phpMyAdmin VS PostgreSQL

phpMyAdmin: This web-based database management tool offers a user-friendly interface (for those who prefer not to work directly with CLIs) to monitor and adjust MySQL system variables.

Log in to your hosting control panel and access phpMyAdmin.

phpMyAdmin

Management Tools - mysql and phpMyAdmin

Mastering SQL is like engaging in a deep conversation with databases. Sometimes, you might feel like you're "chatting" with data. But don't worry, tools like phpMyAdmin for managing MySQL databases ensure that your data stays put, bidding farewell to the nightmare of "lost data." This phase requires mastering SQL and relational databases like MySQL, using tools like phpMyAdmin for data management and optimization. Tools to consider: phpMyAdmin, MySQL Workbench, Sequel Pro, and other database management tools.

phpMyAdmin is the stalwart of free open-source database managers. It’s been around forever, and it’s just plain good at what it does — providing a graphical interface for fully managing every aspect of MySQL databases. It’s built using PHP and runs in the browser.

now any project on this directory will be auto-linked with subdomain .test, let's download PHPMyAdmin from this link and unzip the file inside the Sites directory and rename the folder to PHPMyAdmin, after that go inside the folder and use this command

phpMyAdmin was born in 1998, written in PHP, and is a classic SQL client. It was the default tool for LAMP and MAMP at that time. phpMyAdmin has grown to become one of the leading tools for managing MySQL and MySQL-like databases (e.g. MariaDB) It has over 200,000 direct downloads per month (and countless other users install it using prepackaged installations or using package managers).

Is a Docker WordPress Environment focused on the development of WordPress plugins and themes, supports WP-CLI, phpMyAdmin, Xdebug and WordPress Coding Standards - WPCS.

You're minding your own business, managing AWS infrastructure for a client with a pretty standard e-commerce setup: a Medusa.js backend, a Next.js storefront, and most importantly for this story, a PostgreSQL RDS instance safely stashed away in a private subnet where nothing from the outside world can touch it. Exactly how the AWS gods intended.

Your Database: This is your system's memory. It can be a fast in-memory store like Redis for temporary data (perfect for velocity checks) or a persistent relational database like PostgreSQL for long-term data (like blacklists).

#! /bin/bash set -ex IMG=postgres/test-0.0.1 IMG_ID=`docker images ${IMG} -q` PG_TAG=REL_18_BETA1 if [ "${IMG_ID}" = "" ]; then if [ ! -d "postgres-${PG_TAG}" ]; then wget https://github.com/postgres/postgres/archive/refs/tags/${PG_TAG}.tar.gz && tar -xzf ${PG_TAG}.tar.gz fi ID=991 USR=postgres USR_HOME=/home/postgres cat > Dockerfile << EOF FROM ubuntu:latest RUN groupadd -g ${ID} ${USR} && useradd -r -u ${ID} -g ${USR} ${USR} ADD postgres-${PG_TAG} ${USR_HOME} WORKDIR ${USR_HOME} RUN chown -R ${USR}:${USR} ${USR_HOME} RUN apt-get update && apt-get install -y g++ zlib1g-dev make curl tar gzip perl liblz4-dev libreadline-dev flex bison libicu-dev liburing-dev RUN apt-get install --reinstall -y pkg-config && ./configure --with-liburing --enable-debug --with-lz4 && make -j4 && make all && make install RUN echo "export PATH=/usr/local/pgsql/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin" >> /etc/bash.bashrc && \ chown -R ${USR}:${USR} /usr/local/pgsql USER ${USR} EOF docker build -t ${IMG}:latest . rm Dockerfile rm -rf postgres-${PG_TAG} ${PG_TAG}.tar.gz else echo "Image ${IMG} already exists with ID ${IMG_ID}" fi

Choosing the database was straightforward: PostgreSQL. I have the most experience with it, very easy to spin up, has a lot of trust and is open-source (I guess the last two come hand-in-hand).

I was explaining why it is the way that it is. If you'd like your own version of a parser, here's Postgres' [0]. Personally, I really like SQL's syntax and find that it makes sense when reading it.

[0]: https://github.com/postgres/postgres/tree/master/src/backend...

If you’ve never tried PostgreSQL before, the official site is a great starting point: PostgreSQL: The world’s most advanced open source database.

/* https://github.com/postgres/postgres/blob/144ad723a4484927266a316d1c9550d56745ff67/src/backend/optimizer/path/costsize.c#L3375 */ void final_cost_nestloop(PlannerInfo *root, NestPath *path, JoinCostWorkspace *workspace, JoinPathExtraData *extra) { /* ... */ if (path->jpath.path.param_info) path->jpath.path.rows = path->jpath.path.param_info->ppi_rows; else path->jpath.path.rows = path->jpath.path.parent->rows; /* ... */ } /* https://github.com/postgres/postgres/blob/144ad723a4484927266a316d1c9550d56745ff67/src/backend/optimizer/path/costsize.c#L3873 */ void final_cost_mergejoin(PlannerInfo *root, MergePath *path, JoinCostWorkspace *workspace, JoinPathExtraData *extra) { /* ... */ if (path->jpath.path.param_info) path->jpath.path.rows = path->jpath.path.param_info->ppi_rows; else path->jpath.path.rows = path->jpath.path.parent->rows; /* ... */ } /* https://github.com/postgres/postgres/blob/144ad723a4484927266a316d1c9550d56745ff67/src/backend/optimizer/path/costsize.c#L4305 */ void final_cost_hashjoin(PlannerInfo *root, HashPath *path, JoinCostWorkspace *workspace, JoinPathExtraData *extra) { /* ... */ if (path->jpath.path.param_info) path->jpath.path.rows = path->jpath.path.param_info->ppi_rows; else path->jpath.path.rows = path->jpath.path.parent->rows; /* ... */ }

PostgreSQL as database

> As introduced in the previous chapters, all identifers are hashed, thus we can also hash the known keywords at startup and make comparing them very fast.

One trick that postgres uses [1][2] is perfect hashing [3]. Since you know in advance what your keywords are, you can design such hashing functions that for each w(i) in list of i keywords W, h(w(i)) = i. It essentially means no collisions and it's O(i) for the memory requirement.

[1] https://github.com/postgres/postgres/blob/master/src/tools/P...

[2] https://github.com/postgres/postgres/blob/master/src/tools/g...

[3] https://en.wikipedia.org/wiki/Perfect_hash_function

Understanding a database starts with understanding its structure. For PostgreSQL users, one of the most effective ways to visualize and manage your schema is by using an Entity-Relationship Diagram (ERD). Either if you're working with a large legacy database or starting something new, an ER diagram shows how your tables are connected and how your data is organized.