A comparison of flexible schemas for software as a service

ABSTRACT

A multi-tenant database system for Software as a Service (SaaS) should offer schemas that are flexible in that they can be extended different versions of the application and dynamically modified while the system is on-line. This paper presents an experimental comparison of five techniques for implementing flexible schemas for SaaS. In three of these techniques, the database "owns" the schema in that its structure is explicitly defined in DDL. Included here is the commonly-used mapping where each tenant is given their own private tables, which we take as the baseline, and a mapping that employs Sparse Columns in Microsoft SQL Server. These techniques perform well, however they offer only limited support for schema evolution in the presence of existing data. Moreover they do not scale beyond a certain level. In the other two techniques, the application "owns" the schema in that it is mapped into generic structures in the database. Included here are XML in DB2 and Pivot Tables in HBase. These techniques give the application complete control over schema evolution, however they can produce a significant decrease in performance. We conclude that the ideal database for SaaS has not yet been developed and offer some suggestions as to how it should be designed.

REFERENCES

• 1: Srini Acharya , Peter Carlin , Cesar Galindo-Legaria , Krzysztof Kozielczyk , Pawel Terlecki , Peter Zabback, Relational support for flexible schema scenarios, Proceedings of the VLDB Endowment, v.1 n.2, August 2008 [doi>10.1145/1454159.1454169]
• 2: Rakesh Agrawal , Amit Somani , Yirong Xu, Storage and Querying of E-Commerce Data, Proceedings of the 27th International Conference on Very Large Data Bases, p.149-158, September 11-14, 2001
• 3: Stefan Aulbach , Torsten Grust , Dean Jacobs , Alfons Kemper , Jan Rittinger, Multi-tenant databases for software as a service: schema-mapping techniques, Proceedings of the 2008 ACM SIGMOD international conference on Management of data, June 09-12, 2008, Vancouver, Canada [doi>10.1145/1376616.1376736]
• 4: Jennifer L. Beckmann , Alan Halverson , Rajasekar Krishnamurthy , Jeffrey F. Naughton, Extending RDBMSs To Support Sparse Datasets Using An Interpreted Attribute Storage Format, Proceedings of the 22nd International Conference on Data Engineering, p.58, April 03-07, 2006 [doi>10.1109/ICDE.2006.67]
• 5: P. Boncz. Monet: A Next-Generation DBMS Kernel For Query-Intensive Applications. Phd, University of Amsterdam, The Netherlands, 2002. http://old-www.cwi.nl/htbin/ins1/publications?request=abstract&key=Bo:DISS:02.
• 6: Fay Chang , Jeffrey Dean , Sanjay Ghemawat , Wilson C. Hsieh , Deborah A. Wallach , Mike Burrows , Tushar Chandra , Andrew Fikes , Robert E. Gruber, Bigtable: A Distributed Storage System for Structured Data, ACM Transactions on Computer Systems (TOCS), v.26 n.2, p.1-26, June 2008 [doi>10.1145/1365815.1365816]
• 7: Eric Chu , Jennifer Beckmann , Jeffrey Naughton, The case for a wide-table approach to manage sparse relational data sets, Proceedings of the 2007 ACM SIGMOD international conference on Management of data, June 11-14, 2007, Beijing, China [doi>10.1145/1247480.1247571]
• 8: S. Finkelstein, D. Jacobs, and R. Brendle. Principles for Inconsistency. In CIDR, 2009.
• 9: Michael J. Franklin , Björn Thór Jónsson , Donald Kossmann, Performance tradeoffs for client-server query processing, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.149-160, June 04-06, 1996, Montreal, Quebec, Canada
• 10: J. Gray. Tape is Dead, Disk is Tape, Flash is Disk, RAM Locality is King. http://research.microsoft.com/ Gray/talks/Flash_Is_Good.ppt, 2006.
• 11: HBase. http://hadoop.apache.org/hbase/, 2009.
• 12: Thomas Legler , Wolfgang Lehner , Andrew Ross, Data mining with the SAP NetWeaver BI accelerator, Proceedings of the 32nd international conference on Very large data bases, September 12-15, 2006, Seoul, Korea
• 13: T. McKinnon. Plug Your Code in Here - An Internet Application Platform. www.hpts.ws/papers/2007/hpts_conference_oct_2007.ppt, 2007.
• 14: MegaStore. http://perspectives.mvdirona.com/2008/07/10/GoogleMegastore.aspx, 2009.
• 15: Microsoft SQL Server 2008 Books Online - Using Sparse Columns. http://msdn.microsoft.com/en-us/library/cc280604.aspx.
• 16: M. Nicola. 15 Best Practices for pureXML Performance in DB2. http://www.ibm.com/developerworks/data/library/techarticle/dm-0610nicola/, 2008.
• 17: Oracle Database 10g Release 2 Online Data Reorganization&Redefinition. http://www.oracle.com/technology/deploy/availability/pdf/ha_10gR2_online_reorg_twp.pdf.
• 18: J. F. Roddick. Reduce, Reuse, Recycle: Practical Approaches to Schema Integration, Evolution and Versioning. Lecture Notes in Computer Science, 37:209--216, 2006.
• 19: Salesforce.com: The Force.com Multitenant Architecture. http://www.apexdevnet.com/media/ForcedotcomBookLibrary/Force.com_Multitenancy_WP_101508.pdf.
• 20: C. M. Saracca, D. Chamberlin, and R. Ahuja. DB2 9: pure XML - Overview and Fast Start. IBM, http://ibm.com/redbooks, 2006.
• 21: M. Taylor and C. J. Guo. Data Integration and Composite Business Services, Part 3: Build a Multi-Tenant Data Tier with Access Control and Security. http://www.ibm.com/developerworks/db2/library/techarticle/dm-0712taylor/, 2007.
• 22: Vertica. http://www.vertica.com/, 2009.
• 23: Workday: Forget ERP, Start Over. http://blogs.zdnet.com/SAAS/?p=368.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.m Miscellaneous

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.1 Logical Design

General Terms:
Design, Performance

Keywords:
evolution, extensibility, flexible schemas, multi-tenancy, software as a service