国家重点研发计划(2016YFB1000701)
国家自然科学基金(61802224,71690231)
[1] Fu T. A review on time series data mining. Eng Appl Artificial Intelligence, 2011, 24: 164-181 CrossRef Google Scholar
[2] Stonebraker M, Çetintemel U, Zdonik S. The 8 requirements of real-time stream processing. SIGMOD Rec, 2005, 34: 42-47 CrossRef Google Scholar
[3] Ongaro D, Ousterhout J. In search of an understandable consensus algorithm. In: Proceedings of USENIX Annual Technical Conference, 2014. 305--319. Google Scholar
[4] Karger D. Consistent hashing and random trees: Distributed caching protocols for relieving hot spots on the World Wide Web. In: Proceedings of ACM Symposium on Theory of Computing, 1997. Google Scholar
[5] Kwon Y C, Ren K, Balazinska M, et al. Managing Skew in Hadoop. IEEE Data Eng Bull, 2013, 36: 24-33. Google Scholar
[6] Shvachko K, Kuang H, Radia S, et al. The hadoop distributed file system. In: Proceedings of IEEE 26th Symposium on Mass Storage Systems and Technologies, 2010. 1--10. Google Scholar
[7] Dubreuil M, Gagne C, Parizeau M. Analysis of a master-slave architecture for distributed evolutionary computations.. IEEE Trans Syst Man Cybern B, 2006, 36: 229-235 CrossRef PubMed Google Scholar
[8] Lakshman A, Malik P. Cassandra. SIGOPS Oper Syst Rev, 2010, 44: 35 CrossRef Google Scholar
[9] Stoica I, Morris R, Liben-Nowell D. Chord: a scalable peer-to-peer lookup protocol for internet applications. IEEE/ACM Trans Networking, 2003, 11: 17-32 CrossRef Google Scholar
[10] Chen P M, Lee E K, Gibson G A. RAID: high-performance, reliable secondary storage. ACM Comput Surv, 1994, 26: 145-185 CrossRef Google Scholar
[11] Aguilera M K. Stumbling Over Consensus Research: Misunderstandings and Issues. Berlin: Springer, 2010. 59--72. Google Scholar
[12] Stonebraker M. Retrospection on a database system. ACM Trans Database Syst, 1980, 5: 225-240 CrossRef Google Scholar
[13] Naqvi S N Z, Yfantidou S, Zimanyi E. Time Series Databases and InfluxDB. Studienarbeit, Universite Libre de Bruxelles, 2017. Google Scholar
[14] Prasad S, Avinash S B. Smart meter data analytics using OpenTSDB and Hadoop. In: Proceedings of IEEE Innovative Smart Grid Technologies-Asia, 2013. 1--6. Google Scholar
[15] Vora M N. Hadoop-HBase for large-scale data. In: Proceedings of International Conference on Computer Science and Network Technology, 2011. 1: 601--605. Google Scholar
[16] Van Renesse R, Dumitriu D, Gough V, et al. Efficient reconciliation and flow control for anti-entropy protocols. In: proceedings of the 2nd Workshop on Large-Scale Distributed Systems and Middleware, 2008. 6. Google Scholar
[17] Chang F, Dean J, Ghemawat S. Bigtable. ACM Trans Comput Syst, 2008, 26: 1-26 CrossRef Google Scholar
[18] DeCandia G, Hastorun D, Jampani M. Dynamo. SIGOPS Oper Syst Rev, 2007, 41: 205-220 CrossRef Google Scholar
[19] Chen Z, Yang S, Tan S, et al. Hybrid Range Consistent Hash Partitioning Strategy--A New Data Partition Strategy for NoSQL Database. In: Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2013. 1161--1169. Google Scholar
[20] Hunt P, Konar M, Junqueira F P, et al. ZooKeeper: Wait-free Coordination for Internet-scale Systems. In: Proceedings of USENIX Annual Technical Conference, 2010. 8. Google Scholar
[21] Lamport L. Paxos made simple. ACM Sigact News, 2001, 32: 18--25. Google Scholar
Figure 1
Organization structure of time series
Figure 2
Cluster grouping (5 nodes 3 replicas). (a) Metadata group; (b) data group 1; (c) data group 2; (d) data group 4;protect łinebreak (e) data group 5; (f) data group 6
Figure 3
Consistency hash lookup
Figure 4
Virtual node
Figure 5
Metadata holder
Figure 6
Data partition holder
Figure 7
(Color online) The changes of node processing logic
Figure 8
Prune tree of query filter
Figure 9
(Color online) Total memory occupancy of metadata in different cluster sizes
Figure 10
(Color online) The number of metadata synchronization communication in different cluster sizes
Figure 11
(Color online) Performance test comparison of DMM
Figure 12
(Color online) Write performance comparison between Apache IoTDB and KairosDB
Figure 13
(Color online) Performance comparison between strong consistency query and eventual consistency query
Figure 14
(Color online) Query performance comparison between Apache IoTDB and KairosDB
Configuration | Description |
OS | Ubuntu 16.04.4 LTS |
CPU | 2.40 GHz $\times$ 8 |
Memory | 16 GB |
Disk | 1 T HDD |
Apache IoTDB version | 0.7.0 |
Maven version | 3.5.2 |
JDK version | 1.8 |