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Your goal should be to use as few indexes as possible to meet your performance criteria. There s a price to be paid for having too many indexes, especially in OLTP databases. Each INSERT, UPDATE, and DELETE statement causes changes to be made to the underlying indexes of a table, and can slow down an application in some cases. The following are some broad guidelines you can follow to make sure your indexes help the application instead of hindering it: Index columns with high selectivity. Selectivity here means the percentage of rows in a table with a certain value. High selectivity, as you learned earlier in this chapter, means that there are few rows with identical values. Index all important foreign keys. Index all predicate columns. Index columns used in table joins. Proper indexing of tables involves carefully considering the type of application you re running, the number of DML operations, and the response time expectations. Here are some additional tips that can aid you in selecting appropriate indexes for your application: Try to avoid indexing columns that consist of long character strings, unless you re using the Oracle Text feature. Wherever possible, use index-only plans, meaning a query that can be satisfied completely by just the data in the index alone. This requires that you pay attention to the most common queries and create any necessary composite indexes (indexes that include more than one column attribute). Use secondary indexes on columns frequently involved in ORDER BY and GROUP BY operations, as well as sorting operations such as UNION or DISTINCT.

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The B-tree index (sometimes referred to as the B*tree index) is the default or normal type of Oracle index. You re probably going to use it for almost all the indexes in a typical OLTP application. Although you could use the B-tree index for all your index needs, you ll get better performance by using more specialized indexes for certain kinds of data. Your knowledge of the type of data you have and the nature of your application should determine the index type. In the next few sections, you ll see several alternative types of indexes.

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procedure TReceiver.DoRun; var Payload: String; begin Payload := WaitForNotifications; if (Payload <> '') then HandleEvent(Payload); Sleep(10); end; function TReceiver.WaitForNotifications : String; var Buffer: Pointer; BufferSize, BytesRead: Cardinal; begin Result := ''; if not PeekNamedPipe(NotificationPipe, nil, 0, nil, @BufferSize, nil) then Exit; if (BufferSize = 0) then Exit; GetMem(Buffer, BufferSize); ReadFile(NotificationPipe, Buffer^, BufferSize, BytesRead, nil); Result := String(Buffer); FreeMem(Buffer, BufferSize); end; procedure TReceiver.HandleEvent(ThePayload: String); begin // process the notification end; end. The receiver connects to an existing pipe using the Windows API method CreateFile. The Windows API method PeekNamedPipe is a nonblocking method to check the pipe for data. The method ReadFile reads data from the pipe as if it were a file. You can configure the pipe to cause read operations to block if no data is available. If multiple subscribers are present, the simple design described previously won t work. One solution is to use a separate pipe for each subscriber. The details of such an implementation are beyond the scope of this book.

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Bitmap indexes are ideal for column data that has a low cardinality, which means that the indexed column has few distinct values. The index is compact in size and performs better than the B-tree index for these types of data. However, the bitmap index is going to cause some problems if a lot of DML is going on in the column being indexed.

Index-organized tables (IOTs) are explained in 7. The traditional Oracle tables are called heap-organized tables, where data is stored in the order in which it is inserted. Indexes enable fast access to the rows. However, indexes also mean more storage and the need for accessing both the index and the table rows for most queries (unless the query can be selected just by the indexed columns themselves). IOTs place all the table data in its primary key index, thus eliminating the need for a separate index. IOTs are more akin to B-tree indexes than tables. The data in an IOT is sorted, and rows are stored in primary key order. This type of organization of row values gives you faster access in addition to saving space. To limit the size of the row that s stored in the B-tree leaf blocks, IOTs use an overflow area to store infrequently accessed non-key columns, which leads to lower space consumption in the B-tree.

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