Inter-Process Communication Affects Application Response Time
m (Inter-process communication drives response time moved to The number of inter-process communications affects response time)
Revision as of 02:17, 13 December 2008
Response time is critical to software usability. Few things are as frustrating as waiting for some software system to respond, especially when our interaction with the software involves repeated cycles of stimulus and response. We feel as if the software is wasting our time and affecting our productivity. However the causes of poor response time are less appreciated, especially in modern applications. Application Performance Management literature still discusses data structures and sorting algorithms, issues that were important decades ago, but not likely dominate performance in modern multi-tier enterprise applications.
When performance is a problem, my experience has been that improvements in data structures and algorithms aren't the right place to look. In modern enterprise applications, response time depends most strongly on the number of remote inter-process communications (IPCs) conducted in response to a stimulus. While there can be other local bottlenecks, the number of remote inter-process communications usually dominates. Each remote inter-process communication contributes some non-negligible latency to the overall response time, and these individual contributions add up, especially when they are incurred sequentially.
A prime example is "ripple loading" in an application using object-relational mapping. Ripple loading refers to the sequential execution of many database calls to select the data needed for building a graph of objects (see Lazy Load in Martin Fowler's Patterns of Enterprise Application Architecture). When the database client is a middle-tier application server rendering a web page, these database calls are usually executed sequentially in a single thread, and their individual latencies contribute to the overall response time. Even if each database call takes only 10ms, a page requiring 1000 calls (which is not uncommon) will exhibit at least a 10-second response time. Other examples include web service invocation, HTTP requests from a web browser, distributed object invocation, request-reply messaging, and data grid interaction over custom network protocols. The more remote IPCs needed to respond to a stimulus, the greater the response time will be.
The strategies for reducing the number of remote inter-process communications per stimulus are relatively few, and relatively obvious or well known. One is to apply the principle of parsimony, by optimizing the interface between processes so that exactly the right data for the purpose at hand is exchanged with the minimum amount of interaction. Another is to parallelize the inter-process communications where possible, so that the overall response time becomes driven mainly by the longest-latency IPC. And a third is to cache the results of previous IPCs, so that future IPCs may be avoided by hitting local cache instead.
When you're designing an application, be mindful of the number of inter-process communications in response to each stimulus. When analyzing applications that suffer from poor performance, I have often found IPC-to-stimulus ratios of thousands-to-one. Reducing this ratio, whether by caching or parallelizing or some other technique, will pay off much more than implementing the best data structure choice or list sorting algorithm.
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