Andrew Pavlo

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Michael Stonebraker54
Samuel Madden50
Daniel J. Abadi39
David J. Dewitt28
Stavros Harizopoulos25
36Beng Chin Ooi
30Gang Chen
29Michael R. Stonebraker
19Kian-Lee Tan
17Qian Lin

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Publication Year

2006
2017

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Co-author

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A comparison of approaches to large-scale data analysis
There is currently considerable enthusiasm around the MapReduce (MR) paradigm for large-scale data analysis [17]. Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some have called MR a dramatically new computing model [8, 17]. In this paper, we describe and compare both… (More)
H-store: a high-performance, distributed main memory transaction processing system
Our previous work has shown that architectural and application shifts have resulted in modern OLTP databases increasingly falling short of optimal performance [10]. In particular , the availability of multiple-cores, the abundance of main memory, the lack of user stalls, and the dominant use of stored procedures are factors that portend a clean-slate… (More)
Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems
The advent of affordable, shared-nothing computing systems portends a new class of parallel database management systems (DBMS) for on-line transaction processing (OLTP) applications that scale without sacrificing ACID guarantees [7, 9]. The performance of these DBMSs is predicated on the existence of an optimal database design that is tailored for the… (More)
OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases
Benchmarking is an essential aspect of any database management system (DBMS) effort. Despite several recent advancements, such as pre-configured cloud database images and database-as-a-service (DBaaS) offerings, the deployment of a comprehensive testing platform with a diverse set of datasets and workloads is still far from being trivial. In many cases,… (More)
Benchmarking OLTP/web databases in the cloud: the OLTP-bench framework
Benchmarking is a key activity in building and tuning data management systems, but the lack of reference workloads and a common platform makes it a time consuming and painful task. The need for such a tool is heightened with the advent of cloud computing--with its pay-per-use cost models, shared multi-tenant infrastructures, and lack of control on system… (More)
Staring into the Abyss: An Evaluation of Concurrency Control with One Thousand Cores
Computer architectures are moving towards an era dominated by many-core machines with dozens or even hundreds of cores on a single chip. This unprecedented level of on-chip parallelism introduces a new dimension to scalability that current database management systems (DBMSs) were not designed for. In particular, as the number of cores increases, the problem… (More)
Anti-Caching: A New Approach to Database Management System Architecture
The traditional wisdom for building disk-based relational database management systems (DBMS) is to organize data in heavily-encoded blocks stored on disk, with a main memory block cache. In order to improve performance given high disk latency, these systems use a multi-threaded architecture with dynamic record-level locking that allows multiple transactions… (More)
S-Store: A Streaming NewSQL System for Big Velocity Applications
First-generation streaming systems did not pay much attention to state management via ACID transactions (e.g., [3, 4]). S-Store is a data management system that combines OLTP transactions with stream processing. To create S-Store, we begin with H-Store, a main-memory transaction processing engine, and add primitives to support streaming. This includes… (More)
Bridging the Archipelago between Row-Stores and Column-Stores for Hybrid Workloads
Data-intensive applications seek to obtain trill insights in real-time by analyzing a combination of historical data sets alongside recently collected data. This means that to support such hybrid workloads, database management systems (DBMSs) need to handle both fast ACID transactions and complex analytical queries on the same database. But the current… (More)