PostgreSQL Evangelist. [My Git](https://github.com/digoal/blog/blob/master/README.md)
FollowIn this post, Digoal presents his predictions for what we'll see for PostgreSQL in 2020.
This post describes performance optimization methods to improve store operations by accelerating equivalent and range searches over PostgreSQL Arrays, JSON and Internal Tag Data.
This post outlines the design and performance of PostgreSQL Similarity Search and describes similarity searches for random texts and arrays to see how PostgreSQL standalone performs.
This post describes how PostgreSQL Image Search Plug-in helps to accelerate image-based searches and also highlights how PostgreSQL helps to screen out duplicate videos.
This article describes the application of PostgreSQL along with algorithms to extract keywords from a document for text (keyword) analysis.
This article provides an overview of the smlar plug-in and describes how it supports multiple similarity algorithms.
This post outlines different methods to find the similarity between documents in various scenarios and focuses on how different algorithms can be used to improve efficiency in such scenarios.
This article explains how the PostgreSQL database along with smlar plug-in helps in efficiently retrieving massive volumes of SimHash data on the basis of the hamming distance.
This post discusses how to calculate the similarity between arrays using algorithms. It focuses on similarity calculation for strings, images, and other types of data in PostgreSQL.
This post outlines several ways to filter duplicate e-commerce content and also describes how indexing helps to determine the similarity between documents.
This post outlines the design and practices for PostgreSQL similarity search distributed architecture and describes how you can perform a parallel query by using DBLink asynchronous calls.
This post describes how can we efficiently search tags and filter records that match the tag's weighted value.
This article describes the methods to accelerate compound queries of single-value fields and multi-value fields over 100 times, focusing on PostgreSQL UDF implementation.
This post describes how to perform fuzzy queries or full-text searches on data before it is encrypted and ensure that your database supports encrypted storage.
This article illustrates different optimization methods to accelerate performance under distinct fuzzy search scenarios including prefix, suffix or fully fuzzy search and regex search.
This article lays down the performance differences between PostgreSQL fuzzy searches and regex matches, and further suggests ways to ways for SQL Optimization.
This article describes PostgreSQL best practices to optimize full-text search, fuzzy search, regex matches, and custom fuzzy searches.
This article describes how trgm, a powerful plugin significantly improves text search performance for fuzzy prefix and suffix queries as well as regexp matching.
This article describes how to perform fuzzy searches for all the fields in a table using pg_trgm. It illustrates the implementation of a full-table and full-field fuzzy search with an example.
This article describes how PostgreSQL helps to perform fuzzy prefix or suffix query and regexp query by using database indexes, including GIN, GiST, RUM, and other customized indexes.
