Lesson 1Modelling variations, SKUs, attributes and nested objects vs. parent/childModel product variations, SKUs, and attributes while keeping query simplicity and index size in balance. Compare nested objects with parent-child relations, and learn patterns for colour, size, bundles, and marketplace style multi-seller offers to suit diverse needs.
Flat vs nested attribute modellingRepresenting colour, size, and stylePer-SKU pricing and availabilityWhen to use parent-child relationsHandling bundles and kits modellingMarketplace multi-seller product designLesson 2Designing product index schema: field names, types, examples (keyword, text, numeric, date, nested)Design a full product index schema with practical fields and types. Learn to select between keyword, text, numeric, date, and nested fields, and view real examples that aid search, sorting, filtering, and analytics for better data management.
Core identity and catalog fieldsText fields for titles and descriptionsNumeric and date fields for rankingBoolean and status fields for filtersNested fields for complex attributesExample end-to-end product mappingLesson 3Analysers and tokenization: standard, whitespace, custom lowercase, stopwords, asciifold/icuExplore analysers and tokenization methods for product search. Compare standard and whitespace analysers, create custom lowercase and stopword analysers, and apply asciifolding or ICU to manage accents and multilingual product data effectively.
Standard vs whitespace analysersBuilding custom lowercase analysersManaging stopwords for relevanceUsing asciifolding for accentsICU analysers for multilingual dataTesting analysers with the APILesson 4Time-series and event index modelling: user events schema (searches, clicks, views, purchases)Learn to model user events like searches, clicks, views, and purchases as time-series data. Grasp index naming, event schemas, retention, and how modelling choices impact analytics, funnels, and real-time dashboards for insightful monitoring.
Designing a unified user event schemaIndex-per-time vs rollover strategiesCapturing searches, clicks, views, buysChoosing identifiers and join keysRetention, ILM, and cold storageAggregations for funnels and cohortsLesson 5Normalization for faceting: using keywords and normalized lowercased fieldsUnderstand field normalization for strong faceting and filtering. Learn when to employ keyword fields, normalizers, and lowercased variants to guarantee consistent aggregations, case-insensitive filters, and neat facet labels in the user interface.
Keyword fields for filters and facetsUsing normalizers for lowercase valuesHandling accents and Unicode variantsDealing with whitespace and trimmingMapping enums and controlled vocabulariesFacet label formatting for the UILesson 6Overview of document vs. relational modelling patterns for productsCompare document-oriented and relational modelling for product data. Understand denormalization, joins, and duplication, and learn when Elasticsearch serves as the main source versus a search layer over a relational catalog for optimal use.
Relational product schema recapDenormalization patterns in ElasticsearchHandling joins and reference dataTrade-offs in duplication and updatesSearch index vs system of recordSyncing from relational DB to indexLesson 7Field mapping details: multi-fields, keyword subfields, doc_values, norms, index optionsExamine field mapping choices that manage how product data is indexed and stored. Learn when to use multi-fields, keyword subfields, doc_values, norms, and index options to balance search relevance, aggregations, and storage costs wisely.
Defining text vs keyword field typesConfiguring multi-fields for flexibilityUsing keyword subfields for sortingEnabling and tuning doc_valuesControlling norms and scoring impactIndex options and storage trade-offsLesson 8N-gram and edge n-gram analysers: configs, use cases, trade-offs (autocomplete vs. full-text)Set up n-gram and edge n-gram analysers for autocomplete and partial matching. Learn how token lengths, positions, and filters influence index size, recall, and precision, and avoid noisy matches in full-text search applications.
Basics of n-grams and edge n-gramsDesigning autocomplete analysersChoosing min_gram and max_gram sizesIndex size and performance trade-offsAvoiding noisy and low-quality matchesCombining n-grams with keyword fieldsLesson 9Timestamping, user identifiers, session IDs, and schema for aggregations and pipelinesModel timestamps, user IDs, and session IDs to aid analytics and pipelines. Learn to record event time accurately, design fields for aggregations, and ready data for enrichment, attribution, and further processing in systems.
Event time vs ingestion time fieldsUser identifiers and privacy concernsSession and visit identificationFields optimized for aggregationsSchema for attribution and funnelsSupporting ETL and pipeline stages
