Full-Text Search with Couchbase is a lab-based course in storing and searching JSON data with Couchbase Server, from document design, SQL++ queries and indexes to full-text search with the Search Service, including analysers, BM25 ranking and synonyms. It suits database administrators, developers who build search features and people starting out in NoSQL who already know basic SQL.
Modern applications store data as JSON documents whose structure can change, and users expect to type a search for products, articles or customers and get relevant results immediately, even with typos or different wording. Searching with LIKE in a typical database is slow and has no sense of meaning. Couchbase Server brings a document database, the SQL++ query language and a Search Service for full-text search into one platform, so you can build fast, accurate search without running a separate search system.
This course takes learners through Couchbase Server 8: NoSQL concepts, installation, data modelling with buckets, scopes and collections, SQL++ with nested data and arrays, indexes and query plans, security and Eventing. It then goes deep into the Search Service, covering analysers, mappings, query types, BM25 ranking, synonyms and searching from SQL++, and closes with a workshop that builds a product search engine with an overview of vector search.
Course Details
This course is the 2026 edition of Full Text Search with Couchbase NoSQL, rewritten for Couchbase Server 8. It runs for 3 days, 6 hours per day (18 hours in total, 09:00-16:00), as lectures with labs on each learner's own test cluster. Intermediate level. Day one lays the NoSQL foundations, installs Couchbase, models data and writes SQL++. Day two builds indexes, tunes performance, manages permissions and uses Eventing. Day three goes deep into full-text search with the Search Service, covering analysers, queries, BM25, synonyms and searching from SQL++, and closes with a product search workshop and an overview of vector search. Learners take home the datasets and index definitions for every lab.
Section 1: NoSQL and Document Databases
Section 2: Lab: Installing Couchbase Server 8
Section 3: Lab: Data Modelling for JSON Documents
Section 4: Lab: SQL++ Essentials
Section 5: Lab: SQL++ with Arrays, Joins and Data Changes
Section 6: Lab: The Index Service
Section 7: Lab: Query Planning and EXPLAIN
Section 8: Lab: Tuning Queries and Indexes
Section 9: Lab: RBAC, the REST API and Security
Section 10: Lab: Eventing Functions
Section 11: Lab: Search Service Essentials
Section 12: Lab: Analysers and Field Mappings
Section 13: Lab: Search Queries and Ranking
Section 14: Lab: Search from SQL++ and Applications
Section 15: Workshop: A Product Search Engine and an Overview of Vector Search
No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770
N1QL, now called SQL++ by Couchbase, is a query language with SQL-like syntax for JSON documents. You use SELECT, WHERE, GROUP BY and JOIN as usual, with extras for nested data, such as dot notation for fields, ANY or UNNEST to work with arrays, and a distinction between MISSING fields and NULL values. Current releases also work with scopes and collections and can call full-text search through the SEARCH() function, so people who already know SQL can get started with a document database quickly.
LIKE compares characters literally, and a leading % usually forces a scan of all the data. Full-text search builds a special index that splits text into terms with an analyser, so searches are fast and support fuzzy matching, word stems, synonyms and ranking by relevance, and Couchbase Server 8 supports BM25 ranking. In Couchbase you create a search index in the Web Console or through the REST API, then query it from an SDK or with the SEARCH function inside a query.
Look at which fields the query filters or sorts on and create a secondary index on them in the relevant collection with CREATE INDEX. Use an array index for values inside arrays, and if the index contains every field the query needs, it becomes a covering index that avoids fetching documents. Run EXPLAIN to confirm the plan uses an IndexScan rather than the much slower PrimaryScan, or use ADVISE to get index suggestions, and keep primary indexes for development only.
It suits people new to Couchbase, database administrators, developers who build search features and those starting a career in NoSQL databases. You should have basic database knowledge, be able to write basic SQL and understand JSON at a basic level, and bring a laptop that can run Docker.
You will be able to explain the Couchbase Server architecture, design JSON documents with scopes and collections, write SQL++ for nested data and arrays, create indexes and tune queries with EXPLAIN, manage user permissions, build search indexes with analysers, synonyms and facets, and run full-text search from SQL++, the REST API or an application. Participants receive a Certificate of Completion.
Yes. It can be run in-house for your team, with examples adapted to your own work, such as designing search indexes for the product data or documents your team already uses. You can request a quotation from this page, or contact us on LINE @itgenius or by phone on 02-570-8449.
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