Vedran B.’s Post

In #PostgreSQL, recursive queries for hierarchical data can be implemented using Common Table Expressions (CTEs) with the WITH RECURSIVE clause, allowing efficient traversal of tree-like structures. Proper indexing and query planning are key for optimal performance.#PostgreSQLPerformance @MinervaDB #dba https://zurl.co/R2IH

Check out the article to see how we offloaded the Pgvector’s HNSW index creation from Postgres to external servers. This will allow to create HNSW indexes even on production databases under high-load without affecting the database performance!

Change is constant, and the only way to stay ahead is by keeping our skills sharp. I am excited to share that I've completed the 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 𝐒𝐈 𝐀𝐬𝐬𝐨𝐜𝐢𝐚𝐭𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧! This journey equipped me with a solid foundation in MongoDB, covering the Document Model, CRUD Operations, Aggregation Pipelines, Indexing, and Data Modeling. Even more impressive is the 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦. It seems like a game-changer for developers, freeing them from database complexities and allowing them to focus on what they do best - building amazing applications. MDP offers increased development speed, improved scalability, and enhanced security – all critical features for modern development. My next goal is to complete 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 𝐒𝐈 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭 program to delve deeper into this powerful database. Cheers! 🍻 # MongoDB #SIAssociate #DeveloperPlatform #Database

🚀 Day 28 Highlights 🚀 🚩Dedicated the day to studying data structures and algorithms, along with solving Leetcode problems. Consistency is key! 💪 🚩Worked on CRUD operations in MongoDB. Understanding these basics is crucial for effective database management. 🗃️ #DSA #Leetcode #MongoDB #CRUDOperations #CodingJourney #ContinuousLearning #DeveloperLife

Ever dealt with a ton of data in Mongoose? My new blog shows how mongooseAggregatePaginate makes handling it a breeze! #backend #development #database

🚀 I'm so excited to explain what a CAP theorem is and what uses are ? 🚀 Hi LinkedIn fam 👋 Understanding the CAP Theorem in Distributed Systems: 👉 The CAP theorem is a fundamental principle in distributed database design, formulated by computer scientist Eric Brewer. It stands for Consistency, Availability, and Partition Tolerance, and it states that a distributed system can only guarantee two out of these three properties at any given time. Let’s dive into what each term means and explore the implications with an example. The Three Components of CAP 1.Consistency: - Every read receives the most recent write or an error. - Ensures that all nodes see the same data at the same time. 2.Availability: - Every request (read or write) receives a response, without guarantee that it contains the most recent write. - Ensures the system remains operational 100% of the time. 3.Partition Tolerance: - The system continues to operate despite network partitions. - Ensures that the system can handle any number of communication breakdowns between nodes. 👉 The Trade-offs According to the CAP theorem, a distributed system can only provide two of the three guarantees at the same time. Here's what this means in practice: - CA (Consistency and Availability): - Systems that choose consistency and availability over partition tolerance typically don't handle network failures well. They operate under the assumption that partitions are rare and can be ignored. - Example: Traditional relational databases when deployed on a single server. - CP (Consistency and Partition Tolerance): - Systems that choose consistency and partition tolerance will not be available during a network partition. They sacrifice availability to ensure that data remains consistent. - Example: MongoDB, HBase. - AP (Availability and Partition Tolerance): - Systems that choose availability and partition tolerance allow for some inconsistency during partitions. They prioritize uptime and accessibility over perfect consistency. - Example: Cassandra, DynamoDB.

This blog examines the performance of Materialize vs. Aurora PostgreSQL read replicas for computationally intensive workloads. We demonstrate that Materialize outperforms Aurora for complex queries over relatively small data volumes.  Specifically, for the same on-demand cost, Materialize delivers 100x greater throughput with 1000x lower latency. And unlike other solutions that offload computation from OLTP databases, Materialize does so without sacrificing correctness or requiring external change data capture (CDC) tools to move data between systems. https://lnkd.in/gDtmGitu We found MaterializeDB a great tool for the analytics use cases it designs for. What's your experience?

Speeding up index creation in PostgreSQL 🔍 **Speeding Up Index Creation in PostgreSQL** 🚀 Indexes are the secret sauce of relational databases. Without them, searches, constraints, and primary keys wouldn't even be a thing. But creating indexes on billions of rows? That's a slow-burning ordeal. I cooked up a billion-row dataset to see where the time goes. Spoiler: it’s more than just data size. After creating the dataset (a breezy 26 minutes), I ran VACUUM and built my first index. Sorting an ascending ID column took 5 minutes. Random integers? 7. Adding doubles? Slow grind to 7.5 minutes. And numeric types? Hit the brakes: 13 minutes. Why? The physical order and data type matter. It’s not just about more RAM and CPUs. Playing around with PostgreSQL parameters (max_wal_size, max_parallel_maintenance_workers, maintenance_work_mem, shared_buffers) slashed the index time from 13 minutes to 8. Still couldn't double the speed. The SSD speed hit the ceiling. Moral? More hardware helps, but smart data types and orderings do too. Credit to CYBERTEC PostgreSQL for the deep dive. Always learning from the pros. #PostgreSQL #databasetuning #indexing #performanceoptimization #datageek Link: https://lnkd.in/g3i8cgjy

In #PostgreSQL, recursive queries for hierarchical data can be implemented using Common Table Expressions (CTEs) with the WITH RECURSIVE clause, allowing efficient traversal of tree-like structures. Proper indexing and query planning are key for optimal performance.#PostgreSQLPerformance @MinervaDB #dba https://zurl.co/R2IH

AlloyDB is Unique. Like I say- it is 'PostgreSQL on Steroids' - just a better, faster and cheaper PostgreSQL. Among its many features, AlloyDB's AutoPilot capabilities such as Automatic memory management, Adaptive AutoVacuum, Automatic storage tiering ,Automatic data columnarization and query rewrite- makes management super efficient and easy. AlloyDB eliminates the drudgery of maintaining a PostgreSQL database by using, behind the scenes , advanced self-tuning machine learning algorithm. Want to know how AlloyDB uses Adaptive AutoVacuum? Read the blog to understand a real world example of how AlloyDB Adaptive AutoVacuum works and how AlloyDB Cluster Storage Space is Released- the allocated storage in AlloyDB is your Database used space! https://lnkd.in/eU2J38TS Sridhar Ranganathan Gurmeet G. #PostgreSQL #GCP #AlloyDB