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In this paper we build on our research in data management on native Flash storage. In particular we demonstrate the advantages of intelligent data placement strategies. To effectively manage phsical Flash space and organize the data on it, we utilize novel storage structures such as regions and groups. These are coupled to common DBMS logical structures, thus require no extra overhead for the DBA. The experimental results indicate an improvement of up to 2x, which doubles the longevity of Flash SSD. During the demonstration the audience can experience the advantages of the proposed approach on real Flash hardware.
Asymmetric read/write storage technologies such as Flash are becoming
a dominant trend in modern database systems. They introduce
hardware characteristics and properties which are fundamentally
different from those of traditional storage technologies such
as HDDs.
Multi-Versioning Database Management Systems (MV-DBMSs)
and Log-based Storage Managers (LbSMs) are concepts that can
effectively address the properties of these storage technologies but
are designed for the characteristics of legacy hardware. A critical
component of MV-DBMSs is the invalidation model: commonly,
transactional timestamps are assigned to the old and the new version,
resulting in two independent (physical) update operations.
Those entail multiple random writes as well as in-place updates,
sub-optimal for new storage technologies both in terms of performance
and endurance. Traditional page-append LbSM approaches
alleviate random writes and immediate in-place updates, hence reducing
the negative impact of Flash read/write asymmetry. Nevertheless,
they entail significant mapping overhead, leading to write
amplification.
In this work we present an approach called Snapshot Isolation
Append Storage Chains (SIAS-Chains) that employs a combination
of multi-versioning, append storage management in tuple granularity
and novel singly-linked (chain-like) version organization.
SIAS-Chains features: simplified buffer management, multi-version
indexing and introduces read/write optimizations to data placement
on modern storage media. SIAS-Chains algorithmically avoids
small in-place updates, caused by in-place invalidation and converts
them into appends. Every modification operation is executed
as an append and recently inserted tuple versions are co-located.