ACM Queue - Databases

ACM Queue - Databases http://queue.acm.org/listing.cfm?item_topic=Databases&qc_type=topics_list&filter=Databases&page_title=Databases&order=desc Transactions and Serverless are Made for Each Other: If serverless platforms could wrap functions in database transactions, they would be a good fit for database-backed applications. http://queue.acm.org/detail.cfm?id=3674952 Databases 2024年7月05日 12:47:45 GMT Qian Li, Peter Kraft 3674952 Automatically Testing Database Systems: DBMS testing with test oracles, transaction history, and fuzzing http://queue.acm.org/detail.cfm?id=3639449 Databases 2024年1月12日 12:18:22 GMT Peter Alvaro, Manuel Rigger 3639449 A Conversation with Margo Seltzer and Mike Olson: The history of Berkeley DB http://queue.acm.org/detail.cfm?id=3501713 Databases 2021年11月18日 11:22:22 GMT Margo Seltzer, Mike Olson, Kirk McKusick 3501713 Crashproofing the Original NoSQL Key-Value Store http://queue.acm.org/detail.cfm?id=3487353 Databases 2021年9月19日 12:36:03 GMT Terence Kelly 3487353 Always-on Time-series Database: Keeping Up Where There's No Way to Catch Up: A discussion with Theo Schlossnagle, Justin Sheehy, and Chris McCubbin http://queue.acm.org/detail.cfm?id=3442634 Databases 2020年12月14日 14:19:03 GMT Theo Schlossnagle, Justin Sheehy, Chris McCubbin 3442634 Numbers Are for Computers, Strings Are for Humans: How and where software should translate data into a human-readable form http://queue.acm.org/detail.cfm?id=3379349 Unless what you are processing, storing, or transmitting are, quite literally, strings that come from and are meant to be shown to humans, you should avoid processing, storing, or transmitting that data as strings. Remember, numbers are for computers, strings are for humans. Let the computer do the work of presenting your data to the humans in a form they might find palatable. That's where those extra bytes and instructions should be spent, not doing the inverse. Databases 2020年1月13日 14:22:40 GMT George V. Neville-Neil 3379349 Back under a SQL Umbrella: Unifying serving and analytical data; using a database for distributed machine learning http://queue.acm.org/detail.cfm?id=3371598 Procella is the latest in a long line of data processing systems at Google. What's unique about it is that it's a single store handling reporting, embedded statistics, time series, and ad-hoc analysis workloads under one roof. It's SQL on top, cloud-native underneath, and it's serving billions of queries per day over tens of petabytes of data. There's one big data use case that Procella isn't handling today though, and that's machine learning. But in 'Declarative recursive computation on an RDBMS... or, why you should use a database for distributed machine learning,' Jankov et al. make the case for the database being the ideal place to handle the most demanding of distributed machine learning workloads. Databases 2019年11月06日 14:02:48 GMT Adrian Colyer 3371598 Write Amplification Versus Read Perspiration: The tradeoffs between write and read http://queue.acm.org/detail.cfm?id=3364509 In computing, there's an interesting trend where writing creates a need to do more work. You need to reorganize, merge, reindex, and more to make the stuff you wrote more useful. If you don't, you must search or do other work to support future reads. Databases 2019年9月23日 15:58:19 GMT Pat Helland 3364509 DAML: The Contract Language of Distributed Ledgers: A discussion between Shaul Kfir and Camille Fournier http://queue.acm.org/detail.cfm?id=3357728 "We'll see the same kind of Cambrian explosion we witnessed in the web world once we started using mutualized infrastructure in public clouds and frameworks. It took only three weeks to learn enough Ruby on Rails and Heroku to push out the first version of a management system for that brokerage. And that's because I had to think only about the models, the views, and the controllers. The hardest part, of course, had to do with building a secure wallet." Databases 2019年8月19日 13:25:37 GMT Shaul Kfir, Camille Fournier 3357728 Extract, Shoehorn, and Load: Data doesn’t always fit nicely into a new home. http://queue.acm.org/detail.cfm?id=3339880 It turns out that the business value of ill-fitting data is extremely high. The process of taking the input data, discarding what doesn't fit, adding default or null values for missing stuff, and generally shoehorning it to the prescribed shape is important. The prescribed shape is usually one that is amenable to analysis for deeper meaning. Databases 2019年6月05日 15:42:56 GMT Pat Helland 3339880