{{ !articles[0].partner.isSponsoringArticle ? "Platinum" : "Portal" }} Partner


Here's how to recognize a Faerie Dust request:

  1. We have identified a problem. It can be with almost anything: scalability, reliability, auditability, any Quality Measure.
  2. We're pursuing a specific technology. Typically, something that has the lowest impact on our architecture.
  3. We can't address anything other than this specific technology variation -- we can't change the application software or buy hardware.

Once we're in the Faerie Dust realm, what can we do?

Laughing doesn't help. They have a serious problem, they need a solution. The fact that they won't address the cause isn't completely relevant -- we have to work on the denial, anger, negotiation, depression cycle first. Hopefully skipping past the anger, or assuring the anger is directed elsewhere.

Helping doesn't help. If we join the quest for their Faerie Dust, what will we accomplish? We'll burn billable hours to -- eventually -- reach an equivocal non-solution with a complex write-up and recommendations that won't be implemented.

Not helping doesn't help. If we obstinately refuse to join the quest for the Faerie Dust... well... then we've done nothing. We haven't advanced their understanding of their problem.

What's left? Is there a middle road that allows us to join the Faerie Dust quest, but still point out the side roads, other monsters and other treasures along the way?

Perhaps there is, but it would require a kind of saintly patient persistence. We would have to start with an enumeration of problem causes, prioritize them, and then focus on their selected bit of Faerie Dust. My idea is that enumerating the possible causes allows us to identify the missed opportunities, and the possible magnitude of fixing something essential (algorithm or data structure) instead of throwing up window-dressing to cover problems in something inessential (reducing the time required for a table scan).


Here's a concrete example of Faerie Dust.

  1. Pick a data model that doesn't fit the use cases. i.e., lumped many discrete details into a single text field that has "rich semantic content". Work around this mistake by using wild-card matches.
  2. Complained about performance and dug into nuanced details of LIKE clause and full-text search. Lots of study time spent on LIKE clause processing and how to improvement performance.
  3. Refused to discuss the actual use case or the mismatch between data structures and requirements.

The design didn't match the use cases. Faerie Dust won't help.
{{ tag }}, {{tag}},

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}
{{ parent.authors[0].realName || parent.author}}

{{ parent.authors[0].tagline || parent.tagline }}

{{ parent.views }} ViewsClicks