8,225 research outputs found
B+-tree Index Optimization by Exploiting Internal Parallelism of Flash-based Solid State Drives
Previous research addressed the potential problems of the hard-disk oriented
design of DBMSs of flashSSDs. In this paper, we focus on exploiting potential
benefits of flashSSDs. First, we examine the internal parallelism issues of
flashSSDs by conducting benchmarks to various flashSSDs. Then, we suggest
algorithm-design principles in order to best benefit from the internal
parallelism. We present a new I/O request concept, called psync I/O that can
exploit the internal parallelism of flashSSDs in a single process. Based on
these ideas, we introduce B+-tree optimization methods in order to utilize
internal parallelism. By integrating the results of these methods, we present a
B+-tree variant, PIO B-tree. We confirmed that each optimization method
substantially enhances the index performance. Consequently, PIO B-tree enhanced
B+-tree's insert performance by a factor of up to 16.3, while improving
point-search performance by a factor of 1.2. The range search of PIO B-tree was
up to 5 times faster than that of the B+-tree. Moreover, PIO B-tree
outperformed other flash-aware indexes in various synthetic workloads. We also
confirmed that PIO B-tree outperforms B+-tree in index traces collected inside
the Postgresql DBMS with TPC-C benchmark.Comment: VLDB201
Improving the Performance and Endurance of Persistent Memory with Loose-Ordering Consistency
Persistent memory provides high-performance data persistence at main memory.
Memory writes need to be performed in strict order to satisfy storage
consistency requirements and enable correct recovery from system crashes.
Unfortunately, adhering to such a strict order significantly degrades system
performance and persistent memory endurance. This paper introduces a new
mechanism, Loose-Ordering Consistency (LOC), that satisfies the ordering
requirements at significantly lower performance and endurance loss. LOC
consists of two key techniques. First, Eager Commit eliminates the need to
perform a persistent commit record write within a transaction. We do so by
ensuring that we can determine the status of all committed transactions during
recovery by storing necessary metadata information statically with blocks of
data written to memory. Second, Speculative Persistence relaxes the write
ordering between transactions by allowing writes to be speculatively written to
persistent memory. A speculative write is made visible to software only after
its associated transaction commits. To enable this, our mechanism supports the
tracking of committed transaction ID and multi-versioning in the CPU cache. Our
evaluations show that LOC reduces the average performance overhead of memory
persistence from 66.9% to 34.9% and the memory write traffic overhead from
17.1% to 3.4% on a variety of workloads.Comment: This paper has been accepted by IEEE Transactions on Parallel and
Distributed System
Persistent Buffer Management with Optimistic Consistency
Finding the best way to leverage non-volatile memory (NVM) on modern database
systems is still an open problem. The answer is far from trivial since the
clear boundary between memory and storage present in most systems seems to be
incompatible with the intrinsic memory-storage duality of NVM. Rather than
treating NVM either solely as memory or solely as storage, in this work we
propose how NVM can be simultaneously used as both in the context of modern
database systems. We design a persistent buffer pool on NVM, enabling pages to
be directly read/written by the CPU (like memory) while recovering corrupted
pages after a failure (like storage). The main benefits of our approach are an
easy integration in the existing database architectures, reduced costs (by
replacing DRAM with NVM), and faster peak-performance recovery
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