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6.4 Techniques

With all the basic theory and recommendations covered, it's time to get down to business. When you notice your server is slow, what can you do about it? How do you locate the bottlenecks? What tools are available? What's the thought process?

The first step is to identify the type of bottleneck at the operating-system level. Using standard operating system tools, try to determine which of the server's resources are being taxed. Using top, vmstat, or the Windows Task Manager, check the machine's CPU utilization. If it's at or near 100%, it's obviously CPU-bound. Use top to verify which processes are responsible for the bulk of the CPU utilization. (If you don't have much experience with operating-system performance tools, consult a qualified system administrator.)

If MySQL is indeed consuming lots of CPU time, there are several techniques you can employ in an attempt to reduce the CPU load. See Section 6.4.2 later in this chapter. If the processes using the bulk of the CPU time aren't mysqld, you clearly have to solve a problem unrelated to MySQL. Perhaps it's a runaway process or simply something that should be moved to another machine. Either way, it's not a MySQL issue, so the problem is "solved" from our point of view.

If the CPU is very busy but there doesn't appear to be any obvious process or group of processes using a large amount of CPU time, look at the division between system and user time. If there's an unusually high amount of time being spent on system (kernel) tasks, that may be a sign of a MySQL configuration problem or something completely unrelated. See Section 6.4.4 later in this chapter for an example of why MySQL might be working the kernel too hard.

If the CPU is relatively idle because it's frequently waiting for the disks, see Section 6.4.1. You'll know this because of the higher than normal numbers you see with vmstat and/or iostat. If the CPU is waiting on disk I/O because of swapping activity, however, go to Section 6.4.3.

6.4.1 Solving I/O Bottlenecks

Disk (I/O) bottlenecks tend to be the most common MySQL performance problem. They're typically caused by inefficient queries—meaning that MySQL has to read too many rows to locate the data you're interested in. Usually that means your queries aren't using an index, or they're using an index that's not terribly effective for this particular query. Before going much further, be sure you've reviewed Chapter 5.

Diagnosing a query that's not using an index is relatively easy. If you've enabled the slow query log (see Section 5.3 in Chapter 5) and set log-long-format, MySQL automatically logs any query that doesn't use an index. You really need to start with that query: use EXPLAIN and do simple benchmarks when you have more than one way to write a given query.

After you've looked at any slow queries and fixed them, the next things to look at are more subtle issues. In some cases, queries do use an index and run relatively fast, so MySQL never considers them to be slow, but it's actually the wrong index from a performance point of view. There may be an alternative index MySQL can use to further decrease the I/O required. Wrong index

Finding queries that use the wrong index can be more of a challenge. It requires an intimate understanding of your data and the queries being run against it. A real-world example may help to illustrate how subtle the problem can be.

Jeremy uses the mod_log_sql Apache module to record all his web site hits into a MyISAM table named access_jeremy_zawodny_com. The table is roughly 1.3 GB in size, contains over 6 million records, and looks like this:


| Field            | Type                 | Null | Key | Default | Extra |


| agent            | varchar(255)         | YES  | MUL | NULL    |       |

| bytes_sent       | int(10) unsigned     | YES  |     | NULL    |       |

| child_pid        | smallint(5) unsigned | YES  |     | NULL    |       |

| cookie           | varchar(255)         | YES  |     | NULL    |       |

| request_file     | varchar(255)         | YES  |     | NULL    |       |

| referer          | varchar(255)         | YES  |     | NULL    |       |

| remote_host      | varchar(50)          | YES  | MUL | NULL    |       |

| remote_logname   | varchar(50)          | YES  |     | NULL    |       |

| remote_user      | varchar(50)          | YES  |     | NULL    |       |

| request_duration | smallint(5) unsigned | YES  |     | NULL    |       |

| request_line     | varchar(255)         | YES  |     | NULL    |       |

| request_method   | varchar(6)           | YES  |     | NULL    |       |

| request_protocol | varchar(10)          | YES  |     | NULL    |       |

| request_time     | varchar(28)          | YES  |     | NULL    |       |

| request_uri      | varchar(255)         | YES  | MUL | NULL    |       |

| server_port      | smallint(5) unsigned | YES  |     | NULL    |       |

| ssl_cipher       | varchar(25)          | YES  |     | NULL    |       |

| ssl_keysize      | smallint(5) unsigned | YES  |     | NULL    |       |

| ssl_maxkeysize   | smallint(5) unsigned | YES  |     | NULL    |       |

| status           | smallint(5) unsigned | YES  |     | NULL    |       |

| time_stamp       | int(10) unsigned     | YES  | MUL | NULL    |       |

| virtual_host     | varchar(50)          | YES  |     | NULL    |       |


There are separate indexes on four columns: agent, time_stamp, request_uri, and remote_host. The intention is to provide an efficient way to produce statistics based on time, user agent (browser), the document fetched (request_uri), or the client (remote_host). Notice the indexes on each of those columns.

Most queries ran very quickly, but one particular query was problematic. It seemed to run longer than expected. After repeated execution and watching vmstat output, it became clear that a lot of time was spent waiting on the disk. The query attempts to find out which documents a given client has requested during a particular time range—usually a single day. It is run once for every client that requested anything in the past day. The request looks like this:

  select request_uri from access_jeremy_zawodny_com

   where remote_host = ''

     and time_stamp >= 1056782930

     and time_stamp <= 1056869330

order by time_stamp asc

Running the query through EXPLAIN proved to be quite interesting:

mysql> explain select request_uri from access_jeremy_zawodny_com

    ->    where remote_host = ''

    ->      and time_stamp >= 1056782930

    ->      and time_stamp <= 1056869330

    -> order by time_stamp asc \G

*************************** 1. row ***************************

        table: access_jeremy_zawodny_com

         type: ref

possible_keys: time_stamp,remote_host

          key: remote_host

      key_len: 6

          ref: const

         rows: 4902

        Extra: Using where; Using filesort

1 row in set (0.00 sec)

MySQL chose to use the index on remote_host. But it doesn't always make that choice. Sometimes it decides to use the index on time_stamp. Here's an example:

mysql> explain  select request_uri from access_jeremy_zawodny_com

    ->    where remote_host = ''

    ->      and time_stamp >= 1056782930

    ->      and time_stamp <= 1056869330

    -> order by time_stamp asc \G

*************************** 1. row ***************************

        table: access_jeremy_zawodny_com

         type: range

possible_keys: time_stamp,remote_host

          key: time_stamp

      key_len: 5

          ref: NULL

         rows: 20631

        Extra: Using where

1 row in set (0.01 sec)

The only difference between those two queries is the IP address we're looking for. In each case, MySQL's query optimizer estimates the number of rows it will need to read to satisfy the query using each possible index. In the first example, it decides that there are fewer records with a remote_host of than there are records in the specified 24-hour time range. In the second example, it does just the opposite, deciding the time range will result in fewer rows to read.

By experimenting with various IP addresses, it doesn't take long to find one for which MySQL makes the wrong choice. It chooses the remote_host index when using the time_stamp index is actually faster—even though the remote_host requires reading the fewest rows.[4] How is that possible?

[4] Using a USE INDEX specification in the query, you can test the performance of either index.

The underlying assumption is that all rows cost roughly the same amount of time to read. But this is a case in which that's not always true. Consider how the data will be stored in this MyISAM table. Apache is logging requests to the table all the time and has been doing so for over a year. Rows are never removed, so the data is already sorted by timestamp in the table and on disk (assuming minimal fragmentation).

Once you have a nontrivial amount of information in a table like this, the rules change a bit. If we assume that the records for a given IP address are evenly distributed among the millions of records, it's clear that using the remote_host index may result in many more disk seeks. And since disk seeks are slower than reading consecutive blocks from disk, it follows that MySQL may be doing less work (evaluating fewer rows) but the disk is doing more work—using precious seek time that may slow down other queries too.

In logging applications when you're frequently querying based on a time range as well as another indexed field, this problem is quite common and has no good generalizable solution. If you have some insight into your data and can add it to the software that writes the queries, that can help a lot. The software could be configured to tell MySQL which index to use. For example, if your software knows that a given IP address shows up only very infrequently recently, it can force MySQL to use the time_stamp range:

SELECT ... USE_INDEX(time_stamp) ...

It's not the ideal solution, but it is quite effective when used appropriately. Temporary tables

Another problem that doesn't show up in the slow query log is an excessive use of disk-based temporary tables. In the output of EXPLAIN, you'll often see Using temporary. It indicates that MySQL must create a temporary table to complete the query. However, it doesn't tell you whether that temporary table will be in memory or on disk. That's controlled by the size of the table and MySQL's tmp_table_size variable.

If the space required to build the temporary table is less than or equal to tmp_table_size, MySQL keeps it in memory rather than incur the overhead and time required to write the data to disk and read it again. However, if the space required exceeds tmp_table_size, MySQL creates a disk-based table in its tmpdir directory (often /tmp on Unix systems.) The default tmp_table_size size is 32 MB.

To find out how often that happens, compare the relative sizes of the Created_tmp_tables and Created_tmp_disk_tables counters:

mysql> SHOW STATUS LIKE 'Created_tmp_%';


| Variable_name           | Value |


| Created_tmp_disk_tables | 18    |

| Created_tmp_tables      | 203   |

| Created_tmp_files       | 0     |


If you create a lot of disk-based temporary tables, increase the size of tmp_table_size if you can do so safely. Keep in mind that setting the value too high may result in excessive swapping or MySQL running out of memory if too many threads attempt to allocate in-memory temporary tables at the same time. Otherwise, make sure that tmpdir points to a very fast disk that's not already doing lots of I/O.

As a last resort, consider using a tmpfs (or ramdisk, or mdmfs, or whatever your OS calls memory-backed filesystems) and setting $TMPDIR to point there when starting MySQL. Caching

If your queries are already optimized and using the most efficient indexes, it's still possible to run into I/O bottlenecks at some point. Simply running too many queries, no matter how efficient they are, can become too much for the disk(s) to keep up with. If so, it's time to consider caching.

The easiest thing to do is make sure you're using the MySQL query cache. Available since MySQL 4.0, the query cache keeps the results of frequently executed SELECTs in memory so that MySQL doesn't need to perform any disk I/O at all. See Section 5.4.4 in Chapter 5 for more information.

Taking things a step further, you might consider application-level caching. If there's data that doesn't change frequently at all, query for it once in a while and store it in memory or on local disk until you requery for it. Spread the load

If you've already covered the causes listed earlier and implemented the suggestions, it's likely that you need to spread the I/O load more effectively. As described earlier, installing disks with faster RPMs and lower seek times may help. Using RAID (especially RAID 0, RAID 5, or RAID 10) will spread the work across multiple disks, possibly eliminating or reducing the bottleneck.

Another option, if you have multiple disks and can't easily configure RAID, is to attempt to balance the disk I/O manually. Spend some time with iostat or systat (depending on your OS) to discover where the bulk of the I/O is going. If you have all your MySQL data on a single disk, you can try moving pieces to another disk. If the majority of activity is focused on a small group of tables, consider moving them to a separate disk.

Another approach is to separate predominantly random I/O from that which is mostly serial. Store logs such as the binary logs, replication relay logs, and InnoDB transaction logs, on a separate disk from the actual data files. It's ultimately a game of trial and error. As with benchmarking, keep a close eye on the numbers and try not to change too many things at once.

Finally, replication is always an option. If you've simply outgrown the capacity of a single machine, it's often the least disruptive solution. See Chapter 7 to learn all about replication.

6.4.2 Solving CPU Bottlenecks

CPU bottlenecks in MySQL can be difficult to track down. Unlike some database servers, MySQL currently doesn't provide per-query statistics about the amount of time spent actually doing work versus waiting for disk I/O to complete.

Luckily it doesn't have to be a complete guessing game. If you see a query in the slow query log and suspect that it may be CPU-bound, simply benchmark it. Pull out a copy of MySQL super-smack, and run it a few thousand times in a row. Then, in another window, watch top, vmstat, or your favorite system monitoring tool. If the CPU quickly hits 100% utilization even with a relatively low number of concurrent queries, the query is very likely CPU-bound.

If you find yourself staring at a very large list of slow queries, how do you decide which ones to start analyzing? Easy: look for those that examine a large number of rows (thousands, tens of thousands, or more), and focus on those that use any of MySQL's built-in data-manipulation functions. Common suspects are those that:

  • Format or compare dates

  • Encrypt data or compute hashes

  • Perform complex comparisons, such as regular expressions

You'll often find that something as simple as computing an MD5 hash over millions of values per hour is using too much CPU time. By moving the logic into the application servers that query that database, you'll free up CPU time for work that only MySQL can do efficiently.

If you can't easily ask MySQL to do less work by moving logic into the application layer, you always have the option of throwing hardware at the problem. You can do this in one of two ways. You might simply upgrade the CPUs in your server or add more CPUs if there's room. Alternatively, you may find it less expensive and more scalable to add new servers, replicate the data to them, and spread the load among them. There's nothing wrong with using Moore's Law to your advantage once in a while.

High CPU utilization with MyISAM tables isn't always bad. It may mean that you are doing queries on tables that have been entirely cached in the operating system's cache. This may or may not be a bad thing. It's certainly better than reading from disk, but each time MySQL has to ask the OS for a block of data, that's CPU time that could be better spent processing the rest of the query. Moving to InnoDB or BDB tables lets MySQL cache table data itself, so it doesn't have to ask the OS for records.

6.4.3 Solving Memory Bottlenecks

Tuning memory usage on MySQL servers can be a delicate balancing act. As explained earlier, MySQL has some global memory buffers in addition to a number of per-thread buffers. The trick is to balance the performance gains that come from having large global buffers against the need to service a particular number of concurrent users. At a minimum, you should have enough memory available to handle MySQL's global buffers plus the per-thread buffers multiplied by the maximum number of concurrent connections you will use.

Expressed mathematically, that is:

min_memory_needed = global_buffers + (thread_buffers * max_connections)

where thread_buffers includes the following:


and global_buffers includes:


We say that's the minimum memory required because ideally you'd like some left over for the operating system itself to use. In the case of MyISAM tables, "spare" memory will often be put to use caching records from MyISAM data (.MYD) files.

In addition to any memory the threads may allocate in the process of handling queries, the threads themselves also require a bit of memory simply to exist. The thread_stack variable controls this overhead. On most platforms, 192 KB is the default value.[5]

[5] If you happen to be using LinuxThreads on FreeBSD, the value is hardcoded in the LinuxThreads library. Changing MySQL's thread_stack setting will have no effect. You must recompile the library to change the stack size.

A likely problem is typified by an all-too-common scenario. Imagine you have a server with 1 GB of memory hosting a mix of MyISAM and InnoDB tables—mostly MyISAM. To get the most bang for your buck, you configure a 512-MB key_buffer after watching the key efficiency in mytop (see Appendix B) and a 256-MB innodb_buffer_pool after checking the buffer pool and memory statistics from SHOW INNODB STATUS (see Appendix A). That leaves 256 MB that is used to cache data files at the operating system level as well as the per-thread buffers that are allocated on an as-needed basis. The MySQL server handles a relatively small number of concurrent users, maybe 20-50 most of the time, and the per-thread buffer settings are all left at their default sizes.

Things work very well until a few new applications are built that also use this MySQL server. These new applications need a significant number of concurrent connections. Instead of 20-50 connections on average, the server is handling 300-400. When this happens, the odds of several connections needing to allocate a per-thread buffer (such as the sort_buffer) at the same time increase quite a bit.

This can lead to a particularly nasty series of events. If a large number of threads need to allocate additional memory, it's probably because the server is handling a heavy query load. That can cause MySQL to allocate so much memory that the operating system begins swapping, which causes performance to degrade further, which means that each query takes longer to complete. With queries running more slowly, the odds of more threads needing memory increases. It's a vicious spiral.

The only solution is to restore balance between the system's memory and MySQL's memory needs. That means doing one of the following.

  • Add more memory

  • Decrease max_connections

  • Decrease some of the per-thread buffer sizes

Be proactive. Monitor memory use on your servers. Do the math to ensure that in the worst case (hitting max_connections and each thread allocating additional memory), you'll still have a bit of breathing room.

6.4.4 Solving Kernel Bottlenecks

Though it's not common, you may find that MySQL doesn't appear to be using an overwhelming amount of CPU time, yet the machine is rather busy. There's little idle CPU. Upon looking at it more closely, you find that quite a bit of the time is spent in "system" rather than "user" or "idle." That's likely a sign that MySQL is doing something unusual to exercise the kernel—usually creating and destroying threads.

This happened at Yahoo! during the launch of a new web site. In September 2002, engineers were scrambling to create a September 11th memorial web site known as[6] On it, anyone could create a memorial "tile" by selecting a graphic and adding a customized message. The tile was then viewable by anyone visiting the site. To get the job done as quickly as possible, it was constructed using standard open source tools, including FreeBSD, Apache, PHP, and MySQL

[6] The entire site was conceived, designed, built, and launched in roughly two weeks using the spare time of handful of Yahoo's engineers.

The architecture was relatively straightforward, but we'll simplify it a bit to focus on the main point. A group of frontend web servers was configured to connect to a slave server by way of a hardware load balancer. Using the slave connection, the server could pull the information necessary to display the tiles. When a visitor created a tile, however, the web server needed to connect to the master to insert several records. The master was a beefier machine: dual 1.2-GHz CPUs, 2 GB of RAM, and a SCSI hardware RAID 5 disk array.

At its peak, there were roughly 25-30 web servers that needed to work with the master. Each server was configured to run roughly 30-40 Apache processes. That meant the master would need to support over 1,000 concurrent clients. Knowing that could tie up substantial resources on the master, the designers opted for a simplified approach. Unfortunately, the web application (written in PHP) was configured to use persistent connections. So, to keep connection numbers down on the master, the wait_timeout was set very low—to roughly 10 seconds.

By and large, it worked. Idle connections were dropped after 10 seconds. The number of connections on the master remained below 200, leaving lots of resources free. But there was a problem: the CPUs in the master were quite busy. Most of the time there was less than 10% idle time, and nearly 50% of the CPU time was being spent on system (rather than user) tasks.

After an hour or so of head-scratching, looking at system logs and the output of SHOW STATUS, a light finally flickered on in Jeremy's head. The value of Threads_created was very large and increasing at an alarming rate. The kernel was so busy creating and destroying threads that it was eating into MySQL's ability to use the CPUs effectively.

With that realization, the solution was easy. Increasing the thread_cache from its default value of 0 to roughly 150 resulted in an instant improvement. The system CPU time dropped to roughly 10%, thus freeing up quite a bit of CPU time for MySQL to use. As it turns out, MySQL didn't need it all, so the machine ended up with 20% idle time—breathing room.

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