WHAT THE MARKET-LEADING DBMS VENDORS DON

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• WHAT THE MARKET-LEADING DBMS VENDORS DONT WANT
  YOU TO KNOW
• Disruption is gathering steam

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Curt Monash

• Analyst since 1981
• Covered DBMS since the pre-relational days
• Also analytics, search, etc.
• Own firm since 1987
• Publicly available research
• Blogs, including DBMS2 (www.dbms2.com -- the
  source for most of this talk)
• Feed at www.monash.com/blogs.html
• White papers and more at www.monash.com

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Database diversity

• Mike Stonebraker, PhD
• One size doesnt fit all
• Curt Monash, PhD
• Horses for courses
• Database diversity
• Mike and Curt
• The world needs 9 to 11 different kinds of data
  management software

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The case for grand integrated DBMS

• Theoretical relational model has great advantages
• Actual relational DBMS are versatile and modular
• Software developers have economies of scale
• Vendor consolidation theoretically saves effort
  and money
• So does database consolidation

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The case for database diversity

• Different kinds of data require fundamentally
  different kinds of data management software
• Putting all that together in one system is
  extremely hard
• Nobody has ever done it well

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Application and use cases

• High-end e-commerce
• 100-terabyte analytics
• High-volume call center
• Media-heavy web startup
• Simple departmental application
• General enterprise or SaaS app
• End-user or ISV

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Data management distinctions

• Fundamental
• Data manipulation language
• Data access method
• Practical
• Type of data
• Type of hardware
• Administrative burden
• Performance stresses and metrics

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Very practical

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Major components of DBMS cost

• License and maintenance
• Especially maintenance
• Hardware, power, facilities
• Mainly for VLDB analytics
• Installation and ongoing administration
• Time-to-benefit is a factor too
• Programming
• Sometimes a differentiator

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11 kinds of data management software

1. High-end OLTP/general-purpose DBMS
2. Mid-range OLTP/general-purpose DBMS
3. Row-based analytic RDBMS
4. Column- or array-based analytic RDBMS
5. Text search engines
6. XML and OO DBMS (but these may merge with search)
7. RDF and other graphical DBMS (but these may merge
   with relational)
8. Event/stream processing engines (aka CEP)
9. Embedded DBMS for devices
10. Sub-DBMS file managers (e.g. MapReduce/Hadoop)
11. Science DBMS

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High-end OLTP/general-purpose DBMS

• Oracle, DB2, MS SQL Server, et al.
• Amazing throughput and scale-up
• Bullet-proofing
• 24/7
• Security certifications
• Datatype extensibility
• Expensive, expensive, expensive

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Mid-range OLTP/general-purpose DBMS

• Three main groups
• Crippled high-end (Express editions)
• ISV/VAR-focused (Progress, several
  non-relational)
• Open source-based (Postgres, MySQL)
• Some are comparable to (or better than) the
  systems that ran the world in the 1990s
• What does the Postgres family still lack?
• Generally inexpensive

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Row-based analytic RDBMS

• Data warehouses should be in separate instances
• But thats not enough
• Sequential vs. random reads
• MPP vs. SMP
• Teradata, Netezza, DATAllegro

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Column- or array-based analytic RDBMS

• Retrieving whole rows carries penalties
• I/O
• Optimization
• Columnar is better
• But not in all use cases
• MOLAP may be superceded

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Text search engines

• 85 of all information is in text
• and 16.9 of all statistics are made up out of
  thin air
• There really are a lot of words out there
• And search interfaces are hugely important
• Text search has its own data access methods
• May play more nicely with columnar than row-based
  RDBMS
• Watch integrations with other analytic datatypes
• Attivio (relational, a little XML)
• Mark Logic (a lot of XML)

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XML and OO DBMS

• Reasons for logical XML structures
• Schema flexibility
• Dressed-up text
• XML is the transport format, and its too complex
  to unpack
• The data came from neither an RDMS nor text store
  in the first place
• Native XML data access methods
• Like text and object
• So far mainly in niches

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RDF and other graphical DBMS

• Semantic web is overhyped
• but the world DOES need ontology management
  systems
• Much depends on path length
• Analytic RDBMS may do the job

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Event/stream processing engines

• Design point super-low latency
• but there are other applications
• Data is executed against queries rather than
  vice versa
• Could be the future of BI
• and of social networking

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Embedded DBMS for devices

• Products
• Sybase SQL Anywhere
• solidDB focused on caching post-acquisition?
• Cloudscape vaporized?
• McObject tiny startup
• Features
• Load-and-forget
• Zero-DBA
• Small-footprint
• Sometimes -- subsettable library

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Matching analytic DBMS to use cases

• 100 Tb data mart
• 50 Tb enterprise data warehouse
• 5 Gb 5 Tb OLTP offload

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Matching OLTP/general DBMS to use cases

• Market leader
• High-end e-commerce
• High-volume call center
• Mid-range
• Web startup
• It depends on how locked-in you are
• Simple departmental application
• General enterprise or SaaS app

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Clayton Christensens disruption narrative

• Market leaders have many advantages, including
  top technology.
• Followers come up with good technology too.
• The leaders stay ahead by making their products
  ever better and more complex.
• The followers sell into new or non-mainstream
  markets, at prices the leaders cant match. So
  they dominate new markets.
• Old markets turn into low-margin commodity-fests.
• Unless they diversify, old leaders are doomed.

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Thats whats happening here

• Much DBMS complexity is without benefit
• Other complexity only benefits a few high-end
  customers
• Data warehouse specialists exploit radically
  superior technology (e.g., MPP)
• Open source vendors have radically different
  price points and business models
• Open source adoption has been strongest in
  non-traditional markets.

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And the big vendors know it

• Oracle is diversifying furiously
• Oracle has announced a clear focus on top-end
  customers
• IBM is obviously focused on the high end too
• Oracle and (to some extent) IBM are buying
  alternative DBMS technologies
• Microsoft and IBM arent dependent on the DBMS
  business anyway