Xóa bản ghi trùng lặp trong 1 bảng

Sử dụng cách này nhưng không rõ với db lớn thì sẽ thế nào, sau khi thử sẽ update lại kết quả


delete from table1
USING table1, table1 as vtable
WHERE (table1.ID > vtable.ID)
AND (table1.field_name=vtable.field_name)

PDT Extension

http://p2-dev.pdt-extensions.org/

10 More Common Mistakes Java Developers Make when Writing SQL

I was positively surprised to see how popular my recent listing about 10 Common Mistakes Java Developers Make when Writing SQL was, both on my own blog and on my syndication partner DZone. The popularity shows a couple of things:

• How important SQL is to the professional Java world.
• How common it is to forget about some basic SQL things.
• How well SQL-centric libraries such as jOOQ or MyBatis are responding to market needs, by embracing SQL. An amusing fact is that users have even mentioned my blog post on SLICK’s mailing list. SLICK is a non-SQL-centric database access library in Scala. Like LINQ (and LINQ-to-SQL) it focuses on language integration, not on SQL code generation.

Anyway, the common mistakes I listed previously are far from complete, so I will treat you to a sequel of 10 subtly less common, yet equally interesting mistakes Java developers make when writing SQL.

1. Not using PreparedStatements

Interestingly, this mistake or misbelief still surfaces blogs, forums and mailing lists many years after the appearance of JDBC, even if it is about a very simple thing to remember and to understand. It appears that some developers refrain from using PreparedStatements for any of these reasons:

• They don’t know about PreparedStatements
• They think that PreparedStatements are slower
• They think that writing a PreparedStatement takes more effort

First off, let’s bust the above myths. In 96% of the cases, you’re better off writing a PreparedStatement rather than a static statement. Why? For simple reasons:

• You can omit syntax errors originating from bad string concatenation when inlining bind values.
• You can omit SQL injection vulnerabilities from bad string concatenation when inlining bind values.
• You can avoid edge-cases when inlining more “sophisticated” data types, such as TIMESTAMP, binary data, and others.
• You can keep open PreparedStatements around for a while, reusing them with new bind values instead of closing them immediately (useful in Postgres, for instance).
• You can make use of adaptive cursor sharing (Oracle-speak) in more sophisticated databases. This helps prevent hard-parsing SQL statements for every new set of bind values.

Convinced? Yes. Note, there are some rare cases when you actually want to inline bind values in order to give your database’s cost-based optimiser some heads-up about what kind of data is really going to be affected by the query. Typically, this results in “constant” predicates such as:

• DELETED = 1
• STATUS = 42

But it shouldn’t result in “variable” predicates such as:

• FIRST_NAME LIKE “Jon%”
• AMOUNT > 19.95

Note that modern databases implement bind-variable peeking. Hence, by default, you might as well use bind values for all your query parameters. Note also that higher-level APIs such as JPA CriteriaQuery or jOOQ will help you generate PreparedStatements and bind values very easily and transparently when writing embedded JPQL or embedded SQL.
More background info:

• Caveats of bind value peeking: An interesting blog post by Oracle Guru Tanel Poder on the subject
• Cursor sharing. An interesting Stack Overflow question.

The Cure:
By default, always use PreparedStatements instead of static statements. By default, never inline bind values into your SQL.

2. Returning too many columns

This mistake is quite frequent and can lead to very bad effects both in your database’s execution plan and in your Java application. Let’s look at the second effect first:
Bad effects on the Java application:
If you’re selecting * (star) or a “default” set of 50 columns, which you’re reusing among various DAOs, you’re transferring lots of data from the database into a JDBC ResultSet. Even if you’re not reading the data from the ResultSet, it has been transferred over the wire and loaded into your memory by the JDBC driver. That’s quite a waste of IO and memory if you know that you’re only going to need 2-3 of those columns.
This was obvious, but beware also of…
Bad effects on the database execution plan:
These effects may actually be much worse than the effects on the Java application. Sophisticated databases perform a lot of SQL transformation when calculating the best execution plan for your query. It may well be that some parts of your query can be “transformed away”, knowing that they won’t contribute to the projection (SELECT clause) or to the filtering predicates. I’ve recently blogged about this in the context of schema meta data:
How schema meta data impacts Oracle query transformations
Now, this is quite a beast. Think about a sophisticated SELECT that will join two views:

1 2 3 4

SELECT * FROM   customer_view c JOIN   order_view o   ON   c.cust_id = o.cust_id

Each of the views that are joined to the above joined table reference might again join data from dozens of tables, such as CUSTOMER_ADDRESS, ORDER_HISTORY, ORDER_SETTLEMENT, etc. Given the SELECT * projection, your database has no choice but to fully perform the loading of all those joined tables, when in fact, the only thing that you were interested in was this:

1 2 3 4

SELECT c.first_name, c.last_name, o.amount FROM   customer_view c JOIN   order_view o   ON   c.cust_id = o.cust_id

A good database will transform your SQL in a way that most of the “hidden” joins can be removed, which results in much less IO and memory consumption within the database.
The Cure:
Never execute SELECT *. Never reuse the same projection for various queries. Always try to reduce the projection to the data that you really need.
Note that this can be quite hard to achieve with ORMs.

3. Thinking that JOIN is a SELECT clause

This isn’t a mistake with a lot of impact on performance or SQL correctness, but nevertheless, SQL developers should be aware of the fact that the JOIN clause is not part of the SELECT statement per se. The SQL standard 1992 defines a table reference as such:

6.3 <table reference>

<table reference> ::=
    <table name> [ [ AS ] <correlation name>
      [ <left paren> <derived column list> <right paren> ] ]
  | <derived table> [ AS ] <correlation name>
      [ <left paren> <derived column list> <right paren> ]
  | <joined table>

The FROM clause and also the joined table can then make use of such table references:

7.4 <from clause>

<from clause> ::= 
    FROM <table reference> [ { <comma> <table reference> }... ]

7.5 <joined table>

<joined table> ::=
    <cross join>
  | <qualified join>
  | <left paren> <joined table> <right paren>

<cross join> ::=
    <table reference> CROSS JOIN <table reference>

<qualified join> ::=
    <table reference> [ NATURAL ] [ <join type> ] JOIN
      <table reference> [ <join specification> ]

Relational databases are very table-centric. Many operations are performed on physical, joined or derived tables in one way or another. To write SQL effectively, it is important to understand that the SELECT .. FROM clause expects a comma-separated list of table references in whatever form they may be provided.
Depending on the complexity of the table reference, some databases also accept sophisticated table references in other statements, such as INSERT, UPDATE, DELETE, MERGE. See Oracle’s manuals for instance, explaining how to create updatable views.
The Cure:
Always think of your FROM clause to expect a table reference as a whole. If you write a JOIN clause, think of this JOIN clause to be part of a complex table reference:

1 2 3 4 5 6

SELECT c.first_name, c.last_name, o.amount FROM       customer_view c       JOIN order_view o       ON c.cust_id = o.cust_id

4. Using pre-ANSI JOIN syntax

Now that we’ve clarified how table references work (see the previous point), it should become a bit more obvious that the pre-ANSI JOIN syntax should be avoided at all costs. To execution plans, it usually makes no difference if you specify join predicates in the JOIN .. ON clause or in the WHERE clause. But from a readability and maintenance perspective, using the WHERE clause for both filtering predicates and join predicates is a major quagmire. Consider this simple example:

1 2 3 4 5 6

SELECT c.first_name, c.last_name, o.amount FROM   customer_view c,        order_view o WHERE  o.amount > 100 AND    c.cust_id = o.cust_id AND    c.language = 'en'

Can you spot the join predicate? What if we joined dozens of tables? This gets much worse when applying proprietary syntaxes for outer join, such as Oracle’s (+) syntax.
The Cure:
Always use the ANSI JOIN syntax. Never put JOIN predicates into the WHERE clause. There is absolutely no advantage to using the pre-ANSI JOIN syntax.

5. Forgetting to escape input to the LIKE predicate

The SQL standard 1992 specifies the like predicate as such:

8.5 <like predicate>

<like predicate> ::=
    <match value> [ NOT ] LIKE <pattern>
      [ ESCAPE <escape character> ]

The ESCAPE keyword should be used almost always when allowing for user input to be used in your SQL queries. While it may be rare that the percent sign (%) is actually supposed to be part of the data, the underscore (_) might well be:

1 2 3

SELECT * FROM   t WHERE  t.x LIKE 'some!_prefix%' ESCAPE '!'

The Cure:
Always think of proper escaping when using the LIKE predicate.

6. Thinking that NOT (A IN (X, Y)) is the boolean inverse of A IN (X, Y)

This one is subtle but very important with respect to NULLs! Let’s review what A IN (X, Y) really means:

                  A IN (X, Y)
is the same as    A = ANY (X, Y)
is the same as    A = X OR A = Y

When at the same time, NOT (A IN (X, Y)) really means:

                  NOT (A IN (X, Y))
is the same as    A NOT IN (X, Y)
is the same as    A != ANY (X, Y)
is the same as    A != X AND A != Y

That looks like the boolean inverse of the previous predicate, but it isn’t! If any of X or Y is NULL, the NOT IN predicate will result in UNKNOWN whereas the IN predicate might still return a boolean value.
Or in other words, when A IN (X, Y) yields TRUE or FALSE, NOT(A IN (X, Y)) may still yield UNKNOWN instead of FALSE or TRUE. Note, that this is also true if the right-hand side of the IN predicate is a subquery.
Don’t believe it? See this SQL Fiddle for yourself. It shows that the following query yields no result:

1 2 3 4 5

SELECT 1 WHERE     1 IN (NULL) UNION ALL SELECT 2 WHERE NOT(1 IN (NULL))

More details can be seen in my previous blog post on that subject, which also shows some SQL dialect incompatibilities in that area.
The Cure:
Beware of the NOT IN predicate when nullable columns are involved!

7. Thinking that NOT (A IS NULL) is the same as A IS NOT NULL

Right, so we remembered that SQL implements three-valued logic when it comes to handling NULL. That’s why we can use the NULL predicate to check for NULL values. Right? Right.
But even the NULL predicate is subtle. Beware that the two following predicates are only equivalent for row value expressions of degree 1:

                   NOT (A IS NULL)
is not the same as A IS NOT NULL

If A is a row value expression with a degree of more than 1, then the truth table is transformed such that:

• A IS NULL yields true only if all values in A are NULL
• NOT(A IS NULL) yields false only if all values in A are NULL
• A IS NOT NULL yields true only if all values in A are NOT NULL
• NOT(A IS NOT NULL) yields false only if all values in A are NOT NULL

See more details in my previous blog post on that subject.
The Cure:
When using row value expressions, beware of the NULL predicate, which might not work as expected.

8. Not using row value expressions where they are supported

Row value expressions are an awesome SQL feature. When SQL is a very table-centric language, tables are also very row-centric. Row value expressions let you describe complex predicates much more easily, by creating local ad-hoc rows that can be compared with other rows of the same degree and row type. A simple example is to query customers for first names and last names at the same time.

1 2 3

SELECT c.address FROM   customer c, WHERE (c.first_name, c.last_name) = (?, ?)

As can be seen, this syntax is slightly more concise than the equivalent syntax where each column from the predicate’s left-hand side is compared with the corresponding column on the right-hand side. This is particularly true if many independent predicates are combined with AND. Using row value expressions allows you to combine correlated predicates into one. This is most useful for join expressions on composite foreign keys:

1 2 3 4

SELECT c.first_name, c.last_name, a.street FROM   customer c JOIN   address a   ON  (c.id, c.tenant_id) = (a.id, a.tenant_id)

Unfortunately, not all databases support row value expressions in the same way. But the SQL standard had defined them already in 1992, and if you use them, sophisticated databases like Oracle or Postgres can use them for calculating better execution plans. This is explained on the popular Use The Index, Luke page.
The Cure:
Use row value expressions whenever you can. They will make your SQL more concise and possibly even faster.

9. Not defining enough constraints

So, I’m going to cite Tom Kyte and Use The Index, Luke again. You cannot have enough constraints in your meta data. First off, constraints help you keep your data from corrupting, which is already very useful. But to me, more importantly, constraints will help the database perform SQL transformations, as the database can decide that

• Some values are equivalent
• Some clauses are redundant
• Some clauses are “void” (i.e. they will not return any values)

Some developers may think that constraints are slow. The opposite is the case, unless you insert lots and lots of data, in case of which you can either disable constraints for a large operation, or use a temporary “load table” without constraints, transferring data offline to the real table.
The Cure:
Define as many constraints as you can. They will help your database to perform better when querying.

10. Thinking that 50ms is fast query execution

The NoSQL hype is still ongoing, and many companies still think they’re Twitter or Facebook in dire need of faster, more scalable solutions, escaping ACID and relational models to scale horizontally. Some may succeed (e.g. Twitter or Facebook), others may run into this:

Found here: https://twitter.com/codinghorror/status/347070841059692545

For the others who are forced (or chose) to stick with proven relational databases, don’t be tricked into thinking that modern databases are slow. They’re hyper fast. In fact, they’re so fast, they can parse your 20kb query text, calculate 2000-line execution plans, and actually execute that monster in less than a millisecond, if you and your DBA get along well and tune your database to the max.
They may be slow because of your application misusing a popular ORM, or because that ORM won’t be able to produce fast SQL for your complex querying logic. In that case, you may want to chose a more SQL-centric API like JDBC, jOOQ or MyBatis that will let you get back in control of your SQL.
So, don’t think that a query execution of 50ms is fast or even acceptable. It’s not. If you get these speeds at development time, make sure you investigate execution plans. Those monsters might explode in production, where you have more complex contexts and data.

Conclusion

SQL is a lot of fun, but also very subtle in various ways. It’s not easy to get it right as my previous blog post about 10 common mistakes has shown. But SQL can be mastered and it’s worth the trouble. Data is your most valuable asset. Treat data with respect and write better SQL.

Source: http://blog.jooq.org/2013/08/12/10-more-common-mistakes-java-developers-make-when-writing-sql/

Kiểm soát timeout khi mount NFS

sudo mount localhost:xxx xxx -o bg,intr,soft,timeo=1,retrans=1,actimeo=1,retry=1

Trong đó:

bg,intr,soft

   bg      If  the  first  NFS  mount  attempt times out, retry the mount in the 
           background.  After a mount operation is backgrounded, all subsequent mounts
           on the same NFS  server  will  be  backgrounded immediately, without first
           attempting the mount.  A missing mount point is treated as a timeout, to
           allow for nested NFS mounts.
   soft    If  an  NFS  file operation has a major timeout then report an I/O error
           to the calling program.  The default is to continue retrying NFS file
           operations indefinitely.
   intr    If  an  NFS  file  operation  has  a major timeout and it is hard mounted,
           then allow signals to interupt the file operation and cause it to return
           EINTR to the calling program.  The default is to not allow file operations
           to be interrupted.

You can in addition set:

timeo=5,retrans=5,actimeo=10,retry=5

which should allow the NFS mount to timeout and make the directory inaccessible if the NFS server drops the connection rather then waiting in retries.
Take a look at this link for more information about NFS mount options


Link tham khảo: http://unix.stackexchange.com/questions/31979/stop-broken-nfs-mounts-from-locking-a-directory

Symfony-Doctrine: free memory leak tips

• Disable debug mode. Add following before db connection initialize
  
  
  sfConfig::set('sf_debug', false);
• Set auto query object free attribute for db connection
  
  
  $connection->setAttribute(Doctrine_Core::ATTR_AUTO_FREE_QUERY_OBJECTS, true );
• Free all object after use
  
  
  

      $object = createBigObject();
      $object->save();
      $object->free( true );

      unset($object); 

      $q = Doctrine_Query::create()
          ->from( 'User u' );
      $results = $q->fetchArray();
      $q->free(); 

  
  
  
  
  
  
• Unset all arrays after use unset($array_name)
• Cấu hình file databases.yml:
  

  all:
    doctrine:
      class: sfDoctrineDatabase
      param:
        dsn: 'mysql:host=localhost;dbname=.......'
        username: .....
        password: .....
        profiler: false

   Cấu hình file databases.yml:

  Nguồn: tổng hợp 

Advanced PHP Error Handling via PHP

If you are having trouble handling PHP errors using htaccess, these three items are the first things to check. If it turns out that you are unable to use htaccess to work with PHP errors, don’t despair — this article explains how to achieve the same goals using local php.ini files. To implement this strategy, the following is required:

• Ability to create/edit a php.ini file in your public_html directory
• A server running PHP via CGI (e.g., phpSuExec), not Apache ²
• Ability to edit/change permissions for files on your server
• Access/editing privileges for htaccess files (not required)

Assuming satisfaction of the preceding requirements, the remainder of this tutorial explains how to enable global (sitewide) PHP error handling using a custom php.ini file. After explaining the implementation process for production environments, we will explore several useful functional customizations for both production and development servers ³. Excited? Great, let’s begin..

Step 1: Create a custom php.ini file in your site’s root directory

Using a text editor, create a file named “php.ini” and add the following PHP directives ⁴:

;;; php error handling for production servers
display_startup_errors = off
display_errors = off
html_errors = off
log_errors = on
docref_root = 0
docref_ext = 0
error_log = /var/log/php/errors/php_error.log

Here, we are disabling all public error displays and enabling private error logging in the specified file. After editing the path and file name of the error log in the last line, save the file and upload it to the root directory of your domain. Generally, this directory is named “public_html”, but it may be different on your server. Then, create the specified log file and ensure that it is writable (via 755 or 777) by the server.

Step 2: Enable subdirectory inheritance of custom settings

At this point, error logging should be working, but only for the same directory in which you have placed the php.ini file. Unfortunately, by default, locally specified php.ini directives only affect the directory in which they are located — they are not inherited by subdirectories as they are for htaccess directives. Thus, each directory for which you would like to log errors requires its own copy of the php.ini file. Fortunately, if you are able to access and edit your site’s root htaccess file, there is an easy way to enable subdirectory inheritance of your custom php.ini settings. Simply add the following code to your site’s root htaccess file:

# enable subdirectory inheritance of custom php settings
suPHP_ConfigPath /home/path/public_html

This trick takes advantage of htaccess’ inheritance properties by using them to “point” from each subdirectory to the custom php.ini file in the root (e.g., public_html) directory. Note that you may override the root php.ini directives by placing alternate php.ini files in the target subdirectory.

Step 3: Secure your custom php.ini and log files

Once everything is working, it is important to protect your domain by securing your newly created files. In addition to setting permissions to 600 for your custom php.ini file(s), you may also want to add the following directives to your root htaccess file:

# deny access to php.ini
<Files php.ini>
 order allow,deny
 deny from all
 satisfy all
</Files>

# deny access to php error log
<Files php_error.log>
 order allow,deny
 deny from all
 satisfy all
</Files>

And that’s it — PHP error logging should now be securely enabled on your domain. Now let’s explore some useful functional customizations for both production and development servers..

Controlling the level of PHP error reporting

Using your custom php.ini file, it is possible to set the level of error reporting to suit your particular needs. The general format for controlling the level of PHP errors is as follows:

;;; general directive for setting php error level
error_reporting = integer

There are several common values used for “integer”, including:

• Complete error reporting — for complete PHP error logging, use an error-reporting integer value of “8191”, which will enable logging of everything except run-time notices. ⁵
• Zend error reporting — to record both fatal and non-fatal compile-time warnings generated by the Zend scripting engine, use an error-reporting integer value of “128”.
• Basic error reporting — to record run-time notices, compile-time parse errors, as well as run-time errors and warnings, use “8” for the error-reporting integer value.
• Minimal error reporting — to record only fatal run-time errors, use an error-reporting integer value of “1”, which will enable logging of unrecoverable errors.

Of course, there are many more error-reporting values to use, depending on your particular error-logging needs. For more information on logging PHP errors, refer to the Error Handling and Logging Functions page at php.net.

Setting the maximum file size for your error strings

Using your custom php.ini file, you may specify a maximum size for your PHP errors. This controls the size of each logged error, not the overall file size. Here is the general syntax:

;;; general directive for setting max error size
log_errors_max_len = integer

Here, “integer” represents the maximum size of each recorded error string as measured in bytes. The default value is “1024” (i.e., 1 kilobyte). To unleash your logging powers to their fullest extent, you may use a zero value, “0”, to indicate “no maximum” and thus remove all limits. Note that this value is also applied to displayed errors when they are enabled (e.g., during development).

Disable logging of repeated errors

If you remember the last time you examined a healthy (or sick, depending on your point of view) PHP error log, you may recall countless entries of nearly identical errors, where the only difference for each line is the timestamp of the event. If you would like to disable this redundancy, throw down the following code in your custom php.ini file:

;;; disable repeated error logging
ignore_repeated_errors = true
ignore_repeated_source = true

With these lines in place, repeated errors will not be logged, even if they are from different sources or locations. If you only want to disable repeat errors from the same source or file, simply comment out or delete the last line [note: comments must begin with a semicolon ( ; ) in php.ini files]. Conversely, to ensure that your log file includes all repeat errors, change both of the true values to false.

Putting it all together — Production Environment

Having discussed a few of the useful ways to customize our PHP error-logging experience, let’s wrap it all up with a solid, php.ini-based error-handling strategy for generalized production environments. Here is the code for your custom php.ini file:

;;; php error handling for production servers
display_startup_errors = false
display_errors = false
html_errors = false
log_errors = true
ignore_repeated_errors = false
ignore_repeated_source = false
report_memleaks = true
track_errors = true
docref_root = 0
docref_ext = 0
error_log = /var/log/php/errors/php_error.log
error_reporting = 999999999
log_errors_max_len = 0

Or, if you prefer, an explanatory version of the same code, using comments to explain each line:

;;; php error handling for production servers

; disable display of startup errors
display_startup_errors = false

; disable display of all other errors
display_errors = false

; disable html markup of errors
html_errors = false

; enable logging of errors
log_errors = true

; disable ignoring of repeat errors
ignore_repeated_errors = false

; disable ignoring of unique source errors
ignore_repeated_source = false

; enable logging of php memory leaks
report_memleaks = true

; preserve most recent error via php_errormsg
track_errors = true

; disable formatting of error reference links
docref_root = 0

; disable formatting of error reference links
docref_ext = 0

; specify path to php error log
error_log = /var/log/php/errors/php_error.log

; specify recording of all php errors
error_reporting = 999999999

; disable max error string length
log_errors_max_len = 0

This PHP error-handling strategy is ideal for a generalized production environment. In a nutshell, this code secures your server by disabling public display of error messages, yet also enables complete error transparency for the administrator via private error log. Of course, you may wish to customize this code to suit your specific needs. As always, please share your thoughts, ideas, tips and tricks with our fellow readers. Now, let’s take a look at a generalized error-handling strategy for development environments..

Putting it all together — Development Environment

During project development, when public access to your project is unavailable, you may find it beneficial to catch PHP errors in real time, where moment-by-moment circumstances continue to evolve. Here is a generalized, php.ini-based PHP error-handling strategy for development environments. Place this code in your custom php.ini file:

;;; php error handling for production servers
display_startup_errors = true
display_errors = true
html_errors = true
log_errors = true
ignore_repeated_errors = false
ignore_repeated_source = false
report_memleaks = true
track_errors = true
docref_root = 0
docref_ext = 0
error_log = /var/log/php/errors/php_error.log
error_reporting = 999999999
log_errors_max_len = 0

For this code, we will forego the line-by-line explanations, as they may be extrapolated from the previous section. This PHP error-handling strategy is ideal for a generalized development environment. In a nutshell, this code enables real-time error-handling via public display of error messages, while also enabling complete error transparency for the administrator via private error log. Of course, you may wish to customize this code to suit your specific needs. As always, please share your thoughts, ideas, tips and tricks with our fellow readers.

Whew! That about does it for this article.. — see you next time!

Footnotes

• ¹ To determine if your server is running PHP via phpSuExec (i.e., CGI) instead of Apache, upload a phpinfo() file and check the “Server API” near the top of the file. If it says “Apache”, PHP is running on Apache; if it says “CGI”, PHP is running via phpSuExec.
• ² This is important because it is impossible to manipulate php.ini directives via htaccess while running PHP on phpSuExec.
• ³ For more information, check out the manual on Error Handling and Logging Functions at php.net
• ⁴ Many thanks to Jeff N. at A Small Orange for helping with the information provided in this article :)
• ⁵ Due to the bitwise nature of the various error-reporting values, the value for logging all errors continues to increase. For example, in PHP 5.2.x, its value is 6143, and before that, its value was 2047. Thus, to ensure comprehensive error logging well into the future, it is advisable to set a very large value for error_reporting, such as 2147483647.

Nguồn: http://perishablepress.com/advanced-php-error-handling-via-php/

Cau hinh my.cnf

#Ram 16G
#CPU 8core

[mysqld]
# GENERAL
datadir= /var/lib/mysql
socket = /var/lib/mysql/mysql.sock
pid_file = /var/lib/mysql/mysql.pid
user = mysql
port = 3306
storage_engine = InnoDB

# INNODB
innodb_buffer_pool_size = 12GB
innodb_log_file_size = 128MB
innodb_log_files_in_group = 128MB

innodb_file_per_table = 1
innodb_flush_method = O_DIRECT
innodb_log_buffer_size = 16

sort_buffer_size = 8MB
join_buffer_size = 8M


# MyISAM
key_buffer_size = 512MB

# LOGGING
log_error = /var/lib/mysql/mysql-error.log
log_slow_queries= /var/lib/mysql/mysql-slow.log

# OTHER
tmp_table_size = 32M
max_heap_table_size = 32M
query_cache_type = 1
query_cache_size = 512MB
query_cache_limit = 8MB

thread_concurrency = 16
max_connections = 1000
thread_cache_size = 256
table_cache_size = 10000
open_files_limit = 65535

#Safety and Sanity Settings
expire_logs_days = 7
max_allowed_packet = 64MB
max_connect_errors = 1000
wait_timeout = 7200
connect_timeout = 20
skip_name_resolve
symbolic-links=0

[client]
socket= /var/lib/mysql/mysql.sock
port = 3306