并行存储过程是否会在集群上产生更高的性能？
我目前正在研究加速和扩展长时间运行的匹配作业的方法，该作业当前作为 MSSQL 2005 中的存储过程运行。匹配涉及多个字段，其中有许多不精确的情况。虽然我希望最终将其扩展到数据库之外的大规模数据集，但我还需要考虑一些短期解决方案。
鉴于我对它们如何运行的内部实现了解不多，我想知道是否可以通过将数据集与主过程分开来将过程分成并行过程，然后启动子过程来工作在较小的数据集上。
这会对集群数据库带来任何性能提升吗？ MSSQL 会自动且合理地在集群节点上分配子过程吗？
也许最好在java中拥有master进程并通过jdbc调用worker进程，这可能会有效地使用集群负载平衡？除了有关可维护性的任何争论之外，这还能更快吗？
I'm currently researching ways to speed up and scale up a long running matching job which is currently running as a stored procedure in MSSQL 2005. The matching is involves multiple fields with many inexact cases. While I'd like to ultimately scale it up to large scale data sets outside of the database I need to consider some shorter term solutions also.
Given that I don't know much about the internal implementation of how they are run I'm wondering if it were possible to split the process up into parallel procedures by dividing the data set with a master procedure, which then kicks off subprocs which work on smaller data sets.
Would this yield any performance gains with a clustered database? Will MSSQL distribute the subprocs across the cluster nodes automatically and sensibly?
Perhaps it's better to have the master process in java and call worker procedures through jdbc which would presumably use cluster load balancing effectively? Aside from any arguments about maintainability could this be faster?
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您对集群对于 SQL Server 的意义有一个根本性的误解。集群不允许单个 SQL Server 实例共享多个机器的资源。集群是一种高可用性解决方案，允许在发生故障时将一个设备的功能转移到另一个备用设备。
You have a fundamental misunderstanding of what clustering means for SQL Server. Clustering does not allow a single instance of SQL Server to share the resources of multiple boxes. Clustering is a high availability solution that allows the functionality of one box to shift over to another standby box in case of a failure.