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 Paper
• Yifei's LinkedIn
• Buy Me A Coffee
• Listener Survey

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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 Paper
• Vikramank's LinkedIn

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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 Paper
• OSDI'23 Chardonnay paper
• Tamer's Linkedin

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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.

• Matt's homepage
• VLDB'23 paper
• Tigger's Github repo

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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.

• VLDB'23 Paper
• Gabor's Homepage
• LDBC Homepage
• LDBC GitHub

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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.

• SoCC'23 Paper
• Thaleia's Homepage
• IMDEA Software Homepage
• GitHub Repo

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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!

• Jinkun's Homepage
• Nezha VLDB'23 Paper
• Nezha GitLab Repo

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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 Paper
• Dimitris's Homepage
• Study's source code

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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!

• Mohamed's homepage
• Groundhog EuroSys'23 paper
• Groundhog codebase

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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.

• SIGMOD Paper
• Detock Github Repo
• Cuong's Homepage

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