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Accelerating Similarity Search on Really Big Data with Vector Indexing (Part II)

Many popular artificial intelligence (AI) applications are powered by vector databases, from computer vision to new drug discovery. Indexing, a process of organizing data that drastically accelerates big data search, enables us to efficiently query million, billion, or even trillion-scale vector datasets.

This article is supplementary to the previous blog, "Accelerating Similarity Search on Really Big Data with Vector Indexing," covering the role indexing plays in making vector similarity search efficient and different indexes, including FLAT, IVF_FLAT, IVF_SQ8, and IVF_SQ8H. This article also provides the performance test results of the four indexes. We recommend reading this blog first.

This article provides an overview of the four main types of indexes and continues to introduce four different indexes: IVF_PQ, HNSW, ANNOY, and E2LSH.

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Introduction to Couchbase for Oracle Developers and Experts: Part 6: Indexing

Here are the previous articles comparing architecture, database objects, data types, data modeling, and statements and features of Oracle with Couchbase. This post will focus on indexing.  

"Use the Index, Luke!"  -- Source 

Overview

Oracle

Couchbase

Index Documentation

Index Documentation

Types of Indexes: 

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Full-Text Indexing in Nebula Graph 2.0

1. Introduction

Nebula Graph 2.0 supports full-text indexing by using an external full-text search engine. To understand this new feature, let’s review the architecture and storage model of Nebula Graph 2.0.

1.1 Architecture of Nebula Graph

Architecture of Nebula Graph

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Elasticsearch Index v7.6

Elasticsearch, which is based on Lucene, is a distributed document store. It is a highly effective way of indexing your information for correlation and quick query for analysis. In this blog, I will just walk you through the steps required to create an Index, search, and visualize.

What Is an Index?

In the context of ES an index is a collection of documents.

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The Secrets of Indexes and Foreign Keys

Indexes and foreign keys are great tools when confronted with large databases. They can be the answer to a good design and great performance. In this article, I will go through some tips that helped me understand how to use these tools efficiently and streamline my work with complex databases. 

Every image example was done with DbSchema. I enjoy this tool because it is diagram oriented, integrates many features, and has a very good price. 

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Best Practice for Creating Indexes on Your MySQL Tables

Creating Indexes on Your MySQL Tables

By having appropriate indexes on your MySQL tables, you can greatly enhance the performance of SELECT queries. But, did you know that adding indexes to your tables in itself is an expensive operation, and may take a long time to complete depending on the size of your tables?

During this time, you are also likely to experience a degraded performance of queries as your system resources are busy in index-creation work as well. In this blog post, we discuss an approach to optimize the MySQL index creation process in such a way that your regular workload is not impacted.

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Main Uses for Couchbase Primary Index

 FROM customer USE KEYS ["cx:123"] Couchbase is a distributed database. It supports a flexible data model using JSON. Each document in a bucket will have a user-generated unique document key. This uniqueness is enforced during the insertion or updation of the data. Here’s an example document.

select meta().id, travel
from `travel-sample` travel
where type = 'airline' limit 1;
[
  {
      "id": "airline_10",
      "travel": {
          "callsign": "MILE-AIR",
          "country": "United States",
          "iata": "Q5",
          "icao": "MLA",
          "id": 10,
          "name": "40-Mile Air",
          "type": "airline"
          }
      }
  ]



INSERT INTO customer (key, value) VALUES(“cx:123”, {“name”:”joe”, “zip”: 94040, “state”:ca});

SELECT META().id FROM customer;

cx:123