Alkin Tezuysal’s Post

"Will Postgres development rely on mailing lists forever?" Tomas Vondra raises some excellent points in one of his more recent articles: https://lnkd.in/g99YNTGG "A fundamental reason for my skepticism is that I’m not convinced the number of motivated developers is the primary bottleneck. Go read the recent “commitfest.postgresql.org is no longer fit for purpose” thread (https://lnkd.in/gd8S3HCV) on the developer mailing list. I think Robert is spot on that there are plenty of issues. How difficult it is to find a patch to review, for example. We have a tool to track patches people work on, but it’s very time-consuming to find a patch ready for a review. And if it’s difficult for experienced developers, it’s going to be impossible for new contributors, no matter how motivated they are." I'm excited to see what discussions may arise as a result of this blog, Robert Haas' thread, and others who care enough about the #PostgreSQL community to share their thoughts. Only through speaking up and making an effort to change these practices - while acknowledging how well they have served to move the #Postgres project forward in the past, but also noting that they no longer do - will we be able to see an improvement to the number of contributors. For this is truly the problem at hand, currently. #sql #devcommunity #psql #slonik #contributing #opensource #foss

How it started: await client.query( 'INSERT INTO authors (full_name) VALUES ($1)', [author] WHERE NOT EXISTS( SELECT full_name FROM authors WHERE full_name = author ) ); versus how it's going: await client.query(` INSERT INTO authors (full_name) SELECT $1 WHERE NOT EXISTS ( SELECT 1 FROM authors WHERE full_name = $1 ) `,[author]); #database #fullstack #postgresql

🚀 Exporting PostgreSQL Table Data into Parquet Files with Go! 📊 Working with large datasets? Need a performant way to export your PostgreSQL data into a compressed, analytics-friendly format? Check out how you can use Go and the awesome library https://lnkd.in/gVh_UbVr to achieve this! 🔥 💡 Why Parquet? Columnar Storage: Ideal for analytics workloads Compression: Saves space without sacrificing performance Schema Evolution: Supports flexible schema management Interoperability: Widely supported across data tools like Apache Spark, AWS Athena, etc. 🔧 Steps to Export PostgreSQL Data into Parquet: 1️⃣ Connect to your PostgreSQL database using Go 2️⃣ Query the required table data using pagination (limit & offset or date-based chunks) 3️⃣ Convert the data into Parquet-compatible types 4️⃣ Write the data to Parquet files using parquet-go 5️⃣ Download/Stream the files for further processing 🎯 ✨ Key Benefits: Handle millions of records without breaking a sweat Efficient data storage and fast retrieval Seamless integration with cloud data platforms If you're working with big data exports, this is a solution you don't want to miss. 🚀 💬 Have you used Parquet before? Share your experience or ask questions in the comments below! #GoLang #PostgreSQL #Parquet #BigData #DataEngineering #OpenSource #Cloud

Happy to have completed this book notes project! I read a lot of books but after I finish reading them I often don't remember all the most salient parts of the book. So I started taking notes. This project is built on this idea. In this application, users can store all the books they've read, their notes, ratings, and when they were read. The books are sortable by rating, recency, and title. The application uses a public API to fetch the book covers based on the book's ISBN number. I also aimed to incorporate how to Create Read Update and Delete data in a PostgreSQL Database to persist data. The code for this project can be located here: https://lnkd.in/dAKcvmB2 #WebApplication #PostgreSQL #APIs #Webflow #Webflowdeveloper #Webflowexpert

Tomas Vondra: [PATCH IDEA] Statistics for the file descriptor cache 🚀 **Exploring the Depths of PostgreSQL: New Patch Idea by Tomas Vondra** 🚀 Do you ever wonder what goes on behind the scenes with file descriptors in PostgreSQL? Well, wonder no more! Tomas Vondra (@TomasVondra) has just dropped a new "first patch" idea: Statistics for the file descriptor cache. 📝 Think of it as the 'who, what, where, and how' of your data file access. It's poetry in motion... for geeks. This patch aims to provide runtime statistics on file access, a goldmine for instances overflowing with files. Not as riveting as a summer blockbuster, but oh-so-crucial if your system is a file hoarder. No, this isn't about fancy extensions like his previous work on [PostgreSQL FDW insert batching](https://lnkd.in/evPeT3uB). This dives into the nitty-gritty interaction with file systems, smack at the core of PostgreSQL itself. For those new to PostgreSQL development, it's an isolated yet essential playground. So, if you've been itching to contribute, this is your moment. Kudos to Tomas for pulling back the curtain and sharing these insights. Let’s geek out together and keep PostgreSQL running like a well-oiled machine. 🛠️ #PostgreSQL #DatabaseAdmin #FileDescriptorCache #TomasVondra #DBGeekery You can find the full article here: https://postgr.es/p/6CF

I'm pleased to announce the launch of a very special project to me: "CleanArchitecturePSQL". This initiative began as a solution to a specific technical challenge I encountered while working on another one of my active projects, "DndCharacterSheet". Initially, I planned to use a PostgreSQL database hosted on Supabase for "DndCharacterSheet", but I ran into Jason Taylor's popular Clean Architecture template did not offer native support for PostgreSQL Faced with this challenge, I decided to take matters into my own hands and adapt the Clean Architecture template to work seamlessly with PostgreSQL databases. The result is "CleanArchitecturePSQL", a modified version that not only adheres to clean architecture principles and Taylor's original structure but also opens new possibilities for those in the development community who prefer using PostgreSQL. The project has been a journey of learning and adaptation, during which I've implemented various specific improvements to ensure smooth integration with PostgreSQL. Moreover, I've put effort into creating detailed documentation to help other developers adapt and expand the project according to their own needs. I'm excited about the possibilities that "CleanArchitecturePSQL" can offer to the community, and I'm open to receiving feedback, suggestions, and contributions. Visit the repository on GitHub: https://lnkd.in/dGkhTN3E, and let's see together how we can continue to improve this project. #CleanArchitecture #PostgreSQL #SoftwareDevelopment #DndCharacterSheet #OpenSource

How to use MySQL with Docker and Docker compose a beginners guide

AlloyDB combines our ❤️ of PostgreSQL with the best of Google. In this e-book you’ll learn about the features within AlloyDB that make it faster, better, stronger and smarter than standard PostgreSQL. Download our ebook today and discover how AlloyDB can unlock the full potential of your applications—giving you time to develop your apps. Welcome to the future of PostgreSQL. #tcs #techcommunity #softwaredevelopment #supplychain #manufacturing #healthcare #sustainability #artificialintelligence #indiangovernment #environment #governance #india #mygovindia #wiprocloudstudio #google #adanigroup #RELIANCE ADA GROUP

Peter Eisentraut: PostgreSQL performance with different compilers 🚀 Want to turbocharge your PostgreSQL? 🐘 Peter Eisentraut just dropped some juicy benchmarking insights 📊🔬. He's put PostgreSQL through the wringer with different compilers and optimization settings. Spoiler: it’s not rocket science, but it’s pretty revealing. Hit the link to dive into his blog [https://lnkd.in/eTX39BZi]. Key takeaways: 1. GCC vs. Clang? Neck and neck — less than 1% difference in performance 💨. 2. Upgrading compilers? Don't break a sweat, folks. Only up to 1% bump from old to new versions in the same family 🔄. 3. "Release" setting barely edges out "debugoptimized" — we’re talking a 1% gain here 📈. 4. "Minsize" might shave off some bytes but it's a turtle 🐢 — about 10% slower than "debugoptimized." Peter tips his hat to the default settings for keeping things balanced ⚖️. Unless you're a die-hard performance chaser, stick to the basics. Curious for more numbers and details? Head over to Peter’s full post and feed your geeky soul [https://lnkd.in/eQdTXJ8d]. #PostgreSQL #Performance #Benchmark #pgbench #DatabaseTuning 💡📈 You can find the full article here: https://postgr.es/p/6yg

Hi everyone, As part of my learning journey, I’ve been diving into the world of databases, focusing on MySQL. In this article, I’ll share my experience of building a solid understanding of SQL fundamentals—an essential skill for anyone working with data, whether for analysis, development, or managing robust data-driven applications. This database is designed to handle notifications and inbox functionalities for a Tokopedia-like platform. It supports features like categorized notifications, read/unread status, and counters for unread notifications. Feel free to check out my mini project repository on GitHub: https://lnkd.in/gTG4DM6U Happy learning! #DatabaseDesign #MySQL #DBeaver #SQL