based analyzers are rare, and how normalization via a separate app supports Elasticsearch indexing with full control over character and token filters.

Elasticsearch makes every field optional in the mapping, so documents may include, omit, or set fields to null, and type constraints like integer can be single values or lists.

Learn to configure a custom analyzer in Elasticsearch, apply it to the blog body field, and improve search results with lowercase and stemmer filters, using Kibana for testing.

Skip indexing unused fields in Elasticsearch to save CPU, memory, and disk space, speeding up indexing. Use application-side filtering or mapping with enabled false.

Learn how to select specific documents in elasticsearch by providing an IDs list in the request body, returning only the records matching chosen IDs (for example 1 and 4).

Explore how the Elasticsearch match query computes relevance scores, compares spring and boot, and uses and/or logic in Kibana with live document indexing and scoring insights.

Explore how multi match searches across multiple fields, including title and body, with weighting, tiebreaker, and options like fuzziness and phrase with slop to improve relevance.

Explore the should clause in Elasticsearch bool queries, comparing its scoring with must and filter. Learn when should filters documents and how it contributes to score in multi-condition queries.

Explain how the bool query's should clause creates an or-like filter with boosting, how must and filter interact, and how to prioritize tennis or running shoes by score.

Outline a four-step approach to Elasticsearch bool queries: group conditions in filter, move nots to must_not, move should to should, and place text queries in must for relevance.

Explain how minimum should match works in an Elasticsearch bool query, using a Nike product filter with should conditions and rating or price constraints.

Explore how the should clause in Elasticsearch boosts personalized recommendations by scoring conditions like highly rated or action movies, using user data to rank results on large indexes.

Explore 1-to-1 and nested child objects in Elasticsearch, mapping and indexing, using term and bool queries, and avoid dot notation that breaks parent-child relationships.

Explore elasticsearch nested queries in a 1-to-many model where movies are the parent and actors are nested child documents, using path actors with match or term queries.

Master querying Elasticsearch with strict, flexible, and compound queries, including bool queries with filter, must, must not, and should, and learn how relevance scoring and nested mappings shape results.

Learn how to perform field selection in Elasticsearch using _source, including selecting specific fields, excluding fields, and returning only movie IDs or titles from a sample movies index in Kibana.

demonstrate elasticsearch pagination using size and offset to slice results, and apply a page formula from the first page starting at zero to show two-record pages.

Discover how to sort by id in Elasticsearch for Java Spring developers, why auto generated ids can't be sorted, and embed id in the body with number or keyword types.

Explore bucket terms aggregation in Elasticsearch, using keyword fields and multi field mapping to implement group by facets for size, color, material and brand, and filters affect counts in Kibana.

Explore nested aggregations for stores with nested products, computing price stats (min, max, avg, sum) and grouping by product category via the nested path products.