Summary Solid‐state drives (SSDs) have become an attractive alternative for storing large databases. Solid‐state drives do not present mechanical parts in their assembly. Consequently, SSDs have different characteristics and capabilities than magnetic disks Hard Disk Drives (HDDs). One of the most important features of SSDs is the fact that they implement different levels of internal parallelism for executing read/write operations. In database query processing, the table scan operator is responsible for reading an entire table. Generally, a scan operator is utilized whenever there is no index defined on the table to be accessed. This paper introduces a novel scan operator, denoted DaC Scan, for reading database tables stored in SSDs. The key goal of the proposed operator is to explore IO parallelism. Thus, the idea is to make DaC Scan able to read different regions of a single table simultaneously. For validation of DaC Scan efficiency, experiments with a prototype have been conducted over the TPC‐H benchmark database, scale factors 10 and 20. The results show that DaC Scan is efficient, presenting significant gains w.r.t. classical scan operators.

Nowadays the use of solid state drives (SSDs) is a reality for storing large databases. SSDs are capable to provide IOPS rates up to three orders of magnitude greater than the rates delivered by hard disk drives (HDD). Nonetheless, SSDs presents time asymmetry for executing read/write operations, which poses challenges on the database technology. This is because existing database management systems (DBMS) have been designed by assuming that databases are stored on devices, in which read/write operations are executed in the same amount of time. Thus, we claim that to take full profit from SSD properties, components of DBMS should be aware of read/write asymmetry in SSDs. It is well known that the join operation is the query operator which requires the highest amount of accesses (read/write operations) to the secondary memory. In this paper, we present a new join algorithm, called Bt-Join. The key goal of Bt-Join is to reduce the amount of write operations during the execution of any join operation R S. We have empirically evaluated Bt-Join. The results show that the proposed join operator can be up to 50% faster than FlashJoin, a wellknown join operator proposed to be deployed in SSDs.

The computer industry is moving toward the construction in large scale of chips with hundreds of cores to increase on-chip parallelism. In parallel, computers with solid state memory, the so-called solid state drive (SSD for short), providing petabytes of storage area, is emerging.Nonetheless, database systems (DBSs) were designed based upon two premises. The first is the usage of magnetic disks (HDDs). The second one is that distributed DBSs could scale beyond what a single-node DBMS can support. However, the latter premise only holds for a small number of CPU cores per node and for a limited number of nodes. Thus, to fully exploit benefits of parallelism and high IOPS rates supported by many-core machines with SSDs, database systems should be aware of upcoming CPU architecture and storage technology. In this paper, we present a new join algorithm, denoted DaC-Join, whose key goal is to exploit on-chip parallelism and high IOPS rates delivered by modern hardware. DaC-Join is able to run on multiple cores in parallel, increasing this way intra-operator parallelism. Furthermore, the proposed operator effectively decreases the activity of writing back join temporary results on SSDs, which minimizes the problem of write operation latency in SSDs. In order to validate the novel approach, simulations with a prototype have been conducted over TPC-H benchmark database, scale factor 10. The achieved results evidence the suitability of DaC-Join. KEYWORDS database query processing, parallel join operator, solid state memory 1 INTRODUCTION From a computer hardware perspective, we are witnessing nowadays the existence of two movements toward perhaps a singularity point in computer science. First, the computer industry is moving toward the construction in large scale of chips with hundreds of cores to increase on-chip parallelism. In the near future, we may have several-chip machines, each of which with hundreds of cores. In parallel to the development of several-core chip, a new type of non-volatile memory is emerging, ie, the so-called solid state memory or solid state drive (SSD). Examples of solid state memories are Flash Memory, Phase Change Memory (PCM), Memristors, and Non-Volatile RAM (NV-RAM), among others. SSDs present distinct characteristics and capabilities from hard disks drives (HDDs). The most evident one is the nonexistence of mechanical parts in SSDs. The IOPS rates supported by SSDs may be over 10 2 times greater than 15K RPM HDDs. Write operations on SSDs are much more expensive w.r.t. execution time and energy consumption than read operations phenomenon called read/write asymmetry. A read operation may be up to three times faster and consume up to eight times less energy than write operation. 1 The number of physical write operations on SSD may be far larger than the logical operations, since SSDs internally run two processes (wear leveling and garbage collection), which may induce a rise in the amount of physical write operations. 2 This phenomenon is called write amplification. Another key characteristi...