Question 1 :
MySQL - 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.
Question 2 :
MySQL - Speed of DELETE Queries ?
If you want to delete all rows in the table, you should use TRUNCATE TABLE table_name.
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.
Question 3 :
MySQL - Other Optimization Tips
Unsorted tips for faster systems:
Use persistent connections to the database to avoid the connection overhead. If you can't use persistent connections and you are doing a lot of new connections to the database, you may want to change the value of the thread_cache_size variable.
Always check that all your queries really use the indexes you have created in the tables. In MySQL you can do this with the EXPLAIN command.
Try to avoid complex SELECT queries on tables that are updated a lot. This is to avoid problems with table locking. The new MyISAM tables can insert rows in a table without deleted rows at the same time another table is reading from it. If this is important for you, you should consider methods where you don't have to delete rows or run OPTIMIZE TABLE after you have deleted a lot of rows.
Use ALTER TABLE ... ORDER BY expr1,expr2... if you mostly retrieve rows in expr1,expr2.. order. By using this option after big changes to the table, you may be able to get higher performance.
In some cases it may make sense to introduce a column that is 'hashed' based on information from other columns. If this column is short and reasonably unique it may be much faster than a big index on many columns. In MySQL it's very easy to use this extra column: SELECT * FROM table_name WHERE hash=MD5(concat(col1,col2)) AND col_1='constant' AND col_2='constant' For tables that change a lot you should try to avoid all VARCHAR or BLOB columns. You will get dynamic row length as soon as you are using a single VARCHAR or BLOB column.
It's not normally useful to split a table into different tables just because the rows gets 'big'. To access a row, the biggest performance hit is the disk seek to find the first byte of the row. After finding the data most new disks can read the whole row fast enough for most applications. The only cases where it really matters to split up a table is if it's a dynamic row size table (see above) that you can change to a fixed row size, or if you very often need to scan the table and don't need most of the columns.
If you very often need to calculate things based on information from a lot of rows (like counts of things), it's probably much better to introduce a new table and update the counter in real time. An update of type UPDATE table set count=count+1 where index_column=constant is very fast! This is really important when you use databases like MySQL that only have table locking (multiple readers / single writers). This will also give better performance with most databases, as the row locking manager in this case will have less to do.
If you need to collect statistics from big log tables, use summary tables instead of scanning the whole table. Maintaining the summaries should be much faster than trying to do statistics 'live'. It's much faster to regenerate new summary tables from the logs when things change (depending on business decisions) than to have to change the running application! If possible, one should classify reports as 'live' or 'statistical', where data needed for statistical reports are only generated based on summary tables that are generated from the actual data.
Take advantage of the fact that columns have default values. Insert values explicitly only when the value to be inserted differs from the default. This reduces the parsing that MySQL need to do and improves the insert speed. In some cases it's convenient to pack and store data into a blob. In this case you have to add some extra code in your appliction to pack/unpack things in the blob, but this may save a lot of accesses at some stage. This is practical when you have data that doesn't conform to a static table structure.
Normally you should try to keep all data non-redundant (what is called 3rd normal form in database theory), but you should not be afraid of duplicating things or creating summary tables if you need these to gain more speed.
Stored procedures or UDF (user-defined functions) may be a good way to get more performance. In this case you should, however, always have a way to do this some other (slower) way if you use some database that doesn't support this. You can always gain something by caching queries/answers in your application and trying to do many inserts/updates at the same time. If your database supports lock tables (like MySQL and Oracle), this should help to ensure that the index cache is only flushed once after all updates.
Use INSERT /*! DELAYED */ when you do not need to know when your data is written. This speeds things up because many records can be written with a single disk write.
Use INSERT /*! LOW_PRIORITY */ when you want your selects to be more important.
Use SELECT /*! HIGH_PRIORITY */ to get selects that jump the queue. That is, the select is done even if there is somebody waiting to do a write.
Use the multi-line INSERT statement to store many rows with one SQL command (many SQL servers supports this).
Use LOAD DATA INFILE to load bigger amounts of data. This is faster than normal inserts and will be even faster when myisamchk is integrated in mysqld.
Use AUTO_INCREMENT columns to make unique values.
Use OPTIMIZE TABLE once in a while to avoid fragmentation when using dynamic table format.
Use HEAP tables to get more speed when possible.
When using a normal Web server setup, images should be stored as files. That is, store only a file reference in the database. The main reason for this is that a normal Web server is much better at caching files than database contents. So it it's much easier to get a fast system if you are using files.
Use in memory tables for non-critical data that are accessed often (like information about the last shown banner for users that don't have cookies).
Columns with identical information in different tables should be declared identical and have identical names. Before Version 3.23 you got slow joins otherwise. Try to keep the names simple (use name instead of customer_name in the customer table). To make your names portable to other SQL servers you should keep them shorter than 18 characters.
If you need REALLY high speed, you should take a look at the low-level interfaces for data storage that the different SQL servers support! For example, by accessing the MySQL MyISAM directly, you could get a speed increase of 2-5 times compared to using the SQL interface. To be able to do this the data must be on the same server as the application, and usually it should only be accessed by one process (because external file locking is really slow). One could eliminate the above problems by introducing low-level MyISAM commands in the MySQL server (this could be one easy way to get more performance if needed). By carefully designing the database interface, it should be quite easy to support this types of optimization. In many cases it's faster to access data from a database (using a live connection) than accessing a text file, just because the database is likely to be more compact than the text file (if you are using numerical data), and this will involve fewer disk accesses. You will also save code because you don't have to parse your text files to find line and column boundaries. You can also use replication to speed things up.
Declaring a table with DELAY_KEY_WRITE=1 will make the updating of indexes faster, as these are not logged to disk until the file is closed. The downside is that you should run myisamchk on these tables before you start mysqld to ensure that they are okay if something killed mysqld in the middle. As the key information can always be generated from the data, you should not lose anything by using DELAY_KEY_WRITE.
Question 4 :
MySQL - Using Your Own Benchmarks
You should definately 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.
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 Sasha's recent hack for this - super-smack. 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.
Question 5 :
MySQL - 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:
Table scanning is much slower because you have to read through the indexes to get at the data.
You can't use only the index table to retrieve data for a query.
You lose a lot of space, as you must duplicate indexes from the nodes (as you can't store the row in the nodes).
Deletes will degenerate the table over time (as indexes in nodes are usually not updated on delete).
It's harder to cache ONLY the index data.