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Showing posts with the label benchmarks

Take Out the TraChe: Maximizing (Tra)nsactional Ca(che) Hit Rate

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This paper appeared in OSDI 2023 in July, and is authored by Audrey Cheng, David Chu, Terrance Li, Jason Chan, Natacha Crooks, Joseph M. Hellerstein, and Ion Stoica, UC Berkeley; Xiangyao Yu, University of Wisconsin--Madison. The paper seems to break the acronym rule. It defines one acronym, TraChe, in the title, but uses another one, Detox, for the rest of the paper. I guess I agree, referring to your system as Trache is not very appealing.   I kid, because I love the authors. Looks like I cover almost any paper Audrey Cheng writes these days. Speaking of Audrey, she had written a brief summary of this paper here: https://audreyccheng.com/blog/transaction-caching . Hmm, she didn't use the word "TraChe" in her post even once. I wonder which one of the advisors on the paper coined the typo TraChe, and pushed it to the title. Ok, enough with the trache talk. The Problem           You have been doing caching wrong for your transactional workloads! That ...
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Characterizing Microservice Dependency and Performance: Alibaba Trace Analysis

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This paper got the best paper award at SOCC 2021 . The paper conducts a comprehensive study of large scale microservices deployed in Alibaba clusters.  They analyze the behavior of more than 20,000 microservices in a 7-day period and profile their characteristics based on the 10 billion call traces collected. They find that: microservice graphs are dynamic in runtime most graphs are scattered to grow like a tree size of call graphs follows a heavy-tail distribution Based on their findings, they offer some practical tips about improving microservice runtime performance. They also develop a stochastic model to simulate microservice call graph dependencies and show that it approximates the dataset they collected (which is available at https://github.com/alibaba/clusterdata ). What are microservices? Microservices is a software development approach that divides an application into independently deployable services, owned by small teams organized around business capabilities. Each servi...
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Polyjuice: High-Performance Transactions via Learned Concurrency Control (OSDI'21)

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This paper appeared in OSDI 2021 . I really like this paper. It is informative, it taught me new things about concurrency control techniques. It is novel, it shows a practical application of simple machine learning to an important systems problem, concurrency control. It shows significant benefits for a limited but reasonable setup. It has good evaluation coverage and explanation. It is a good followup paper to the benchmarking papers we have been looking at recently. So let's go on learning about how Polyjuice was brewed. Problem and motivation There is no supreme concurrency control (CC) algorithm for all conditions. Different CC algorithms return the best outcome under different conditions.  Consider two extreme CC algorithms. Two phase locking (2PL) waits-for every dependent transactions to finish. Optimistic concurrency control (OCC) don't track or wait for any dependent transaction but validate at the end. We find that OCC is better with less contention, because it avoids...
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TAOBench: An End-to-End Benchmark for Social Network Workloads

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TAOBench is an opensource benchmarking framework that captures the social graph workload at Meta (who am I kidding, I'll call it Facebook). This paper (which will appear at VLDB'2022) studies the production workloads of Facebook's social graph datastore TAO, and distills them to a small set of representative features.  The integrity of TAOBench's workloads are validated by testing them against their production counterparts. The paper also describes several use cases of TAOBench at Facebook. Finally, the paper uses TAOBench to evaluate five popular distributed database systems (Spanner, CockroachDB, Yugabyte, TiDB, PlanetScale). The paper is a potpourri of many subquests. It feels a bit unfocused, but maybe I shouldn't complain because the paper is full to the brim, and provides three papers for the price of one. TAO TAO is a read-optimized geographically distributed in-memory data store that provides access to Facebook's social graph for diverse products and b...
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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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My Time at MIT

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Twenty years ago, in 2004-2005, I spent a year at MIT’s Computer Science department as a postdoc working with Professor Nancy Lynch. It was an extraordinary experience. Life at MIT felt like paradise, and leaving felt like being cast out. MIT Culture MIT’s Stata Center was the best CS building in the world at the time. Designed by Frank Gehry, it was a striking abstract architecture masterpiece ( although like all abstractions it was a bit leaky ). Furniture from Herman Miller complemented this design. I remember seeing price tags of $400 on simple yellow chairs. The building buzzed with activity.  Every two weeks, postdocs were invited to the faculty lunch on Thursdays, and alternating weeks we had group lunches. Free food seemed to materialize somewhere in the building almost daily, and the food trucks outside were also good. MIT thrived on constant research discussions, collaborations, and talks. Research talks were advertised on posters at the urinals, as a practical touch of M...
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Scalable OLTP in the Cloud: What’s the BIG DEAL?

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This paper is from Pat Helland, the apostate philosopher of database systems, overall a superb person, and a good friend of mine. The paper appeared this week at CIDR'24. (Check out the program for other interesting papers). The motivating question behind this work is: " What are the asymptotic limits to scale for cloud OLTP (OnLine Transaction Processing) systems? " Pat says that the CIDR 2023 paper "Is Scalable OLTP in the Cloud a Solved Problem?" prompted this question.  The answer to the question? Pat says that the answer lies in the joint responsibility of database and the application. If you know of Pat's work, which I have summarized several in this blog , you would know that Pat has been advocating along these lines before. But this paper provides a very crisp, specific, concrete answer. Read on for my summary of the paper. Disclaimer: This is a wisdom and technical information/detail packed 13-page paper, so I will try my best to summarize the sa...
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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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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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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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Distributed Transactions at Scale in Amazon DynamoDB

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This paper appeared in July at USENIX ATC 2023. If you haven't read about the architecture and operation of DynamoDB, please first read my summary of the DynamoDB ATC 2022 paper . The big omission in that paper was discussion about transactions. This paper amends that. It is great to see DynamoDB, and AWS in general, is publishing/sharing more widely than before. Overview A killer feature of DynamoDB is predictability at any scale. Do read Marc Brooker's post to fully appreciate this feature. Aligned with this predictability tenet, when adding transactions to DynamoDB, the first and primary constraint was to preserve the predictable high performance of single-key reads/writes at any scale. The second big constraint was to implement transactions using update in-place operation without multi-version concurrency control. The reason for this was they didn't want to mock with the storage layer which did not support multi-versioning. Satisfying both of the above constraints may s...
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Making database systems usable

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C. J. Date's Sigmod 1983 keynote, "Database Usability", was prescient. Usability is the most important thing to the customers. They care less about impressive benchmarks or clever algorithms, and more about whether they can operate and use a database efficiently to query, update, analyze, and persist their data with minimal headache. (BTW, does anyone have a link to the contents of this Sigmod'83 talk? There is no transcript around, except for this short abstract .) The paper we cover today is from Sigmod 2007. It takes on the database usability problem raised in that 1983 keynote head-on, and calls out that the king is still naked.  Let's give some context for the year 2007. Yes, XML format was still popular then. The use-case in the paper is XQuery. The paper does not contain any  reference to json. MongoDB would be released in 2009 with the document model; and that seems to be great timing for some of the usability pains mentioned in the paper! Web 2.0 was in ...
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Looming Liability Machines (LLMs)

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As part of our zoom reading group ( wow, 4.5 years old now ), we discussed a paper that uses LLMs for automatic root cause analysis (RCA) for cloud incidents. This was a pretty straightforward application of LLMs. The proposed system employs an LLM to match incoming incidents to incident handlers based on their alert types, predicts the incident's root cause category, and provides an explanatory narrative. The only customization is through prompt-engineering. Since this is a custom domain, I think a more principled and custom-designed  machine learning system would be more appropriate rather than adopting LLMs. Anyways, the use of LLMs for RCAs spooked me vicerally. I couldn't find the exact words during the paper discussion, but I can articulate this better now. Let me explain. RCA is serious business Root cause analysis (RCA) is the process of identifying the underlying causes of a problem/incident, rather than just addressing its symptoms. One RCA heuristic is asking 5 Why...
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Analyzing Metastable Failures in Distributed Systems

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So it goes: your system is purring like a tiger, devouring requests, until, without warning, it slumps into existential dread. Not a crash. Not a bang. A quiet, self-sustaining collapse. The system doesn’t stop. It just refuses to get better. Metastable failure is what happens when the feedback loops in the system go feral. Retries pile up, queues overflow, recovery stalls. Everything runs but nothing improves. The system is busy and useless. In an earlier post, I reviewed the excellent OSDI ’22 paper on metastable failures , which dissected real-world incidents and laid the theoretical groundwork. If you haven’t read that one, start there. This HotOS ’25 paper picks up the thread. It introduces tooling and a simulation framework to help engineers identify potential metastable failure modes before disaster strikes. It’s early stage work. A short paper. But a promising start. Let’s walk through it. Introduction Like most great tragedies, metastable failure doesn't begin with villain...
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