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

As more RTO news hits my feed, I wanted to highlight some awesome, fully remote roles that my team is hiring for at Timescale! ⭐ 🐯 🦄 Solutions Architect  Enterprise Account Executive Senior Analyst, Sales Operations Database Internals Software Engineer + more on timescale.com/careers

As part of my onboarding, I’ve been diving into some amazing resources, and one that really grabbed my attention was all about how Timescale scaled Postgres to handle a mind-blowing 10 billion new records per day! The coolest part? It’s Timescale using Timescale! 🐯 In this case, Timescale (our super powerful extension built on PostgreSQL) was the hero, managing over 350 TB of data while handling those billions of daily records. This was all for a project that helped build Insights—a feature that collects and analyzes query stats from all of our customers’ databases, storing everything in a single Timescale service powered by TimescaleDB. ✏️ What’s even more impressive is that the team was "dogfooding" (yes, they used our own tech to test and improve it!). They leaned on some key TimescaleDB features like hypertables, continuous aggregates, compression, and tiered storage to make this massive scale happen. 👏🏻 It’s super inspiring to see our platform being put to the test and performing so well! 🚀 If you’re into serious database magic, definitely check it out! #PostgreSQL #BigData #RealTimeAnalytics #Timescale

For someone who works closely with customers in the #DBaaS world, I see a lot of businesses struggling to balance fast, real-time data needs with complex analytics. It’s tough! But Timescale’s Hypercore is a real lifesaver in this space, and I’m excited to talk about it. Most of the time, clients are trying to manage large volumes of real-time data—whether that’s tracking IoT devices, keeping up with financial transactions, or analyzing user behavior. These businesses need two things: speed and accuracy. They need data to flow in fast but also be ready for instant analysis. The problem is, most databases make you pick one or the other. 🥲 This is where Hypercore really shines. It’s a smart hybrid storage engine that automatically handles both fast inserts and efficient analytics. Hypercore lets your data be written quickly, but when it's ready for analysis, it automatically shifts into a format that’s optimized for fast queries—without you having to lift a finger. For example, Toyota Motor Corporation uses TimescaleDB and Hypercore to monitor their NASCAR race cars in real time. They can ingest data on car performance instantly, while also running deeper queries to analyze trends—all without slowing down the system. And then there’s OVHCloud, who saw a big drop in their storage needs (and costs) thanks to Hypercore’s 98% compression. Now they handle high-speed billing data with ease, while keeping their storage and costs under control. Hypercore is a game-changer because it automatically switches between row-based and column-based storage, meaning you get the best of both worlds: fast data ingestion and quick analysis. Plus, it’s all built into PostgreSQL, so you don’t need to learn anything new—just plug it in and let Hypercore do the heavy lifting. For businesses handling real-time analytics, whether it’s for tracking, monitoring, or decision-making, Hypercore makes life so much easier. You can focus on your data and insights, and let Timescale handle the performance and storage.

🐘 𝐩𝐠𝐯𝐞𝐜𝐭𝐨𝐫𝐬𝐜𝐚𝐥𝐞: 𝐚𝐧 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞 𝐞𝐱𝐭𝐞𝐧𝐬𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐛𝐨𝐨𝐬𝐭𝐬 𝐯𝐞𝐜𝐭𝐨𝐫 𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐢𝐧 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 And it's now available on more PostgreSQL versions -- PostgreSQL 13, 14, 15, 16, and 17. This is the way. https://lnkd.in/g4Nrysw9

🐘 𝐩𝐠𝐯𝐞𝐜𝐭𝐨𝐫𝐬𝐜𝐚𝐥𝐞 𝐢𝐬 𝐚𝐧 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞 𝐞𝐱𝐭𝐞𝐧𝐬𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐛𝐨𝐨𝐬𝐭𝐬 𝐯𝐞𝐜𝐭𝐨𝐫 𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐢𝐧 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋. And it's now available on more PostgreSQL versions -- PostgreSQL 13, 14, 15, 16, and 17 to be exact! Supporting pgvectorscale in more versions of PostgreSQL has been a popular ask from our community. ⚡ The latest release of pgvectorscale makes 𝐡𝐢𝐠𝐡-𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞, 𝐜𝐨𝐬𝐭-𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐯𝐞𝐜𝐭𝐨𝐫 𝐬𝐞𝐚𝐫𝐜𝐡 on PostgreSQL accessible to more devs building with older/ newer version of PostgreSQL. 🧑💻Instructions for getting started are on the pgvectorscale GitHub repo: https://lnkd.in/eeWBjyjr 📚 And for details on pgvectorscale's StreamingDiskANN index and Statistical Binary Quantization features, check out this technical explainer: https://lnkd.in/eFD5wpeC #postgresql17 #pgvector #vectordatabase

𝗪𝗵𝗮𝘁 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗔𝗜 𝗮𝗽𝗽𝘀 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘄𝗶𝘁𝗵 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟? 🐘 🤖 Now everyday application developers with no specialized AI/ML background can build AI apps with PostgreSQL. Thanks to its robust relational foundation and ecosystem of extensions like pgvector, pgvectorscale, and pgai, PostgreSQL is all you need to build a state of the art AI application. Just like the timescaledb extension turned Postgres into a time series database, now the pgvectorscale and pgai extensions have turned Postgres into a vector database. Here are some AI systems you can build using PostgreSQL, with resources to learn more: ➡️ RAG: https://lnkd.in/gg-T6MQ7 ➡️ Search: https://lnkd.in/gY5KYnaY ➡️ Agents: https://lnkd.in/g6nGsY9n ➡️ Text to SQL: https://lnkd.in/grJtr_9c ➡️ Recommendation Systems: https://lnkd.in/gvpN_dzc No wonder PostgreSQL is catching fire as the default database choice for AI applications! That’s something covered in detail in our recent webinar: https://lu.ma/0v7nwfxd #PostgreSQL #Timescale #ArtificialIntelligence #AI #Vectors #LLM #RAG #SQL #Postgres #SQL 

Watching demos from our recent team hackathon in Lisbon. And wow is this team talented (and creative)! Can't wait to share these with you all. 

Just successfully deployed a major Timescale schema refactor to production for two customers, achieving zero downtime in real-time processing and near-instant recovery of data post-deployment. This was made possible by our platform and its seamless GitOps integration. Huge kudos to Cosimo Meli and matteo parmi—just another day at Zaphiro Technologies! Credits to ChatGPT for the image.

As your application grows, so do your PostgreSQL tables—sometimes to billions of rows. That’s when performance lags and storage costs start to bite. But fear not, you don’t have to say goodbye to your favorite relational database! At Timescale, we've developed a powerful columnar compression feature that scales PostgreSQL databases effortlessly. With compression rates reaching over 95%, you can store more data with smaller disks, save money, and boost query performance. What makes it even better? Timescale's compression is flexible and fully mutable—making it easy to add, update, and delete data, or alter table schemas without the hassle of decompressing the whole dataset. 🗣️ Here’s what our customers are saying: “Timescale is solving a big headache for us... I can spin up two tables in an hour with 94% compression rates!” Björn Olafur Johannsson, AVO Whether you're dealing with real-time analytics, sensor data, or AI workloads, TimescaleDB unlocks incredible efficiency by combining the best of both row and column-oriented databases—keeping your queries fast and your costs low. Ready to scale PostgreSQL without the pain? Stay with Postgres, scale with Postgres! 💪 #PostgreSQL #Databases #DataCompression #DataAnalytics #ScalingSolutions https://lnkd.in/guieQjgy