Middleware-based Database Replication: The Gaps between Theory and Practice

The need for high availability and performance in data management systems has been fueling a long running interest in database replication from both academia and industry. However, academic groups often attack replication problems in isolation, overlooking the need for completeness in their solutions, while commercial teams take a holistic approach that often misses opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. This paper aims to characterize the gap along three axes: performance, availability, and administration. We build on our own experience developing and deploying replication systems in commercial and academic settings, as well as on a large body of prior related work. We sift through representative examples from the last decade of open-source, academic, and commercial database replication systems and combine this material with case studies from real systems deployed at Fortune 500 customers. We propose two agendas, one for academic research and one for industrial R&D, which we believe can bridge the gap within 5-10 years. This way, we hope to both motivate and help researchers in making the theory and practice of middleware-based database replication more relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, June 200

Byzantine Fault Tolerance for Nondeterministic Applications

All practical applications contain some degree of nondeterminism. When such applications are replicated to achieve Byzantine fault tolerance (BFT), their nondeterministic operations must be controlled to ensure replica consistency. To the best of our knowledge, only the most simplistic types of replica nondeterminism have been dealt with. Furthermore, there lacks a systematic approach to handling common types of nondeterminism. In this paper, we propose a classification of common types of replica nondeterminism with respect to the requirement of achieving Byzantine fault tolerance, and describe the design and implementation of the core mechanisms necessary to handle such nondeterminism within a Byzantine fault tolerance framework.Comment: To appear in the proceedings of the 3rd IEEE International Symposium on Dependable, Autonomic and Secure Computing, 200

Experience with the Open Source based implementation for ATLAS Conditions Data Management System

Conditions Data in high energy physics experiments is frequently seen as every data needed for reconstruction besides the event data itself. This includes all sorts of slowly evolving data like detector alignment, calibration and robustness, and data from detector control system. Also, every Conditions Data Object is associated with a time interval of validity and a version. Besides that, quite often is useful to tag collections of Conditions Data Objects altogether. These issues have already been investigated and a data model has been proposed and used for different implementations based in commercial DBMSs, both at CERN and for the BaBar experiment. The special case of the ATLAS complex trigger that requires online access to calibration and alignment data poses new challenges that have to be met using a flexible and customizable solution more in the line of Open Source components. Motivated by the ATLAS challenges we have developed an alternative implementation, based in an Open Source RDBMS. Several issues were investigated land will be described in this paper: -The best way to map the conditions data model into the relational database concept considering what are foreseen as the most frequent queries. -The clustering model best suited to address the scalability problem. -Extensive tests were performed and will be described. The very promising results from these tests are attracting the attention from the HEP community and driving further developments.Comment: 8 pages, 4 figures, 3 tables, conferenc

Centrally Banked Cryptocurrencies

Current cryptocurrencies, starting with Bitcoin, build a decentralized blockchain-based transaction ledger, maintained through proofs-of-work that also generate a monetary supply. Such decentralization has benefits, such as independence from national political control, but also significant limitations in terms of scalability and computational cost. We introduce RSCoin, a cryptocurrency framework in which central banks maintain complete control over the monetary supply, but rely on a distributed set of authorities, or mintettes, to prevent double-spending. While monetary policy is centralized, RSCoin still provides strong transparency and auditability guarantees. We demonstrate, both theoretically and experimentally, the benefits of a modest degree of centralization, such as the elimination of wasteful hashing and a scalable system for avoiding double-spending attacks.Comment: 15 pages, 4 figures, 2 tables in Proceedings of NDSS 201

On Predictive Modeling for Optimizing Transaction Execution in Parallel OLTP Systems

A new emerging class of parallel database management systems (DBMS) is designed to take advantage of the partitionable workloads of on-line transaction processing (OLTP) applications. Transactions in these systems are optimized to execute to completion on a single node in a shared-nothing cluster without needing to coordinate with other nodes or use expensive concurrency control measures. But some OLTP applications cannot be partitioned such that all of their transactions execute within a single-partition in this manner. These distributed transactions access data not stored within their local partitions and subsequently require more heavy-weight concurrency control protocols. Further difficulties arise when the transaction's execution properties, such as the number of partitions it may need to access or whether it will abort, are not known beforehand. The DBMS could mitigate these performance issues if it is provided with additional information about transactions. Thus, in this paper we present a Markov model-based approach for automatically selecting which optimizations a DBMS could use, namely (1) more efficient concurrency control schemes, (2) intelligent scheduling, (3) reduced undo logging, and (4) speculative execution. To evaluate our techniques, we implemented our models and integrated them into a parallel, main-memory OLTP DBMS to show that we can improve the performance of applications with diverse workloads.Comment: VLDB201

Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the cloud. While the provisioning of a database cluster is usually fully automated by cloud providers, customers typically still have to make important design decisions which were traditionally made by the database administrator such as selecting the partitioning schemes. In this paper we introduce a learned partitioning advisor for analytical OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea is that a DRL agent learns its decisions based on experience by monitoring the rewards for different workloads and partitioning schemes. We evaluate our learned partitioning advisor in an experimental evaluation with different databases schemata and workloads of varying complexity. In the evaluation, we show that our advisor is not only able to find partitionings that outperform existing approaches for automated partitioning design but that it also can easily adjust to different deployments. This is especially important in cloud setups where customers can easily migrate their cluster to a new set of (virtual) machines