SQL:2011 standardized temporal tables over a decade ago. Most databases moved on it fairly quickly. Postgres took its time. Postgres 19 is changing that, and Shaun Thomas breaks it all down in this week's PG Phriday. The old approach required btree_gist, hand-written exclusion constraints, and making your application responsible for every time-range split on update or delete. It worked. But Postgres didn't actually understand your data was temporal - it was just columns and an unusual index type. The new way: PRIMARY KEY (product_id, valid_at WITHOUT OVERLAPS) No extension. No exclusion constraint. Postgres handles overlap prevention natively. You also get FOR PORTION OF for partial-range updates and deletes - update a two-year price record for just one quarter and Postgres handles the row splitting automatically. And temporal foreign keys using the PERIOD keyword, where referenced records must fully cover the referencing row's entire validity period. System time isn't there yet - the docs acknowledge it and point to trigger-based emulation in the meantime. But application-time is a meaningful step. Hetti Dombrovskaya and others have been making the case for this since 2015 through pg_bitemporal... now it's in core. ✨ Read the full article: 🔗 hubs.la/Q04lcrqD0 #PostgreSQL #Postgres #TemporalTables #SQL #DatabaseEngineering #pgEdge #OpenSource #Tech #TechNews

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Your database crashes mid-transaction. Which rows got saved? Which didn't? How does Postgres even know? #PostgreSQL #SQL #DatabaseEngineering #DataEngineering #BackendDev

AWS Summit Mumbai is a wrap! 🇮🇳🚀 We had incredible conversations with teams running AI in production about what it really takes to handle massive concurrency and state at scale. Thank you to everyone who stopped by to explore how native distributed SQL is powering the AI era! #OceanBase #AWSSummit #DatabaseEngineering #DistributedSQL

If you've ever stared at a table that's still 138 MB after deleting 600,000 rows, you already understand the problem. Postgres doesn't return bloated space to the OS after a VACUUM - it just marks dead tuples as reusable. The only built-in ways to actually reclaim space, VACUUM FULL and CLUSTER, require an ACCESS EXCLUSIVE lock for the entire operation. On a 1TB table in a production system, that's a non-starter. So DBAs have reached for tools like pg_repack and pg_squeeze - third-party utilities that rewrite tables with minimal locking by leveraging logical decoding. They work, mostly. But they operate outside the core engine's safety guarantees, and that's enough to make conservative shops nervous. Postgres 19 may change the calculus entirely. The new REPACK command brings this functionality into the core engine, with a CONCURRENTLY option that holds an ACCESS EXCLUSIVE lock only during the brief final swap - not for the entire rewrite. Shaun Thomas walks through the full demo: 138 MB down to 52 MB, space genuinely returned to the OS. There's also a USING INDEX option that replaces CLUSTER, reordering rows physically to match an index. In his benchmark, that dropped a query from 3,300 buffer reads to 49, and execution time from 3ms to 0.6ms. Worth noting: REPACK CONCURRENTLY isn't MVCC-safe in a narrow edge case (transactions that have a snapshot open but haven't yet touched the table). Shaun explains when that matters and when it doesn't. Postgres 19 is still in development, so this could still change. But if it ships, it'll be one of the more practically useful additions for anyone running large, write-heavy databases. Read the full post: hubs.la/Q04jqCKS0 #PostgreSQL #Postgres19 #DatabaseEngineering #DBA #OpenSource #Postgres #DatabasePerformance

I’ll be speaking at the 5th DB Mastery Series: PostgreSQL on Kubernetes. My session: “ProxySQL on Kubernetes: the PostgreSQL Traffic Layer for Production Workloads” A lot of the PostgreSQL on Kubernetes discussion focuses on operators, pods, storage, services, and failover automation. All important. But in production, another question becomes critical: What happens to application traffic when the database topology changes? That is where the proxy layer matters. Connections, routing, read/write split, failover behavior, observability, and runtime control directly affect how applications behave during normal operations and incidents. 📅 May 29 🌐 Virtual 🎟️ Free to attend Register: buildevcon.com/register #PostgreSQL #Kubernetes #ProxySQL #DatabaseEngineering

Tarmo Kople ran databases at Bolt. Now he's at LHV Bank. At #TiDBSCaiLE Europe, he's sharing what TiDB looks like across both — startup scale to regulated banking. No fluff, just field experience. 🔍 June 4 | Stockholm 👉 ow.ly/aL9950YXKV2 #TiDB #DistributedSQL #DatabaseEngineering

📊If you work with financial data, you've probably been through this: query your data, export a CSV, switch to Python, then finally get your chart. medium.com/@DolphinDB_Inc/fr… There's a built-in plot() function that handles 2D and 3D financial charts directly inside the database — yield curves, vol smiles, interactive 3D volatility surfaces — all in one script, no external libraries needed. We put together a step-by-step guide using a USDCNY FX option vol surface as the running example, covering 5 chart types from yield curve comparison to interactive 3D surfaces. 👉 Learn more about us dolphindb.com #DolphinDB #TimeSeries #DatabaseEngineering #DataEngineering #BigData #Developer #DataInfrastructure

In case you missed it, “Real-Time Database Management on Raw Flash Memory: From Complex Infrastructure to Streamlined Drivers”. Read the technical article in @embedded_online t.ly/sIb2e #EmbeddedSystems #flashmemory #DBMS #databaseengineering #RTOS #EdgeComputing

Your SSD is a cache. S3 is the real database. That sounds wrong until you see the math: • S3: $0.023/GB/month • EBS gp3: $0.08/GB/month • 3x replication on EBS: $0.24/GB/month At 10TB, that's $230 vs $2,400/month. The trick is making S3 feel like local disk. That's what disaggregated storage engines do — hot path stays fast, cold path stays cheap. The future of databases isn't faster disks. It's smarter caching on infinite storage. The next challenging question is how to build a latency-sensitive OLTP database based on S3. #CloudNative #S3 #ObjectStorage #DatabaseEngineering #DistributedSystems #TiDB #DataInfra

New episode of #datainthehallway 🎙️ Cassio Perin and #TiDB Champion Everton Silva from Bling share their migration journey to #TiDB, the lessons they learned along the way, and how they’re now contributing back to the TiDB community. Watch full video: ow.ly/Lycf50Yv8yX #TiDB #DatabaseEngineering #Reliability #MySQL #DevOps

Found a bug in Dolt's TIMESTAMPDIFF: dates >292 years returned wrong values. Fixed using microsecond arithmetic. dolthub.com/blog/2026-02-20-… #GoLang #DatabaseEngineering #TimeBug

We shipped Commit Verification in Dolt. Block bad commits with SQL validation rules and automatic tests. See full details → dolthub.com/blog/2026-02-12-… #DatabaseEngineering #DataQuality

Announcing Agent Mode in the Dolt Workbench. Type English. Agent writes SQL. Version control makes it safe. Welcome to #CursorForSQL Free, open source, available now. dolthub.com/blog/2026-02-09-… #DatabaseEngineering

🔥 How to Scale Your Database to 1 Billion Users: A Sharding Deep Dive Hit a database bottleneck? Here's how Netflix, Instagram, and Uber handle billions of queries. 📊 The Problem: - Single database maxed out at 10K writes/sec - Read replicas aren't enough - Vertical scaling has limits 🎯 The Solution: Sharding What is Sharding? Splitting your data horizontally across multiple databases. Each "shard" holds a subset of your data. Sharding Strategies: 1️⃣ Hash-based Sharding • Use: User ID % number_of_shards • Pros: Even distribution • Cons: Hard to add new shards 2️⃣ Range-based Sharding • Use: Users 1-1M → Shard1, 1M-2M → Shard2 • Pros: Simple queries • Cons: Hotspot problems 3️⃣ Geographic Sharding • Use: US users → US shard, EU → EU shard • Pros: Lower latency, data compliance • Cons: Uneven load ⚠️ Challenges You'll Face: • Cross-shard joins become expensive • Maintaining data consistency • Rebalancing when adding shards • Transaction complexity increases 💡 Pro Tips: ✅ Choose your shard key carefully (it's hard to change!) ✅ Monitor shard sizes continuously ✅ Plan for resharding from day one ✅ Consider virtual shards for flexibility Real-world example: Instagram shards by User ID using consistent hashing. This lets them scale to 1B users while keeping response times under 100ms. Have you implemented sharding? What challenges did you face? #DatabaseEngineering #SystemDesign #ScalabilityEngineering #BackendDevelopment

We just hit MySQL-level performance with Dolt (0.99x on Sysbench)! 🚀 Full benchmarks: ow.ly/S9FV50XCZT3 #DatabaseEngineering #DevOps #MySQL #DatabasePerformance #OpenSourceDatabase #DataOps

📢 Dept. of IT, RIT hosted a Two-Day Industry Workshop on Database Engineering (13–14 Sept 2025) with Mr. Sunil Devakate, Epicacy Pvt. Ltd. 💻📊 Students gained core DB concepts, industry practices & interview-ready skills. 👩‍🎓✨ #RITIT #DatabaseEngineering #SkillBuilding

Boost 𝐃𝐁𝐀 & 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 with Generative AI 🤖 Automate SQL query writing & optimization 📊 Simplify schema design & documentation 🔍 Get faster insights from data ⚡ Speed up migration & troubleshooting 👉𝐑𝐞𝐚𝐝 𝐦𝐨𝐫𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: c-sharpcorner.com/article/ho… #GenerativeAI #DatabaseEngineering #DBA #AIProductivity #CSharpCorner

Boost 𝐃𝐁𝐀 & 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 with Generative AI 🤖 Automate SQL query writing & optimization 📊 Simplify schema design & documentation 🔍 Get faster insights from data ⚡ Speed up migration & troubleshooting 👉𝐑𝐞𝐚𝐝 𝐦𝐨𝐫𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 : c-sharpcorner.com/article/ho… #GenerativeAI #DatabaseEngineering #DBA #AIProductivity #CSharpCorner