ﻻ يوجد ملخص باللغة العربية
Although significant recent progress has been made in improving the multi-core scalability of high throughput transactional database systems, modern systems still fail to achieve scalable throughput for workloads involving frequent access to highly contended data. Most of this inability to achieve high throughput is explained by the fundamental constraints involved in guaranteeing ACID --- the addition of cores results in more concurrent transactions accessing the same contended data for which access must be serialized in order to guarantee isolation. Thus, linear scalability for contended workloads is impossible. However, there exist flaws in many modern architectures that exacerbate their poor scalability, and result in throughput that is much worse than fundamentally required by the workload. In this paper we identify two prevalent design principles that limit the multi-core scalability of many (but not all) transactional database systems on contended workloads: the multi-purpose nature of execution threads in these systems, and the lack of advanced planning of data access. We demonstrate the deleterious results of these design principles by implementing a prototype system, ORTHRUS, that is motivated by the principles of separation of database component functionality and advanced planning of transactions. We find that these two principles alone result in significantly improved scalability on high-contention workloads, and an order of magnitude increase in throughput for a non-trivial subset of these contended workloads.
Research in transaction processing has made significant progress in improving the performance of multi-core in-memory transactional systems. However, the focus has mainly been on low-contention workloads. Modern transactional systems perform poorly o
Current main memory database system architectures are still challenged by high contention workloads and this challenge will continue to grow as the number of cores in processors continues to increase. These systems schedule transactions randomly acro
It is important for big data systems to identify their performance bottleneck. However, the popular indicators such as resource utilizations, are often misleading and incomparable with each other. In this paper, a novel indicator framework which can
Arguably data is the new natural resource in the enterprise world with an unprecedented degree of proliferation. But to derive real-time actionable insights from the data, it is important to bridge the gap between managing the data that is being upda
This paper focuses on the contention resolution problem on a shared communication channel that does not support collision detection. A shared communication channel is a multiple access channel, which consists of a sequence of synchronized time slots.