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At the core of MPbase is the unique way it organizes information.
This organization is very different from a traditional DBMS. In
a traditional database the various tables, rows and columns can
be viewed as boxes. The access to each box is by its label. The
more precisely defined the label, the less you can put in one
box. So you can either have lots of precise little boxes or a
few big, but vague, boxes.
The problem is that neither design makes a good general choice.
This would tend to imply that the best choice would be the midpoint
between the two. The unfortunate reality is that the midpoint
suffers from the problems of both while having few of the benefits
of either. It really is the worst of both worlds.
This is what causes high-performance database designs to specialize
in only one type of transaction. This is also what causes the
more general-purpose database designs to be tolerable for anything,
really good at nothing. Any attempt to improve some single aspect
of the general-purpose database design will tend to trash some
other area of functionality.
This either/or mentality is so ingrained in the current database
paradigm that no one dares to ask "What if we could have
both?" As long as you are predefining boxes to hold your
data you cannot. If you were to give up the fixed boxes, how on
earth would you ever find anything? The answer is to let the data
sort itself out. This is only possible under a content-addressable
schema.
Using this content-addressable model, the need for fixed meta-data
goes away. If you do not have fixed boxes you do not need fixed-box
labels. What you get in its place is virtual meta-data, or the
ability to define and label your boxes on the fly. By definition,
if the data content determines its location in the data store,
the more alike the data, the closer together it is stored.
In this model you could say: like data attracts. Hence the most
common data elements are the best attractors. This creates the
most natural possible structure for loading, updating, finding
or analyzing the data. It allows the ability to "zoom"
in and out. From a high-level macro view to a detailed analysis
of subtle differences between almost matching data.
Just looking at the resulting structure can be very informative. Data found in large clumps is going to be very similar. Data found off by itself is normally going to be ether in error or interesting. Some types of analysis will focus on the "stray" data, others on one or more of the data "clumps." In ether case the other type of data can easily be kept out of the way.
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