1. Why does POE pass parameters as array slices? http://poe.perl.org/?POE_FAQ/calling_convention 2. perl regular expression fast referecnces metacharacters are {}[]() ^ $ . | * + ? a metacharacter can be matched by putting a backslash befo
1. Why does POE pass parameters as array slices?
http://poe.perl.org/?POE_FAQ/calling_convention
2. perl regular expression fast referecnces
metacharacters are
{ } [ ] ( ) ^ $ . | * + ?
a metacharacter can be matched by putting a backslash before it
anchor metacharacters ^ and $ .
The anchor ^ means match at the beginning of the string and the anchor $ means match at the end of the string, or before a newline at the end of the string.
"housekeeper" =~ /keeper/; # matches
"housekeeper" =~ /^keeper/; # doesn't match
"housekeeper" =~ /keeper$/; # matches
"housekeeper " =~ /keeper$/; # matches
/cat/; #matches 'cat'
/[bcr]at/; #matches 'bat', 'cat', or 'rat'
/item[0123456789]/; #matches 'item0' or ... or 'item9'
"abc" =~ /[cab]/; #matches 'a'
/[yY][eE][sS]/ can be rewritten as /yes/i.
The special characters for a character class are - ] / ^ $ (and the pattern delimiter, whatever it is). ] is special because it denotes the end of a character class. $ is special because it denotes a scalar variable. / is special because it is used in escape sequences.
/[/]c]def/; # matches ']def' or 'cdef'
$x = 'bcr';
/[$x]at/; # matches 'bat', 'cat', or 'rat'
/[/$x]at/; # matches '$at' or 'xat'
/[//$x]at/; # matches '/at', 'bat, 'cat', or 'rat'
/item[0-9]/; # matches 'item0' or ... or 'item9'
/[0-9bx-z]aa/; # matches '0aa', ..., '9aa',
# 'baa', 'xaa', 'yaa', or 'zaa'
/[0-9a-fA-F]/; # matches a hexadecimal digit
/[0-9a-zA-Z_]/; # matches a "word" character, # like those in a Perl variable name
The special character ^ in the first position of a character class denotes a negated character class, which matches any characters but those in the brackets.
/[^a]at/; # doesn't match 'aat' or 'at', but matches
# all other 'bat', 'cat, '0at', '%at', etc.
/[^0-9]/; # matches a non-numeric character
/[a^]at/; # matches 'aat' or '^at'; here '^' is ordinary
/d matches a digit, not just [0-9] but also digits from non-roman scripts
/s matches a whitespace character, the set [/ /t/r/n/f] and others
/w matches a word character (alphanumeric or _), not just [0-9a-zA-Z_] but also digits and
characters from non-roman scripts
/D is a negated /d; it represents any other character than a digit, or [^/d]
/S is a negated /s; it represents any non-whitespace character [^/s]
/W is a negated /w; it represents any non-word character [^/w]
The period '.' matches any character but "/n" (unless the modifier //s is in effect, as explained
below).
//d/d:/d/d:/d/d/; # matches a hh:mm:ss time format
/[/d/s]/; # matches any digit or whitespace character
//w/W/w/; # matches a word char, followed by a
# non-word char, followed by a word char
/..rt/; # matches any two chars, followed by 'rt'
/end/./; # matches 'end.'
/end[.]/; # same thing, matches 'end.'
regexp dog|cat. As before, Perl will try to match the regexp at the earliest possible point in the
string. At each character position, Perl will first try to match the first alternative, dog. If dog doesn't
match, Perl will then try the next alternative, cat. If cat doesn't match either, then the match fails and
Perl moves to the next position in the string.
"cats and dogs" =~ /cat|dog|bird/; # matches "cat"
"cats and dogs" =~ /dog|cat|bird/; # matches "cat"
/(a|b)b/; # matches 'ab' or 'bb'
/(ac|b)b/; # matches 'acb' or 'bb'
/(^a|b)c/; # matches 'ac' at start of string or 'bc' anywhere
/(a|[bc])d/; # matches 'ad', 'bd', or 'cd'
/house(cat|)/; # matches either 'housecat' or 'house'
/house(cat(s|)|)/; # matches either 'housecats' or 'housecat' or
# 'house'. Note groups can be nested.
/(19|20|)/d/d/; # match years 19xx, 20xx, or the Y2K problem, xx
"20" =~ /(19|20|)/d/d/; # matches the null alternative '()dd',
# because '20dd' can't match

MySQL is an open source relational database management system, mainly used to store and retrieve data quickly and reliably. Its working principle includes client requests, query resolution, execution of queries and return results. Examples of usage include creating tables, inserting and querying data, and advanced features such as JOIN operations. Common errors involve SQL syntax, data types, and permissions, and optimization suggestions include the use of indexes, optimized queries, and partitioning of tables.

MySQL is an open source relational database management system suitable for data storage, management, query and security. 1. It supports a variety of operating systems and is widely used in Web applications and other fields. 2. Through the client-server architecture and different storage engines, MySQL processes data efficiently. 3. Basic usage includes creating databases and tables, inserting, querying and updating data. 4. Advanced usage involves complex queries and stored procedures. 5. Common errors can be debugged through the EXPLAIN statement. 6. Performance optimization includes the rational use of indexes and optimized query statements.

MySQL is chosen for its performance, reliability, ease of use, and community support. 1.MySQL provides efficient data storage and retrieval functions, supporting multiple data types and advanced query operations. 2. Adopt client-server architecture and multiple storage engines to support transaction and query optimization. 3. Easy to use, supports a variety of operating systems and programming languages. 4. Have strong community support and provide rich resources and solutions.

InnoDB's lock mechanisms include shared locks, exclusive locks, intention locks, record locks, gap locks and next key locks. 1. Shared lock allows transactions to read data without preventing other transactions from reading. 2. Exclusive lock prevents other transactions from reading and modifying data. 3. Intention lock optimizes lock efficiency. 4. Record lock lock index record. 5. Gap lock locks index recording gap. 6. The next key lock is a combination of record lock and gap lock to ensure data consistency.

The main reasons for poor MySQL query performance include not using indexes, wrong execution plan selection by the query optimizer, unreasonable table design, excessive data volume and lock competition. 1. No index causes slow querying, and adding indexes can significantly improve performance. 2. Use the EXPLAIN command to analyze the query plan and find out the optimizer error. 3. Reconstructing the table structure and optimizing JOIN conditions can improve table design problems. 4. When the data volume is large, partitioning and table division strategies are adopted. 5. In a high concurrency environment, optimizing transactions and locking strategies can reduce lock competition.

In database optimization, indexing strategies should be selected according to query requirements: 1. When the query involves multiple columns and the order of conditions is fixed, use composite indexes; 2. When the query involves multiple columns but the order of conditions is not fixed, use multiple single-column indexes. Composite indexes are suitable for optimizing multi-column queries, while single-column indexes are suitable for single-column queries.

To optimize MySQL slow query, slowquerylog and performance_schema need to be used: 1. Enable slowquerylog and set thresholds to record slow query; 2. Use performance_schema to analyze query execution details, find out performance bottlenecks and optimize.

MySQL and SQL are essential skills for developers. 1.MySQL is an open source relational database management system, and SQL is the standard language used to manage and operate databases. 2.MySQL supports multiple storage engines through efficient data storage and retrieval functions, and SQL completes complex data operations through simple statements. 3. Examples of usage include basic queries and advanced queries, such as filtering and sorting by condition. 4. Common errors include syntax errors and performance issues, which can be optimized by checking SQL statements and using EXPLAIN commands. 5. Performance optimization techniques include using indexes, avoiding full table scanning, optimizing JOIN operations and improving code readability.


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