Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
The VLDB Journal
Published in: The VLDB Journal 5/2016

01-10-2016 | Special Issue Paper

Characterization of the Impact of Hardware Islands on OLTP

Authors: Danica Porobic, Ippokratis Pandis, Miguel Branco, Pınar Tözün, Anastasia Ailamaki

Published in: The VLDB Journal | Issue 5/2016

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and processor caches, which causes variability in the communication costs. Unfortunately, database systems mostly assume that all processing cores are the same and that microarchitecture differences are not significant enough to appear in critical database execution paths. As we demonstrate in this paper, however, non-uniform core topology does appear in the critical path and conventional database architectures achieve suboptimal and even worse, unpredictable performance. We perform a detailed performance analysis of OLTP deployments in servers with multiple cores per CPU (multicore) and multiple CPUs per server (multisocket). We compare different database deployment strategies where we vary the number and size of independent database instances running on a single server, from a single shared-everything instance to fine-grained shared-nothing configurations. We quantify the impact of non-uniform hardware on various deployments by (a) examining how efficiently each deployment uses the available hardware resources and (b) measuring the impact of distributed transactions and skewed requests on different workloads. We show that no strategy is optimal for all cases and that the best choice depends on the combination of hardware topology and workload characteristics. Finally, we argue that transaction processing systems must be aware of the hardware topology in order to achieve predictably high performance.
previous article Special Issue: Modern Hardware
next article Exploiting SSDs in operational multiversion databases

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Footnotes
1
Such as VoltDB, MongoDB, MemSQL, NuoDB.
 
3
Explaining, among other reasons, the high compensation for skilled database administrators.
 
Literature
1.
Accetta, M.J., Baron, R.V., Bolosky, W.J., Golub, D.B., Rashid, R.F., Tevanian, A., Young, M.: Mach: A new kernel foundation for UNIX development. In: USENIX Summer, pp. 93–112 (1986)
2.
Ailamaki, A., DeWitt, D.J., Hill, M.D., Wood, D.A.: DBMSs on a modern processor: where does time go? In: VLDB, pp. 266–277 (1999)
3.
Albutiu, M.C., Kemper, A., Neumann, T.: Massively Parallel sort-merge joins in main memory multi-core database systems. PVLDB 5(10), 1064–1075 (2012)
4.
5.
Bailis, P., Fekete, A., Franklin, M.J., Ghodsi, A., Hellerstein, J.M., Stoica, I.: Coordination avoidance in database systems. PVLDB 8(3), 185–196 (2015)
6.
Balkesen, C., Alonso, G., Teubner, J., Ozsu, M.T.: Multi-core, main-memory joins: sort vs. hash revisited. PVLDB 7(1), 85–96 (2014)
7.
Barroso, L.A., Gharachorloo, K., Bugnion, E.: Memory system characterization of commercial workloads. In: ISCA, pp. 3–14 (1998)
8.
Baumann, A., Barham, P., Dagand, P.E., Harris, T., Isaacs, R., Peter, S., Roscoe, T., Schüpbach, A., Singhania, A.: The multikernel: a new OS architecture for scalable multicore systems. In: SOSP, pp. 29–44 (2009)
9.
Beckmann, B.M., Wood, D.A.: Managing wire delay in large chip-multiprocessor caches. In: IEEE MICRO, pp. 319–330 (2004)
10.
11.
Blagodurov, S., Zhuravlev, S., Fedorova, A., Kamali, A.: A case for NUMA-aware contention management on multicore systems. In: PACT, pp. 557–558 (2010)
12.
Brewer, E.A.: Towards robust distributed systems (abstract). In: PODC, pp. 7–7 (2000)
13.
Carey, M.J., DeWitt, D.J., Franklin, M.J., Hall, N.E., McAuliffe, M.L., Naughton, J.F., Schuh, D.T., Solomon, M.H., Tan, C.K., Tsatalos, O.G., White, S.J., Zwilling, M.J.: Shoring up persistent applications. In: SIGMOD, pp. 383–394 (1994)
14.
15.
Corbett, J.C., Dean, J., Epstein, M., Fikes, A., Frost, C., Furman, J., Ghemawat, S., Gubarev, A., Heiser, C., Hochschild, P., Hsieh, W., Kanthak, S., Kogan, E., Li, H., Lloyd, A., Melnik, S., Mwaura, D., Nagle, D., Quinlan, S., Rao, R., Rolig, L., Saito, Y., Szymaniak, M., Taylor, C., Wang, R., Woodford, D.: Spanner: Google’s globally-distributed database. In: OSDI, pp. 261–264 (2012)
16.
Curino, C., Jones, E., Zhang, Y., Madden, S.: Schism: a workload-driven approach to database replication and partitioning. PVLDB 3, 48–57 (2010)
17.
Dashti, M., Fedorova, A., Funston, J., Gaud, F., Lachaize, R., Lepers, B., Quema, V., Roth, M.: Traffic management: a holistic approach to memory placement on NUMA systems. In: ASPLOS, pp. 381–394 (2013)
18.
David, T., Guerraoui, R., Trigonakis, V.: Everything you always wanted to know about synchronization but were afraid to ask. In: SOSP, pp. 33–48 (2013)
19.
Engler, D.R., Kaashoek, M.F., O’Toole Jr., J.: Exokernel: an operating system architecture for application-level resource management. In: SOSP, pp. 251–266 (1995)
20.
Giceva, J., Alonso, G., Roscoe, T., Harris, T.: Deployment of query plans on multicores. PVLDB 8(3), 233–244 (2014)
21.
22.
Hardavellas, N., Ferdman, M., Falsafi, B., Ailamaki, A.: Reactive NUCA: near-optimal block placement and replication in distributed caches. In: ISCA, pp. 184–195 (2009)
23.
Harizopoulos, S., Abadi, D.J., Madden, S., Stonebraker, M.: OLTP through the looking glass, and what we found there. In: SIGMOD, pp. 981–992 (2008)
24.
Helland, P.: Life beyond distributed transactions: an apostate’s opinion. In: CIDR, pp. 132–141 (2007)
25.
26.
Johnson, R., Pandis, I., Ailamaki, A.: Improving OLTP scalability using speculative lock inheritance. PVLDB 2(1), 479–489 (2009)
27.
Johnson, R., Pandis, I., Ailamaki, A.: Eliminating unscalable communication in transaction processing. Vldb J. 23(1), 1–23 (2014)CrossRef
28.
Johnson, R., Pandis, I., Hardavellas, N., Ailamaki, A., Falsafi, B.: Shore-MT: a scalable storage manager for the multicore era. In: EDBT, pp. 24–35 (2009)
29.
Johnson, R., Pandis, I., Stoica, R., Athanassoulis, M., Ailamaki, A.: Aether: a scalable approach to logging. PVLDB 3, 681–692 (2010)
30.
Jones, E., Abadi, D.J., Madden, S.: Low overhead concurrency control for partitioned main memory databases. In: SIGMOD, pp. 603–614 (2010)
31.
Jung, H., Han, H., Fekete, A.D., Heiser, G., Yeom, H.Y.: A Scalable lock manager for multicores. In: SIGMOD, pp. 73–84 (2013)
32.
Kemper, A., Neumann, T.: HyPer – a hybrid OLTP&OLAP main memory database system based on virtual memory snapshots. In: ICDE, pp. 195–206 (2011)
33.
Kimura, H.: FOEDUS: OLTP engine for a thousand cores and NVRAM. In: SIGMOD, pp. 691–706 (2015)
34.
Kimura, H., Graefe, G., Kuno, H.: Efficient locking techniques for databases on modern hardware. In: ADMS (2012)
35.
Kissinger, T., Kiefer, T., Schlegel, B., Habich, D., Molka, D., Lehner, W.: ERIS: A NUMA-aware in-memory storage engine for analytical workload. In: ADMS, pp. 74–85 (2014)
36.
Kung, H.T., Robinson, J.T.: On optimistic methods for concurrency control. ACM TODS 6(2), 213–226 (1981)CrossRef
37.
Lahiri, T., Neimat, M.A., Folkman, S.: Oracle TimesTen: an in-memory database for enterprise applications. IEEE Data Eng. Bull. 36(2), 6–13 (2013)
38.
Lahiri, T., Srihari, V., Chan, W., MacNaughton, N., Chandrasekaran, S.: Cache fusion: extending shared-disk clusters with shared caches. In: VLDB, pp. 683–686 (2001)
39.
Larson, P.A., Blanas, S., Diaconu, C., Freedman, C., Patel, J.M., Zwilling, M.: High-performance concurrency control mechanisms for main-memory databases. PVLDB 5(4), 298–309 (2011)
40.
Levandoski, J.J., Lomet, D.B., Sengupta, S.: The bw-tree: a b-tree for new hardware platforms. In: ICDE, pp. 302–313 (2013)
41.
42.
Li, Y., Pandis, I., Mueller, R., Raman, V., Lohman, G.: NUMA-aware algorithms: the case of data shuffling. In: CIDR (2013)
43.
Lindström, J., Raatikka, V., Ruuth, J., Soini, P., Vakkila, K.: IBM solidDB: in-memory database optimized for extreme speed and availability. IEEE Data Eng. Bull. 36(2), 14–20 (2013)
44.
Mao, Y., Kohler, E., Morris, R.T.: Cache craftiness for fast multicore key-value storage. In: Eurosys, pp. 183–196 (2012)
45.
46.
47.
Pandis, I., Johnson, R., Hardavellas, N., Ailamaki, A.: Data-oriented transaction execution. PVLDB 3(1), 928–939 (2010)
48.
Pandis, I., Tözün, P., Johnson, R., Ailamaki, A.: PLP: page latch-free shared-everything OLTP. PVLDB 4(10), 610–621 (2011)
49.
Pavlo, A., Curino, C., Zdonik, S.: Skew-Aware Automatic database partitioning in shared-nothing, parallel OLTP systems. In: SIGMOD, pp. 61–72 (2012)
50.
Pavlo, A., Jones, E.P.C., Zdonik, S.: On predictive modeling for optimizing transaction execution in parallel OLTP systems. PVLDB 5(2), 85–96 (2011)
51.
Polychroniou, O., Ross, K.A.: A comprehensive study of main-memory partitioning and its application to large-scale comparison- and radix-sort. In: SIGMOD, pp. 755–766 (2014)
52.
Porobic, D., Liarou, E., Tözün, P., Ailamaki, A.: ATraPos: adaptive transaction processing on hardware islands. In: ICDE (2014)
53.
Porobic, D., Pandis, I., Branco, M., Tözün, P., Ailamaki, A.: OLTP on hardware Islands. PVLDB 5(11), 1447–1458 (2012)
54.
Quamar, A., Kumar, K.A., Deshpande, A.: Sword: scalable workload-aware data placement for transactional workloads. In: EDBT, pp. 430–441 (2013)
55.
Salomie, T.I., Subasu, I.E., Giceva, J., Alonso, G.: Database engines on multicores, why parallelize when you can distribute? In: EuroSys, pp. 17–30 (2011)
56.
Somogyi, S., Wenisch, T.F., Hardavellas, N., Kim, J., Ailamaki, A., Falsafi, B.: Memory coherence activity prediction in commercial workloads. In: WMPI, pp. 37–45 (2004)
57.
Stonebraker, M.: The case for shared nothing. IEEE Database Eng. Bull. 9, 4–9 (1986)
58.
Stonebraker, M., Madden, S., Abadi, D.J., Harizopoulos, S., Hachem, N., Helland, P.: The end of an architectural era: (it’s time for a complete rewrite). In: VLDB, pp. 1150–1160 (2007)
59.
Tang, L., Mars, J., Vachharajani, N., Hundt, R., Soffa, M.L.: The impact of memory subsystem resource sharing on datacenter applications. In: ISCA, pp. 283–294 (2011)
60.
Thomson, A., Diamond, T., Weng, S.C., Ren, K., Shao, P., Abadi, D.J.: Calvin: Fast distributed transactions for partitioned database systems. In: SIGMOD, pp. 1–12 (2012)
61.
Tözün, P., Pandis, I., Johnson, R., Ailamaki, A.: Scalable and dynamically balanced shared-everything OLTP with physiological partitioning. VLDB J. 22(2), 151–175 (2013)CrossRef
62.
Tözün, P., Pandis, I., Kaynak, C., Jevdjic, D., Ailamaki, A.: From A to E: analyzing TPC’s OLTP Benchmarks—The obsolete, the ubiquitous, the unexplored. In: EDBT, pp. 17–28 (2013)
63.
64.
65.
66.
Tran, K.Q., Naughton, J.F., Sundarmurthy, B., Tsirogiannis, D.: JECB: A join-extension, code-based approach to OLTP data partitioning. In: SIGMOD, pp. 39–50. ACM
67.
Tu, S., Zheng, W., Kohler, E., Liskov, B., Madden, S.: Speedy transactions in multicore in-memory databases. In: SOSP, pp. 18–32 (2013)
68.
69.
Wagle, M., Booss, D., Schreter, I.: NUMA-aware memory management with in-memory databases. In: TPCTC (2015)
70.
71.
Yu, X., Bezerra, G., Pavlo, A., Devadas, S., Stonebraker, M.: Staring into the abyss: an evaluation of concurrency control with one thousand cores. PVLDB 8(3), 209–220 (2014)
72.
Zhang, C., Ré, C.: Dimmwitted: a study of main-memory statistical analytics. PVLDB 7(12), 1283–1294 (2014)
Metadata
Title
Characterization of the Impact of Hardware Islands on OLTP
Authors
Danica Porobic
Ippokratis Pandis
Miguel Branco
Pınar Tözün
Anastasia Ailamaki
Publication date
01-10-2016
Publisher
Springer Berlin Heidelberg
Published in
The VLDB Journal / Issue 5/2016
Print ISSN: 1066-8888
Electronic ISSN: 0949-877X
DOI
https://doi.org/10.1007/s00778-015-0413-2

Other articles of this Issue 5/2016

The VLDB Journal 5/2016 Go to the issue

Special Issue Paper

Read/write-optimized tree indexing for solid-state drives

Special Issue Paper

Flash as cache extension for online transactional workloads

Special Issue Paper

GPU-accelerated string matching for database applications

Guest Editorial

Special Issue: Modern Hardware

Special Issue Paper

Exploiting SSDs in operational multiversion databases

Premium Partner

    Image Credits