Scalability and Performance Tradeoffs in Java

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Scalability and Performance Tradeoffs
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Now that we've clarified the two basic forms of communication between systems, how do we decide which is the best one to use Although each has some performance and scalability tradeoffs, the most important factor obviously has to do with the business requirements of a given situation Certainly, interactive Web applications need to operate in a mostly synchronous manner They have many processes (transferring money, purchasing a product, etc) where synchronous communication is simply mandatory because a user needs to be assured that the operation just requested has been performed However, there may be opportunities to solve problems asynchronously as well Let's look at some of these tradeoffs in the context of the challenges we outlined earlier Connection Management Synchronous communication brings with it a difficult connection management issue Interactive Web applications must be able to maintain a large number of available connections at all times to service high demand and to handle longer usage times per connection For example, the HTTP 11 persistent connections feature improves performance for a series of HTTP exchanges, but it also frees connections at a lower rate than nonpersistent connections In contrast, asynchronous communication has to worry only about providing enough connections to ensure highly available queuing These connections are typically short-lived and don't sit idle during periods of client think time, as a persistent connection might Caching and Data Locality
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Synchronous communication has much stricter requirements for efficient memory management than does asynchronous communication This has to do with the difference in processing between the two Synchronous clients, by definition, must wait for an answer Good caching schemes can translate into shorter average latencies; bad caching schemes (or none at all) obviously can result in the opposite Shorter latencies mean better request processing performance and thus better throughput Although caching can also improve the efficiency of asynchonrous communication models, it is not always a necessary feature For example, many asynchronous solutions involve large "pushes" of data (ie, batch loads) that with or without caching take hours (not milliseconds) to complete While businesses generally strive to build efficient systems, waiting 3 hours for the consumption of data instead of the 2 that a caching solution might yield may not be a major problem The point at which a delay becomes unacceptable depends on the nature of the application Some asynchronous applications may in fact require good consumer caching Consider a stock market information system that works using a broadcast paradigm Suppose that a client receives stock quotes from an information producer and must flash the ticker symbols on the user screen along with some other data (such as the name of the company) Caching this "other data" is going to be very important in this scenario or else the lag time between when the message is produced and when it's consumed and processed will be too great to be useful (ie, the report of a price drop or rise will arrive too late for the user to do anything about it) In such cases, data is changing fast and client/listener processing needs to be as quick as possible In short, caching and data locality are usually more critical for synchronous systems because the client can't make progress until he receives a reply However, they can also be critical for some highly dynamic asynchronous scenarios One thing is for sure: Good caching improves the performance of both approaches as long as the complexity in implementing it isn't unreasonable Data Marshalling Here's a case where synchronous systems are often more efficient, for two reasons First, a messaging intermediary represents yet another layer in the communication between two parties, which potentially means
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Another level of reconstructing and deconstructing serialized forms into an intermediate data structure Possibly another level of request comprehension (so that the right consumer can be contacted)
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In addition, the actual application data may need to be converted to another format This is very common in messaging systems, many of which support transformation rule building so that deployers can easily customize just how data is to be translated between producer and consumer Consider the transfer of records that consist of several codes that indicate employee history information These codes may mean something to the producer but mean nothing to the consumer, who has his own codes The producer's codes may need to be translated to make sense in the consumer's application or data model In contrast, synchronous systems usually have fewer data transformations and don't have to worry about this intermediate hop in the request-processing network flow Still, keep in mind that the client is waiting in the synchronous case, so the lack of efficient data marshalling has a greater impact here than it does in the asynchronous case
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