| dc.description.abstract | Driven by the growing demands from big-data applications, the focus of their data
management has been largely shifted from traditional SQL databases to NoSQL (Not-only-
SQL) databases, such as key-value (KV) stores, which provides essential functionalities and
much higher performance for storing and retrieving data. Correspondingly new hardware
technologies have been developed to support the fast data accesses, such as NVMe SSDs
and Infiniband network. However, existing designs of NoSQL databases usually see sub-
optimal performance on fast hardware. Traditionally the computing overhead of a database
system is overshadowed by the slow storage and network. With the adoption of the new
hardware technologies the inefficiency at the software side is now the major source of
bottlenecks in today’s systems.
In this dissertation we propose solutions to overcome barriers on the adoption of new
technologies, such as large DRAMs, fast SSDs, and low-latency Infiniband network, into
existing stacks of software systems. Accordingly introduce new designs, including Search
Lookaside Buffer (SLB), zExapnder, LSM-trie, and NVMcached, to improve the systems’
efficiency on accessing SSD, DRAM, non-volatile main memory, and CPU cache. We
identify false temporal locality and false spatial locality in index search and propose SLB
to effectively improve index search by removing the false localities. In zExpander we use
compression to increase the effective capacity in KV caches without adding DRAM, which
can substantially reduce misses in the KV cache. In LSM-trie we introduce a trie-based data
structure to reduce the overhead of internal data reorganization by an order of magnitude for
KV stores on SSDs. In NVMcached we remove expansive FLUSH operations for persistent
and crash-consistent KV caches on byte-addressable NVM. | |
