Page 395 - 《软件学报》2024年第6期

胡梓锐 等: HTAP 数据库系统数据共享模型和优化策略 2971

[22] Raza A, Chrysogelos P, Anadiotis AC, Ailamaki A. Adaptive htap through elastic resource scheduling. In: Proc. of the 2020 ACM
SIGMOD Int’l Conf. on Management of Data. Portland: ACM, 2020. 2043–2054. [doi: 10.1145/3318464.3389783]
[23] Coelho F, Paulo J, Vilaça R, Pereira J, Oliveira R. HTAPBench: Hybrid transactional and analytical processing benchmark. In: Proc. of
the 8th ACM/SPEC on Int’l Conf. on Performance Engineering. L’Aquila: ACM, 2017. 293–304. [doi: 10.1145/3030207.3030228]
[24] Sirin U, Dwarkadas S, Ailamaki A. Performance characterization of HTAP workloads. In: Proc. of the 37th IEEE Int’l Conf. on Data
Engineering (ICDE). Chania: IEEE, 2021. 1829–1834. [doi: 10.1109/ICDE51399.2021.00162]
[25] Cole R, Funke F, Giakoumakis L, Guy W, Kemper A, Krompass S, Kuno H, Nambiar R, Neumann T, Poess M, Sattler KU, Seibold M,
Simon E, Waas F. The mixed workload CH-benchmark. In: Proc. of the 4th Int’l Workshop on Testing Database Systems. Athens: ACM,
2011. 8. [doi: 10.1145/1988842.1988850]
[26] Wu YJ, Arulraj J, Lin JX, Xian R, Pavlo A. An empirical evaluation of in-memory multi-version concurrency control. Proc. of the VLDB
Endowment, 2017, 10(7): 781–792. [doi: 10.14778/3067421.3067427]
[27] Vinçon T, Knödler C, Solis-Vasquez L, Bernhardt A, Tamimi S, Weber L, Stock F, Koch A, Petrov I. Near-data processing in database
systems on native computational storage under HTAP workloads. Proc. of the VLDB Endowment, 2022, 15(10): 1991–2004. [doi: 10.
14778/3547305.3547307]
[28] Böttcher J, Leis V, Neumann T, Kemper A. Scalable garbage collection for in-memory mvcc systems. Proc. of the VLDB Endowment,
2019, 13(2): 128–141. [doi: 10.14778/3364324.3364328]
[29] Kim J, Kim K, Cho H, Yu J, Kang S, Jung H. Rethink the scan in MVCC databases. In: Proc. of the 2021 Int’l Conf. on Management of
Data. ACM, 2021. 938–950. [doi: 10.1145/3448016.3452783]
[30] Özcan F, Tian YY, Tözün P. Hybrid transactional/analytical processing: A survey. In: Proc. of the 2017 ACM Int’l Conf. on Management
of Data. ACM, 2017. 1771–1775. [doi: 10.1145/3035918.3054784]
[31] Hieber D, Grambow G. Hybrid transactional and analytical processing databases: A systematic literature review. In: Proc. of the 9th Int’l
Conf. on Data Analytics. Nice: IARIA, 2020. 90–98.
[32] Psaroudakis I, Wolf F, May N, Neumann T, Böhm A, Ailamaki A, Sattler KU. Scaling up mixed workloads: A battle of data freshness,
flexibility, and scheduling. In: Proc. of the 6th Technology Conf. on Performance Evaluation and Benchmarking. Hangzhou: Springer,
2014. 97–112. [doi: 10.1007/978-3-319-15350-6_7]
[33] Zhang C, Li GL, Feng JH, Zhang JT. A survey of key techniques of HTAP databases. Ruan Jian Xue Bao/Journal of Software, 2023,
34(2): 761–785 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6713.htm [doi: 10.13328/j.cnki.jos.006713]
[34] Li GL, Zhang C. HTAP databases: What is new and what is next. In: Proc. of the 2022 Int’l Conf. on Management of Data. Philadelphia:
ACM, 2022. 2483–2488. [doi: 10.1145/3514221.3522565]
[35] Li GL, Zhou XH, Sun J, Yu X, Yuan HT, Liu JB, Han Y. A survey of machine learning based database techniques. Chinese Journal of
Computers, 2020, 43(11): 2019–2049 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2020.02019]
[36] Terry D. Replicated data consistency explained through baseball. Communications of the ACM, 2013, 56(12): 82–89. [doi: 10.1145/
2500500]
[37] Herlihy MP, Wing JM. Linearizability: A correctness condition for concurrent objects. ACM Trans. on Programming Languages and
Systems, 1990, 12(3): 463–492. [doi: 10.1145/78969.78972]
[38] Gilbert S, Lynch N. Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant Web services. ACM SIGACT News,
2002, 33(2): 51–59. [doi: 10.1145/564585.564601]
[39] Peng D, Dabek F. Large-scale incremental processing using distributed transactions and notifications. In: Proc. of the 9th USENIX Symp.
on Operating Systems Design and Implementation. Vancouver: USENIX Association, 2010. 251–264.
[40] Prout A, Wang SP, Victor J, Sun Z, Li YZ, Chen J, Bergeron E, Hanson E, Walzer R, Gomes R, Shamgunov N. Cloud-native transactions
and analytics in singleStore. In: Proc. of the 2022 Int’l Conf. on Management of Data. Philadelphia: ACM, 2022. 2340–2352. [doi: 10.
1145/3514221.3526055]
[41] Chen J, Jindel S, Walzer R, Sen R, Jimsheleishvilli N, Andrews M. The MemSQL query optimizer: A modern optimizer for real-time
analytics in a distributed database. Proc. of the VLDB Endowment, 2016, 9(13): 1401–1412. [doi: 10.14778/3007263.3007277]
[42] Ongaro D, Ousterhout J. In search of an understandable consensus algorithm. In: Proc. of the 2014 USENIX Annual Technical Conf.
Philadelphia: USENIX Association, 2014. 305–319.
[43] TiFlash architecture and principles. PingCAP. 2022 (in Chinese). https://book.tidb.io/session1/chapter9/tiflash-architecture.html
[44] Corbett JC, Dean J, Epstein M, et al. Spanner: Google’s globally distributed database. ACM Trans. on Computer Systems, 2013, 31(3): 8.
[doi: 10.1145/2491245]
[45] Lamport L. The part-time parliament. In: Malkhi D, ed. Concurrency: The Works of Leslie Lamport. New York: ACM, 2019. 277–317.