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Christian Steiger

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Datum

10/10/2010

A Graphic Guide To Facebook Portraits

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Gartner 2010 Hype Cycle – How to Get Into the Trough of Disillusionment

The annual 2010 Gartner technology hype-cycle report is out – and wouldn’t you know cloud computing, cloud-web platforms and private clouds are all at or near the top of the peak of expectations section of the curve.

Now to be clear – Gartner, which is focused on large enterprise IT departments , uses the term cloud computing a bit differently than I do on this blog – they generally mean the idea of large IT CIOs using „iron in the sky“ like Amazon EC2 as a place to run their computing workloads instead of on servers in their own data centers. Gartner rates Software as a Service applications like Intact and Salesforce much closer to the plateau of productivity.

What is interesting to me in this is the speed of adoption that Gartner are predicting for cloud computing – note that most of the other dots for technologies at the top of the „peak of expectations“ are dark blue, meaning it will take 5 to 10 years for them to become mainstream (Remember – to Gartner mainstream means even information technology laggards will adopt – so mainstream really does mean ubiquitous). But all the various permutations of cloud computing carry light blue dots – mainstream in 2 to 5 years.

Now remember that all of this is for large enterprises – the benefits of Software as a Service and cloud computing are far larger for small and midsized businesses than they are for giant corporations. This is because large corporates operate at sufficient scale that they can gain some of the operating efficiencies that the large cloud vendors accrue. The beauty of cloud computing for small and midsized businesses is that they can tap into those very same efficiencies – that’s why I call cloud computing the great democratizer – offering SMBs cost and operational benefits formerly only available to large enterprises.

What’s the bottom line – Gartner is predicting cloud computing will be mainstream in the large enterprise in 2 to 5 years. That says you should come in with a skeptical eye and beware the hype – but at the same time you should start piloting now or risk getting left behind your peer companies that are already jumping in with both feet.

Where would I be the most skeptical – with legacy software vendors that are offering up hosted versions of their old on-premises applications on Amazon or Rackspace that you access via Citrix – and calling this cloud computing to try to take advantage of the cloud computing buzz. Going this route will put you right into the trough of disillusionment…

SaaS 2.0: New Morgan Stanley Research – Cloud Computing Keeps on Rolling

I just received Morgan Stanley’s October 2010 CIO Survey. This is always very interesting primary research about what is on the mind of enterprise CIOs – and since it is focused on where they plan to spend their money it gives great insight into what is going to happen over the next couple of years.

Remember that the conventional wisdom is that cloud computing is threatening to enterprise CIOs – since the cloud is all about outsourcing headcount and capital budget that would formerly have been in house – so the way the story goes is that the cloud is supposedly a threat to the job security of the IT department. Morgan Stanley’s data pretty clearly shows that CIOs are less concerned about conventional wisdom and more concerned about driving business performance.

There were two particularly interesting pieces of information in this year’s survey. First from this chart:
The punchline is that 70% of the CIOs said they plan to adopt cloud computing for at least 10% of their applications – up from just 36% last year and very low numbers the prior year. And 30% say they will have more than 20% of their applications in the cloud.

Now let’s look at their plans for cloud platforms and for Platform as a Service:

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Again here 40% is very high adoption for enterprise CIOs – also extremely interesting is that Google has lept into the lead here among cloud platforms – I think most people associate Google with consumer or small business, not with large enterprise.

So what’s the bottom line – even enterprise CIOs are clearly flocking to cloud computing. Contrary to popular wisdom, they aren’t avoiding it because it is a threat to their empires – they see the ROI and flexibility it gives them and as such they are adopting in increasing numbers.

Another 10 Mistakes Made by API Providers

There was a recent post on ReadWriteCloud about 10 common mistakes made by API providers. I think this is a very thoughtful post, but I think it’s an inward look at the problem. In other words, they are looking at problems that developers face while implementing their own APIs. I think biggest mistakes are not necessarily how to implement your API, but how API consumers will perceive, implement and use the API. So I came up with my own list based on nearly a decade of implementing APIs from the receiving end.

Guest author Marcelo Calbucci is the Chief Startup Officer at Conceivian, a Seattle-based Startup Lab, and the founder of Seattle 2.0, an organization providing resources for tech entrepreneurs and startups. Marcelo is also a software developer since the age of 12. You can follow him @calbucci.

1) Naming Convention

Naming convention in the software world is a debate as old as the first programming language was invented. Independent of which convention you use, be consistent. It’s very annoying to implement an API that uses lowercase for all XML elements and attributes, except for a couple of them. Any developer can tell you stories of countless hours chasing a bug because of case-mismatch. My preference is all lowercase because it helps with HTTP compression.

2) URL Consistencies

This is similar to naming convention, and it’s just too common to see APIs where the URL stems and query string have not a strong logic to it. As in, to get users use api.mydomain.com/getusers.php, but to get pictures use api.mydomain.com/pictures/get.php. A little bit of thought goes a long way, even if you are starting with a single API. Thinking as „objects“ and „actions“ is a good start.

3) Authentication

I love APIs that just ask me to pass a single API-Key on the URL. It’s much simpler than having to do some digest authentication (although digest is simple too), and a lot of heck simpler than having to do a separate call to get a session cookie to use on subsequent calls.

4) Simplicity

Some engineers over-think and over-engineer the problem. YouTube used to have a beautifully simple API where you could get meta-data from a YouTube video. Now they decided to normalize and use the Google standard ATOM response, which is pretty awful and bloated. Awful and bloated is one of the reasons SOAP has not caught up. Trying to create a common response for all APIs is silly. Getting user information is different from getting a video information, which is different from posting a comment, which is different from getting a list of followers. Keep it obvious. Keep it simple.

5) Object normalization

If you are going to return an object of type User on the XML element (or JSON) then make sure that every API that returns the element is consistent and returns similar fields. It’s very hard when the same provider has three different APIs that might return a similar, but not the same object. It means I have to parse it three different ways. Making attributes/values optional is fine, but don’t overload their meaning.

6) Documentation

This is the most awful part of implementing APIs, particularly for newly released APIs. Don’t make me figure out how each call, each element and each attribute works. I spend way too much time looking into many responses to see what’s optional, what’s not, what’s the date format, is it a string or an integer, is it long-lat or lat-long, etc. It doesn’t have to be an extensive MSDN-like documentation, but clearly stating what are the call parameters, what values are valid, what are the default values, and on the response side giving an XML-response example and describing what each element/attribute is.

7) Be Forward and Backward Thoughtful

Don’t break my code! There is nothing worse when using a third-party API to learn that your live production code stopped working because the provider changed how an API works. It can be as simple as a change on the format of an element or sometimes as bad as a new XML format that is completely different from the previous format. I know you wrote on your blog, told on your Twitter account and, maybe, emailed everyone you could about this change, but don’t assume people pay attention to that. The best way is to make sure the URL itself has versioning, as in api.mydomain.com/1/myapi.xml. Be committed to keep old versions for at least six months after you release a new version and be very proactive at alerting consumers of your API.

8) Error Messages Are Important

There are two points I want to make: First, „Internal Error“ is not a satisfactory error message, and, second, don’t overload the meaning of HTTP response status codes. The best error messages have both an English description of what they are and a parser-friendly code, as in „783“. I don’t want to parse English-language error messages to figure out what I should tell my user. A standard error code to indicate the Full-Name field must be present is much better. Now, we might get into preferences now, but I prefer every HTTP call to respond with status code 200 and error messages to be embedded inside of the response body (or on an HTTP header), but using 401, 403, 500 to indicate success or error is just confusing. The HTTP status code is not supposed to understand the semantic inside of the response.

9) Making it Parsing Friendly

It’s important to remember as an API provider, that the cost of generating the output might be different from the cost of receiving that output and converting into a usable data structure. This goes both for the computational cost and for the implementation (lines of code) cost. Stay clear of custom date-time formats, stay clear of creating custom data types (for example, multiple pieces of information concatenated into a single string, e.g. „374|Mike Wallace|yes“). It also means don’t get too creative of embedding a CSV file inside a JSON inside an XML.

10) Allow for Subsets

I really like when APIs give me the ability of choosing a subset of data on the response. Amazon e-commerce API supports that and it’s great because if you just need a few fields back why would you get a 30Kb response? Depending on how I’m using the API, the CPU, network and monetary costs can be significant for me. I might also have some of the data cached, so returning a „user-id“ might be enough instead of returning a full „user“ object.

I don’t think this is an extensive list of best practices of implementing your own API for others to consume, but I think the more you wear the hat of the consumer side of things, the more adopted your API will be. Before you even start implementing your API think of three or four applications that would be built using it, and understand what the needs would be. Maybe you should go ahead and use a bit of test-driven development, on this case usage-driven development, and implement those applications first.

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