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CockroachDB Serverless: Sub-second Scaling from Zero with Multi-region Cluster Virtualization

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This paper describes the architecture behind CockroachDB Serverless. At first glance, the design can feel like cheating. Rather than introducing a new physically disaggregated architecture with log and page stores, CRDB retrofits its existing implementation through logical disaggregation: It splits the binary into separate SQL and KV processes and calls it serverless. But dismissing this as fake disaggregation would miss the point. I came to appreciate this design choice as I read the paper (a well written paper!). This logical disaggregation (the paper calls it cluster virtualization) provides a pragmatic evolution of the shared-nothing model. CRDB pushes the SQL–KV boundary (as in systems like TiDB and FoundationDB) to its logical extreme to provide the basis for a multi-tenant storage layer. From here on, they solve the sub-second cold starts problem and admission control problems with good engineering rather than an architectural overhaul. System Overview If you split the stack at...
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Paper summary. Cloud programming simplified: A Berkeley view on Serverless Computing

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This position paper by UC Berkeley RISE lab is about serverless computing, its shortcomings, and its potential. It is easy reading, and is still useful even if you have a pretty good understanding about serverless computing due to some insights and forecasts in the paper. As you will read below, the paper provides a very strong endorsement for serverless computing. Instead of explaining the paper in my terms, I quote some of my highlights from the paper below, and at the end, in the MAD questions section, I discuss some of my thoughts on serverless computing. Introduction We believe the main reason for the success of low-level virtual machines was that in the early days of cloud computing users wanted to recreate the same computing environment in the cloud that they had on their local computers to simplify porting their workloads to the cloud. To set up your own environment in cloud (using virtual machines), you need to address these 8 issues. Redundancy for availability, ...
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Hints for Distributed Systems Design

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This is with apologies to Butler Lampson, who published the " Hints for computer system design " paper 40 years ago in SOSP'83. I don't claim to match that work of course. I just thought I could draft this post to organize my thinking about designing distributed systems and get feedback from others. I start with the same  disclaimer Lampson gave. These hints are not novel, not foolproof recipes, not laws of design, not precisely formulated, and not always appropriate. They are just hints.  They are context dependent, and some of them may be controversial. That being said, I have seen these hints successfully applied in distributed systems design throughout my 25 years in the field, starting from the theory of distributed systems (98-01), immersing into the practice of wireless sensor networks (01-11), and working on cloud computing systems both in the academia and industry ever since. These heuristic principles have been applied knowingly or unknowingly and has proven...
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The Agentic Self: Parallels Between AI and Self-Improvement

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2025 was the year of the agent. The goalposts for AGI shifted; we stopped asking AI to merely "talk" and demanded that it "act". As an outsider looking at the architecture of these new agents and agentic system, I noticed something strange. The engineering tricks used to make AI smarter felt oddly familiar. They read less like computer science and more like … self-help advice . The secret to agentic intelligence seems to lie in three very human habits: writing things down, talking to yourself, and pretending to be someone else. They are almost too simple. The Unreasonable Effectiveness of Writing One of the most profound pieces of advice I ever read as a PhD student came from Prof. Manuel Blum, a Turing Award winner. In his essay "Advice to a Beginning Graduate Student", he wrote: "Without writing, you are reduced to a finite automaton. With writing you have the extraordinary power of a Turing machine." If you try to hold a complex argument enti...
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Learning about distributed systems: where to start?

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This is definitely not a "learn distributed systems in 21 days" post. I recommend a principled, from the foundations-up, studying of distributed systems, which will take a good three months in the first pass, and many more months to build competence after that. If you are practical and coding oriented you may not like my advice much. You may object saying, "Shouldn't I learn distributed systems with coding and hands on? Why can I not get started by deploying a Hadoop cluster, or studying the Raft code." I think that is the wrong way to go about learning distributed systems, because seeing similar code and programming language constructs will make you think this is familiar territory, and will give you a false sense of security. But, nothing can be further from the truth. Distributed systems need radically different software than centralized systems do.  --A. Tannenbaum This quotation is literally the first sentence in my distributed systems syllabus. Inst...
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5 Lessons at 50

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Looking at my peak male physique, and my Keanu Reeves baby face, you would never suspect it, but I recently turned 50. As is the tradition, I thought about writing a post titled "50 Lessons at 50". Unfortunately, I don't have that kind of wisdom. The thing is, I still feel like I'm 18, same age as my son. Turns out this is the secret old guys have been hiding from us all along. You get older only on the outside, but inside you still see yourself as the same young lad. Still, fifty years should count for something. So what did I actually learn? How am I mentally different than my 18-year-old self. Here is my attempt to tally it up.  1. Caution is warranted I finally understand my parents. As you age, you accumulate battle scars, and the scars turn into habits. Anything that can go wrong will go wrong. You forget the cooktop on once, and suddenly you check it three times before leaving the house. True story. You stop diving head-first into a pile of leaves, because ther...
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Foundational distributed systems papers

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I talked about the importance of reading foundational papers last week. To followup, here is my compilation of foundational papers in the distributed systems area. (I focused on the core distributed systems area, and did not cover networking, security, distributed ledgers, verification work etc. I even left out distributed transactions, I hope to cover them at a later date.)  I classified the papers by subject, and listed them in chronological order. I also listed expository papers and blog posts at the end of each section. Time and State in Distributed Systems Time, Clocks, and the Ordering of Events in a Distributed System. Leslie Lamport, Commn. of the ACM,  1978. Distributed Snapshots: Determining Global States of a Distributed System. K. Mani Chandy Leslie Lamport, ACM Transactions on Computer Systems, 1985. Virtual Time and Global States of Distributed Systems.  Mattern, F. 1988. Practical uses of synchronized clocks in distributed systems. B. Liskov, 1991. Exp...
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Building a Database on S3

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Hold your horses, though. I'm not unveiling a new S3-native database. This paper is from 2008. Many of its protocols feel clunky today. Yet it nails the core idea that defines modern cloud-native databases: separate storage from compute. The authors propose a shared-disk design over Amazon S3, with stateless clients executing transactions. The paper provides a blueprint for serverless before the term existed. SQS as WAL and S3 as Pagestore The 2008 S3 was painfully slow, and 100 ms reads weren't unusual. To hide that latency, the database separates "commit" from "apply". Clients write small, idempotent redo logs to Amazon Simple Queue Service (SQS) instead of touching S3 directly. An asynchronous checkpoint by a client applies those logs to B-tree pages on S3 later. This design shows strong parallels to modern disaggregated architectures . SQS becomes the write-ahead log (WAL) and logstore. S3 becomes the pagestore. Modern Aurora follows a similar logic : t...
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Cloudspecs: Cloud Hardware Evolution Through the Looking Glass

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This paper (CIDR'26) presents a comprehensive analysis of cloud hardware trends from 2015 to 2025, focusing on AWS and comparing it with other clouds and on-premise hardware. TL;DR: While network bandwidth per dollar improved by one order of magnitude (10x), CPU and DRAM gains (again in performance per dollar terms) have been much more modest. Most surprisingly, NVMe storage performance in the cloud has stagnated since 2016. Check out the NVMe SSD discussion below for data on this anomaly. CPU Trends Multi-core parallelism has skyrocketed in the cloud. Maximum core counts have increased by an order of magnitude over the last decade. The largest AWS instance u7in now boasts 448 cores. However, simply adding cores hasn't translated linearly into value. To measure real evolution, the authors normalized benchmarks (SPECint, TPC-H, TPC-C) by instance cost. SPECint benchmarking shows that cost-performance improved roughly 3x over ten years. A huge chunk of that gain comes from AWS G...
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Aurora DSQL: Scalable, Multi-Region OLTP

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The Aurora DSQL paper finally dropped . Reading it yesterday was an interesting experience, because I spent two years (2022-23) working with the AWS team that designed and built Aurora DSQL. Since I have been very familiar with the architecture, the paper's overview description of the system didn't excite me much. And if I am being honest, reading the paper also felt a bit dry, most likely because I am not doing the usual extra thinking to explore/understand the ideas in the paper. But taking a step back today, and leaving my subjective experience aside, I will try to elaborate on how the DSQL architecture is actually built on a set of aggressive and opinionated engineering bets.  If I had to explain this architecture in a single sentence (just as I used to do for other teams at AWS) it would be this: We took a traditional monolithic database and blew out every single component into an independent, horizontally scalable service. Exploding the Monolith DSQL divides the database ...
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TLA+ modeling tips

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Model minimalistically Start from a tiny core, and always keep a working model as you extend. Your default should be omission. Add a component only when you can explain why leaving it out would not work. Most models are about a slice of behavior, not the whole system in full glory: E.g., Leader election, repair, reconfiguration. Cut entire layers and components if they do not affect that slice. Abstraction is the art of knowing what to cut . Deleting should spark joy.  Model specification, not implementation Write declaratively. State what must hold, not how it is achieved. If your spec mirrors control flow, loops, or helper functions, you are simulating code. Cut it out. Every variable must earn its keep. Extra variables multiply the state space (model checking time) and hide bugs. Ask yourself repeatedly: can I derive this instead of storing it? For example, you do not need to maintain a WholeSet variable if you can define it as a state function of existing variables: WholeSet =...
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Supporting our AI overlords: Redesigning data systems to be Agent-first

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This Berkeley systems group paper opens with the thesis that LLM agents will soon dominate data system workloads. These agents, acting on behalf of users, do not query like human analysts or even like the applications written by them. Instead, the LLM agents bombard databases with a storm of exploratory requests: schema inspections, partial aggregates, speculative joins, rollback-heavy what-if updates. The authors calls this behavior agentic speculation . Agentic speculation is positioned as both the problem and the opportunity. The problem is that traditional DBMSs are built for exact intermittent workloads and cannot handle the high-throughput redundant and inefficient querying of LLM agents. The opportunity also lies here. Agentic speculation has recognizable properties and features that invite new designs. Databases should adapt by offering approximate answers, sharing computation across repeated subplans, caching grounding information in an agentic memory store, and even steering...
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