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Auto-WLM: machine learning enhanced workload management in Amazon Redshift

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This paper appeared in Sigmod'23. What? Auto-WLM is a machine learning based *automatic workload manager* currently used in production in Amazon Redshift. I thought this would be a machine learning paper, you know deep learning and stuff. But this paper turned out to be a practical/applied data systems paper. At its core, this paper is about improving query performance and resource utilization in data warehouses, possibly the first for a database system in production at scale.  They are not using deep learning, and rightly so! The main take-away from the paper is that locally-trained simple models (using XGBoost , a decision tree-based model built from the query plan trees) outperformed globally trained models, likely due to their ability to "instance optimize" to specific databases and workloads. They are using simple ML. And it works. Why? This is an important problem. If tuning is done prematurely, resources are unnecessarily wasted, and if it is done too late, overall...
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TiDB: A Raft-based HTAP Database

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This paper is from VLDB 2020. TiDB is an opensource Hybrid Transactional and Analytical Processing (HTAP) database, developed by PingCap. The TiDB server, written in Go, is the query/transaction processing component; it is stateless, in the sense that  it does not store data and it is for computing only. The underlying key-value store, TiKV, is written in Rust, and it uses RocksDB as the storage engine. They add a columnar store called TiFlash, which gets most of the coverage in this paper. In this figure PD stands for Placement Driver (PD), which is responsible for managing Raft ranges, and automatically moving ranges to balance workloads. PD also hosts the timestamp oracle (TSO), which provides strictly increasing and globally unique timestamps to serve as transaction IDs. Each timestamp includes the physical time and logical time. The physical time refers to the current time with millisecond accuracy, and the logical time takes 18 bits. If you know about CockroachDB/CRDB ( he...
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Amazon Redshift Re-invented

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This paper (SIGMOD'22) discusses the evolution of Amazon Redshift since 2015 when it launched. Redshift is a cloud data warehouse. Data warehouse basically means a place where analysis/querying/reporting is done for shitload of data coming from multiple sources. Tens of thousands of customers use Redshift to process Exabytes of data daily. Redshift is fully managed to make it simple and cost-effective to efficiently analyze BIG data. The concept art in this blog post are creations of Stable Diffusion. Since its launch in 2015, the use cases for Redshift have evolved, and the teams focused on meeting the following customer needs High-performance execution of complex analytical queries using innovative query execution via C++ code generation Enabling fast scalability in response to changing workloads by disaggregating storage and compute layers Ease of use via incorporating machine learning based autonomics Seamless integration with the AWS ecosystem and other AWS purpose built serv...
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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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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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