WEDDED ASYNCHRONY in .NET framework

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Applications can send their transactions into the queue and then continue with other tasks, safe in the knowledge that the queue manager will forward the transactions on to their next destination. Generally, it s a bit faster to insert a transaction into a queue than into the whole database, because there s no data manipulation work involved in the queue. The transaction is copied to disk and logged, and the writing application is free to continue. The queue manager then forwards the transaction on, possibly replicating it to other queues on multiple machines, or doing the replication in-place by replaying the transaction across several machines before removing it. Queues allow transactions to be routed, merged, and distributed without impacting the application, provided the application can handle asynchronous notification of transaction completion or failure, and that the application is pipelined enough to benefit from asynchronous operation.
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WEDDED ASYNCHRONY
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When talking about asynchronous queuing systems, I often point out that they work best in optimistic designs that is, in cases where you try to cover the average case instead of the oddball but serious failure mode. After I used the term in a conversation, a customer asked me, What do you mean by asynchronous but optimistic transactional systems It s a model that was popularized inside of Sun by Alfred Chuang (who went on to become the A in BEA Systems), but the examples seemed particular to Sun and computer sales operations. Here s the example I used to explain asynchronous operation: Consider how you and your spouse manage your checkbook. In a synchronous world, you would write checks together, go to the ATM together, and even balance the checkbook register together in a true fit of marital bliss. This model is impractical because it assumes you re physically together and in total agreement for every single financial transaction. In real life, you operate asynchronously; you each visit the ATM separately and write your own checks, confident that your spouse won t suddenly withdraw 99 percent of your available funds and head for Las Vegas. Your ability to handle your own slice of the financials is the asynchronous part; the assurance that most transactions don t produce exceptions is the optimistic part; the monthly discussion over who gets to sort through receipts and checks to balance the checkbook and produce the truth is the transactional part. Eventually, all assumptions are cleared and exceptions processed, and you re back running asynchronously again. Hal
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Applications that can operate optimistically while recovering an asynchronous queue are ideal. It s possible to execute ahead, with the optimistic assumption that transactions in the queue won t interfere with or invalidate transactions being currently processed. As the queue of transactions is applied, those assumptions are tested and any exception conditions are handled, allowing the system to continue functioning even while it performs recovery. This is vaguely similar to a disk mirroring system that does disk rebuild after hot-spare replacement on the fly. The system makes the optimistic assumption that you won t suffer another failure while it is copying data onto the new disk, and it allows the mirror repair to proceed asynchronously with respect to the other disk activity that is generating independent I/O operations. Be careful not to leave single points of failure when designing with queues. If you leave the receiving queue on the same machine as your primary database, but have the queue write to two intermediate queues on other machines, you re exposed if one of the intermediate machines crashes and leaves the queue inactive during the downtime. See Figure 18.4 for an example of two databases that populate an intermediate queue used to replicate the transaction stream into two disparate databases. If the intermediate node fails without any redundancy, transactions in the queue are left in limbo until the node is repaired. When using asynchronous queues, keep the queues close to the databases that they re responsible for updating so that they fail and recover consistently with the database servers.
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