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The lowest level blocks in the tree, called leaf nodes or leaf blocks, contain every indexed key and a rowid that points to the row it is indexing. The interior blocks, above the leaf nodes, are known as branch blocks. They are used to navigate through the structure. For example, if we wanted to find the value 42 in the index, we would start at the top of the tree and go to the left. We would inspect that block and discover we needed to go to the block in the range 42..50 . This block would be the leaf block and point us to the rows that contained the number 42. It is interesting to note that the leaf nodes of the index are actually a doubly linked list. Once we find out where to start in the leaf nodes (i.e., once we have found that first value), doing an ordered scan of values (also known as an index range scan) is very easy. We don t have to navigate the structure anymore; we just go forward or backward through the leaf nodes as needed. That makes satisfying a predicate, such as the following, pretty simple: where x between 20 and 30 Oracle finds the first index leaf block that contains the lowest key value that is 20 or greater, and then it just walks horizontally through the linked list of leaf nodes until it finally hits a value that is greater than 30.

Once the statement has reached the end of execution, it must be finalized. You can either finalize or reset the statement using one of the following functions: int sqlite3_finalize(sqlite3_stmt *pStmt); int sqlite3_reset(sqlite3_stmt *pStmt); sqlite3_finalize() will close out the statement. It frees resources and commits or rolls back any implicit transactions (if the connection is in autocommit mode), clearing the journal file and freeing the associated lock. If you want to reuse the statement, you can do so using sqlite3_reset(). It will keep the compiled SQL statement (and any bound parameters) but commits any changes related to the current statement to the database. It also releases its lock and clears the journal file if autocommit is enabled. The primary difference between sqlite3_finalize() and sqlite3_reset() is that the latter preserves the resources associated with the statement so that it can be executed again, avoiding the need to call sqlite3_ prepare() to compile the SQL command. Let s go through an example. Listing 6-5 shows a simple, complete program using a prepared query. It is taken from select.c in the examples. Listing 6-5. Using Prepared Queries int main(int argc, char **argv) { int rc, i, ncols; sqlite3 *db; sqlite3_stmt *stmt; char *sql; const char *tail; rc = sqlite3_open_v2("foods.db", &db); if(rc) { fprintf(stderr, "Can't open database: %s\n", sqlite3_errmsg(db)); sqlite3_close(db); exit(1); } sql = "select * from episodes;"; rc = sqlite3_prepare_v2(db, sql, -1, &stmt, &tail); if(rc != SQLITE_OK) { fprintf(stderr, "SQL error: %s\n", sqlite3_errmsg(db)); } rc = sqlite3_step(stmt); ncols = sqlite3_column_count(stmt);

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There really is no such thing as a nonunique entry in a B*Tree index. In a nonunique index, Oracle simply stores the rowid by appending it to the key as an extra column with a length byte to make the key unique. For example, an index such as CREATE INDEX I ON T(X,Y) is conceptually CREATE UNIQUE INDEX I ON T(X,Y,ROWID). In a unique index, as defined by you, Oracle does not add the rowid to the index key. In a nonunique index, you will find that the data is sorted first by index key values (in the order of the index key) and then by rowid ascending. In a unique index, the data is sorted only by the index key values. One of the properties of a B*Tree is that all leaf blocks should be at the same level in the tree. This level is also known as the height of the index, meaning that any traversal from the root block of the index to a leaf block will visit the same number of blocks. That is, to get to the leaf block to retrieve the first row for a query of the form "SELECT INDEXED_COL FROM T WHERE INDEXED_COL = :X" will take the same number of I/Os regardless of the value of :X that is used. In other words, the index is height balanced. Most B*Tree indexes will have a height of 2 or 3, even for millions of records. This means that it will take, in general, two or three I/Os to find your key in the index which is not too bad.

Probably the most common data source for a pivot table is Excel data, in the same workbook as the pivot table. The data may be contained in only a few rows of records or there may be thousands of rows. No matter how much data there is, some common requirements exist when preparing to create a pivot table from the Excel data. Organizing the Data in Rows and Columns The Excel data should be organized in a table of rows and columns, as shown in Figure 1-1. This shows the first few rows of data from the sample file named ProductSales.xlsx.

Note Oracle uses two terms with slightly different meanings when referring to the number of blocks involved in

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