Timescale

PostgreSQL and pgvector: Now Faster than Pinecone, 75% cheaper, 100% open-source. Introducing pgvectorscale, an open-source PostgreSQL extension that builds on pgvector, enabling greater performance and scalability. Here’s how pgvectorscale helps pgvector outperform specialized vector database like Pinecone: 1️⃣ StreamingDiskANN:  A new vector search index that overcomes limitations of in-memory indexes like HNSW the index on disk, making it more cost-efficient to run and scale as vector workloads grow. Inspired by the DiskANN paper from Microsoft. 2️⃣ Statistical Binary Quantization (SBQ): Developed by researchers at Timescale, this technique improves on standard binary quantization techniques by improving accuracy when using quantization to reduce the space needed for vector storage 3️⃣ Written in Rust, giving the PostgreSQL community to contribute to vector support. 📈The result? On our benchmark of 50 million Cohere embeddings (768 dimensions each), PostgreSQL with pgvector and pgvectorscale achieves 28x lower p95 latency and 16x higher query throughput compared to Pinecone for approximate nearest neighbor queries at 99 % recall, all at 75 % less cost when self-hosted on AWS EC2. We also tested it against Pinecone’s p2 high performance index, see the blog post at the end of this post for full results (spoiler: It’s just as impressive). Pgvectorscale is open-source under the PostgreSQL license and free for you to use on any PostgreSQL database for your AI projects. To get started, see the pgvectorscale github repo: https://lnkd.in/ghXj2e-U Or try it on Timescale Cloud on any new database service. Eager to learn more about pgvectorscale and how it works? Head over to our blog post with all the details: https://lnkd.in/gcMcxrVb

PostgreSQL has overtaken MySQL and has become the top choice among many developers. 🌟 Explore how it’s more than just an SQL database, with powerful extensions like Timescale, PostGIS, pgvector, and pgvectorscale transforming data management. 👇 🏅 Shout out to Uma Abu for this amazing video!

Learn how to install TimescaleDB on AWS to ensure a scalable and high-performance database for your time-series data needs. 💪 TimescaleDB is an open-source database built on PostgreSQL for efficiently managing time-series data, events, and analytics. It supports full SQL, making it user-friendly like traditional relational databases while scaling like NoSQL databases. This guide covers setting up TimescaleDB on AWS. Here's what it covers: 1️⃣ Getting started with AWS 2️⃣ Setting up an EC2 instance 3️⃣ Installing & Using TimescaleDB 4️⃣ Configuring PostgreSQL ...and more! Install TimescaleDB on AWS now and learn more: https://tsdb.co/ts-aws

Top 16 Open-Source Databases you should know! Whether you're developing for web, mobile, or enterprise, this list covers the essential databases for all your needs. Highlights: 1. MariaDB - High performance and rich features, ideal for web and enterprise applications. 2. PostgreSQL - Advanced, robust relational database known for extensibility and standards compliance. 3. MongoDB - Flexible, scalable NoSQL database using JSON-like documents. 4. CockroachDB - Distributed SQL database with strong consistency and horizontal scalability. 5. MySQL - Reliable, high-performance relational database widely used in web applications. 6. Redis - In-memory data store for database, cache, and message broker applications. 7. Neo4j - Graph database for storing and querying complex relationships. 8. Couchbase - High-performance NoSQL database combining document and key-value stores. 9. SQLite - Lightweight, serverless SQL database engine for embedded systems and mobile apps. 10. ScyllaDB - High-performance NoSQL database compatible with Cassandra. 11. PrestoDB - Distributed SQL query engine for fast analytics across various data sources. 12. Cassandra - Scalable NoSQL database for high availability and performance across many servers. 13. Apache HBase - Scalable, distributed big data store for real-time read/write access. 14. InfluxDB - Time-series database for fast, high-availability data storage and retrieval. 15. TimescaleDB - Time-series database built on PostgreSQL, optimized for fast ingest and complex queries. 16. ArangoDB - Multi-model database supporting graph, document, and key/value models. Join our Newsletter with 130k+ subscribers — www.theravitshow.com #data #ai #databases #opensource #theravitshow

Timescale's pgvectorscale has received phenomenal feedback! 🎉 It’s heartening to see such positive responses from our community and users. Your support and feedback are invaluable to us, and they motivate us to keep innovating and improving. 🐯 Ajay Kulkarni 🐯 Michael Freedman Ramon Guiu A big thank you to everyone who has been part of this journey. We're just getting started, and there's so much more to come. Stay tuned for exciting updates and new features! #VectorSearch #GenerativeAI #DatabaseInnovation #PostgreSQL

🐘 Ever wonder what happens under the hood when your PostgreSQL queries slow down? Use EXPLAIN to decipher query plans, understand operation costs, and see a real-world example of query optimization in action in our latest article with Timescale:

PostgreSQL is perfect for real-time monitoring and historical analysis, thanks to its robust SQL support. However, writing efficient, readable queries can be challenging. 🤔 In this webinar, learn to write 5 simple, efficient queries to gain deeper insights from your data, whether monitoring real-time changes or analyzing historical patterns. You'll get practical guidance to enhance your monitoring and analytical queries. 👇 During the session, you'll: 1️⃣ Learn essential PostgreSQL functions and build queries for common monitoring scenarios, like calculating deltas and rates. 2️⃣ Use TimescaleDB SQL functions for time-series analysis, such as filling data gaps and examining metrics over time. 3️⃣ Hear best practices and customizable guidance for your projects. ...and more! We'll focus on code and step-by-step demos using a real-world example and time-series dataset. Check it out: https://tsdb.co/pg-monitor

Artificial intelligence has evolved from science fiction to reality, thanks to innovations in generative AI like ChatGPT and Microsoft’s Copilot. To better understand the future of AI, 🐯 Ajay Kulkarni examines its past and offers insights in our latest newsletter. Learn more about the history of AI— its evolution, innovations, impact, and more.👇

🌍 Geo-Spatial Analysis with PostgreSQL and PostGIS: Mapping the Future 🗺️ Discover how to leverage the power of PostgreSQL and the PostGIS extension to perform advanced geo-spatial analysis and mapping. PostGIS is a powerful extension for PostgreSQL that adds support for geographic objects, allowing you to store, query, and analyze spatial data. This combination enables you to build location-based applications, perform spatial analysis, and create interactive maps. Code: sql -- Creating a table with a geographic column CREATE TABLE locations ( id SERIAL PRIMARY KEY, name TEXT, geom GEOMETRY(POINT, 4326) ); -- Inserting a new location INSERT INTO locations (name, geom) VALUES ('New York', ST_GeomFromText('POINT(-74.0060 40.7128)', 4326)); #PostgreSQL #PostGIS #GIS #Geospatial #Spatial

Public datasets provide valuable insights, but combining them with other data often requires a series of steps to normalize the data. 🔁 💡 Data normalization is the process of organizing data to reduce redundancy and improve data integrity. It typically involves dividing a database into smaller, related tables and defining relationships between them, following rules known as normal forms to help ensure consistent and efficient data storage and retrieval. In a recent project, we combined two public datasets— the San Francisco police incident and NOAA weather database, and encountered two challenges: 1️⃣ Different date formats (yyyy-mm-dd vs. mm/dd/yyyy) made joining data difficult. 2️⃣ Gaps in weather data complicated time-series graphs. Read our latest blog to learn how to solve these issues and tackle other common problems: https://lnkd.in/gqpVef4q