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5 MySQL Optimization

Optimization is a complicated task because it ultimately requires understanding of the whole system. While it may be possible to do some local optimizations with small knowledge of your system or application, the more optimal you want your system to become the more you will have to know about it.

This chapter will try to explain and give some examples of different ways to optimize MySQL. Remember, however, that there are always some (increasingly harder) additional ways to make the system even faster.

5.1 Optimization Overview

The most important part for getting a system fast is of course the basic design. You also need to know what kinds of things your system will be doing, and what your bottlenecks are.

The most common bottlenecks are:

5.1.1 MySQL Design Limitations/Tradeoffs

Because MySQL uses extremely fast table locking (multiple readers / single writers) the biggest remaining problem is a mix of a steady stream of inserts and slow selects on the same table.

We believe that for a huge number of systems the extremely fast performance in other cases make this choice a win. This case is usually also possible to solve by having multiple copies of the table, but it takes more effort and hardware.

We are also working on some extensions to solve this problem for some common application niches.

5.1.2 Portability

Because all SQL servers implement different parts of SQL, it takes work to write portable SQL applications. For very simple selects/inserts it is very easy, but the more you need the harder it gets. If you want an application that is fast with many databases it becomes even harder!

To make a complex application portable you need to choose a number of SQL servers that it should work with.

You can use the MySQL crash-me program/web-page http://www.mysql.com/information/crash-me.php to find functions, types, and limits you can use with a selection of database servers. Crash-me now tests far from everything possible, but it is still comprehensive with about 450 things tested.

For example, you shouldn't have column names longer than 18 characters if you want to be able to use Informix or DB2.

Both the MySQL benchmarks and crash-me programs are very database-independent. By taking a look at how we have handled this, you can get a feeling for what you have to do to write your application database-independent. The benchmarks themselves can be found in the `sql-bench' directory in the MySQL source distribution. They are written in Perl with DBI database interface (which solves the access part of the problem).

See http://www.mysql.com/information/benchmarks.html for the results from this benchmark.

As you can see in these results, all databases have some weak points. That is, they have different design compromises that lead to different behavior.

If you strive for database independence, you need to get a good feeling for each SQL server's bottlenecks. MySQL is VERY fast in retrieving and updating things, but will have a problem in mixing slow readers/writers on the same table. Oracle, on the other hand, has a big problem when you try to access rows that you have recently updated (until they are flushed to disk). Transaction databases in general are not very good at generating summary tables from log tables, as in this case row locking is almost useless.

To get your application really database-independent, you need to define an easy extendable interface through which you manipulate your data. As C++ is available on most systems, it makes sense to use a C++ classes interface to the databases.

If you use some specific feature for some database (like the REPLACE command in MySQL), you should code a method for the other SQL servers to implement the same feature (but slower). With MySQL you can use the /*! */ syntax to add MySQL-specific keywords to a query. The code inside /**/ will be treated as a comment (ignored) by most other SQL servers.

If REAL high performance is more important than exactness, as in some Web applications, a possibility is to create an application layer that caches all results to give you even higher performance. By letting old results 'expire' after a while, you can keep the cache reasonably fresh. This is quite nice in case of extremely high load, in which case you can dynamically increase the cache and set the expire timeout higher until things get back to normal.

In this case the table creation information should contain information of the initial size of the cache and how often the table should normally be refreshed.

5.1.3 What Have We Used MySQL For?

During MySQL initial development, the features of MySQL were made to fit our largest customer. They handle data warehousing for a couple of the biggest retailers in Sweden.

From all stores, we get weekly summaries of all bonus card transactions, and we are expected to provide useful information for the store owners to help them find how their advertisement campaigns are affecting their customers.

The data is quite huge (about 7 million summary transactions per month), and we have data for 4-10 years that we need to present to the users. We got weekly requests from the customers that they want to get 'instant' access to new reports from this data.

We solved this by storing all information per month in compressed 'transaction' tables. We have a set of simple macros (script) that generates summary tables grouped by different criteria (product group, customer id, store ...) from the transaction tables. The reports are Web pages that are dynamically generated by a small Perl script that parses a Web page, executes the SQL statements in it, and inserts the results. We would have used PHP or mod_perl instead but they were not available at that time.

For graphical data we wrote a simple tool in C that can produce GIFs based on the result of a SQL query (with some processing of the result). This is also dynamically executed from the Perl script that parses the HTML files.

In most cases a new report can simply be done by copying an existing script and modifying the SQL query in it. In some cases, we will need to add more fields to an existing summary table or generate a new one, but this is also quite simple, as we keep all transactions tables on disk. (Currently we have at least 50G of transactions tables and 200G of other customer data.)

We also let our customers access the summary tables directly with ODBC so that the advanced users can themselves experiment with the data.

We haven't had any problems handling this with quite modest Sun Ultra SPARCstation (2x200 Mhz). We recently upgraded one of our servers to a 2 CPU 400 Mhz UltraSPARC, and we are now planning to start handling transactions on the product level, which would mean a ten-fold increase of data. We think we can keep up with this by just adding more disk to our systems.

We are also experimenting with Intel-Linux to be able to get more CPU power cheaper. Now that we have the binary portable database format (new in Version 3.23), we will start to use this for some parts of the application.

Our initial feelings are that Linux will perform much better on low-to-medium load and Solaris will perform better when you start to get a high load because of extreme disk IO, but we don't yet have anything conclusive about this. After some discussion with a Linux Kernel developer, this might be a side effect of Linux giving so much resources to the batch job that the interactive performance gets very low. This makes the machine feel very slow and unresponsive while big batches are going. Hopefully this will be better handled in future Linux Kernels.

5.1.4 The MySQL Benchmark Suite

This should contain a technical description of the MySQL benchmark suite (and crash-me), but that description is not written yet. Currently, you can get a good idea of the benchmark by looking at the code and results in the `sql-bench' directory in any MySQL source distributions.

This benchmark suite is meant to be a benchmark that will tell any user what things a given SQL implementation performs well or poorly at.

Note that this benchmark is single threaded, so it measures the minimum time for the operations. We plan to in the future add a lot of multi-threaded tests to the benchmark suite.

For example, (run on the same NT 4.0 machine):

Reading 2000000 rows by index
Seconds Seconds
mysql 367 249
mysql_odbc 464
db2_odbc 1206
informix_odbc 121126
ms-sql_odbc 1634
oracle_odbc 20800
solid_odbc 877
sybase_odbc 17614
Inserting (350768) rows
Seconds Seconds
mysql 381 206
mysql_odbc 619
db2_odbc 3460
informix_odbc 2692
ms-sql_odbc 4012
oracle_odbc 11291
solid_odbc 1801
sybase_odbc 4802

In the above test MySQL was run with a 8M index cache.

We have gather some more benchmark results at http://www.mysql.com/information/benchmarks.html.

Note that Oracle is not included because they asked to be removed. All Oracle benchmarks have to be passed by Oracle! We believe that makes Oracle benchmarks VERY biased because the above benchmarks are supposed to show what a standard installation can do for a single client.

To run the benchmark suite, you have to download a MySQL source distribution, install the perl DBI driver, the perl DBD driver for the database you want to test and then do:

cd sql-bench
perl run-all-tests --server=#

where # is one of supported servers. You can get a list of all options and supported servers by doing run-all-tests --help.

crash-me tries to determine what features a database supports and what its capabilities and limitations are by actually running queries. For example, it determines:

We can find the result from crash-me on a lot of different databases at http://www.mysql.com/information/crash-me.php.

5.1.5 Using Your Own Benchmarks

You should definitely benchmark your application and database to find out where the bottlenecks are. By fixing it (or by replacing the bottleneck with a 'dummy module') you can then easily identify the next bottleneck (and so on). Even if the overall performance for your application is sufficient, you should at least make a plan for each bottleneck, and decide how to solve it if someday you really need the extra performance.

For an example of portable benchmark programs, look at the MySQL benchmark suite. See section 5.1.4 The MySQL Benchmark Suite. You can take any program from this suite and modify it for your needs. By doing this, you can try different solutions to your problem and test which is really the fastest solution for you.

It is very common that some problems only occur when the system is very heavily loaded. We have had many customers who contact us when they have a (tested) system in production and have encountered load problems. In every one of these cases so far, it has been problems with basic design (table scans are NOT good at high load) or OS/Library issues. Most of this would be a LOT easier to fix if the systems were not already in production.

To avoid problems like this, you should put some effort into benchmarking your whole application under the worst possible load! You can use Super Smack for this, and it is available at: http://www.mysql.com/Downloads/super-smack/super-smack-1.0.tar.gz. As the name suggests, it can bring your system down to its knees if you ask it, so make sure to use it only on your development systems.

5.2 Optimizing SELECTs and Other Queries

First, one thing that affects all queries: The more complex permission system setup you have, the more overhead you get.

If you do not have any GRANT statements done, MySQL will optimize the permission checking somewhat. So if you have a very high volume it may be worth the time to avoid grants. Otherwise more permission check results in a larger overhead.

If your problem is with some explicit MySQL function, you can always time this in the MySQL client:

mysql> select benchmark(1000000,1+1);
+------------------------+
| benchmark(1000000,1+1) |
+------------------------+
|                      0 |
+------------------------+
1 row in set (0.32 sec)

The above shows that MySQL can execute 1,000,000 + expressions in 0.32 seconds on a PentiumII 400MHz.

All MySQL functions should be very optimized, but there may be some exceptions, and the benchmark(loop_count,expression) is a great tool to find out if this is a problem with your query.

5.2.1 EXPLAIN Syntax (Get Information About a SELECT)

    EXPLAIN tbl_name
or  EXPLAIN SELECT select_options

EXPLAIN tbl_name is a synonym for DESCRIBE tbl_name or SHOW COLUMNS FROM tbl_name.

When you precede a SELECT statement with the keyword EXPLAIN, MySQL explains how it would process the SELECT, providing information about how tables are joined and in which order.

With the help of EXPLAIN, you can see when you must add indexes to tables to get a faster SELECT that uses indexes to find the records. You can also see if the optimizer joins the tables in an optimal order. To force the optimizer to use a specific join order for a SELECT statement, add a STRAIGHT_JOIN clause.

For non-simple joins, EXPLAIN returns a row of information for each table used in the SELECT statement. The tables are listed in the order they would be read. MySQL resolves all joins using a single-sweep multi-join method. This means that MySQL reads a row from the first table, then finds a matching row in the second table, then in the third table and so on. When all tables are processed, it outputs the selected columns and backtracks through the table list until a table is found for which there are more matching rows. The next row is read from this table and the process continues with the next table.

Output from EXPLAIN includes the following columns:

table
The table to which the row of output refers.
type
The join type. Information about the various types is given below.
possible_keys
The possible_keys column indicates which indexes MySQL could use to find the rows in this table. Note that this column is totally independent of the order of the tables. That means that some of the keys in possible_keys may not be usable in practice with the generated table order. If this column is empty, there are no relevant indexes. In this case, you may be able to improve the performance of your query by examining the WHERE clause to see if it refers to some column or columns that would be suitable for indexing. If so, create an appropriate index and check the query with EXPLAIN again. See section 6.5.4 ALTER TABLE Syntax. To see what indexes a table has, use SHOW INDEX FROM tbl_name.
key
The key column indicates the key that MySQL actually decided to use. The key is NULL if no index was chosen. If MySQL chooses the wrong index, you can probably force MySQL to use another index by using myisamchk --analyze, See section 4.4.6.1 myisamchk Invocation Syntax, or by using USE INDEX/IGNORE INDEX. See section 6.4.1.1 JOIN Syntax.
key_len
The key_len column indicates the length of the key that MySQL decided to use. The length is NULL if the key is NULL. Note that this tells us how many parts of a multi-part key MySQL will actually use.
ref
The ref column shows which columns or constants are used with the key to select rows from the table.
rows
The rows column indicates the number of rows MySQL believes it must examine to execute the query.
Extra
This column contains additional information of how MySQL will resolve the query. Here is an explanation of the different text strings that can be found in this column:
Distinct
MySQL will not continue searching for more rows for the current row combination after it has found the first matching row.
Not exists
MySQL was able to do a LEFT JOIN optimization on the query and will not examine more rows in this table for the previous row combination after it finds one row that matches the LEFT JOIN criteria. Here is an example for this:
SELECT * FROM t1 LEFT JOIN t2 ON t1.id=t2.id WHERE t2.id IS NULL;
Assume that t2.id is defined with NOT NULL. In this case MySQL will scan t1 and look up the rows in t2 through t1.id. If MySQL finds a matching row in t2, it knows that t2.id can never be NULL, and will not scan through the rest of the rows in t2 that has the same id. In other words, for each row in t1, MySQL only needs to do a single lookup in t2, independent of how many matching rows there are in t2.
range checked for each record (index map: #)
MySQL didn't find a real good index to use. It will, instead, for each row combination in the preceding tables, do a check on which index to use (if any), and use this index to retrieve the rows from the table. This isn't very fast but is faster than having to do a join without an index.
Using filesort
MySQL will need to do an extra pass to find out how to retrieve the rows in sorted order. The sort is done by going through all rows according to the join type and storing the sort key + pointer to the row for all rows that match the WHERE. Then the keys are sorted. Finally the rows are retrieved in sorted order.
Using index
The column information is retrieved from the table using only information in the index tree without having to do an additional seek to read the actual row. This can be done when all the used columns for the table are part of the same index.
Using temporary
To resolve the query MySQL will need to create a temporary table to hold the result. This typically happens if you do an ORDER BY on a different column set than you did a GROUP BY on.
Where used
A WHERE clause will be used to restrict which rows will be matched against the next table or sent to the client. If you don't have this information and the table is of type ALL or index, you may have something wrong in your query (if you don't intend to fetch/examine all rows from the table).
If you want to get your queries as fast as possible, you should look out for Using filesort and Using temporary.

The different join types are listed below, ordered from best to worst type:

system
The table has only one row (= system table). This is a special case of the const join type.
const
The table has at most one matching row, which will be read at the start of the query. Because there is only one row, values from the column in this row can be regarded as constants by the rest of the optimizer. const tables are very fast as they are read only once!
eq_ref
One row will be read from this table for each combination of rows from the previous tables. This is the best possible join type, other than the const types. It is used when all parts of an index are used by the join and the index is UNIQUE or a PRIMARY KEY.
ref
All rows with matching index values will be read from this table for each combination of rows from the previous tables. ref is used if the join uses only a leftmost prefix of the key, or if the key is not UNIQUE or a PRIMARY KEY (in other words, if the join cannot select a single row based on the key value). If the key that is used matches only a few rows, this join type is good.
range
Only rows that are in a given range will be retrieved, using an index to select the rows. The key column indicates which index is used. The key_len contains the longest key part that was used. The ref column will be NULL for this type.
index
This is the same as ALL, except that only the index tree is scanned. This is usually faster than ALL, as the index file is usually smaller than the data file.
ALL
A full table scan will be done for each combination of rows from the previous tables. This is normally not good if the table is the first table not marked const, and usually very bad in all other cases. You normally can avoid ALL by adding more indexes, so that the row can be retrieved based on constant values or column values from earlier tables.

You can get a good indication of how good a join is by multiplying all values in the rows column of the EXPLAIN output. This should tell you roughly how many rows MySQL must examine to execute the query. This number is also used when you restrict queries with the max_join_size variable. See section 5.5.2 Tuning Server Parameters.

The following example shows how a JOIN can be optimized progressively using the information provided by EXPLAIN.

Suppose you have the SELECT statement shown below, that you examine using EXPLAIN:

EXPLAIN SELECT tt.TicketNumber, tt.TimeIn,
            tt.ProjectReference, tt.EstimatedShipDate,
            tt.ActualShipDate, tt.ClientID,
            tt.ServiceCodes, tt.RepetitiveID,
            tt.CurrentProcess, tt.CurrentDPPerson,
            tt.RecordVolume, tt.DPPrinted, et.COUNTRY,
            et_1.COUNTRY, do.CUSTNAME
        FROM tt, et, et AS et_1, do
        WHERE tt.SubmitTime IS NULL
            AND tt.ActualPC = et.EMPLOYID
            AND tt.AssignedPC = et_1.EMPLOYID
            AND tt.ClientID = do.CUSTNMBR;

For this example, assume that:

Initially, before any optimizations have been performed, the EXPLAIN statement produces the following information:

table type possible_keys                key  key_len ref  rows  Extra
et    ALL  PRIMARY                      NULL NULL    NULL 74
do    ALL  PRIMARY                      NULL NULL    NULL 2135
et_1  ALL  PRIMARY                      NULL NULL    NULL 74
tt    ALL  AssignedPC,ClientID,ActualPC NULL NULL    NULL 3872
      range checked for each record (key map: 35)

Because type is ALL for each table, this output indicates that MySQL is doing a full join for all tables! This will take quite a long time, as the product of the number of rows in each table must be examined! For the case at hand, this is 74 * 2135 * 74 * 3872 = 45,268,558,720 rows. If the tables were bigger, you can only imagine how long it would take.

One problem here is that MySQL can't (yet) use indexes on columns efficiently if they are declared differently. In this context, VARCHAR and CHAR are the same unless they are declared as different lengths. Because tt.ActualPC is declared as CHAR(10) and et.EMPLOYID is declared as CHAR(15), there is a length mismatch.

To fix this disparity between column lengths, use ALTER TABLE to lengthen ActualPC from 10 characters to 15 characters:

mysql> ALTER TABLE tt MODIFY ActualPC VARCHAR(15);

Now tt.ActualPC and et.EMPLOYID are both VARCHAR(15). Executing the EXPLAIN statement again produces this result:

table type   possible_keys   key     key_len ref         rows    Extra
tt    ALL    AssignedPC,ClientID,ActualPC NULL NULL NULL 3872    where used
do    ALL    PRIMARY         NULL    NULL    NULL        2135
      range checked for each record (key map: 1)
et_1  ALL    PRIMARY         NULL    NULL    NULL        74
      range checked for each record (key map: 1)
et    eq_ref PRIMARY         PRIMARY 15      tt.ActualPC 1

This is not perfect, but is much better (the product of the rows values is now less by a factor of 74). This version is executed in a couple of seconds.

A second alteration can be made to eliminate the column length mismatches for the tt.AssignedPC = et_1.EMPLOYID and tt.ClientID = do.CUSTNMBR comparisons:

mysql> ALTER TABLE tt MODIFY AssignedPC VARCHAR(15),
                      MODIFY ClientID   VARCHAR(15);

Now EXPLAIN produces the output shown below:

table type   possible_keys   key     key_len ref            rows     Extra
et    ALL    PRIMARY         NULL    NULL    NULL           74
tt    ref    AssignedPC,ClientID,ActualPC ActualPC 15 et.EMPLOYID 52 where used
et_1  eq_ref PRIMARY         PRIMARY 15      tt.AssignedPC  1
do    eq_ref PRIMARY         PRIMARY 15      tt.ClientID    1

This is almost as good as it can get.

The remaining problem is that, by default, MySQL assumes that values in the tt.ActualPC column are evenly distributed, and that isn't the case for the tt table. Fortunately, it is easy to tell MySQL about this:

shell> myisamchk --analyze PATH_TO_MYSQL_DATABASE/tt
shell> mysqladmin refresh

Now the join is perfect, and EXPLAIN produces this result:

table type   possible_keys   key     key_len ref            rows    Extra
tt    ALL    AssignedPC,ClientID,ActualPC NULL NULL NULL    3872    where used
et    eq_ref PRIMARY         PRIMARY 15      tt.ActualPC    1
et_1  eq_ref PRIMARY         PRIMARY 15      tt.AssignedPC  1
do    eq_ref PRIMARY         PRIMARY 15      tt.ClientID    1

Note that the rows column in the output from EXPLAIN is an educated guess from the MySQL join optimizer. To optimize a query, you should check if the numbers are even close to the truth. If not, you may get better performance by using STRAIGHT_JOIN in your SELECT statement and trying to list the tables in a different order in the FROM clause.

5.2.2 Estimating Query Performance

In most cases you can estimate the performance by counting disk seeks. For small tables, you can usually find the row in 1 disk seek (as the index is probably cached). For bigger tables, you can estimate that (using B++ tree indexes) you will need: log(row_count) / log(index_block_length / 3 * 2 / (index_length + data_pointer_length)) + 1 seeks to find a row.

In MySQL an index block is usually 1024 bytes and the data pointer is usually 4 bytes. A 500,000 row table with an index length of 3 (medium integer) gives you: log(500,000)/log(1024/3*2/(3+4)) + 1 = 4 seeks.

As the above index would require about 500,000 * 7 * 3/2 = 5.2M, (assuming that the index buffers are filled to 2/3, which is typical) you will probably have much of the index in memory and you will probably only need 1-2 calls to read data from the OS to find the row.

For writes, however, you will need 4 seek requests (as above) to find where to place the new index and normally 2 seeks to update the index and write the row.

Note that the above doesn't mean that your application will slowly degenerate by N log N! As long as everything is cached by the OS or SQL server things will only go marginally slower while the table gets bigger. After the data gets too big to be cached, things will start to go much slower until your applications is only bound by disk-seeks (which increase by N log N). To avoid this, increase the index cache as the data grows. See section 5.5.2 Tuning Server Parameters.

5.2.3 Speed of SELECT Queries

In general, when you want to make a slow SELECT ... WHERE faster, the first thing to check is whether or not you can add an index. See section 5.4.3 How MySQL Uses Indexes. All references between different tables should usually be done with indexes. You can use the EXPLAIN command to determine which indexes are used for a SELECT. See section 5.2.1 EXPLAIN Syntax (Get Information About a SELECT).

Some general tips:

5.2.4 How MySQL Optimizes WHERE Clauses

The WHERE optimizations are put in the SELECT part here because they are mostly used with SELECT, but the same optimizations apply for WHERE in DELETE and UPDATE statements.

Also note that this section is incomplete. MySQL does many optimizations, and we have not had time to document them all.

Some of the optimizations performed by MySQL are listed below:

Some examples of queries that are very fast:

mysql> SELECT COUNT(*) FROM tbl_name;
mysql> SELECT MIN(key_part1),MAX(key_part1) FROM tbl_name;
mysql> SELECT MAX(key_part2) FROM tbl_name
           WHERE key_part_1=constant;
mysql> SELECT ... FROM tbl_name
           ORDER BY key_part1,key_part2,... LIMIT 10;
mysql> SELECT ... FROM tbl_name
           ORDER BY key_part1 DESC,key_part2 DESC,... LIMIT 10;

The following queries are resolved using only the index tree (assuming the indexed columns are numeric):

mysql> SELECT key_part1,key_part2 FROM tbl_name WHERE key_part1=val;
mysql> SELECT COUNT(*) FROM tbl_name
           WHERE key_part1=val1 AND key_part2=val2;
mysql> SELECT key_part2 FROM tbl_name GROUP BY key_part1;

The following queries use indexing to retrieve the rows in sorted order without a separate sorting pass:

mysql> SELECT ... FROM tbl_name ORDER BY key_part1,key_part2,... ;
mysql> SELECT ... FROM tbl_name ORDER BY key_part1 DESC,key_part2 DESC,... ;

5.2.5 How MySQL Optimizes DISTINCT

DISTINCT is converted to a GROUP BY on all columns, DISTINCT combined with ORDER BY will in many cases also need a temporary table.

When combining LIMIT # with DISTINCT, MySQL will stop as soon as it finds # unique rows.

If you don't use columns from all used tables, MySQL will stop the scanning of the not used tables as soon as it has found the first match.

SELECT DISTINCT t1.a FROM t1,t2 where t1.a=t2.a;

In the case, assuming t1 is used before t2 (check with EXPLAIN), then MySQL will stop reading from t2 (for that particular row in t1) when the first row in t2 is found.

5.2.6 How MySQL Optimizes LEFT JOIN and RIGHT JOIN

A LEFT JOIN B in MySQL is implemented as follows:

RIGHT JOIN is implemented analogously as LEFT JOIN.

The table read order forced by LEFT JOIN and STRAIGHT JOIN will help the join optimizer (which calculates in which order tables should be joined) to do its work much more quickly, as there are fewer table permutations to check.

Note that the above means that if you do a query of type:

SELECT * FROM a,b LEFT JOIN c ON (c.key=a.key) LEFT JOIN d (d.key=a.key) WHERE b.key=d.key

MySQL will do a full scan on b as the LEFT JOIN will force it to be read before d.

The fix in this case is to change the query to:

SELECT * FROM b,a LEFT JOIN c ON (c.key=a.key) LEFT JOIN d (d.key=a.key) WHERE b.key=d.key

5.2.7 How MySQL Optimizes LIMIT

In some cases MySQL will handle the query differently when you are using LIMIT # and not using HAVING:

5.2.8 Speed of INSERT Queries

The time to insert a record consists approximately of:

where the numbers are somewhat proportional to the overall time. This does not take into consideration the initial overhead to open tables (which is done once for each concurrently running query).

The size of the table slows down the insertion of indexes by N log N (B-trees).

Some ways to speed up inserts:

To get some more speed for both LOAD DATA INFILE and INSERT, enlarge the key buffer. See section 5.5.2 Tuning Server Parameters.

5.2.9 Speed of UPDATE Queries

Update queries are optimized as a SELECT query with the additional overhead of a write. The speed of the write is dependent on the size of the data that is being updated and the number of indexes that are updated. Indexes that are not changed will not be updated.

Also, another way to get fast updates is to delay updates and then do many updates in a row later. Doing many updates in a row is much quicker than doing one at a time if you lock the table.

Note that, with dynamic record format, updating a record to a longer total length may split the record. So if you do this often, it is very important to OPTIMIZE TABLE sometimes. See section 4.5.1 OPTIMIZE TABLE Syntax.

5.2.10 Speed of DELETE Queries

If you want to delete all rows in the table, you should use TRUNCATE TABLE table_name. See section 6.4.7 TRUNCATE Syntax.

The time to delete a record is exactly proportional to the number of indexes. To delete records more quickly, you can increase the size of the index cache. See section 5.5.2 Tuning Server Parameters.

5.2.11 Other Optimization Tips

Unsorted tips for faster systems:

5.3 Locking Issues

5.3.1 How MySQL Locks Tables

You can find a discussion about different locking methods in the appendix. See section G.4 Locking methods.

All locking in MySQL is deadlock-free. This is managed by always requesting all needed locks at once at the beginning of a query and always locking the tables in the same order.

The locking method MySQL uses for WRITE locks works as follows:

The locking method MySQL uses for READ locks works as follows:

When a lock is released, the lock is made available to the threads in the write lock queue, then to the threads in the read lock queue.

This means that if you have many updates on a table, SELECT statements will wait until there are no more updates.

To work around this for the case where you want to do many INSERT and SELECT operations on a table, you can insert rows in a temporary table and update the real table with the records from the temporary table once in a while.

This can be done with the following code:

mysql> LOCK TABLES real_table WRITE, insert_table WRITE;
mysql> insert into real_table select * from insert_table;
mysql> TRUNCATE TABLE insert_table;
mysql> UNLOCK TABLES;

You can use the LOW_PRIORITY options with INSERT, UPDATE or DELETE or HIGH_PRIORITY with SELECT if you want to prioritize retrieval in some specific cases. You can also start mysqld with --low-priority-updates to get the same behaveour.

Using SQL_BUFFER_RESULT can also help making table locks shorter. See section 6.4.1 SELECT Syntax.

You could also change the locking code in `mysys/thr_lock.c' to use a single queue. In this case, write locks and read locks would have the same priority, which might help some applications.

5.3.2 Table Locking Issues

The table locking code in MySQL is deadlock free.

MySQL uses table locking (instead of row locking or column locking) on all table types, except BDB tables, to achieve a very high lock speed. For large tables, table locking is MUCH better than row locking for most applications, but there are, of course, some pitfalls.

For BDB and InnoDB tables, MySQL only uses table locking if you explicitely lock the table with LOCK TABLES or execute a command that will modify every row in the table, like ALTER TABLE. For these table types we recommend you to not use LOCK TABLES at all.

In MySQL Version 3.23.7 and above, you can insert rows into MyISAM tables at the same time other threads are reading from the table. Note that currently this only works if there are no holes after deleted rows in the table at the time the insert is made. When all holes has been filled with new data, concurrent inserts will automatically be enabled again.

Table locking enables many threads to read from a table at the same time, but if a thread wants to write to a table, it must first get exclusive access. During the update, all other threads that want to access this particular table will wait until the update is ready.

As updates on tables normally are considered to be more important than SELECT, all statements that update a table have higher priority than statements that retrieve information from a table. This should ensure that updates are not 'starved' because one issues a lot of heavy queries against a specific table. (You can change this by using LOW_PRIORITY with the statement that does the update or HIGH_PRIORITY with the SELECT statement.)

Starting from MySQL Version 3.23.7 one can use the max_write_lock_count variable to force MySQL to temporary give all SELECT statements, that wait for a table, a higher priority after a specific number of inserts on a table.

Table locking is, however, not very good under the following senario:

Some possible solutions to this problem are:

5.4 Optimizing Database Structure

5.4.1 Design Choices

MySQL keeps row data and index data in separate files. Many (almost all) other databases mix row and index data in the same file. We believe that the MySQL choice is better for a very wide range of modern systems.

Another way to store the row data is to keep the information for each column in a separate area (examples are SDBM and Focus). This will cause a performance hit for every query that accesses more than one column. Because this degenerates so quickly when more than one column is accessed, we believe that this model is not good for general purpose databases.

The more common case is that the index and data are stored together (like in Oracle/Sybase et al). In this case you will find the row information at the leaf page of the index. The good thing with this layout is that it, in many cases, depending on how well the index is cached, saves a disk read. The bad things with this layout are:

5.4.2 Get Your Data as Small as Possible

One of the most basic optimization is to get your data (and indexes) to take as little space on the disk (and in memory) as possible. This can give huge improvements because disk reads are faster and normally less main memory will be used. Indexing also takes less resources if done on smaller columns.

MySQL supports a lot of different table types and row formats. Choosing the right table format may give you a big performance gain. See section 7 MySQL Table Types.

You can get better performance on a table and minimize storage space using the techniques listed below:

5.4.3 How MySQL Uses Indexes

Indexes are used to find rows with a specific value of one column fast. Without an index MySQL has to start with the first record and then read through the whole table until it finds the relevant rows. The bigger the table, the more this costs. If the table has an index for the columns in question, MySQL can quickly get a position to seek to in the middle of the data file without having to look at all the data. If a table has 1000 rows, this is at least 100 times faster than reading sequentially. Note that if you need to access almost all 1000 rows it is faster to read sequentially because we then avoid disk seeks.

All MySQL indexes (PRIMARY, UNIQUE, and INDEX) are stored in B-trees. Strings are automatically prefix- and end-space compressed. See section 6.5.7 CREATE INDEX Syntax.

Indexes are used to:

Suppose you issue the following SELECT statement:

mysql> SELECT * FROM tbl_name WHERE col1=val1 AND col2=val2;

If a multiple-column index exists on col1 and col2, the appropriate rows can be fetched directly. If separate single-column indexes exist on col1 and col2, the optimizer tries to find the most restrictive index by deciding which index will find fewer rows and using that index to fetch the rows.

If the table has a multiple-column index, any leftmost prefix of the index can be used by the optimizer to find rows. For example, if you have a three-column index on (col1,col2,col3), you have indexed search capabilities on (col1), (col1,col2), and (col1,col2,col3).

MySQL can't use a partial index if the columns don't form a leftmost prefix of the index. Suppose you have the SELECT statements shown below:

mysql> SELECT * FROM tbl_name WHERE col1=val1;
mysql> SELECT * FROM tbl_name WHERE col2=val2;
mysql> SELECT * FROM tbl_name WHERE col2=val2 AND col3=val3;

If an index exists on (col1,col2,col3), only the first query shown above uses the index. The second and third queries do involve indexed columns, but (col2) and (col2,col3) are not leftmost prefixes of (col1,col2,col3).

MySQL also uses indexes for LIKE comparisons if the argument to LIKE is a constant string that doesn't start with a wild-card character. For example, the following SELECT statements use indexes:

mysql> select * from tbl_name where key_col LIKE "Patrick%";
mysql> select * from tbl_name where key_col LIKE "Pat%_ck%";

In the first statement, only rows with "Patrick" <= key_col < "Patricl" are considered. In the second statement, only rows with "Pat" <= key_col < "Pau" are considered.

The following SELECT statements will not use indexes:

mysql> select * from tbl_name where key_col LIKE "%Patrick%";
mysql> select * from tbl_name where key_col LIKE other_col;

In the first statement, the LIKE value begins with a wild-card character. In the second statement, the LIKE value is not a constant.

Searching using column_name IS NULL will use indexes if column_name is an index.

MySQL normally uses the index that finds the least number of rows. An index is used for columns that you compare with the following operators: =, >, >=, <, <=, BETWEEN, and a LIKE with a non-wild-card prefix like 'something%'.

Any index that doesn't span all AND levels in the WHERE clause is not used to optimize the query. In other words: To be able to use an index, a prefix of the index must be used in every AND group.

The following WHERE clauses use indexes:

... WHERE index_part1=1 AND index_part2=2 AND other_column=3
... WHERE index=1 OR A=10 AND index=2      /* index = 1 OR index = 2 */
... WHERE index_part1='hello' AND index_part_3=5
          /* optimized like "index_part1='hello'" */
... WHERE index1=1 and index2=2 or index1=3 and index3=3;
          /* Can use index on index1 but not on index2 or index 3 */

These WHERE clauses do NOT use indexes:

... WHERE index_part2=1 AND index_part3=2  /* index_part_1 is not used */
... WHERE index=1 OR A=10                  /* Index is not used in both AND parts */
... WHERE index_part1=1 OR index_part2=10  /* No index spans all rows */

Note that in some cases MySQL will not use an index, even if one would be available. Some of the cases where this happens are:

5.4.4 Column Indexes

All MySQL column types can be indexed. Use of indexes on the relevant columns is the best way to improve the performance of SELECT operations.

The maximum number of keys and the maximum index length is defined per table handler. See section 7 MySQL Table Types. You can with all table handlers have at least 16 keys and a total index length of at least 256 bytes.

For CHAR and VARCHAR columns, you can index a prefix of a column. This is much faster and requires less disk space than indexing the whole column. The syntax to use in the CREATE TABLE statement to index a column prefix looks like this:

KEY index_name (col_name(length))

The example below creates an index for the first 10 characters of the name column:

mysql> CREATE TABLE test (
           name CHAR(200) NOT NULL,
           KEY index_name (name(10)));

For BLOB and TEXT columns, you must index a prefix of the column. You cannot index the entire column.

In MySQL Version 3.23.23 or later, you can also create special FULLTEXT indexes. They are used for full-text search. Only the MyISAM table type supports FULLTEXT indexes. They can be created only from VARCHAR and TEXT columns. Indexing always happens over the entire column and partial indexing is not supported. See section 6.9 MySQL Full-text Search for details.

5.4.5 Multiple-Column Indexes

MySQL can create indexes on multiple columns. An index may consist of up to 15 columns. (On CHAR and VARCHAR columns you can also use a prefix of the column as a part of an index).

A multiple-column index can be considered a sorted array containing values that are created by concatenating the values of the indexed columns.

MySQL uses multiple-column indexes in such a way that queries are fast when you specify a known quantity for the first column of the index in a WHERE clause, even if you don't specify values for the other columns.

Suppose a table is created using the following specification:

mysql> CREATE TABLE test (
           id INT NOT NULL,
           last_name CHAR(30) NOT NULL,
           first_name CHAR(30) NOT NULL,
           PRIMARY KEY (id),
           INDEX name (last_name,first_name));

Then the index name is an index over last_name and first_name. The index will be used for queries that specify values in a known range for last_name, or for both last_name and first_name. Therefore, the name index will be used in the following queries:

mysql> SELECT * FROM test WHERE last_name="Widenius";

mysql> SELECT * FROM test WHERE last_name="Widenius"
                          AND first_name="Michael";

mysql> SELECT * FROM test WHERE last_name="Widenius"
                          AND (first_name="Michael" OR first_name="Monty");

mysql> SELECT * FROM test WHERE last_name="Widenius"
                          AND first_name >="M" AND first_name < "N";

However, the name index will NOT be used in the following queries:

mysql> SELECT * FROM test WHERE first_name="Michael";

mysql> SELECT * FROM test WHERE last_name="Widenius"
                          OR first_name="Michael";

For more information on the manner in which MySQL uses indexes to improve query performance, see section 5.4.3 How MySQL Uses Indexes.

5.4.6 How MySQL Opens and Closes Tables

table_cache, max_connections, and max_tmp_tables affect the maximum number of files the server keeps open. If you increase one or both of these values, you may run up against a limit imposed by your operating system on the per-process number of open file descriptors. However, you can increase the limit on many systems. Consult your OS documentation to find out how to do this, because the method for changing the limit varies widely from system to system.

table_cache is related to max_connections. For example, for 200 concurrent running connections, you should have a table cache of at least 200 * n, where n is the maximum number of tables in a join. You also need to reserve some extra file descriptors for temporary tables and files.

The cache of open tables can grow to a maximum of table_cache (default 64; this can be changed with the -O table_cache=# option to mysqld). A table is never closed, except when the cache is full and another thread tries to open a table or if you use mysqladmin refresh or mysqladmin flush-tables.

When the table cache fills up, the server uses the following procedure to locate a cache entry to use:

A table is opened for each concurrent access. This means that if you have two threads accessing the same table or access the table twice in the same query (with AS) the table needs to be opened twice. The first open of any table takes two file descriptors; each additional use of the table takes only one file descriptor. The extra descriptor for the first open is used for the index file; this descriptor is shared among all threads.

You can check if your table cache is too small by checking the mysqld variable opened_tables. If this is quite big, even if you haven't done a lot of FLUSH TABLES, you should increase your table cache. See section 4.5.5.3 SHOW STATUS.

5.4.7 Drawbacks to Creating Large Numbers of Tables in the Same Database

If you have many files in a directory, open, close, and create operations will be slow. If you execute SELECT statements on many different tables, there will be a little overhead when the table cache is full, because for every table that has to be opened, another must be closed. You can reduce this overhead by making the table cache larger.

5.4.8 Why So Many Open tables?

When you run mysqladmin status, you'll see something like this:

Uptime: 426 Running threads: 1 Questions: 11082 Reloads: 1 Open tables: 12

This can be somewhat perplexing if you only have 6 tables.

MySQL is multithreaded, so it may have many queries on the same table simultaneously. To minimize the problem with two threads having different states on the same file, the table is opened independently by each concurrent thread. This takes some memory and one extra file descriptor for the data file. The index file descriptor is shared between all threads.

5.5 Optimizing the MySQL Server

5.5.1 System/Compile Time and Startup Parameter Tuning

We start with the system level things since some of these decisions have to be made very early. In other cases a fast look at this part may suffice because it not that important for the big gains. However, it is always nice to have a feeling about how much one could gain by changing things at this level.

The default OS to use is really important! To get the most use of multiple CPU machines one should use Solaris (because the threads works really nice) or Linux (because the 2.2 kernel has really good SMP support). Also on 32-bit machines Linux has a 2G file size limit by default. Hopefully this will be fixed soon when new filesystems are released (XFS/Reiserfs). If you have a desperate need for files bigger than 2G on Linux-intel 32 bit, you should get the LFS patch for the ext2 file system.

Because we have not run MySQL in production on that many platforms, we advice you to test your intended platform before choosing it, if possible.

Other tips:

5.5.2 Tuning Server Parameters

You can get the default buffer sizes used by the mysqld server with this command:

shell> mysqld --help

This command produces a list of all mysqld options and configurable variables. The output includes the default values and looks something like this:

Possible variables for option --set-variable (-O) are:
back_log              current value: 5
bdb_cache_size        current value: 1048540
binlog_cache_size     current_value: 32768
connect_timeout       current value: 5
delayed_insert_timeout  current value: 300
delayed_insert_limit  current value: 100
delayed_queue_size    current value: 1000
flush_time            current value: 0
interactive_timeout   current value: 28800
join_buffer_size      current value: 131072
key_buffer_size       current value: 1048540
lower_case_table_names  current value: 0
long_query_time       current value: 10
max_allowed_packet    current value: 1048576
max_binlog_cache_size current_value: 4294967295
max_connections       current value: 100
max_connect_errors    current value: 10
max_delayed_threads   current value: 20
max_heap_table_size   current value: 16777216
max_join_size         current value: 4294967295
max_sort_length       current value: 1024
max_tmp_tables        current value: 32
max_write_lock_count  current value: 4294967295
myisam_sort_buffer_size  current value: 8388608
net_buffer_length     current value: 16384
net_retry_count       current value: 10
net_read_timeout      current value: 30
net_write_timeout     current value: 60
query_buffer_size     current value: 0
record_buffer         current value: 131072
record_rnd_buffer     current value: 131072
slow_launch_time      current value: 2
sort_buffer           current value: 2097116
table_cache           current value: 64
thread_concurrency    current value: 10
tmp_table_size        current value: 1048576
thread_stack          current value: 131072
wait_timeout          current value: 28800

If there is a mysqld server currently running, you can see what values it actually is using for the variables by executing this command:

shell> mysqladmin variables

You can find a full description for all variables in the SHOW VARIABLES section in this manual. See section 4.5.5.4 SHOW VARIABLES.

You can also see some statistics from a running server by issuing the command SHOW STATUS. See section 4.5.5.3 SHOW STATUS.

MySQL uses algorithms that are very scalable, so you can usually run with very little memory. If you, however, give MySQL more memory, you will normally also get better performance.

When tuning a MySQL server, the two most important variables to use are key_buffer_size and table_cache. You should first feel confident that you have these right before trying to change any of the other variables.

If you have much memory (>=256M) and many tables and want maximum performance with a moderate number of clients, you should use something like this:

shell> safe_mysqld -O key_buffer=64M -O table_cache=256 \
           -O sort_buffer=4M -O record_buffer=1M &

If you have only 128M and only a few tables, but you still do a lot of sorting, you can use something like:

shell> safe_mysqld -O key_buffer=16M -O sort_buffer=1M

If you have little memory and lots of connections, use something like this:

shell> safe_mysqld -O key_buffer=512k -O sort_buffer=100k \
           -O record_buffer=100k &

or even:

shell> safe_mysqld -O key_buffer=512k -O sort_buffer=16k \
           -O table_cache=32 -O record_buffer=8k -O net_buffer=1K &

If you are doing a GROUP BY or ORDER BY on files that are much bigger than your available memory you should increase the value of record_rnd_buffer to speed up the reading of rows after the sorting is done.

When you have installed MySQL, the `support-files' directory will contain some different my.cnf example files, `my-huge.cnf', `my-large.cnf', `my-medium.cnf', and `my-small.cnf', you can use as a base to optimize your system.

If there are very many connections, ``swapping problems'' may occur unless mysqld has been configured to use very little memory for each connection. mysqld performs better if you have enough memory for all connections, of course.

Note that if you change an option to mysqld, it remains in effect only for that instance of the server.

To see the effects of a parameter change, do something like this:

shell> mysqld -O key_buffer=32m --help

Make sure that the --help option is last; otherwise, the effect of any options listed after it on the command line will not be reflected in the output.

5.5.3 How Compiling and Linking Affects the Speed of MySQL

Most of the following tests are done on Linux with the MySQL benchmarks, but they should give some indication for other operating systems and workloads.

You get the fastest executable when you link with -static.

On Linux, you will get the fastest code when compiling with pgcc and -O3. To compile `sql_yacc.cc' with these options, you need about 200M memory because gcc/pgcc needs a lot of memory to make all functions inline. You should also set CXX=gcc when configuring MySQL to avoid inclusion of the libstdc++ library (it is not needed). Note that with some versions of pgcc, the resulting code will only run on true Pentium processors, even if you use the compiler option that you want the resulting code to be working on all x586 type processors (like AMD).

By just using a better compiler and/or better compiler options you can get a 10-30 % speed increase in your application. This is particularly important if you compile the SQL server yourself!

We have tested both the Cygnus CodeFusion and Fujitsu compilers, but when we tested them, neither was sufficiently bug free to allow MySQL to be compiled with optimizations on.

When you compile MySQL you should only include support for the character sets that you are going to use. (Option --with-charset=xxx). The standard MySQL binary distributions are compiled with support for all character sets.

Here is a list of some measurements that we have done:

The MySQL-Linux distribution provided by MySQL AB used to be compiled with pgcc, but we had to go back to regular gcc because of a bug in pgcc that would generate the code that does not run on AMD. We will continue using gcc until that bug is resolved. In the meantime, if you have a non-AMD machine, you can get a faster binary by compiling with pgcc. The standard MySQL Linux binary is linked statically to get it faster and more portable.

5.5.4 How MySQL Uses Memory

The list below indicates some of the ways that the mysqld server uses memory. Where applicable, the name of the server variable relevant to the memory use is given:

ps and other system status programs may report that mysqld uses a lot of memory. This may be caused by thread-stacks on different memory addresses. For example, the Solaris version of ps counts the unused memory between stacks as used memory. You can verify this by checking available swap with swap -s. We have tested mysqld with commercial memory-leakage detectors, so there should be no memory leaks.

5.5.5 How MySQL uses DNS

When a new thread connects to mysqld, mysqld will span a new thread to handle the request. This thread will first check if the hostname is in the hostname cache. If not the thread will call gethostbyaddr_r() and gethostbyname_r() to resolve the hostname.

If the operating system doesn't support the above thread-safe calls, the thread will lock a mutex and call gethostbyaddr() and gethostbyname() instead. Note that in this case no other thread can resolve other hostnames that is not in the hostname cache until the first thread is ready.

You can disable DNS host lookup by starting mysqld with --skip-name-resolve. In this case you can however only use IP names in the MySQL privilege tables.

If you have a very slow DNS and many hosts, you can get more performance by either disabling DNS lookop with --skip-name-resolve or by increasing the HOST_CACHE_SIZE define (default: 128) and recompile mysqld.

You can disable the hostname cache with --skip-host-cache. You can clear the hostname cache with FLUSH HOSTS or mysqladmin flush-hosts.

If you don't want to allow connections over TCP/IP, you can do this by starting mysqld with --skip-networking.

5.5.6 SET Syntax

SET [OPTION] SQL_VALUE_OPTION= value, ...

SET OPTION sets various options that affect the operation of the server or your client. Any option you set remains in effect until the current session ends, or until you set the option to a different value.

CHARACTER SET character_set_name | DEFAULT
This maps all strings from and to the client with the given mapping. Currently the only option for character_set_name is cp1251_koi8, but you can easily add new mappings by editing the `sql/convert.cc' file in the MySQL source distribution. The default mapping can be restored by using a character_set_name value of DEFAULT. Note that the syntax for setting the CHARACTER SET option differs from the syntax for setting the other options.
PASSWORD = PASSWORD('some password')
Set the password for the current user. Any non-anonymous user can change his own password!
PASSWORD FOR user = PASSWORD('some password')
Set the password for a specific user on the current server host. Only a user with access to the mysql database can do this. The user should be given in user@hostname format, where user and hostname are exactly as they are listed in the User and Host columns of the mysql.user table entry. For example, if you had an entry with User and Host fields of 'bob' and '%.loc.gov', you would write:
mysql> SET PASSWORD FOR bob@"%.loc.gov" = PASSWORD("newpass");

or

mysql> UPDATE mysql.user SET password=PASSWORD("newpass") where user="bob' and host="%.loc.gov";
SQL_AUTO_IS_NULL = 0 | 1
If set to 1 (default) then one can find the last inserted row for a table with an auto_increment row with the following construct: WHERE auto_increment_column IS NULL. This is used by some ODBC programs like Access.
AUTOCOMMIT= 0 | 1
If set to 1 all changes to a table will be done at once. To start a multi-command transaction, you have to use the BEGIN statement. See section 6.7.1 BEGIN/COMMIT/ROLLBACK Syntax. If set to 0 you have to use COMMIT / ROLLBACK to accept/revoke that transaction. See section 6.7.1 BEGIN/COMMIT/ROLLBACK Syntax. Note that when you change from not AUTOCOMMIT mode to AUTOCOMMIT mode, MySQL will do an automatic COMMIT on any open transactions.
SQL_BIG_TABLES = 0 | 1
If set to 1, all temporary tables are stored on disk rather than in memory. This will be a little slower, but you will not get the error The table tbl_name is full for big SELECT operations that require a large temporary table. The default value for a new connection is 0 (that is, use in-memory temporary tables).
SQL_BIG_SELECTS = 0 | 1
If set to 0, MySQL will abort if a SELECT is attempted that probably will take a very long time. This is useful when an inadvisable WHERE statement has been issued. A big query is defined as a SELECT that probably will have to examine more than max_join_size rows. The default value for a new connection is 1 (which will allow all SELECT statements).
SQL_BUFFER_RESULT = 0 | 1
SQL_BUFFER_RESULT will force the result from SELECT's to be put into a temporary table. This will help MySQL free the table locks early and will help in cases where it takes a long time to send the result set to the client.
SQL_LOW_PRIORITY_UPDATES = 0 | 1
If set to 1, all INSERT, UPDATE, DELETE, and and LOCK TABLE WRITE statements wait until there is no pending SELECT or LOCK TABLE READ on the affected table.
SQL_MAX_JOIN_SIZE = value | DEFAULT
Don't allow SELECTs that will probably need to examine more than value row combinations. By setting this value, you can catch SELECTs where keys are not used properly and that would probably take a long time. Setting this to a value other than DEFAULT will reset the SQL_BIG_SELECTS flag. If you set the SQL_BIG_SELECTS flag again, the SQL_MAX_JOIN_SIZE variable will be ignored. You can set a default value for this variable by starting mysqld with -O max_join_size=#.
SQL_SAFE_UPDATES = 0 | 1
If set to 1, MySQL will abort if an UPDATE or DELETE is attempted that doesn't use a key or LIMIT in the WHERE clause. This makes it possible to catch wrong updates when creating SQL commands by hand.
SQL_SELECT_LIMIT = value | DEFAULT
The maximum number of records to return from SELECT statements. If a SELECT has a LIMIT clause, the LIMIT takes precedence over the value of SQL_SELECT_LIMIT. The default value for a new connection is ``unlimited.'' If you have changed the limit, the default value can be restored by using a SQL_SELECT_LIMIT value of DEFAULT.
SQL_LOG_OFF = 0 | 1
If set to 1, no logging will be done to the standard log for this client, if the client has the process privilege. This does not affect the update log!
SQL_LOG_UPDATE = 0 | 1
If set to 0, no logging will be done to the update log for the client, if the client has the process privilege. This does not affect the standard log!
SQL_QUOTE_SHOW_CREATE = 0 | 1
If set to 1, SHOW CREATE TABLE will quote table and column names. This is on by default, for replication of tables with fancy column names to work. section 4.5.5.8 SHOW CREATE TABLE.
TIMESTAMP = timestamp_value | DEFAULT
Set the time for this client. This is used to get the original timestamp if you use the update log to restore rows. timestamp_value should be a UNIX Epoch timestamp, not a MySQL timestamp.
LAST_INSERT_ID = #
Set the value to be returned from LAST_INSERT_ID(). This is stored in the update log when you use LAST_INSERT_ID() in a command that updates a table.
INSERT_ID = #
Set the value to be used by the following INSERT or ALTER TABLE command when inserting an AUTO_INCREMENT value. This is mainly used with the update log.

5.6 Disk Issues

5.6.1 Using Symbolic Links

You can move tables and databases from the database directory to other locations and replace them with symbolic links to the new locations. You might want to do this, for example, to move a database to a file system with more free space or increase the speed of your system by spreading your tables to different disk.

The recommended may to do this, is to just symlink databases to different disk and only symlink tables as a last resort.

5.6.1.1 Using Symbolic Links for Databases

The way to symlink a database is to first create a directory on some disk where you have free space and then create a symlink to it from the MySQL database directory.

shell> mkdir /dr1/databases/test
shell> ln -s /dr1/databases/test mysqld-datadir

MySQL doesn't support that you link one directory to multiple databases. Replacing a database directory with a symbolic link will work fine as long as you don't make a symbolic link between databases. Suppose you have a database db1 under the MySQL data directory, and then make a symlink db2 that points to db1:

shell> cd /path/to/datadir
shell> ln -s db1 db2

Now, for any table tbl_a in db1, there also appears to be a table tbl_a in db2. If one thread updates db1.tbl_a and another thread updates db2.tbl_a, there will be problems.

If you really need this, you must change the following code in `mysys/mf_format.c':

if (flag & 32 || (!lstat(to,&stat_buff) && S_ISLNK(stat_buff.st_mode)))

to

if (1)

On Windows you can use internal symbolic links to directories by compiling MySQL with -DUSE_SYMDIR. This allows you to put different databases on different disks. See section 2.6.2.5 Splitting Data Across Different Disks on Windows.

5.6.1.2 Using Symbolic Links for Tables

Before MySQL 4.0 you should not symlink tables, if you are not very carefully with them. The problem is that if you run ALTER TABLE, REPAIR TABLE or OPTIMIZE TABLE on a symlinked table, the symlinks will be removed and replaced by the original files. This happens because the above command works by creating a temporary file in the database directory and when the command is complete, replace the original file with the temporary file.

You should not symlink tables on system that doesn't have a fully working realpath() call. (At least Linux and Solaris support realpath())

In MySQL 4.0 symlinks is only fully supported for MyISAM tables. For other table types you will probably get strange problems when doing any of the above mentioned commands.

The handling of symbolic links in MySQL 4.0 works the following way (this is mostly relevant only for MyISAM tables).

Things that are not yet supported:


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