Abstract
As the volume of relational data is increased significantly, big data technologies have been noticed for recent years. Hadoop File System (HDFS) [14] is a basis of several big data systems and enables large data sets to be stored across the big data environment which is composed of many computers. HDFS divides large data into several blocks and each block is distributed and stored in a computer. To support reliability of data, HDFS replicates the data block. Generally, HDFS provides high-throughput when a client accesses to data. However, the architecture of HDFS is mainly designed to process data whose pattern is large and sequential. A data input whose pattern is small and random is not appropriate for applying HDFS. The data can result in several weak points of HDFS in terms of performance. HBase [2], one of Hadoop eco-systems, is a distributed data store which can process random, small read/write data efficiently. HBase utilizes HDFS structure but the block size of HBase is smaller than one of HDFS and a file of HBase is composed by blocks which is arranged by index structure. As many softwares which are related with big data science have emerged, many researches improving the system performance also have emerged steadily.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 6th International Conference on Emerging Databases |
| Subtitle of host publication | Technologies, Applications, and Theory, EDB 2016 |
| Editors | Carson K. Leung |
| Publisher | Association for Computing Machinery |
| Pages | 35-42 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781450347549 |
| DOIs | |
| Publication status | Published - 2016 Oct 17 |
| Event | 6th International Conference on Emerging Databases: Technologies, Applications, and Theory, EDB 2016 - Jeju Island, Korea, Republic of Duration: 2016 Oct 17 → 2016 Oct 19 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Other
| Other | 6th International Conference on Emerging Databases: Technologies, Applications, and Theory, EDB 2016 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju Island |
| Period | 16/10/17 → 16/10/19 |
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All Science Journal Classification (ASJC) codes
- Software
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
Cite this
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Replica parallelism to utilize the granularity of data. / Choi, Won Gi; Park, Sang Hyun.
Proceedings of the 6th International Conference on Emerging Databases: Technologies, Applications, and Theory, EDB 2016. ed. / Carson K. Leung. Association for Computing Machinery, 2016. p. 35-42 (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Replica parallelism to utilize the granularity of data
AU - Choi, Won Gi
AU - Park, Sang Hyun
PY - 2016/10/17
Y1 - 2016/10/17
N2 - As the volume of relational data is increased significantly, big data technologies have been noticed for recent years. Hadoop File System (HDFS) [14] is a basis of several big data systems and enables large data sets to be stored across the big data environment which is composed of many computers. HDFS divides large data into several blocks and each block is distributed and stored in a computer. To support reliability of data, HDFS replicates the data block. Generally, HDFS provides high-throughput when a client accesses to data. However, the architecture of HDFS is mainly designed to process data whose pattern is large and sequential. A data input whose pattern is small and random is not appropriate for applying HDFS. The data can result in several weak points of HDFS in terms of performance. HBase [2], one of Hadoop eco-systems, is a distributed data store which can process random, small read/write data efficiently. HBase utilizes HDFS structure but the block size of HBase is smaller than one of HDFS and a file of HBase is composed by blocks which is arranged by index structure. As many softwares which are related with big data science have emerged, many researches improving the system performance also have emerged steadily.
AB - As the volume of relational data is increased significantly, big data technologies have been noticed for recent years. Hadoop File System (HDFS) [14] is a basis of several big data systems and enables large data sets to be stored across the big data environment which is composed of many computers. HDFS divides large data into several blocks and each block is distributed and stored in a computer. To support reliability of data, HDFS replicates the data block. Generally, HDFS provides high-throughput when a client accesses to data. However, the architecture of HDFS is mainly designed to process data whose pattern is large and sequential. A data input whose pattern is small and random is not appropriate for applying HDFS. The data can result in several weak points of HDFS in terms of performance. HBase [2], one of Hadoop eco-systems, is a distributed data store which can process random, small read/write data efficiently. HBase utilizes HDFS structure but the block size of HBase is smaller than one of HDFS and a file of HBase is composed by blocks which is arranged by index structure. As many softwares which are related with big data science have emerged, many researches improving the system performance also have emerged steadily.
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UR - http://www.scopus.com/inward/citedby.url?scp=85018359819&partnerID=8YFLogxK
U2 - 10.1145/3007818.3007835
DO - 10.1145/3007818.3007835
M3 - Conference contribution
AN - SCOPUS:85018359819
T3 - ACM International Conference Proceeding Series
SP - 35
EP - 42
BT - Proceedings of the 6th International Conference on Emerging Databases
A2 - Leung, Carson K.
PB - Association for Computing Machinery
ER -