ﻻ يوجد ملخص باللغة العربية
Transaction logging is an essential constituent to guarantee the atomicity and durability in online transaction processing (OLTP) systems. It always has a considerable impact on performance, especially in an in-memory database system. Conventional implementations of logging rely heavily on a centralized design, which guarantees the correctness of recovery by enforcing a total order of all operations such as log sequence number (LSN) allocation, log persistence, transaction committing and recovering. This strict sequential constraint seriously limits the scalability and parallelism of transaction logging and recovery, especially in the multi-core hardware environment. In this paper, we define recoverability for transaction logging and demonstrate its correctness for crash recovery. Based on recoverability, we propose a recoverable logging scheme named Poplar, which enables scalable and parallel log processing by easing the restrictions. Its main advantages are that (1) Poplar enables the parallel log persistence on multiple storage devices; (2) it replaces the centralized LSN allocation by calculating a partially ordered sequence number in a distributed manner, which allows log records to only track RAW and WAW dependencies among transactions; (3) it only demands transactions with RAW dependencies to be committed in serial order; (4) Poplar can concurrently restore a consistent database state based on the partially constrained logs after a crash. Experimental results show that Poplar scales well with the increase of IO devices and outperforms other logging approaches on both SSDs and emulated non-volatile memory.
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
A blockchain is an append-only linked-list of blocks, which is maintained at each participating node. Each block records a set of transactions and their associated metadata. Blockchain transactions act on the identical ledger data stored at each node
Interactive tools make data analysis more efficient and more accessible to end-users by hiding the underlying query complexity and exposing interactive widgets for the parts of the query that matter to the analysis. However, creating custom tailored
Interactive tools make data analysis both more efficient and more accessible to a broad population. Simple interfaces such as Google Finance as well as complex visual exploration interfaces such as Tableau are effective because they are tailored to t
The query log of a DBMS is a powerful resource. It enables many practical applications, including query optimization and user experience enhancement. And yet, mining SQL queries is a difficult task. The fundamental problem is that queries are symboli