The following table shows an example of an inverted index substructures build based on a set of documents: the postings list which creates a list of each term, indicating the documents where the term appears.the term dictionary which groups all the terms included in the documents in a sorted list.The inverted index is composed of two substructures: When you need to search for a specific term in the book, you use the book index and check which pages contain information about the queried term. The concept of the inverted index is close to the concept of a book index. A simple document is illustrated in the following figure:Īn inverted index is a data structure storing information in a complex HashMap, aiming to facilitate the search of terms contained within the fields of the documents. Fields are the minimal unit of storage in the Lucene ecosystem. A document contains a set of fields and is generally stored in JSON format. Documents do not have a specific scheme and every document pushed into the index is tagged with a unique identifier. ![]() Given that Elasticsearch is a distributed system and clusters can be added on demand, there is virtually no limit to the number of documents an Elasticsearch server can store.Ī document is a record containing information related to the index. You can think of an index as a folder with multiple related documents. I will briefly describe these concepts below.Īn index is a collection of documents sharing conceptual and logical similarities. The fundamental concepts required to understand the theory behind Apache Lucene are indexes, documents, inverted indexes, scoring, and tokenisation. The figure below depicts the integration between Elasticsearch and Lucene, and how they interact with external systems: The core of Elasticsearch is the Apache Lucene library, which includes features for indexing, searching, retrieving and updating documents, and text analysis. FUNDAMENTAL CONCEPTS OF THE APACHE LUCENE LIBRARY Additionally, I will present a case where these Elasticsearch features were evaluated and Elasticsearch was proposed as the main data repository for an internal project. ![]() This article explores fundamental Elasticsearch concepts such as indexes, documents, and inverted indexes, and how these concepts work together to provide storage and relevance scoring. Which is why I want to share some fundamental information on the topic.Ĭurrently, Elasticsearch is ranked as the most popular search engine according to DB-Engines. That could be the reason Google became so popular, and Google certainly resolved that problem.Īs the amount of content is growing daily and with an increased pace, giving such powerful search capabilities to users is getting more important as well. I still remember those days of using search engines in different portals and not getting even one relevant result.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |