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Functions such as List.map are called aggregate operators, and they are powerful constructs, especially when combined with the other features of F#. Here is a longer example that uses the aggregate operators List.filter and List.map to count the number of URL links in an HTML page and then collects stats on a group of pages (this sample uses the function http defined in 2): let delimiters = [ ' '; '\n'; '\t'; '<'; '>'; '=' ] let getWords s = String.split delimiters s let getStats site = let url = "http://" + site let html = http url let hwords = html |> getWords let hrefs = html |> getWords |> List.filter (fun s -> s = "href") (site,html.Length, hwords.Length, hrefs.Length) Here we use the function getStats with three web pages: > let sites = [ "www.live.com";"www.google.com";"search.yahoo.com" ];; val sites : string list > sites |> List.map getStats;; val it : (string * int * int * int) list = [("www.live.com", 7728, 1156, 10); ("www.google.com", 2685, 496, 14); ("search.yahoo.com", 10715, 1771, 38)] The function getStats computes the length of the HTML for the given website, the number of words in the text of that HTML, and the approximate number of links on that page. The previous code sample extensively uses the |> operator to pipeline operations, discussed in the Pipelining with |> sidebar. The F# library design ensures that a common, consistent set of aggregate operations is defined for each structured type. Table 3-11 shows how the same design patterns occur for the map abstraction.

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PCTTHRESHOLD 50 OVERFLOW PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 LOGGING TABLESPACE "USERS" ops$tkyte%ORA11GR2> select dbms_metadata.get_ddl( 'TABLE', 'T3' ) from dual; DBMS_METADATA.GET_DDL('TABLE','T3') ------------------------------------------------------------------------------CREATE TABLE "OPS$TKYTE"."T3" ( "X" NUMBER(*,0), "Y" VARCHAR2(25), "Z" DATE, PRIMARY KEY ("X") ENABLE ) ORGANIZATION INDEX NOCOMPRESS PCTFREE 10 INITRANS 2 MAXTRANS 255 LOGGING TABLESPACE "USERS" PCTTHRESHOLD 50 INCLUDING "Y" OVERFLOW PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 LOGGING TABLESPACE "USERS" So, now we have PCTTHRESHOLD, OVERFLOW, and INCLUDING left to discuss. These three items are intertwined, and their goal is to make the index leaf blocks (the blocks that hold the actual index data) able to efficiently store data. An index is typically on a subset of columns. You will generally find many more times the number of row entries on an index block than you would on a heap table block. An index counts on being able to get many rows per block. Oracle would spend large amounts of time maintaining an index otherwise, as each INSERT or UPDATE would probably cause an index block to split in order to accommodate the new data. The OVERFLOW clause allows you to set up another segment (making an IOT a multisegment object, much like having a CLOB column does) where the row data for the IOT can overflow onto when it gets too large.

Notice that an OVERFLOW reintroduces the PCTUSED clause to an IOT when using MSSM. PCTFREE and PCTUSED have the same meanings for an OVERFLOW segment as they did for a heap table. The conditions for using an overflow segment can be specified in one of two ways: PCTTHRESHOLD: When the amount of data in the row exceeds that percentage of the block, the trailing columns of that row will be stored in the overflow. So, if PCTTHRESHOLD was 10 percent and your block size was 8KB, any row that was greater than about 800 bytes in length would have part of it stored elsewhere, off the index block. INCLUDING: All of the columns in the row up to and including the one specified in the INCLUDING clause are stored on the index block, and the remaining columns are stored in the overflow.

: ('a -> 'b) -> 'a list : ('a -> 'b) -> 'a[] -> 'b list -> 'b[]

Given the following table with a 2KB block size: ops$tkyte@ORA11GR2> create table iot 2 ( x int, 3 y date, 4 z varchar2(2000), 5 constraint iot_pk primary key (x) 6 ) 7 organization index 8 pctthreshold 10 9 overflow 10 / Table created. Graphically, it could look like Figure 10-6.