Column-oriented database has gained popularity as
“Data Warehousing” data and performance issues for
“Analytical Queries” have increased. Each attribute of a
relation is physically stored as a separate column, which will
help analytical queries to work fast. The overhead is incurred
in tuple reconstruction for multi attribute queries. Each tuple
reconstruction is joining of two columns based on tuple IDs,
making it significant cost component. For reducing cost,
physical design have multiple presorted copies of each base
table, such that tuples are already appropriately organized in
different orders across the various columns.
This paper proposes a novel design, called
partitioning, that minimizes the tuple reconstruction cost. It
achieves performance similar to using presorted data, but
without requiring the heavy initial presorting step. In addition,
it handles dynamic, unpredictable workloads with no idle time
and frequent updates. Partitioning provides the direct loading
of the data in respective partitions. Partitions are created on
the fly and depend on distribution of data, which will work
nicely in limited storage space environments.
%0 Journal Article
%1 tejaswiniapte2011performance
%A Tejaswini Apte, Dr. Maya Ingale
%A A.K.Goyal, Dr.
%D 2011
%E Das, Dr. Vinu V
%J International Journal on Network Security
%K Database_Cracking Self-organization
%N 2
%P 4
%T Performance Improvement Technique in
Column-Store
%U http://doi.searchdl.org/01.IJNS.2.2.202
%V 2
%X Column-oriented database has gained popularity as
“Data Warehousing” data and performance issues for
“Analytical Queries” have increased. Each attribute of a
relation is physically stored as a separate column, which will
help analytical queries to work fast. The overhead is incurred
in tuple reconstruction for multi attribute queries. Each tuple
reconstruction is joining of two columns based on tuple IDs,
making it significant cost component. For reducing cost,
physical design have multiple presorted copies of each base
table, such that tuples are already appropriately organized in
different orders across the various columns.
This paper proposes a novel design, called
partitioning, that minimizes the tuple reconstruction cost. It
achieves performance similar to using presorted data, but
without requiring the heavy initial presorting step. In addition,
it handles dynamic, unpredictable workloads with no idle time
and frequent updates. Partitioning provides the direct loading
of the data in respective partitions. Partitions are created on
the fly and depend on distribution of data, which will work
nicely in limited storage space environments.
@article{tejaswiniapte2011performance,
abstract = {Column-oriented database has gained popularity as
“Data Warehousing” data and performance issues for
“Analytical Queries” have increased. Each attribute of a
relation is physically stored as a separate column, which will
help analytical queries to work fast. The overhead is incurred
in tuple reconstruction for multi attribute queries. Each tuple
reconstruction is joining of two columns based on tuple IDs,
making it significant cost component. For reducing cost,
physical design have multiple presorted copies of each base
table, such that tuples are already appropriately organized in
different orders across the various columns.
This paper proposes a novel design, called
partitioning, that minimizes the tuple reconstruction cost. It
achieves performance similar to using presorted data, but
without requiring the heavy initial presorting step. In addition,
it handles dynamic, unpredictable workloads with no idle time
and frequent updates. Partitioning provides the direct loading
of the data in respective partitions. Partitions are created on
the fly and depend on distribution of data, which will work
nicely in limited storage space environments.},
added-at = {2012-09-25T08:34:34.000+0200},
author = {Tejaswini Apte, Dr. Maya Ingale and A.K.Goyal, Dr.},
biburl = {https://www.bibsonomy.org/bibtex/28a24af8106c69861fc57608f7139ea9f/ideseditor},
editor = {Das, Dr. Vinu V},
interhash = {a894f5199c807dc7c6d35d1504d6c5db},
intrahash = {8a24af8106c69861fc57608f7139ea9f},
journal = {International Journal on Network Security},
keywords = {Database_Cracking Self-organization},
month = {April},
number = 2,
pages = 4,
timestamp = {2012-09-25T08:34:34.000+0200},
title = {Performance Improvement Technique in
Column-Store},
url = {http://doi.searchdl.org/01.IJNS.2.2.202},
volume = 2,
year = 2011
}