DBOS, Inc.

Here's the recording of yesterday's live stream on building durable #Python backends with #opensource DBOS and DBOS Cloud: https://lnkd.in/gXXKWGVK DBOS architects Qian Li and Peter Kraft explained how: - DBOS wraps Python workflows, operations, and steps with functionality that makes them durable and observable by default. - #RAG workflows are made reliable  - Durability solves problems in #AI apps like #LLM wait time cost, time outs, and rate limiting,  Check it out and let us know what you think!

Come and get it! 🔔🔔 DBOS for #Python is now available! Open source DBOS Transact for Python is a lightweight durable execution library that makes it way easier to build fast, #resilient Python apps.  - Durable by default (way less coding!) - Exactly once processing (even less coding!) - Scheduled jobs (no need for cron) - Reliable by default - Observable by default (automatic distributed #OpenTelemetry traces) Run your durable Python apps and workflows anywhere--or deploy to DBOS Cloud with 1 click for serverless auto-scaling that's 25x faster than #AWS #Lambda and Step Functions. Check it out and let us know what you think!

Come and get it! 🔔🔔 DBOS for #Python is now available! Open source DBOS Transact for Python is a lightweight durable execution library that makes it way easier to build fast, #resilient Python apps.  - Durable by default (way less coding!) - Exactly once processing (even less coding!) - Scheduled jobs (no need for cron) - Reliable by default - Observable by default (automatic distributed #OpenTelemetry traces) Run your durable Python apps and workflows anywhere--or deploy to DBOS Cloud with 1 click for serverless auto-scaling that's 25x faster than #AWS #Lambda and Step Functions. Check it out and let us know what you think!

DBOS is coming to #Python! Join us on Sep 12 for a walkthrough of the #opensource DBOS Transact framework for Python.  Learn an easier way to build durable Python backends and workflows that can be deployed to the cloud and scaled to millions with a single click. https://lnkd.in/gXXKWGVK DBOS Architects and Co-Founders, Qian Li and Peter Kraft will show you:: - How DBOS Transact simplifies Python backends - How to build resilient AI apps with DBOS and #LangChain / #LlamaIndex - How to integrate DBOS Transact into your Python code - How to deploy to the cloud - How DBOS makes durable Python execution 25x more performant

Here's a great read by DBOS engineer, Henri Maxime Demoulin on #observability and different ways to solve the problem of #Distributed tracing. Shows options for instrumenting code with #OpenTelemetry, including a new #LowCode approach with open source DBOS Transact. Cloud-native applications become observable by default! https://lnkd.in/eqWGAgha #ObservableByDefault Cloud Native Computing Foundation (CNCF) OpenTelemetry

Just finished building my first app with DBOS Python! Every hour, search Hacker News for people commenting on a topic and post the comments to Slack. Entire app is <60 lines of code, and you can serverlessly deploy it to the cloud in one command. Check out the full app here: https://lnkd.in/gTwZtzc3

In Postgres (and many other databases), the 𝘀𝗲𝗿𝗶𝗮𝗹 𝗼𝗿𝗱𝗲𝗿 of serializable transactions isn't determined by their 𝘀𝘁𝗮𝗿𝘁 𝗼𝗿 𝗰𝗼𝗺𝗺𝗶𝘁 𝗼𝗿𝗱𝗲𝗿, which makes debugging tricky. For example, consider two concurrent transactions, Tx1 and Tx2, where Tx1 starts first but Tx2 commits first. Postgres allows this because the final effect is as if Tx1 ran and committed before Tx2, meaning the serial order is Tx1 -> Tx2, even though the commit order is Tx2 -> Tx1. If you swap the contents of these transactions, the serial order would reverse as well. According to Postgres docs, "any concurrent execution of a set of Serializable transactions is guaranteed to produce the same effect as running them one at a time in some order." However, this "some order" isn't tied to the transaction start or commit order, and the actual serial order can't be retrieved from Postgres. This complicates debugging -- replaying transactions by start or commit order won't reproduce the same results. The challenge is even greater with more commonly used levels like snapshot isolation when no serial order exists. I discussed more and proposed a solution in my VLDB'23 paper: https://lnkd.in/gsMM9RNr I'm writing a new blog post about how the DBOS time travel debugger can correctly reproduce past transactions under snapshot or serializable isolation. Stay tuned!

Final reminder: webcast today 11AM Pacific: "Stored Procedures: The Good, The Bad, and The Elegant" with DBOS engineer, Harry Pierson. Learn one of the ways DBOS makes backends faster and easier to build. Hope to see you there! https://lnkd.in/g8Ufr9c7 #PostgreSQL #AWS

We are super excited to announce Jeremy Edberg has joined DBOS, Inc. as CEO! 🙌 A long-time fan of #Serverless computing, he brings a lot of great experience to the team. "I'm incredibly excited to step into this role and join the team on its important mission. #DBOS is the culmination of the last decade plus of my career. I've always believed that serverless is the way of the future, but it's always been held up by all the complexity one had to deal with in order to make it reliable and secure. DBOS solves those problems, making serverless truly the wave of the future" Get to know Jeremy better here: https://lnkd.in/ea3HZHs4 Welcome aboard 🚀Jeremy!

DBOS is coming to #Python! Development of the DBOS Transact framework for Python is well underway. DBOS Transact makes it much simpler to build & scale durable Python backends. Join the early access program, we'd love your feedback! https://lnkd.in/ex35bbmN @Python_Dv