QlikView depends entirely on processor speed, processor cache performance, memory latency and memory throughput. This makes QlikView an ideal reference for Intel, who uses QlikView to show off the latest product improvements. It also adds to the challenge of adapting QlikView to cloud platforms such as Amazon Web Services, Mosso, Joyent, etc.
The problem is virtualization. Virtualization is valuable to customers and service providers, but it’s also a thief! It adds overhead for the processor, cache and memory–everything that impacts QlikView performance!
The cloud, as in real life, is ever changing. You have no idea how many people are sharing your hardware and what their load will be from second to second. I would bet that nearly all deployed QlikView servers spend most of their time idle and the rest of their time at peak processing power. In the cloud, the goal is to spend as little time as possible idle for which we sacrifice peak processing power. QlikView depends on peak processing power and that type of application will suffer the most in the cloud.
But exactly how much will it suffer? Success in the cloud will need to be measured by the end-user experience. The cost of being in the cloud is vigilant monitoring and smart responses. What’s the right way to monitor the end-user experience in a company that uses OCX vs. AJAX, or is spread out geographically? Will bringing up more servers in the cloud improve response time? Should every QlikView server deliver the same set of apps, or should each app be served by a dynamic set of servers? Similarly, do some apps need sub-second response time while others can wait?
One thing stays the same. If you deploy large QlikView data sets you’re already sensitive to response times and what to consider when designing an app. In the cloud, smaller apps will need to think about costly chart expressions, messy data models and design choices that work fine on dedicated servers.
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