大規(guī)模時間序列數(shù)據(jù)存儲系統(tǒng)的研究與實(shí)現(xiàn)
[Abstract]:Time series data, which is the sequence of data points collected at a fixed time interval in a certain period of time, has become an important record form of information in production and life. RRDtool is a widely used database tool for storing time series data. However, the time series data storage system based on RRDtool is heavily loaded with I / O, and the number of RRD files per unit time can not meet the requirements. The rapid growth of data requires better scalability of storage systems, in particular the ability to adjust storage capacity without affecting the online operation of the system, and, on the other hand, considering that the system may be anomalous or partially affected, There is a need for a storage scheme that ensures the availability of the system in these cases. In order to solve the above problems, a large-scale time series data storage system is studied and implemented in this paper. The key part of this system is mem-RRD, which is a better implementation of I / O for RRDtool, and MooseFS, which is a distributed file system that can guarantee the availability and scalability of storage system. This paper first introduces the design and implementation of an improved RRDtool scheme based on user space buffer named mem-RRD, and then gives a scheme to build and deploy a storage system for large-scale time series data using mem-RRD and MooseFS. Finally, the I / O performance, availability and scalability of the storage system are tested in detail, and the test data are compared and analyzed. The test results show that the storage system for large-scale time series data based on mem-RRD and MooseFS has great improvement or good performance in the aspects of I / O performance, availability, capacity expansion and so on.
【學(xué)位授予單位】:華中科技大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2013
【分類號】:TP333
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