Web crawling is the last resort

Data source analysis is one of the crucial parts of an enterprise search deployment project. Search engine results quality strongly depends on an indexed data quality. In case of web-based sources, there are two basic ways of reaching the data: internal and external. Internal method involves reading the data directly from its storage place, such as a database, filesystem files or API. Documents are read by some criteria or all documents are read, depending on requirements. External technique relies on reading a rendered HTML with content via HTTP, the same way as it is read by human users. Reaching further documents (so called content discovery) is achieved by following hyperlinks present in the content or with a sitemap. This method is called a web crawling.

The crawling, in contrary to a direct source reading, does not require particular preparations. In a minimal variant, just a starting URL is required and that’s it. Content encoding is detected automatically, off the shelf components extract text from the HTML. The web crawling may appear as a quick and easy way to collect a content to be indexed. But after deeper analysis, it turns out to have multiple serious drawbacks.

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LDAP Connector for Openpipeline Used for Indexing Organisational Information

Finding people within your organisation, also denoted as People Search, is something that is a key ingredient in a findability solutions. People catalogs are often based on an LDAP structure which holds the important information about each employee.

The LDAP connector for Openpipeline is the result of the latest activity at the Findwise development department which makes it easy to make the LDAP structure searchable. As always with a connector, you get direct access to the source which ensures a very efficent indexing and good control over the indexed information.

The LDAP connector has a number of features, some noted below:

  • SSL support – Supports LDAP over SSL
  • Pagination – LDAP entries can be retrieved in batches if the LDAP server supports the PagedResultControl. This increases performance and reduces memory consumption drastically
  • Incremental indexing – If the LDAP server flag each update to an entry with a timestamp, the connector can use this timestamp to only fetch updated entries.
  • Delete entries – LDAP entries that has been removed since the last run will be removed from the index
  • Attribute specification – Specify what attributes that should be returned for each entry. By only retrieving the attributes you need, performance is increased.

Interested of knowing more about the connector, or do you have any experience you like to share when indexing LDAP directories? Please drop a comment!

Solr Processing Pipeline

Hi again Internet,

For once I have had time to do some thinking. Why is there no powerful data processing layer between the Lucene Connector Framework and Solr? I´ve been looking into the Apache Commons Processing Pipeline. It seems like a likely candidate to do some cool stuff.  Look at the diagram below.

A schematic drawing of a Solr Pipeline concept. (Click to enlarge)

What I´m thinking of is to make a transparent Solr processing pipeline that speaks the Solr REST protocol on each end. This means that you would be able to use SolrJ or any other API to communicate with the Pipeline.

Has anyone attempted this before?  If you’re interested in chatting about the pipeline drop me a mail or just grab me at Eurocon in Prague this year.

To Crawl or Not to Crawl in Enterprise Search

Having an Enterprise Search Engine, there are basically two ways of getting content into the index; using a web crawler or a connector. Both methods have their advantages and disadvantages. In this post I’ll try to poinpoint the differences with the two methods.

Web crawler

Most systems of today have a web-interface. Let it be your time reporting system, intranet, document management, you’ll probably access those with your web browser. Because of this, it’s very easy to use a web crawler to index this content as well.

The web crawler index the pages by starting at one page. From there, it follows all outbound links and index those. From those pages, it follows all links, and so on. This process continues until all links at a web site has been followed and the pages been indexed. The crawler thus uses the same technique as a human, visit a page and clicking the links.

Most Enterprise Search Engines are bundled with a web crawler. Thus, it’s usually very easy to get started. Just enter a start page and within minutes you’ll have searchable content in your index. No extra installation or license fee are required. For some sources, this may also be the only option, i.e if you’re indexing external sources that your company has no control of.

The main disadvantage though, is that web pages are designed for humans, not crawlers. This means that there are a lot of extra information for presentation purposes, such as navigation menus, sticky information messages, headers and footers and so on. All of this makes it a more pleasant experience for the user, and also making it easier to navigate on the page. The crawler on the other hand has no use of this information when retrieving pages. It’s actually reducing information quality in the index. For example, a navigation menu will be displayed on every page, thus the crawler will index the navigation content for all pages. So if you have a navigation item called “Customers” and a user searches for customers, he/she will get a hit in ALL pages in the index.

There are ways to get around this, but it requires either altering of the produced HTML or adjustments in the search engine. Also, if the design of the site change, you have to do these adjustments again.


Even though the majority of systems has a web-interface, the content is stored in a data source of some format. It might be a database, structured file system, etc. By using a connector, you connect either to the underlying data source or to the system directly by its programming API.

Using a connector, the search engine does not get any presentation information but only the pure content, making the information quality in the index better. The connector can also retrieve all metadata associated with the information which further increases the quality. Often, you’ll also have more fine-grained control over what will be indexed with a connector than a web crawler.

Though, using a connector requires more configuration. It might also cost some extra money to buy one for your system, and require additional hardware. Though, once set up, it’s most likely to produce more relevant results compared to a web crawler.

Bottom line is it’s a consideration between quality and cost, as most decisions in life 🙂