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A Deep And Fuzzy Dive Into Search
Estimated reading time: 16 minutes
Together with my team, I took a deep dive into the available fuzzy search approaches and algorithms for quite a while, in order to find a performant solution for the various projects ArangoSearch gets used for.
Since the introduction of ArangoSearch back in 2018, many of our users have asked for fuzzy search support. We worked hard on getting this done, and the whole team is now excited to finally make fuzzy search available with the upcoming ArangoDB 3.7.
In the following, we will share our learnings and hope they are useful for you.
(more…)ArangoSearch architecture overview
In this article, we’re going to dive deeper into our recently released feature preview in Milestone ArangoDB 3.4 – ArangoSearch which provides a rich set of information retrieval capabilities. In particular, we’ll give you an idea of how our search engine works under the hood.
Essentially ArangoSearch consists of 2 components: A search engine and an integration layer. The former is responsible for managing the index, querying and scoring, whereas latter exposes search capabilities to the end user in a convenient way.
Milestone ArangoDB 3.4:
ArangoSearch – Information retrieval with ArangoDB
For the upcoming ArangoDB 3.4 release we’ve implemented a set of information retrieval features exposed via new database object `View`. The `View` object is meant to be treated as another data source accessible via AQL and the concept itself is pretty similar to a classical “materialized” view in SQL.
While we are still working on completing the feature, you can already try our retrieval engine in the Milestone of the upcoming ArangoDB 3.4 released today. Read more
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