Abstract
Partitioning is the process of dividing a logical database into distinct independent datasets. Partitions are database objects itself and can be managed independently. The main reason to apply data partitioning is to achieve data-level parallelism Data-level parallelism and thus to enable performance gains. Nowadays, multi-core CPUs that are capable to process several distinct data areas in parallel harness partitioned data structures.
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Notes
- 1.
Transparent in IT means that something is completely invisible to the user, not that the user can inspect the implementation through the cover. Except of their effects like improvements in speed or usability, transparent components should not be noticeable at all.
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NP-complete means that the problem can not be solved in polynomial time.
- 3.
Based on the assumption that the companies’ customers mainly live nowadays and are between 0 and 100 years old.
- 4.
A hash function maps a potentially large amount of data with often variable length to a smaller value of fixed length. In the figurative sense, hash functions generate a digital fingerprint of the input data.
Reference
R. Karp, Reducibility among combinatorial problems, in Complexity of Computer Computations, ed. by R. Miller, J. Thatcher (Plenum Press, New York, 1972), pp. 85–103
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© 2014 Springer-Verlag Berlin Heidelberg
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Plattner, H. (2014). Partitioning. In: A Course in In-Memory Data Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55270-0_9
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DOI: https://doi.org/10.1007/978-3-642-55270-0_9
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