| 摘要: |
| 针对传统并行处理技术在海量数据处理中存在的实际应用问题，利用云计算技术强大
的计算能力、高效的海量数据处理方式，结合关系数据库实时访问的优点，在Hadoop分布式
计算框架基础上，采用Map Reduce架构，设计并实现了基于云计算的海量数据处理平台。
实践证明，该系统在计算能力、稳定性、可扩展性等方面都优于传统并行处理的技术，能有
效解决海量数据大并发访问。 |
| 关键词: 云计算 海量数据 Hadoop分布式计算 并行处理技术 |
| DOI： |
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| 基金项目: |
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| Mass data processing platform design and implementation based on cloud computing |
| SONG Jun,ZHU Lin |
| () |
| Abstract: |
| According to the shortcomings of the massive data processing metho
ds based on traditional parallel processing techniques in practical applications
,by using the powerful computing abilities and effcient ways of mass data proce
ssing of cloud computing, and taking the advantages of real-time access to relat
ional databases,a cloud computing platform for mass data processing based on the
Hadoop distributed computing framework and Map-Reduce model is developed.
Practice shows that the system proposed is superior to the traditional parallel
processing techniques in computing ability, stabi
lity, scalability,etc., and what′s more, it can effectively solve the concurren
t access to mass data simultaneously. |
| Key words: cloud computing mass data hadoop distributed computing parallel processing techn
ique |
