Native XML databases - Making Sense of NoSQL - page 119

experience with native XML systems nor the XQuery language. One member of the
staff, a historian by training, learned XQuery over the course of several months and
created a prototype website using online examples and assistance from other mem-
bers of the eXist and TEI community.
There are currently hundreds of completed FRUS volumes in the system, with
more being added each month. Search performance has met all the requirements for
the site with page rendering and web searches all averaging well under 500ms.

5.7 Case study: managing financial derivatives

with MarkLogic
In this case study, we'll look at how a financial institution implemented a commercial,
native XML database (MarkLogic) to manage a high-stakes financial derivatives system.
This study is an excellent example of how organizations with highly-variable data
are moving away from relational databases even if they're managing high-stakes finan-
cial transactions. High-variability data is difficult to store in relational databases, since
each variation may need new columns and tables created in a RDBMS as well as new
reports.
After reading this study, you'll understand how organizations with high-variability
data can use document stores for transactional data. You'll also see how these organi-
zations manage ACID transactions and use database triggers to process event streams.

5.7.1 Why financial derivatives are difficult to store in RDBMSs

This section presents an overview of financial derivatives and provides insight as to
why they're not well suited for storage in tables within a RDBMS .
Let's start with a quick comparison. If you purchase items from any web retailer,
the information you enter for each item you want to purchase is limited. When you
purchase a dress or shirt, you choose the item name or number, size, color, and per-
haps a few other details such as the material type or item length. This information fits
neatly into the rows of an RDBMS .
Now consider purchasing a complex financial instrument like a derivative, where
each item has thousands of parameters and the parameters for every item are differ-
ent. Most derivatives contain a product ID , but they also contain conditional logic,
mathematical equations, lookup tables, decision trees, and even the full text of legal
contracts. In short, the information doesn't lend itself to an RDBMS table. Note that
it's possible to store the item as a binary large object ( BLOB ) in a traditional RDBMS ,
but you wouldn't be able to access any property inside the BLOB for reporting.

5.7.2 An investment bank switches from 20 RDBMSs

to one native XML system
A large investment bank was using 20 different RDBMS s to store complex financial
instruments called over-the-counter derivative contracts , as shown in figure 5.12.