Group by an expression… and limitations of MDX vs SQL

Some queries can be easily expressed with MDX while it’s not possible to get the same result using SQL. For example, if we use Adventure Works sample cube and would like to display internet gross profits by country in a way so that we have separate column for each year it can be easily achieved with below MDX.

  non empty [Date].[Calendar Year].[Calendar Year] on 0,
  [Customer].[Country].[Country] * [Measures].[Internet Gross Profit] on 1
from [Adventure Works]

It displays column for each year which has data for internet gross profits. In SQL, however, all columns must be explicitly listed in a query (or derived from table structure in case of select *) – whether in select list or in pivot clause. Generically speaking, SQL is designed to return two-dimensional datasets with arbitrary number of rows and pre-defined number of columns while MDX can return data on multiple axes (dimensions) although it’s a tricky question how to visualize the output if we use more than 2 axes.

On the other hand, sometimes it quite challenging to implement the logic in MDX which can be expressed using SQL in a very trivial manner. Let’s consider a couple of cases.
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Domain indexes. Built-in vs DIY.

In this I’ll do a quick performance comparison of built-in Oracle Text index versus the one created using Extensible indexing interface (described in this post).

First of all, Oracle Text option must be installed to use built-in text indexes. This functionality is getting installed under CTXSYS schema.

Speaking about DIY text indexes, it’s mandatory to have privileges to create index types and operators or simply RESOURCE role.

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Scala external procedures in Oracle

I assume it would be more correct to say “using scala functions in Oracle” but Oracle documentation uses terms like External Procedures or External Subprograms.

There are no specific details about scala with regard to external procedures in Oracle but given that scala code is translated into Java byte code there should be no difficulties in using scala as a language for stored code as well.

Of course, there is no scala compiler in Oracle so we cannot run statement like create or replace and compile java source but we can compile scala code in advance and upload classes using loadjava utility.

All java methods which are mapped to PL/SQL routines must be static and since there are no static methods in scala the typical approach to mimic them is by declaring functions in an object rather than in a class.
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Domain indexes. Quick dive.

Extensible indexing interface

Domain indexes are used to speed up data retrieval for specific domains like text, spatial or image data. Oracle has built in indexes for above mentioned domains but developers can create their own using extensible indexing interface (ODCIIndex).

Let’s consider specific task when this can be useful and discover process end-to-end.

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Evolution of regular expressions

Frankly speaking “evoulution” is not quite proper word to title this post.
I’m going to show some extensions in regular expressions in different languages in compare to their implementation in Oracle.

Oracle supports regular expressions according to POSIX standard draft 1003.2/D11.2 and two extensions

  • matching capabilities for multilingual data
  • some commonly used PERL regular expression operators (for example, character class shortcuts and the nongreedy modifier “?”)

Let’s consider quizzes which cannot be easily resolved using built-in capabilities in Oracle.
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Differentiation of functions in Python and R

Calculating value of derivative function is a part of many applied tasks such
– gradient descent/ascent for finding minimum/maximum of a function
– Newton’s method for finding approximations to the roots (or zeroes) of a real-valued function

There are three main ways to calculate derivatives
1. Finite differences i.e. calculate approximation using (f(x+Δx)-f(x))/Δx when Δx is close to zero.
2. Symbolic differentiation – generates exact formula for derivative function.
3. Automatic differentiation – generates evaluations (and not formulas) of the derivatives. All intermediate expressions are evaluated as soon as possible.
A brilliant explanation of automatic differentiation may be found here in a paper by Warwick Tucker “One-dimensional, first-order, and Taylor-mode automatic differentiation with programming examples”.

So let’s have a closer look on some technics for derivation in Python/R.
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