Normalize Denormalizing: Data Design for Performant Search

The June meeting for the Ohio North Database Training user group will be Tuesday, June 6, 2023. This will be a HYBRID event and our featured speaker, Eddie Mitchell, will be joining us in person.

Our in-person meeting location is in the Artemis offices at:

6161 Oak Tree Blvd, Ste 300
Independence, OH 44131

5:00 PM EST: Online and in-person meeting begins with a social hour. This is an unstructured hour where you can join us to catch up and meet other group members before the session starts. There will be pizza brought in for in-person attendees.

6:00 PM EST: Updates and announcements, followed by Eddie's session (please review the abstract below for more details).

7:15 PM EST: optionally after the main presentation, the in-person crowd may go out for snacks and drinks at a local establishment.

We hope to see you there!

*Please note, we will be using Microsoft Teams for the online portion of this meeting. You may want to join a few minutes early to ensure you do not have any issues. If you are attending in person, there are large TVs at the office and you do not need to bring a laptop or use Teams.

Session Abstract:

Normalize Denormalizing: Data Design for Performant Search

Few applications ship without a search bar these days.  To achieve relevant full-text search and highly performant aggregations, you must make sure your data is structured appropriately.
Such structure resides outside of typical B-Tree indexes, table joins, and WHERE LIKE %% clauses, and into the world of Inverted Indexes, TF/IDF (Term Frequency/Inverted Document Frequency), and Vectors, like those in Elasticsearch.

In this talk, we'll compare Elasticsearch to traditional relational databases, review denormalized data schemas vs their Third Normal Form (3NF) counterparts, demonstrate ways to maintain those pesky relationships, and discuss best practices for field mappings (schema) such as Keyword, Text, Numbers, Boolean, Nested, and more.

By the end of this session, you will understand the key data design differences between Elasticsearch and traditional relational databases, the appropriate mindset for data design in search, and a beginner's understanding of Elasticsearch field mappings and their usage to achieve maximum performance.