Metadata

This paper (to appear in VLDB'25) proposes a consensus algorithm called "Cabinet", which dynamically adjusts node weights based on responsiveness. As in the past three weeks, Aleksey and I live-recorded our first blind read and discussion of the paper to capture in real time how experts approach and dissect a paper. Below is the link to the video.  The paper I annotated during our read is available here . Frankly, we regretted reading this paper, as it had big flaws. Motivation The paper began by revisiting the role of consensus protocols in distributed systems: ensuring consistent state across replicas. It claimed that consensus often struggles with scalability because majority quorums scale poorly. But this is misleading because it omits that flexible quorums already address this issue. The paper ignores flexible quorums work, and mentions it only with one sentence. Ironically, as we will see later, flexible quorums resurface and undermine all of the paper's premis...

This Google paper (to appear in VLDB'25) is about not blowing up your production system. That is harder than it sounds, especially with stateful applications with memories. When rolling out new versions of stateful applications, the "shared, persistent, mutable" data means bugs can easily propagate across versions. Modern rollout tricks (canaries, blue/green deployments) don't save you from this. Subtle cross-version issues often slip through pre-production testing and surface in production, sometimes days or weeks later. These bugs can be severe, and the paper categorizes them as data corruption, data incompatibility, and false data assumptions. The paper mentions real-world incidents from Google and open-source projects to emphasize these bugs' long detection and resolution times, and the production outages and revenue loss they cause. So, we need tooling that directly tests v1/v2 interactions on realistic data before a rollout. The paper delivers a prototype o...

This paper (VLDB'2024) looks at boosting transaction throughput through better scheduling. The idea is to explore the schedule-space more systematically and pick execution orders that reduce conflicts. The paper's two main contributions are a scheduling policy called Shortest Makespan First (SMF) and a MVTSO concurrency control variant called MVSchedO. SMF uses a greedy heuristic to pick low-conflict schedules that minimize the increase in total execution time (makespan) at each step. MVSchedO enforces the chosen schedule at a fine-grained level by adapting multi-version timestamp ordering (MVTSO). The authors implement SMF and MVSchedO in RocksDB (R-SMF) and show up to 3.9x higher throughput and 3.2x lower tail latency on benchmarks as well as Meta's TAO workload. I mean, this is a stellar systems work, and it makes a convincing case that search-based scheduling is a promising direction for extracting higher throughput from database systems. Motivation Reducing contention...

The paper (arXiv 2020, also AI review 2023) opens up with discussing recent high-profile AI debates: the Montréal AI Debate and the AAAI 2020 fireside chat with Kahneman, Hinton, LeCun, and Bengio. A consensus seems to be emerging: for AI to be robust and trustworthy, it must combine learning with reasoning. Kahneman's "System 1 vs. System 2" dual framing of cognition maps well to deep learning and symbolic reasoning. And AI needs both. Neurosymbolic AI promises to  combine data-driven learning with structured reasoning, and provide modularity, interpretability, and measurable explanations. The paper moves from philosophical context to representation, then to system design and technical challenges in neurosymbolic AI.  Neurons and Symbols: Context and Current Debate This section lays out the historic divide within symbolic AI and neural AI. Symbolic approach supports logic, reasoning, and explanation. Neural approach excels at perception and learning from data. Symbolic...

The paper (2023) argues for integrating two historically divergent traditions in artificial intelligence (neural networks and symbolic reasoning) into a unified paradigm called Neurosymbolic AI. It argues that the path to capable, explainable, and trustworthy artificial intelligence lies in marrying perception-driven neural systems with structure-aware symbolic models.  The authors lean on Daniel Kahneman’s story of two systems in the mind ( Thinking Fast and Slow ). Neural networks are the fast ones: pattern-hungry, intuitive, good with unstructured mess. Symbolic methods are the slow ones: careful, logical, good with rules and plans. Neural networks, especially in their modern incarnation as large language models (LLMs), excel at pattern recognition, but fall short in tasks demanding multi-step reasoning, abstraction, constraint satisfaction, or explanation. Conversely, symbolic systems offer interpretability, formal correctness, and composability, but tend to be brittle (not in...

This paper from HotStorage'25 presents OrcaCache, a design proposal for a coordinated caching framework tailored to disaggregated storage systems. In a disaggregated architecture, compute and storage resources are physically separated and connected via high-speed networks. These became increasingly common in modern data centers as they enable flexible resource scaling and improved fault isolation. (Follow the money as they say!) But accessing remote storage introduces serious latency and efficiency challenges. The paper positions OrcaCache as a solution to mitigate these challenges by orchestrating caching logic across clients and servers. Important note: in the paper's terminology the server means the storage node, and the client means the compute node. As we did last week for another paper , Aleksey and I live-recorded our reading/discussion of this paper. We do this to teach t he thought-process and mechanics of how experts read papers in real time. Check our discussion vi...

This paper from SIGMOD 2016 proposes a transaction healing approach to improve the scalability of Optimistic Concurrency Control (OCC) in main-memory OLTP systems running on multicore architectures. Instead of discarding the entire execution when validation fails, the system repairs only the inconsistent operations to improve throughput in high-contention scenarios. If this sounds familiar, it's because we recently reviewed the Morty paper from EuroSys 2023 , which applied healing ideas to interactive transactions using continuations to support re-execution. This 2016 Transaction Healing paper is scoped to static stored procedures, and focuses more on integrating healing into OCC for stored procedures.  Key Ideas OCC works well under low contention because it separates reads from writes and  keeps critical sections short (only for validation). But under high contention, especially in workloads with skewed access patterns (like Zipfian distributions), transactions are frequent...

This paper (PODC'2016) presents a clean and declarative treatment of Snapshot Isolation (SI) using dependency graphs. It builds on the foundation laid by prior work, including the SSI paper we reviewed recently , which had already identified that SI permits cycles with two adjacent anti-dependency (RW) edges, the so-called inConflict and outConflict edges. While the SSI work focused on algorithmic results and implementation, this paper focuses more on the theory (this is PODC after all) of defining a declarative dependency-graph-based model for SI. It strips away implementation details such as commit timestamps and lock management, and provides a purely symbolic framework. It also proves a soundness result (Theorem 10), and leverages the model for two practical static analyses: transaction chopping and robustness under isolation-level weakening. Soundness result and dependency graph model Let's begin with Theorem 10, which establishes both the soundness and completeness of the...