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Introduction Expression Index is an extremely powerful feature of PostgreSQL, which can be used for any data type (such as text, numbers, time, arrays and more) as long as the result of the expression returns a Deterministic value You can use Expression Index for all index types ( B-Tree , Hash , GIN , GiST and more) as long as the expression you write in the CREATE INDEX command returns a data type that the Index type supports for processing The nature of Expression Index is that instead of indexing on the original column value, Postgres precomputes the result of the function or expression as soon as you INSERT/UPDATE and saves the result directly into the Index file Characteristics After creating an Expression Index, when using a query, you must write it exactly like the expression in the Index, otherwise the index will not work Every time a row of data is modified, Postgres must run that function or expression to get the result to create the Index. Therefore, avoid using function...

Introduction pg-promise is a powerful library for Node.js dedicated to interacting with PostgreSQL databases. This library is built on top of the native pg driver but provides a high-level abstraction layer with a Promise-based architecture, making the source code cleaner, easier to read and more maintainable. Advantages Automatically manages connections and transactions safely Features a built-in powerful SQL formatting system to prevent SQL Injection attacks Supports handling external SQL files via Query Files and offers exceptionally high performance for bulk data insertion thanks to optimized utilities like pgp.helpers.insert and pgp.helpers.update . Prerequisites In the previous article, I provided instructions on using Prisma to seed data, but using ORMs always introduces limitations such as lack of support for all data types as well as advanced database features. Since ORMs are designed to work with various database types, they sacrifice specific functionalities to solve gener...

Introduction GiST (Generalized Search Tree) is also a high-order balanced tree, which has a hierarchical structure similar to the traditional B-Tree structure. However, the core difference lies in the storage content inside nodes including Root Node and Internal Nodes: each entry in a branch node contains two pieces of information: Predicate: This is the data area created by Postgres from a general level (at the root node) to a detailed level (at the internal node) Nodes at higher levels will contain a general description for all child nodes below them With such a structure, it helps to eliminate extremely quickly data areas that definitely do not satisfy the condition, instead of having to check each row one by one. TID points to lower-level child nodes. Leaf Nodes contain information Specific actual data of the field value TID points to the row data in the Heap Data type GiST supports well for handling data types without linear order (cannot be sorted from smallest to largest like re...

Introduction In this article, we will continue exploring GIN Index with the JsonB data type. JsonB, short for JSON Binary, is a data type developed from the JSON data type and supported by PostgreSQL since version 9.2. The key difference between JSON and JsonB lies in how they are stored. JsonB supports binary storage and resolves the limitations of the JSON data type by optimizing the insert process and supporting indexing. Creating an Index When creating an index for a JsonB column, Postgres supports two strategies as follows: jsonb_ops : This is the default strategy that flattens the JSON to extract independent paths, keys and values to create Entries. For example, if you have a JSON like this: { "shop" : "ShopA" , "products" : [ { "product_name" : "mouse" , "price" : 50 } , { "product_name" : "keyboard" , "price" : 100 } ] } It will create a GIN Index as follows, n...