=Paper=
{{Paper
|id=Vol-2367/invited_2
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2367/invited_2.pdf
|volume=Vol-2367
|dblpUrl=https://dblp.org/rec/conf/gvd/Habich19
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2367/invited_2.pdf</pdf>
<pre>
 MorphStore – From an Idea to a Novel In-Memory Column
                 Store Database System
                                                              [Abstract]
                                                             Dirk Habich
                                              TU Dresden, Database Systems Group
                                                      Dresden, Germany
                                                  dirk.habich@tu-dresden.de

ABSTRACT                                                              re. As I will show, the unique features of MorphStore are: (i)
In-memory database systems pursue a main memory-centric               support of a large variety of lightweight integer compression
architecture approach and assume that all relevant data (ba-          algorithms, (ii) a continuous handling of compression from
se data as well as intermediates) can be fully kept in the main       base data through intermediate results, (iii) a cost-based
memory of a computer or of a computer network. For OLAP               decision for the best-suited compression algorithm, and (iv)
workloads, in-memory column store systems are perfectly               morphing intermediates from one to another compression
suited, because relational tables are organized by column             scheme to dynamically adapt the physical representation to
rather than by row and based on that, queries only need               the changing data characteristics at query run-time.
to read relevant data columns. In these systems, lightweight
integer compression algorithms already play an important
role. Aside from reducing the amount of data, compressed
data offers several advantages such as less time spent on load
and store instructions and a better utilization of the cache
hierarchy. Moreover, a direct processing of the compressed
data is possible in many cases. However, existing systems
only provide a very limited set of compression algorithms
for base data. Furthermore, during query processing, these
systems only keep the data compressed until an operator
cannot process the compressed data directly, whereupon the
data is decompressed, but not recompressed. Thus, the full
optimization potential is not exploited.
   To overcome that, several years ago we had the idea to
make lightweight compression a first class citizen in query
optimization and query processing, because the effort to ac-
cess intermediate results is equivalent to the effort to access
the base data. Thus, the optimization of intermediate re-
sults is interesting and has a high impact on the performance
of the query execution. For this domain, we envisioned the
continuous use of lightweight compression methods for base
data as well as intermediate results and developed a novel
compression-aware query processing concept. However, to
minimize the overall query execution time, it is important
to find a balance between the reduced transfer times and the
increased computational effort during query optimization.
   In my talk, I will give an overview of our research jour-
ney starting from an idea to the development of a novel
in-memory column store database system called MorphSto-




31st GI-Workshop on Foundations of Databases (Grundlagen von Daten-
banken), 11.06.2019 - 14.06.2019, Saarburg, Germany.
Copyright is held by the author/owner(s).

</pre>