Application servers for specific data sets. in Java

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Alternative 4: Dialog-Oriented Servers
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The design principle of this alternative is also partitioning, but a partitioning from the end-user perspective and not from a database perspective. The goal is to group the
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Architecture of the Distributed, Multitier Railway Application DaRT
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dialogs in categories that are related together; that is, when an end-user is working with dialog A and normally then moves to dialog B, both dialogs belong to the same dialog category. For instance, there will be dialogs for maintaining the locomotive data, the carriage data, their model data, the maintenance schedules, the access control, etc., so that application servers for exactly these categories are required. If an end-user maintains the data of locomotives he or she also might want to read some data of the according locomotive model. Therefore, the locomotive application server has to access some data that are mainly controlled by the model application server. This is the principal difference to data-specific servers: dialogue-oriented servers access overlapping object sets. If the object sets of the application servers are overlapping, locking conflicts in the concurrent database transactions can occur so that an application server might be blocked or, in the case of a deadlock, might even be reset. Therefore, the application server program has to consider that a transaction can be aborted due to a deadlock detected by the database server. This is routine work for a programmer. A more challenging task than handling deadlocks is to reduce the likelihood that the application servers' transaction can be blocked or even aborted. If the dialog categories are defined carefully then only a small subset of the persistent objects can be accessed via different dialog categories. Figure 18.9 illustrates the partitioning of the DaRT application servers according to different dialog groups. As mentioned for the dataspecific servers, each dialog-oriented server has its own ObjectStore cache with an adjusted size. For instance the locomotive application server requires a cache size of 40MB, and the application server for the model data requires only 15MB. The selection and query servers can also be adapted to various dialog categories. The selection servers are partitioned in the same way as the update servers, so that there are selection servers, for example, for locomotives, carriages, model data, maintenance schedules, and access control. According to the access profiles there are different numbers of selection servers for each selection category. For instance, four selection servers for locomotives are required for good throughput, but only three selection servers for model data. The partitioning of the query servers is the same as for data-specific servers; that is, different query servers handle queries on locomotives, on carriages, and on all vehicles. Dialog-oriented servers consider overlapping object sets from the very beginning because they are designed with the assumption that the dialogs and therefore the accessed persistent objects will overlap. This design issue is promising for future requirements. If new or changed requirements have to be taken into account in a new DaRT release then the according dialogs might access objects that are already addressed by another dialog. However, there will be no problem because locking conflicts of concurrent transactions were already considered. The only design issue is to examine the amount of objects in the overlapping object set and to ensure that there will be a reasonable size in the intersection.
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Comparing the four alternatives for DaRT, obviously the uniform servers were a reasonable choice for the first prototype, but they do not scale at all. To guarantee the required response time the cache size has to be the same as the database size in order to avoid periodic cache overflows resulting in periodic cache evictions. Thus the
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response times will be significantly longer when the cache size is too small. Therefore, the alternative with uniform servers cannot be considered seriously. Scalability can be improved by having multithreaded servers and concurrent client requests will be handled in parallel so that a good throughput can be provided. However, maintaining multithreaded C++ code is not a simple job. Finding and fixing bugs can be a very challenging task because the debuggers are not well suited for handling multithreaded programs. This consideration was the knockout criteria for multithreaded DaRT servers. The third alternative of data-specific servers is separating the objects in nonoverlapping sets. Specialized servers can handle concurrent client requests in a multiprocess environment (i.e., without multithreading). Due to multiple concurrent server processes the required scalability and throughput can be ensured. The servers can access and modify objects concurrently and will not block if the objects are in different
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