In a SQL world, post-relational databases are holding their own.
NEWPORT BEACH, Calif.-In
the modern IT world, SQL and XML have become such key components in the
database sector that it's difficult to remember a time when they weren't part
of the architectural story.
However, in the late 1960s and early '70s, database
architectures with flexible field sizes, nested tables and loose-data typing
options were winning market share and mind share. While these earlier systems
lacked the tagging that in many ways defines XML as XML, they behaved in a
remarkably similar manner in all other respects.
Because these systems-the current terms to describe them are
"post-relational" or "multivalue"-don't get a lot of press coverage, it is easy
to write them off as one of the many noble experiments that have fallen by the
wayside. The IT industry is littered with better mousetraps that eventually
disappear.
However, post-relational technologies haven't fallen. There
are many companies that still offer such database products, from such
tech-sector mainstays as IBM to lesser-known
companies such as InterSystems, Northgate and jBase. At the International
Spectrum Conference here in late March, seven of the major database vendors in
this arena presented their wares, touted new partnerships and detailed plans
for expansion.
A quick look through the conference agenda reveals the same
topics that can be seen at a relational database conference: how to develop
robust Web-to-data integration, change control management, security, document
management and all the other usual subjects.
In addition, just like their better-known counterparts in
the SQL world, these databases have strongly partisan supporters.
In reviewing the information at the event and talking with
participants at the conference, here's the summary of the partisan case for why
a business might consider post-relational databases in addition to-or instead
of-RDBMS (relational database management system) technology:
Scale
SQL scales exceptionally well when scale means increasing
the number of users without loss of speed. Post-relational systems scale
exceptionally well when scale means increasing the complexity of the
application.
The secret is in the data structure. Due to the XML-style
nesting, data integrity is inherent in the model. This requires fewer computational
resources to check and protect the completeness of the data. Additionally,
nesting allows for dramatically fewer reads to achieve the equivalent volume of
data retrieval. That means fewer read cycles and therefore longer MTBF (mean
time between failures) relative to the volume of business. It also means that a
programmer or analyst can see the primary relationships by looking directly at
the data, without actually needing to see the schema.
IBM has documented
excellent throughput with thousands of active users, so even user scaling works
well in these environments.
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