Elasticsearch for Product Searches
Make Product Search Better
Our Elasticsearch training classes have a 4.67/5 rating based on 15 reviews
- 2-hour online training
- A digital copy of the training material
- Docker Compose files, configs, scripts, etc.
- Certificate of Completion
Next Class: Dec 12 See Upcoming Classes
Full day classes available upon request
Looking for a more general and extended knowledge-based Elasticsearch training?
Who should attend?
This Elasticsearch online course is designed for anyone who:
- Has a basic understanding of Elasticsearch’s core concepts (documents, shards, basic queries)
- Is looking to implement product search.
What attendees say
Sematext was an ideal training partner for Parse.ly. We had just recently adopted Elasticsearch on a new project, and they gave us two days of solid training that was tailored to our team’s needs. The material was built atop strong foundations and moved quickly into advanced areas around querying, Lucene internals, and cluster performance. It was clear that it was all informed by real-world experience operating these systems at scale.
- Using ngrams and shingles to tolerate typos, while boosting relevant matches
- How is relevancy score calculated?
- Searching across fields to detect user intent
- Balance precision and recall by using different variants of the same field
- Using the Function Score Query to boost documents by recency, region and other business criteria
- Using ngrams and shingles to boost more exact matches
- Boosting documents based on recency
- Dealing with stopwords efficiently
Relational data in Elasticsearch
- Arrays and objects: fastest way to deal with one-to-one relations
- Nested documents: quick searches for low-velocity data
- Parent-child relationships: fastest way to update
- Dealing with many-to-many relationships via application-side joins
- Searching across a relationship between two documents
- Showing both sides of the relationship in the results
- Term suggester: providing did-you-mean for each word
- Phrase suggester: did-you-mean for the whole query
- Quick autocomplete with the completion suggester
- Filter autocomplete results with the context suggester
- Implementing did-you-mean functionality
- Implementing autocomplete functionality
Highlighting query results
- Default highlighting: fast and flexible for short fields
- Term offsets: quick highlighting for content fields
- Term vectors: paying with storage to get the best of both worlds
- Add highlighting for query results
- Balance precision and recall through analysis tweaks
- Using DisMax and Function Score to make relevant documents rank higher
- Suggesters for did-you-mean and autocomplete functionality
- Highlighting query results