The Notoriously Tricky "Step 0"

-

I recently posted about "Git for Dummies" and noticed that commented that they had followed my instructions and had a strange permission issue. Wanting to verify everything was correct, I did a quick check and, sure enough I was having the same problem. Upon investigation, I discovered that I had posted a which required a public/private key pair and folks who hadn't already set this up on their machines would get an error. The post was supposed to have been written for a new user and they typically wouldn't have done this. As a long time github user, I did this one time setup ages ago and had totally forgotten about it.

This is common occurrence, so common, that I give it a name... I call it "Step 0". This is a variation of the curse of (prior) knowledge and can be frustrating for both people trying to learn something new as well as people who are trying to explain how to that something to a newbie. In essence, "Step 0" is the prior knowledge necessary and fundamental to performing a task that is so fundamental that it is routinely forgotten until a novice attempts the task for the first time.

As with many things, there is no sure fire solution or way to avoid this, but being aware of the situation can certainly reduce the amount of frustration when it happens. I was about to amend my previous blog post to add the necessary detail, but decided to change the url to the "read only" version. Instead of editing the post to add the 5 steps necessary to set up and use the ssh version, I changed to use the anonymous version that most newbies should start with.

I chose the simpler solution over trying to explain "setting up a github account" and "setting up ssh public/private key pairs" because I think those topics are different and trying to put too much detail can be just a detrimental as not having enough. If someone follows the instructions and they don't work, they can ask a question.

Comments

Post a Comment

Popular posts from this blog

Please use ANSI-92 SQL Join Syntax

-
Image
Way back in 1992, a standard was published for SQL. In it, the syntax for specifying joins in SQL Database was finally standardized. This was a good thing because (for folks who've been doing this a while), trying to decode the vagaries of how various Databases handled outer joins could drive you bonkers yielding different results depending on the parse order of things in the from and where clauses. Unfortunately, some DBMS vendors took a while to adopt this standard (Oracle didn't support it until 2001), but at this point if you're using a major RDBMS and NOT using it, you're probably mired in a tradition or habit that bears changing. As an example of what I'm describing, here are some examples of what I mean. Old School (no standard syntax + implementation and evaluation of predicates varied by vendor [and query]): select * from customer, order where customer.customer = order.customerid ANSI way (agreed to standard, predicate evaluation has some rules ...
Read more

The difference between Scalability, Performance, Efficiency, and Concurrency explained

-
I thought I'd give some explanation of the difference between scalability, performance, Efficiency, and concurrency. To make things more accessible (and because I'm watching a cooking show on TV) I'm going to use a baking metaphor to help explain the differences. In our case, the system in question is baking cookies for consumers. Scalability, is how well something responds to adding more resources. If we can double our capacity by doubling the number of resources, we've got linear scalability. In our baking example, if we add more ingredients, we can scale to a larger number of cookies. To contrast with our other metrics, this doesn't actually increase the performance of creating a single cookie (they still take 10 minutes to bake), and the efficiency is the same (we just have more capacity). We also cannot bake more cookies in a fixed time period, but over a larger time period we can produce more cookies. Performance is how fast something can get done, u...
Read more

the myth of asynchronous JDBC

-
Image
I keep seeing people (especially in the scala/typesafe world) posting about async jdbc libraries. STOP IT! Under the current APIs, async JDBC belongs in a realm with Unicorns, Tiger Squirrels, and 8' spiders. While you might be able to move the blocking operations and queue requests and keep your "main" worker threads from blocking, jdbc is synchronous. At some point, somewhere, there's going to be a thread blocking waiting for a response. It's frustrating to see so many folks hyping this and muddying the waters. Unless you write your own client for a dbms and have a dbms that can multiplex calls over a single connection (or using some other strategy to enable this capability) db access is going to block. It's not impossible to make the calls completely async, but nobody's built it yet. Yes, I know ajdbc is taking a stab at this capability, but even IT uses a thread pool for the blocking calls (be default). Someday we'll have async databa...
Read more