Big Data. Big Decisions
InformationWeek
Special Coverage Series

Commentary


Indexable versus Iterable Collections

The C++ standard template library design emphasizes collections that support generic iterators. In this post I propose an alternative design approach for collections that uses generic indexers.



The C++ standard template library design emphasizes collections that support generic iterators. In this post I propose an alternative design approach for collections that uses generic indexers.

In the C++ standard library many collections are iterable. They provide an iterator type that  may support assignment (if the collection is not read-only) and can be incremented and maybe  decremented. The collection returns an iterator representing the first value in the collection  using a "begin()" method, and an "end()" method returns an iterator indicating that the end of  the collection has been reached.

Let's look at how a generic "ScaleVector" procedure might work on an iterable Array collection in C++.

void ScaleVector(Array& xs, int x) {
  for (Array::iterator i = xs.begin();
       i != xs.end();
       ++i)
  {
     *i = *i + x;
  }
}

What this code does is it decouple the notion of iterator from the collection itself. We ask the collection for its iterator, and let the collection class and the iterator takes care of the details of initialization, incrementing, and checking the invariant.

One characteristic of many collections and arrays in particular, is that elements can be accessed using an index. In other words they are indexable. Sometimes having access to the index in a loop is useful. So consider if instead of abstracting the notion of iterator, we abstracted the notion of an  indexer? Then we might be able to write:

void ScaleVector(Array& xs, int x) {
  for (Array::indexer i = xs.begin_index();
       i < xs.end_index();
       ++i)
  {
     xs[i] = xs[i] + x;
  }
}

In this example, the most obvious gains is the improvement to the readability of the assignment statement. An observer can quickly see that the statement "xs[i] = xs[i] + x;" will modify the collection, whereas that information is a bit less obvious in the earlier example's statement "*i = *i + x;".

The power of indexing lies in the fact that they remain valid as long as they remain in a valid range of indexes. So for example, if an array is resized the index remains valid as long as it stays within the valid range.

Consider the following  bad code in C++:

int DontDoThis(std::vector<int> xs, int x) {
  // this example assumes a non-empty vector
  assert(xs.begin() != xs.end());
  std::vector::iterator i = xs.begin();
  xs.resize(xs.size() + 100);
  // bad, bad, bad
  return *i;  
}

STL iterators aren't valid after a collection is resized. This design decision was so that they could be dependent on the memory locations for fast implementation.

An indexer is assumed to remain valid if it is within the valid range, regardless of how memory is arranged. So the following would remain valid:

int DoDoThis(Array<int>& xs, int x) {
  // this example assumes a non-empty array
  assert(xs.begin_index() != xs.end_index());
  Array::index i = xs.begin_index();
  xs.resize(xs.size() + 100);
  return xs[i];  
}

The final question I should address is "why not just use ints"? The answer is another question: Why not use size_t's? Or unsigned longs? Or why not use a pair of value because the internal layout is a two-dimensional array?

The problem with hard-coding ints as your indexing mechanism, or any other particular types is that it locks you into a particular implementation. The internal implementation of the collection is exposed to the consumer of the collection, and any refactoring of the collection type, forces all usage to be rewritten as well. As I imply above, one very nice implementation of an index is to use a pair of values.

So here is one way you might construct an indexable collection:

template<T>
struct Array
{
  std::vector<T> mvec;
  typedef size_t indexer;
  indexer begin_index() const { return 0; }
  indexer end_index() const { return mvec; }
  T& operator[](indexer i) { return mvec[i]; }
  const T& operator[](indexer i) const { return mvec[i]; }
  size_t size() { return mvec.size(); }
  void resize(size_t sz) { mvec.resize(sz); }
  void push(const T& x) { mvec.push_back(x); }
  T& top() { return mvec.back(); }
  const T& top() const { return mvec.back(); }
  void pop() { mvec.pop_back(); }
  void swap(indexer i, indexer j) { if (i != j) std::swap(mvec[i], mvec[j]); }
}; 

 



Related Reading


More Insights




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.