.NET/Java PDF, Tiff, Barcode SDK Library

where each SQL*Plus session was passed a different number (that was number 1; there was a 2, 3, and so on). The script test2.sql they each ran is as follows: begin for i in 1 .. 5000 loop update small set y = i where x= &1; commit; end loop; end; / exit

ssrs code 128 barcode font, ssrs code 39, ssrs fixed data matrix, winforms pdf 417 reader, winforms qr code reader, winforms upc-a reader, c# remove text from pdf, replace text in pdf c#, winforms ean 13 reader, c# remove text from pdf,

We ll first show how to develop a parser for a simple programming language. A sample fragment of the BASIC-like language we want to parse is shown here: a := 1; b := 0; if a then d := 20 + 20; if b then d := 40 * 20 + 20; print d; while d do begin d := d + 1; print d end; print d For simplicity we will call this language Kitty. As the previous example shows, Kitty supports naming values, printing values, basic arithmetic operators, and a while and conditional construct. The Ast module (shown in Listing 16-5) defines the internal representation for Kitty programs. Listing 16-5. kittyAst.fs: Defining the AST for Kitty Programs type expr = | Val | Int | Plus | Minus | Times

execute immediate 'select ts.segment_space_management from dba_segments seg, dba_tablespaces ts where seg.segment_name = :p_segname and (:p_partition is null or seg.partition_name = :p_partition) and seg.owner = :p_owner and seg.tablespace_name = ts.tablespace_name' into l_segment_space_mgmt using p_segname, p_partition, p_partition, p_owner; exception when too_many_rows then dbms_output.put_line ( 'This must be a partitioned table, use p_partition => '); return; end; -- if the object is in an ASSM tablespace, we must use this API -- call to get space information, else we use the FREE_BLOCKS -- API for the user managed segments if l_segment_space_mgmt = 'AUTO' then dbms_space.space_usage ( p_owner, p_segname, p_type, l_unformatted_blocks, l_unformatted_bytes, l_fs1_blocks, l_fs1_bytes, l_fs2_blocks, l_fs2_bytes, l_fs3_blocks, l_fs3_bytes, l_fs4_blocks, l_fs4_bytes, l_full_blocks, l_full_bytes, p_partition); p( 'Unformatted Blocks ', l_unformatted_blocks ); p( 'FS1 Blocks (0-25) ', l_fs1_blocks ); p( 'FS2 Blocks (25-50) ', l_fs2_blocks ); p( 'FS3 Blocks (50-75) ', l_fs3_blocks ); p( 'FS4 Blocks (75-100)', l_fs4_blocks ); p( 'Full Blocks ', l_full_blocks ); else dbms_space.free_blocks( segment_owner => p_owner, segment_name => p_segname, segment_type => p_type, freelist_group_id => 0, free_blks => l_free_blks); p( 'Free Blocks', l_free_blks ); end if; -- and then the unused space API call to get the rest of the -- information dbms_space.unused_space ( segment_owner => p_owner, segment_name => p_segname, segment_type => p_type, partition_name => p_partition, total_blocks => l_total_blocks,

total_bytes => l_total_bytes, unused_blocks => l_unused_blocks, unused_bytes => l_unused_bytes, LAST_USED_EXTENT_FILE_ID => l_LastUsedExtFileId, LAST_USED_EXTENT_BLOCK_ID => l_LastUsedExtBlockId, LAST_USED_BLOCK => l_LAST_USED_BLOCK ); p( p( p( p( p( p( p( p( end; / 'Total Blocks', l_total_blocks ); 'Total Bytes', l_total_bytes ); 'Total MBytes', trunc(l_total_bytes/1024/1024) ); 'Unused Blocks', l_unused_blocks ); 'Unused Bytes', l_unused_bytes ); 'Last Used Ext FileId', l_LastUsedExtFileId ); 'Last Used Ext BlockId', l_LastUsedExtBlockId ); 'Last Used Block', l_LAST_USED_BLOCK );

Listing 16-6 shows a lexer for the language in the file kittyLexer.fsl. It is similar to lexers developed earlier in this chapter. The one exception is that we use a keyword table. Matching against lexemes to identify tokens is a sensible solution only if there are relatively few cases. Tokenizing a large set of keywords and operators using explicit rules can lead to large lexers. This situation is often handled using tables that contain the possible lexeme matches and the tokens to be returned. Listing 16-6 uses simple dictionaries (maps).

For examples throughout this book, I use a table called BIG_TABLE. Depending on which system I use, this table has between one record and four million records and varies in size from 200MB to 800MB. In all cases, the table structure is the same. To create BIG_TABLE, I wrote a script that does the following: Creates an empty table based on ALL_OBJECTS. This dictionary view is used to populate the BIG_TABLE. Makes this table NOLOGGING. This is optional. I did it for performance. Using NOLOGGING mode for a test table is safe; you won't use it in a production system, so features like Oracle Data Guard will not be enabled. Populates the table by seeding it with the contents of ALL_OBJECTS and then iteratively inserting into itself, approximately doubling its size on each iteration. Creates a primary key constraint on the table. Gathers statistics.

Listing 16-6. kittyLexer.fsl: Lexer for Kitty { open System open KittyParser open Lexing let ids = [ ("while", ("begin", ("end", ("do", ("if", ("then", ("else", ("print",