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Best of metadata in 2025

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It is that time of year again to look back on a year of posts. I average about sixty posts annually. I don't explicitly plan for the number, and I sometimes skip weeks for travel or work, yet I somehow hit the number by December. Looking back, I always feel a bit proud. The posts make past Murat look sharp and sensible, and I will not argue with that. Here are some of the more interesting pieces from the roughly sixty posts of 2025. Advice Looks like I wrote several advice posts this year. I must be getting old. The Invisible Curriculum of Research Academic chat: On PhD What I'd do as a College Freshman in 2025 My Time at MIT What makes entrepreneurs entrepreneurial? Publish and Perish: Why Ponder Stibbons Left the Ivory Tower Databases Concurrency Control book reading was fun. Also the series on use of time in distributed databases. And it seems like I got hyperfocused on transaction isolation this year.  Concurrency Control and Recovery in Database Systems Book reading series...
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Best of Metadata in 2024

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I can't believe we wasted another good year. It is time to reflect back on the best posts at Metadata blog in 2024. (I think you guys should tip me just because I didn't call this post "Metadata wrapped".) Distributed systems posts Transactional storage for geo-replicated systems(SOSP11):  I like this paper because it asked the right questions, and introduced parallel snapshot isolation. No individual part is novel (vector clocks, csets) but their composition together and application to WAN web applications have been novel. Walter showed how to limit WAN coordination, while still developing useful applications. An Hourglass Architecture for Distributed Systems (SRDS24 Keynote):  This work successfully bridges theoretical research and practical implementation in large-scale distributed systems in Facebook/Meta control plane. The shared log abstraction proposes to separate the system into two layers: the database layer (proposers/learners) and the log layer (acceptors)....
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Best of Metadata in 2023

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It is that most wonderful time of the year again. Time to reflect back on the best posts at Metadata blog in 2023. Distributed systems Hints for Distributed Systems Design :   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. Metastable failures in the wild : Metastable failure is defined as permanent overload with low throughput even after the fault-trigger is removed. It is an emergent behavior of a system, and it naturally arises from the optimizations for the common case that lead to sustained work amplification. Towards Modern Development of Cloud Applications : This is an easy-to-read paper, but it is not an easy-to-agree-with paper. The message is controversial: Don't do microservices, write a monolith,...
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Best of Metadata in 2022

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It is time to reflect back on the year. Here are some selected papers I covered in 2022. Production systems Amazon Aurora: Design Considerations + On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes Aurora provides a a high-throughput relational database on AWS cloud by decoupling the storage to a multi-tenant scale-out storage service. Each database instance acts as a SQL endpoint and supports query processing and transactions. Many database functions, such as redo logging, materialization of data blocks, garbage collection, and backup/restore, are offloaded to the storage nodes. Aurora performs replication among the storage nodes by pushing the redo log; this reduces networking traffic and enables fault-tolerant storage that heals without database involvement. Amazon DynamoDB: A Scalable, Predictably Performant, and Fully Managed NoSQL Database Service (USENIX ATC 2022) DynamoDB has massive scale; it powers Alexa, Amazon.com sites, and all Amazon fulfillment c...
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Best of metadata in 2021

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As it became our tradition, here are some highlights from my 2021 posts. Systems Foundational distributed systems papers There is plenty of room at the bottom Graviton2 and Graviton3 Fail-silent Corruption Execution Errors (CEEs) at CPU/cores SOSP21 conference (Day1) Using Lightweight Formal Methods to Validate a Key-Value Storage Node in Amazon S3 Building Distributed Systems With Stateright Sundial: Fault-tolerant Clock Synchronization for Datacenters Do tightly synchronized clocks help consensus? Databases Linearizability What's Really New with NewSQL? A read-only transaction anomaly under snapshot isolation FoundationDB Record Layer: A Multi-Tenant Structured Datastore Misc Learning a technical subject Your attitude determines your success Humans of Computer Systems: Obdurodon Facebook: The Inside Story (2020) by Steven Levy Previous years in review Year in review 2020 Year in review 2019 Year in review 2018 Research, writing, and career advice
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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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Decade in Review

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We are entering 2020, and this is a good time to retrospect and review the past decade from the lens of this blog, which started late 2010. I started posting regularly on September 2010. I wanted to get into the cloud computing domain, so I needed to accumulate background on cloud computing work. I decided that as I read papers on cloud computing, I will post a summary to this blog. I thought if I could explain what I learned from the papers in my own words, I would internalize those lessons better. And if others read those summaries and benefit, that is an extra plus. Initially I reviewed and posted paper summaries on big data processing systems and NoSQL distributed databases to catch up on these areas. Around 2010s, misrepresentation of the CAP theorem by NoSQL proponents was a problematic issue. I covered some papers that try to clarify this issue. Throughout the years, I covered many papers that discussed the different consistency guarantees offered by distributed databases....
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Year in Review

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As is the custom in a year-end post, here are some highlights among my 2018 posts. Blockchains Spring 18 Distributed Systems Seminar   Remember peer-to-peer systems?   Replying to why decentralization matters   Snowflake to Avalanche: A Novel Metastable Consensus Protocol Family for Cryptocurrencies   IPFS: Content addressed, versioned, P2P file system   The last mile problem in trust   TLA+  Why you should use modeling [with TLA+]   Debugging designs with TLA+  Modeling the DAO attack in PlusCal  Azure Cosmos DB Azure Cosmos DB   Schema-Agnostic Indexing with Azure Cosmos DB   Logical index organization in Cosmos DB   Academia Academic/research impact  Research and climbing  How to fix scholarly peer review Paxos  Anatomical similarities and differences between Paxos and blockchain consensus protocols  Paxos derived  SDPaxos: Building efficient semi-decent...
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Best of 2017 in MuratBuffalo

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This is my 77th post for the year. As is the custom in a year-end post , I mention some highlights among these posts in 2017. Machine Learning My first impressions after a week of using TensorFlow Google DistBelief paper: Large Scale Distributed Deep Networks Learning Machine Learning: Deep Neural Networks Google Federated Learning DeepXplore, Automated Whitebox Testing of Deep Learning Systems Dynet: The Dynamic Neural Network Toolkit A Comparison of Distributed Machine Learning Platforms The Case for Learned Index Structures Cloud Computing and Big Data Analytics Occupy the Cloud: Distributed Computing for the 99% Cloud fault-tolerance Mesos: A platform for fine-grained resource sharing in the data center Retroscope: Retrospective cut-monitoring of distributed systems (part 3) Making sense of Performance in Data Analytics Frameworks Performance clarity as a first-class design principle On dataflow systems, Naiad and Tensorflow Distributed Coordination ...
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Popular posts from this blog

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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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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Advice to the young

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I notice I haven't written any advice posts recently. Here is a collection of my advice posts pre 2020. I've been feeling all this elderly wisdom pent up in me, ready to pour at any moment. So here it goes. Get ready to quench your thirst from my fount of wisdom. No man, think for yourself, only get what works for you. It is called foundations, not theory Foundations of computer science (or rather any field of study) are the most important topics you can learn. These lay down the frame of thinking/perspective for that area of study. Yet, I am saddened to hear these called as "theory", and labeled as "unpractical". This couldn't be farther from the truth. Take a look at how I recommend studying distributed systems . Don't you dare call this "theory" and "unpractical". This lays the bedrock that you build your practice on. Don't skimp on the foundations. Don't build your home on quicksand. Keep your hands dirty, your mind cl...
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