The document discusses search capabilities within the Ektron CMS. It notes that while search was initially useful, most websites only perform text-based searches within one area, limiting functionality. Later versions of Ektron have added powerful search tools through integration with Solr or Microsoft Search Server. These tools can index Ektron data and perform fast searches across large volumes of content through indexing, unlike direct database searches. Facets also allow filtering of results.

1. Empower Your Ektron Websites – Think Beyong The Search
When enterprise was introduced as a new added feature to Ektron 8.5 version, users
immedietly witnessed the benefits. It was accurate, fast and help in attainable search results
on your website. With the lastest version (9), Ektron has become better than ever. Today,
developers are using either Solr or Microsoft Search Server as their key search server.
We all know that with every shaded of white there comes shades of black. The same is for
Ektron. Most websites, as per my observation, limit their search to one area – performing
text­based searches. It is not a good sign at all. However, there are set of powerful tools
which can help lifting heavy search results, as much as they can. Isn't it interesting?
I am working as an Ektron developers since 2006. Believe me I have never faced so many
challenges before. But I can say one thing that the added power of the search functionality
can be considered as one of the most exciting and valuable enhancements of the said
content management system (CMS).
PROBLEM
There are problems with the last and latest version of Ektron CMS. But that does not mean
that we are discouraging people to use it for their website development projects. I would
not go into details. But I must say about one problem which can be solved if you are
accustomed with this content management system.
To illustrate, we will use the example of news articles on various websites. Let’s say we start
from scratch and in our first year we develop 1,000 news articles for our website. Chances
are that querying news articles on different criteria like date, category or title will still be
very quick (fractions of a second). But what happens when we produce thousands more
articles over the next few years or have a lot of archived content from previous years. There

2. could be 50,000 or even 500,000 articles in our system. The count may be higher than what
we could expect. You will notice that the same queries perform decidedly slower than
before. In fact, it's so much slower that it affects the performance of pages and delays
loading times by seconds. Sound familiar?
The reason these queries are not scaling well over time is because they are directly tied to
the quantity of the content they are searching. In order to find out what news articles meet
the specified criteria, all records need to be inspected on the appropriate fields.
RESCUE MEASURE
Under the almost same scenario, a product like Solr or Microsoft Search Server can filter
through all of your news articles and give results in fractions of a second. How? By
indexing your Ektron data and querying on those indexes.
What is an index? A thorough definition can be found here. But the simplest way to think
about it is essentially the same way indexes work in a book. If I need to find a particular
topic or category, the index tells me what page to go find it on. Otherwise, I would need to
scan through the whole book looking for that information, page by page.
The same applies to database indexing. Although the actual implementation technique of
an index can vary. What is important is the part to take away is that we only have to look
through a fraction of the data to get our results—not all of them. When I enter content into
the Ektron work area, I can specify what fields need to be indexed. This include different
types of information such as smart form fields, metadata, and taxonomies.
What’s more is that queries running through providers like Solr or the latest version of
Microsoft Search Server yield another powerful mechanism: facets. Facets are a list of filters
that can be used to further refine your search results. You have no doubt seen facets when
shopping at an online retailer like Amazon. These are the sub­categories that appear to help
you refine your original search results. Facets are returned at the time of the original search
request and do not require additional steps or programming to populate.