How To Dev

Introduction In the previous article, I provided a basic guide on how to use @snaplet/seed to seed data quickly. In this article, I will show you how to generate a large amount of data in a short time, approximately 1,000,000 records, which is useful when you need to perform performance testing. You can apply a similar approach to larger datasets like several million records, using a streaming mechanism to avoid Out of Memory (OOM) errors caused by allocating too much memory at once and overloading the system. I will explain two different approaches here: Coding: chunking data and writing each small part to the database Database: creating a CSV file and using a command to copy that data directly into PostgreSQL Prerequisites You need to set up Prisma, PostgreSQL and @snaplet/seed before continuing, you can check out the previous articles for instructions. Detail First, let us create the file prisma/seed/seed.ts import {faker} from '@faker-js/faker' import {createSeedClient}...

Introduction @snaplet/seed is a highly powerful library that supports automatic mock data generation for databases based on the Prisma schema. Instead of manually writing hundreds of lines of complex insert code, @snaplet/seed automatically analyzes relations in the database to generate logical, consistent and data-integrity-assured data. Standout advantages include the ability to automatically reset the database, inherently understand foreign key constraints and support concise syntax for easily creating nested data structures. Prerequisites This article is used alongside Prisma in a NestJS project, I will not specifically mention how to set up Prisma anymore, you can review the previous article to have the necessary preparation before proceeding Detail First, install the package yarn add -D @snaplet/seed Then, add the following scripts to package.json { "scripts" : { "seed:init" : "npx @snaplet/seed init prisma/seed" , "seed:sync...

Introduction GraphQL is a powerful query language for APIs which optimizes performance by allowing the client side to accurately define the required data structure, completely overcoming the over-fetching and under-fetching disadvantages of traditional REST APIs. In this article we will set up the connection environment as follows: Client connects to NextJS server which acts as a middleware to forward the GraphQL payload to NestJS. You will see that most of the implementation on NestJS will be quite simple and most of the content is auto-generated code via prisma-nestjs-graphql. It is suitable for projects that have complex processing requirements concentrated on the frontend and applying GraphQL will bring high flexibility in querying data so that the team can focus on building features for the frontend effectively. Prerequisites Please take a look at this article where I have specific instructions on setting up GraphQL for NextJS, here on the FE side we will use @tanstack/react-query...