In this episode, we talk to Eleni Zapridou and delve into the challenges of data processing within enterprises, where multiple applications operate concurrently on shared resources. Traditional resource boundaries between applications often lead to increased costs and resource consumption. However, as Eleni explains the principle of functional isolation offers a solution by combining cross-task optimizations with performance isolation. We explore GroupShare, an innovative strategy that reduces CPU consumption and query latency, transforming data processing efficiency. Join us as we discuss the implications of functional isolation with Eleni and its potential to revolutionize enterprise data processing.Links:CIDR'24 PaperEleni's TwitterEleni's LinkedIn Hosted on Acast. See acast.com/privacy for more information.

In this thought-provoking podcast episode, we dive into the world of scalable OLTP (OnLine Transaction Processing) systems with the insightful Pat Helland. As a seasoned expert in the field, Pat shares his insights on the critical role of isolation semantics in the scalability of OLTP systems, emphasizing its significance as the "BIG DEAL." By examining the interface between OLTP databases and applications, particularly through the lens of RCSI (READ COMMITTED SNAPSHOT ISOLATION) SQL databases, Pat talks about the limitations imposed by current database architectures and application patterns on scalability.Through a compelling thought experiment, Pat explores the asymptotic limits to scale for OLTP systems, challenging the status quo and envisioning a reimagined approach to building both databases and applications that empowers scalability while adhering to established to RCSI. By shedding light on how today's popular databases and common app patterns may unnecessarily hinder scalability, Pat sparks discussions within the database community, paving the way for new opportunities and advancements in OLTP systems. Join us as we delve into this conversation with Pat Helland, where every insight shared could potentially catalyze significant transformations in the realm of OLTP scalability.Papers mentioned during the episode:Scalable OLTP in the Cloud: What’s the BIG DEAL?Autonomous ComputingDecoupled TransactionsDon't Get Stuck in the "Con" GameThe Best Place to Build a SubwayBuilding on QuicksandSide effects, front and centerImmutability changes everythingIs Scalable OLTP in the Cloud a solved problem?You can find Pat on:Twitter/XLinkedInScattered Thoughts on Distributed Systems Hosted on Acast. See acast.com/privacy for more information.

In this episode, we talk to Rui Liu and explore the transformative potential of Ratchet, a groundbreaking resource-adaptive query execution framework. We delve into the challenges posed by ephemeral resources in modern cloud environments and the innovative solutions offered by Ratchet. Rui guides us through the intricacies of Ratchet's design, highlighting its ability to enable adaptive query suspension and resumption, sophisticated resource arbitration for diverse workloads, and a fine-grained pricing model to navigate fluctuating resource availability. Join us as we uncover the future of cloud-native databases and workloads, and discover how Ratchet is poised to revolutionize the way we harness the power of dynamic cloud resources.Links:CIDR'24 PaperRui's LinkedIn Rui's Twitter/XRui's HomepageYou can find links to all Rui's work from his Google Scholar profile. Hosted on Acast. See acast.com/privacy for more information.

In this episode, Yifei Yang introduces predicate transfer, a revolutionary method for optimizing join performance in databases. Predicate transfer builds on Bloom joins, extending its benefits to multi-table joins. Inspired by Yannakakis's theoretical insights, predicate transfer leverages Bloom filters to achieve significant speed improvements. Yang's evaluation shows an average 3.3× performance boost over Bloom join on the TPC-H benchmark, highlighting the potential of predicate transfer to revolutionize database query optimization. Join us as we explore the transformative impact of predicate transfer on database operations.Links:CIDR'24 PaperYifei's LinkedInBuy Me A CoffeeListener Survey Hosted on Acast. See acast.com/privacy for more information.

In this episode, Vikramank Singh introduces the Panda framework, aimed at refining Large Language Models' (LLMs) capability to address database performance issues. Vikramank elaborates on Panda's four components—Grounding, Verification, Affordance, and Feedback—illustrating how they collaborate to contextualize LLM responses and deliver actionable recommendations. By bridging the divide between technical knowledge and practical troubleshooting needs, Panda has the potential to revolutionize database debugging practices, offering a promising avenue for more effective and efficient resolution of performance challenges in database systems. Tune in to learn more! Links:CIDR'24 PaperVikramank's LinkedIn Hosted on Acast. See acast.com/privacy for more information.

In this episode, Tamer Eldeeb sheds light on the challenges faced by geo-distributed database management systems (DBMSes) in supporting strictly-serializable transactions across multiple regions. He discusses the compromises often made between low-latency regional writes and restricted programming models in existing DBMS solutions. Tamer introduces Chablis, a groundbreaking geo-distributed, multi-versioned transactional key-value store designed to overcome these limitations.Chablis offers a general interface accommodating range and point reads, along with writes within multi-step strictly-serializable ACID transactions. Leveraging advancements in low-latency datacenter networks and innovative DBMS designs, Chablis eliminates the need for compromises, ensuring fast read-write transactions with low latency within a single region, while enabling global strictly-serializable lock-free snapshot reads. Join us as we explore the transformative potential of Chablis in revolutionizing the landscape of geo-distributed DBMSes and facilitating seamless transactional operations across distributed environments.CIDR'24 Chablis PaperOSDI'23 Chardonnay paperTamer's Linkedin Hosted on Acast. See acast.com/privacy for more information.

Summary: In this episode, we chat to Matt Butrovich about his research on database proxies. We discuss the inefficiencies of traditional database proxies, which operate in user-space, causing overhead due to buffer copying and system calls. Matt introduces "user-bypass" which leverages Linux's eBPF infrastructure to move application logic into kernel-space. Matt then tells us about Tigger, a PostgreSQL-compatible DBMS proxy, showcasing user-bypass benefits. Tune in to hear about the experiments that demonstrate how Tigger can achieve up to a 29% reduction in transaction latencies and a 42% reduction in CPU utilization compared to other widely-used proxies.Links: Matt's homepageVLDB'23 paperTigger's Github repo Hosted on Acast. See acast.com/privacy for more information.

Summary: In this episode, Gábor Szárnyas takes us on a journey through the LDBC Social Network Benchmark's Business Intelligence workload (SNB BI). Developed through collaboration between academia and industry the SNB BI is a comprehensive graph OLAP benchmark. It pushes the boundaries of synthetic and scalable analytical database benchmarks, featuring a sophisticated data generator and a temporal graph with small-world phenomena. The benchmark's query workload, rooted in LDBC's innovative design methodology, aims to drive future technical advancements in graph database systems. Gabor highlights SNB BI's unique features, including the adoption of "parameter curation" for stable query runtimes across diverse parameters. Join us for a succinct yet insightful exploration of SNB BI, where Gábor Szárnyas unveils the intricacies shaping the forefront of analytical data systems and graph workloads.Links: VLDB'23 PaperGabor's HomepageLDBC HomepageLDBC GitHub Hosted on Acast. See acast.com/privacy for more information.

Summary:In this week's episode, we talk with Thaleia Doudali and explore the realm of cloud resource forecasting, focusing on the use of Long Short Term Memory (LSTM) neural networks, a popular machine learning model. Drawing from her research, Thaleia discusses the surprising discovery that, despite the complexity of ML models, accurate predictions often boil down to a simple shift of values by one time step. The discussion explores the nuances of time series data, encompassing resource metrics like CPU, memory, network, and disk I/O across different cloud providers and levels. Thaleia highlights the minimal variations observed in consecutive time steps, prompting a critical question: Do we really need complex machine learning models for effective forecasting? The episode concludes with Thaleia's vision for practical resource management systems, advocating for a thoughtful balance between simple solutions, such as data shifts, and the application of machine learning. Tune in as we unravel the layers of cloud resource forecasting with Thaleia Doudali.Links:SoCC'23 PaperThaleia's HomepageIMDEA Software HomepageGitHub Repo Hosted on Acast. See acast.com/privacy for more information.

Summary: In this episode Jinkun Geng talks to us about Nezha, a high-performance consensus protocol. Nezha can be deployed by cloud tenants without support from cloud providers. Nezha bridges the gap between protocols such as MultiPaxos and Raft, which can be readily deployed, and protocols such as NOPaxos and Speculative Paxos, that provide better performance, but require access to technologies such as programmable switches and in-network prioritization, which cloud tenants do not have. Tune in to learn more! Links: Jinkun's HomepageNezha VLDB'23 PaperNezha GitLab Repo Hosted on Acast. See acast.com/privacy for more information.

Summary: In this episode, Dimitris Koutsoukos talks to us about Persistent or Non Volatile Memory (PMEM) and we answer the question: Is it Not Very Meaningful for Databases? PMEM offers expanded memory capacity and faster access to persistent storage. However, (before Dimitris's work) there was no comprehensive empirical analysis of existing database engines under diferent PMEM modes, to understand how databases can benefit from the various hardware configurations. Dimitris and his colleagues have then analyzes multiple diferent engines under common benchmarks with PMEM in AppDirect mode and Memory mode - tune in to hear the findings!Links:VLDB'23 PaperDimitris's HomepageStudy's source code Hosted on Acast. See acast.com/privacy for more information.

Summary:Security is a core responsibility for Function-as-a-Service (FaaS) providers. The prevailing approach has each function execute in its own container to isolate concurrent executions of different functions. However, successive invocations of the same function commonly reuse the runtime state of a previous invocation in order to avoid container cold-start delays when invoking a function. Although efficient, this container reuse has security implications for functions that are invoked on behalf of differently privileged users or administrative domains: bugs in a function’s implementation, third-party library, or the language runtime may leak private data from one invocation of the function to subsequent invocations of the same function.In this episode, Mohamed Alzayat tells us about Groundhog, which isolates sequential invocations of a function by efficiently reverting to a clean state, free from any private data, after each invocation. Tune in to learn more about how Groundhog works and how it improves security in FaaS!Links:Mohamed's homepageGroundhog EuroSys'23 paperGroundhog codebase Hosted on Acast. See acast.com/privacy for more information.

Summary: In this episode Cuong Nguyen tells us about Detock, a geographically replicated database system. Tune in to learn about its specialised concurrency control and deadlock resolution protocols that enable processing strictly-serializable multi-region transactions with near-zero performance degradation at extremely high conflict and improves latency by up to a factor of 5.Links: SIGMOD PaperDetock Github RepoCuong's Homepage Hosted on Acast. See acast.com/privacy for more information.

Concurrency bugs are difficult to detect, reproduce, and diagnose, as they manifest under rare timing conditions. Recently, active delay injection has proven efficient for exposing one such type of bug — thread-safety violations — with low over-head, high coverage, and minimal code analysis. However, how to efficiently apply active delay injection to broader classes of concurrency bugs is still an open question.In this episode, Bogdan Stoica tells us about how answered this question by focusing on MemOrder bugs — a type of concurrency bug caused by incorrect timing between a memory access to a particular object and the object’s initialization or deallocation. Tune to learn about Waffle — a delay injection tool that tailors key design points to better match the nature of MemOrder bugs. Links: EuroSys'23 PaperBogdan's HomepageWaffle's GitHub Repo Hosted on Acast. See acast.com/privacy for more information.

Summary: In this episode, Roger Waleffe talks about Graph Neural Networks (GNNs) for large-scale graphs. Specifically, he reveals all about MariusGNN, the first system that utilises the entire storage hierarchy (including disk) for GNN training. Tune in to find out how MaruisGNN works and just how fast it goes (and how much more cost-efficient it is!) Links: Marius ProjectRoger's Homepage Roger's TwitterEuroSys'23 PaperSupport the podcast through Buy Me a Coffee Hosted on Acast. See acast.com/privacy for more information.

Summary:The success of deep learning has sparked interest in improving relational table tasks, like data preparation and search, with table representation models trained on large table corpora. Existing table corpora primarily contain tables extracted from HTML pages, limiting the capability to represent offline database tables. To train and evaluate high-capacity models for applications beyond the Web, we need resources with tables that resemble relational database tables. In this episode, Madelon Hulsebos tells us all about such a resource! Tune in to learn more about GitTables!! Links: Madelon's websiteGitTables homepageSIGMOD'23 paperBuy Me A Coffee! Hosted on Acast. See acast.com/privacy for more information.

Summary:Compared to hard disk drives (HDDs), solid-state drives (SSDs) have two fundamentally different properties: (i) read/write asymmetry (writes are slower than reads) and (ii) access concurrency (multiple I/Os can be executed in parallel to saturate the device bandwidth). But, database operators are often designed without considering storage asymmetry and concurrency resulting in device under utilization. In thie episode, Tarikul Islam Papon tells us about his work on a new Asymmetry & Concurrency aware bufferpool management (ACE) that batches writes based on device concurrency and performs them in parallel to amortize the asymmetric write cost. Tune in to learn more! Links:ICDE'23 PaperPapon's HomepagePapon's LinkedInBuy me a coffee Hosted on Acast. See acast.com/privacy for more information.

Summary:Snapshot isolation is supported by most commercial databases and is widely used by applications. However, checking, if given a set of transactions, a database ensures Snapshot Isolation is either slow or gives up soundness. In this episode, Jian Zhang tells us about VIPER, an SI checker that is sound, complete, and fast. Tune in to learn more!! Links:PaperGitHub repoJian's homepage Hosted on Acast. See acast.com/privacy for more information.

Summary: Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device capabilities, and participant availability as deployments scale, which can impact both model convergence and bias. Existing FL schemes use random participant selection to improve fairness; however, this can result in inefficient use of resources and lower quality training. In this episode, Ahmed Sayed talks about how he and his colleagues address the question of resource efficiency in FL. He talks about the benefits of intelligent participant selection, and incorporation of updates from straggling participants. Tune in to learn more!Links:EuroSys'23 PaperAhmed's LinkedIn Ahmed's HomepageAhmed's TwitterREFL Github Hosted on Acast. See acast.com/privacy for more information.

Summary:Log-structured merge (LSM) trees have emerged as one of the most commonly used storage-based data structures in modern data systems as they offer high throughput for writes and good utilization of storage space. In this episode, Subhadeep Sarkar presents the fundamental principles of the LSM paradigm. He tells us about recent research on improving write performance and the various optimization techniques and hybrid designs adopted by LSM engines to accelerate reads. Tune in to find out more! Links:Personal websiteICDE'23 tutorialLinkedIn Hosted on Acast. See acast.com/privacy for more information.