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As you can see, most of the object-related data is stored in a single cluster (the C_OBJ# cluster): 16 tables sharing the same block. It is mostly column-related information stored there, so all of the information about the set of columns of a table or index is stored physically on the same block. This makes sense, as when Oracle parses a query, it wants to have access to the data for all of the columns in the referenced table. If this data were spread all over the place, it would take a while to get it together. Here, it is on a single block typically and readily available. When would you use a cluster It is easier perhaps to describe when not to use one: If you anticipate the tables in the cluster will be modified heavily: You must be aware that an index cluster will have certain negative performance side effects on DML performance, INSERT statements in particular. It takes more work to manage the data in a cluster. The data has to be put away carefully, so it takes longer to put the data away (to insert it). If you need to perform full scans of tables in clusters: Instead of just having to full scan the data in your table, you have to full scan the data for (possibly) many tables. There is more data to scan through, so full scans will take longer. If you need to partition the tables: Tables in a cluster cannot be partitioned, nor can the cluster be partitioned. If you believe you will frequently need to TRUNCATE and load the table: Tables in clusters cannot be truncated. That is obvious since the cluster stores more than one table on a block, you must delete the rows in a cluster table.

So, if you have data that is mostly read (that does not mean never written ; it is perfectly OK to modify cluster tables) and read via indexes, either the cluster key index or other indexes you put on the tables in the cluster, and join this information together frequently, a cluster would be appropriate. Look for tables that are logically related and always used together, like the people who designed the Oracle data dictionary when they clustered all column-related information together.

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It is, by default, an all-or-nothing relationship, which means that once you implement your own IQueryProvider, you re expected to handle the entire set of extension methods defined on Enumerable (or at least, pass back a NonSupportedException) However, in a case like CSLA NET, a special problem is presented Because you re overriding only part of LINQ, you have no desire to re-implement the other more than 100 members that constitute LINQ To help with this, CSLA NET matches the Expression passed to CreateQuery() or Execute() against one of the static methods on the Enumerable class The following snippet from the CreateQuery() implementation shows an example of this: Dim mex = DirectCast(expression, MethodCallExpression) Dim listType = GetType(Enumerable) Dim listMethods = listTypeGetMethods() For Each method As MethodInfo In listMethods If MethodsEquivalent(mex, method) Then Dim genericArguments As Type() = mexMethodGetGenericArguments() Dim genericMethodInfo As MethodInfo = method.

Clustered tables give you the ability to physically prejoin data together. You use clusters to store related data from many tables on the same database block. Clusters can help read-intensive operations that always join data together or access related sets of data (e.g., everyone in department 10). Clustered tables reduce the number of blocks that Oracle must cache. Instead of keeping ten blocks for ten employees in the same department, Oracle will put them in one block and therefore increase the efficiency of your buffer cache. On the downside, unless you can calculate your SIZE parameter setting correctly, clusters may be inefficient with their space utilization and can tend to slow down DML-heavy operations.

Hash clustered tables are very similar in concept to the index clustered tables just described with one main exception: the cluster key index is replaced with a hash function The data in the table is the index; there is no physical index Oracle will take the key value for a row, hash it using either an internal function or one you supply, and use that to figure out where the data should be on disk One side effect of using a hashing algorithm to locate data, however, is that you cannot range scan a table in a hash cluster without adding a conventional index to the table In an index cluster, the query select * from emp where deptno between 10 and 20 would be able to make use of the cluster key index to find these rows.

MakeGenericMethod(genericArguments) Dim testObject = genericMethodInfoInvoke(Nothing, paramListToArray()) Dim objectQuery As IQueryable(Of TElement) = _ DirectCast(testObject, IEnumerable(Of TElement))AsQueryable(Of TElement)() Return objectQuery End If Next The code is fairly straightforward, with MethodsEquivalent() doing most of the work to determine whether a given MethodCallExpression (which all calls to CreateQuery() or Execute() pass) is logically equivalent to a given reflected method definition that comes from the Enumerable static class Once the correct MethodInfo from Enumerable is found, it is converted into a generic method call, which is then invoked dynamically via reflection and returned to the caller The process is similar regardless of whether CreateQuery() or Execute() is being called..

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