Current computer systems have evolved from featuring only a single processing unit and limited RAM, in the order of kilobytes or few megabytes, to include several multicore processors, o↵ering in the order of several tens of concurrent execution contexts, and have main memory in the order of several tens to hundreds of gigabytes. This allows to keep all data of many applications in the main memory, leading to the development of inmemory databases. Compared to disk-backed databases, in-memory databases (IMDBs) are expected to provide better performance by incurring in less I/O overhead.
In this dissertation, we present a scalability study of two general purpose IMDBs on
multicore systems. The results show that current general purpose IMDBs do not scale
on multicores, due to contention among threads running concurrent transactions. In
this work, we explore di↵erent direction to overcome the scalability issues of IMDBs in
multicores, while enforcing strong isolation semantics.
First, we present a solution that requires no modification to either database systems
or to the applications, called MacroDB. MacroDB replicates the database among several engines, using a master-slave replication scheme, where update transactions execute on the master, while read-only transactions execute on slaves. This reduces contention, allowing MacroDB to o↵er scalable performance under read-only workloads, while updateintensive workloads su↵er from performance loss, when compared to the standalone engine.
Second, we delve into the database engine and identify the concurrency control mechanism used by the storage sub-component as a scalability bottleneck. We then propose a new locking scheme that allows the removal of such mechanisms from the storage sub-component. This modification o↵ers performance improvement under all workloads, when compared to the standalone engine, while scalability is limited to read-only workloads.
Next we addressed the scalability limitations for update-intensive workloads, and
propose the reduction of locking granularity from the table level to the attribute level.
This further improved performance for intensive and moderate update workloads, at a
slight cost for read-only workloads. Scalability is limited to intensive-read and read-only
workloads.
Finally, we investigate the impact applications have on the performance of database
systems, by studying how operation order inside transactions influences the database performance. We then propose a Read before Write (RbW) interaction pattern, under
which transaction perform all read operations before executing write operations. The
RbW pattern allowed TPC-C to achieve scalable performance on our modified engine for all workloads. Additionally, the RbW pattern allowed our modified engine to achieve scalable performance on multicores, almost up to the total number of cores, while enforcing strong isolation
