PostgreSQL 17 Connection Pooling: pgBouncer, pgpool-II, pgjdbc-ng Compared

Your core business isn't database operations. Ours is. Managing databases across multiple platforms like MySQL, MongoDB, PostgreSQL, TiDB, and more can drain valuable engineering time and resources. Mydbops becomes your extended database operations team, ensuring reliability, performance, and scalability across your entire data infrastructure while helping you reduce costs and operational overhead. With Mydbops, you can: 🔹Achieve lower operational costs. 🔹Ensure seamless scaling and smooth migrations. 🔹Allow your developers to focus on building great products instead of managing infrastructure. 📅 Reach out to schedule a short meeting, let’s discuss your current setup and how Mydbops can add value. #CTO #VPofEngineering #ManagedServices #Opensource #Databases #PostgreSQL #Mysql #MongoDB #Tidb #CostOptimization #mydbops

Migrating from PostgreSQL, MySQL, or Oracle to MongoDB? It doesn't have to be complicated. Join our Developer Advocates, Ricardo Mello and Jesse Hall in this tutorial to the MongoDB Relational Migrator— to simplify your migration process. Watch it here. https://lnkd.in/dFt7e5nm

Is your monolithic database groaning under the weight of your microservices? As applications scale, the database often becomes the biggest bottleneck. You can throw more CPU and RAM at it (vertical scaling), but you eventually hit a wall. So, how do you scale your database horizontally, just like your stateless services? Enter Vitess. Vitess is a CNCF graduated project that makes MySQL scalable on an epic level. Originally born at YouTube to handle massive traffic, it’s now a go-to for running MySQL on Kubernetes. So, what’s the magic? In simple terms, Vitess acts as a smart proxy layer on top of multiple MySQL instances. It takes a large database and breaks it into smaller, faster pieces called shards. To your application, it still looks like you're talking to a single, giant MySQL database. But behind the scenes, Vitess is routing queries to the correct shard, handling connection pooling, and rewriting queries for better performance. This means you get: → Horizontal scaling for your database writes. → Resilience against failures. → Improved performance without changing your application code. It effectively brings the scalability and operational ease of a cloud-native database to the familiar and battle-tested MySQL ecosystem. If you’re running stateful workloads on Kubernetes and feeling the scaling pain, Vitess is a project worth exploring. #DevOps #Kubernetes #CNCF #Vitess #MySQL #Database #CloudNative #OpenSource #K8s

🚀 From 22GB to 8GB — How I Boosted My AWS RDS Performance ⚙️ As a Java developer, I recently faced a real performance issue — Every time I opened MySQL Workbench, it took almost 2 minutes to connect to my AWS RDS database. After a bit of digging, I discovered the culprit: 👉 14 GB of image data (stored as BLOBs) inside the database! 🧠 Problem: My RDS instance size was small (cost-efficient tier) The database was 22 GB in total 14 GB of that was just image files Workbench startup + backups were painfully slow Queries using these tables lagged noticeably 🧩 Solution: I decided to move all images (BLOB data) out of MySQL and into Amazon S3, and store only the image URLs in MySQL. Here’s what I did 👇 1️⃣ Created an S3 bucket (your-app-images) 2️⃣ Added a new column photo_url in the table 3️⃣ Wrote a Java migration script that: Reads BLOBs from MySQL Uploads them to S3 Updates each row with the public S3 URL 4️⃣ Dropped the old LONGBLOB column after verification 5️⃣ Ran OPTIMIZE TABLE to reclaim space -- ⚙️ Before vs After: Metric After DB Size : 22 GB Workbench Startup : 120 sec Backups : Slow Query Response : Laggy Cost : Higher Metric After DB Size : 10 GB Workbench Startup : 3-4 sec Backups : 3x Faster Query Response : Smoother Cost : Low --- ✅ Result: ✔️ Faster RDS startup ✔️ Cheaper storage ✔️ Simpler queries ✔️ Zero data loss ✔️ And best of all — Workbench loads almost instantly! 🚀 --- 🧰 Tech Stack: Java + AWS SDK (v2) for migration Amazon RDS (MySQL) Amazon S3 for storage SQL + JDBC for data updates 💬 My Key Takeaway: > Don’t store large binary files inside MySQL. Use the database for structured data, and use S3 for storage. Your future self (and your RDS bill) will thank you. 💪 --- #AWS #MySQL #RDS #S3 #JavaDeveloper #CloudOptimization #DatabasePerformance #AWSCommunity #DevJourney #LinkedInLearning

Migrating from PostgreSQL, MySQL, or Oracle to MongoDB? It doesn't have to be complicated. Join our Developer Advocates, Ricardo Mello and Jesse Hall in this tutorial to the MongoDB Relational Migrator— to simplify your migration process. Watch it here. https://lnkd.in/dd3nKVfi

Migrating from PostgreSQL, MySQL, or Oracle to MongoDB? It doesn't have to be complicated. Join our Developer Advocates, Ricardo Mello and Jesse Hall in this tutorial to the MongoDB Relational Migrator— to simplify your migration process. Watch it here. https://lnkd.in/d2jiDmmn

🚀 Automating MongoDB Backups with Bash + Email Notifications 🗄️ Recently, I created a simple yet powerful Bash script that automates the MongoDB schema backup process for Non-Production environments. Here’s what the script does 👇 ✅ Verifies if the MongoDB database exists before taking backup ✅ Performs schema-only mongodump ✅ Logs all activities ✅ Sends a neat HTML-formatted email notification with backup status (✅ Success / ❌ Failure) ✅ Uses msmtp for lightweight email delivery It’s a handy automation for DB admins and DevOps engineers who want to keep things clean and monitored without manual intervention. 💻 Check out the full script on GitHub: 🔗 https://lnkd.in/g72B633Y If you’re working on database or infrastructure automation, would love to hear your thoughts or improvements! 💬 #MongoDB #Bash #Automation #DevOps #DatabaseAdministration #Scripting #Linux #Cloud

Today, I explored the fundamentals of databases and AWS RDS CloudDevOpsHub by Vikas Ratnawat 💡 Database Basics: A structured collection of data, managed by a DBMS. CRUD operations: Check, Update, Retrieve, Delete. 📚 Types of Databases: Relational (RDBMS): Tables, rows, columns. SQL for queries. NoSQL: Document, key-value, graph, wide-column stores. Time-Series: Stores time-stamped data for analytics. 🛠 Popular Databases: A) MySQL: Open-source, cross-platform, secure, integrates with Python/Java/PHP. B) Oracle SQL: Enterprise-grade, high performance, cloud-ready, licensed software. C) MongoDB: NoSQL, document-oriented, handles large unstructured data. Apache Cassandra: NoSQL, distributed, optimized for fast writes, highly scalable. ☁️ AWS RDS: Fully managed relational database in the cloud. Automated backups, patching, and recovery. Supports MySQL, PostgreSQL, Oracle, SQL Server, Aurora. 💻 I also explored one by using MySQL Commands on AWS RDS: Backup & restore: use snapshots or mysql -u username -p database_name < backup_file.sql. Learning and practicing these basics helps in database management and cloud deployments. #AWS #RDS #MySQL #OracleSQL #MongoDB #Cassandra #Database #CloudComputing #DevOps #Learning

🚀 Amazon RDS PostgreSQL Upgrade Guide from Version 13.2 to 17.6! This upgrade brings enhanced performance, improved security, and access to the latest PostgreSQL features. In my latest article, I walk through the key steps, considerations, and lessons learned from the major version upgrade process. If you're planning a similar migration, this guide will help you prepare, execute, and validate your upgrade. 👉 Read the full article here: https://lnkd.in/gF2dsNcw #AWS #PostgreSQL #RDS #DatabaseUpgrade #DevOps #CloudEngineering

Migrating from PostgreSQL, MySQL, or Oracle to MongoDB? It doesn't have to be complicated. Join our Developer Advocates, Ricardo Mello and Jesse Hall in this tutorial to the MongoDB Relational Migrator— to simplify your migration process. Watch it here. https://lnkd.in/dwN8sVgd