Do the operating systems kill your MySQL instances from time to time? Are some database servers swapping constantly? These are relatively common problems. Why? How to prevent them?
When a running program needs some additional memory, it can typically allocate it dynamically with malloc() function. It finds an unused continuous block that is at least as large as the requested size, reserves as much as it needs, and returns a pointer to that space. No initialization of the memory contents is performed at the time. When malloc() returns NULL instead of a valid address, it is an information to the calling program that there wasn’t enough memory available and the call has failed to allocate anything. In such cases applications typically take appropriate actions to notify users about the problem and terminate some of their activity or completely shut down.
In Linux it can be a little bit more complicated. Calling malloc() may succeed even if there should be no more memory available. It is because Linux implements optimistic memory allocation, which is an element of the legacy system architecture. Optimistic memory allocation allows applications to ask for and receive more than can be possibly offered. The system hopes and assumes that all running applications won’t ever require all of the allocated memory all at once. It is a bit like with airlines, which like to overbook their flights assuming they can get away with it as in most cases there are a few no-shows or last minute cancellations. That way all applications may think they have the memory they allocated, just the same way you think you have a seat on the flight you booked, but eventually the system may not be able to provide it.
There are a few scenarios. At first the system can start swapping some of the memory contents to disk, because the whole reason the swap space is there is to extend the physical memory. But as applications keep using it more and more, there is a physical limit they cannot cross. At that point either some of them crash miserably or, more typically, system decides to kill one of them and this way quickly free some resources.
The optimistic memory allocation is also implemented in FreeBSD, while on the other hand Solaris never overcommits memory.
The Linux kernel has a functionality called Out Of Memory Killer (or OOM Killer) responsible for dealing with memory exhaustion. If system reaches a point where it may soon run out of all memory, OOM Killer looks for a process it could kill and ends its life.
May 16 00:12:33 mysql-server-01 kernel: Out of Memory: Killed process 3154 (mysqld).
How does it work and why does it often kill MySQL?
OOM Killer uses a heuristic system to choose a processes for termination. It is based on a score associated with each running application, which is calculated by oom_badness() call, formerly named badness(), inside Linux kernel. Those interested in more details can check the source code in mm/oom_kill.c.
The algorithm is relatively simple and usually the more memory a process uses, the higher score it receives, which makes it more likely to be killed. But there are actually more factors that are considered:
- memory consumption
- process ownership
- process age (older kenerls only)
- CPU time used (older kernels only)
- process nice value (older kernels only)
- process flags
- oom_adj/oom_score_adj setting
The complete list can be different for different Linux versions as the algorithm was mostly re-written in kernel 2.6.29.
In the past, the modifiers used to impact the score significantly and for example if a task had niceness above zero, its score was doubled. If it was owned by a privileged user, the score was divided by eight. In new kernels it is no longer the case. For instance, a process belonging to root now only receive a very small bonus of 30 points out of the possible 1000. With these changes the developers wanted a more predictable algorithm and apparently this was the way to achieve that.
So why does it kill MySQL so often? The answer is simple — because MySQL typically uses the most memory out of all processes running in a system.
To demonstrate the problem mentioned above, here’s a little experiment. I wrote a tiny program that does nothing except it allocates some memory and then fills it with values in 50MB blocks every now and then. On a machine with 1.5GB of memory and 128MB swap space and running a Linux distribution, I started two instances of the program:
USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND root 3142 3.0 3.3 1052552 51816 pts/0 S 18:42 0:00 ./test-malloc root 3143 3.0 3.4 1052552 52684 pts/0 S 18:42 0:00 ./test-malloc
Each process was granted 1GB of memory as shown in VSZ, which together was more than RAM and swap size combined. However, at the time each process actually only used 50MB as reported in RSS, while the rest of it was never even initialized. The system could easily offer 2GB, and it did, when most of it was not used at all.
USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND root 3143 10.1 49.2 1052552 757232 pts/0 R 18:43 0:01 ./test-malloc root 3142 6.3 35.3 1052552 542756 pts/0 D 18:43 0:01 ./test-malloc
As both programs kept writing into the memory they allocated, their respective resident set sizes grew larger and larger. At that point the system ran out of physical RAM and started swapping out. Eventually, a few moments later, it decided it couldn’t let both growing programs live and decided to kill one of them:
test-malloc invoked oom-killer: gfp_mask=0x280da, order=0, oom_adj=0, oom_score_adj=0 test-malloc cpuset=/ mems_allowed=0 [..] Out of memory: Kill process 3143 (test-malloc) score 465 or sacrifice child Killed process 3143, UID 0, (test-malloc) total-vm:1052552kB, anon-rss:862564kB, file-rss:8kB
Only then the other instance managed to use its full allocation of 1GB, even though in the beginning both were told they could have 1GB each.
From MySQL DBA perspective
A similar problem may appear when running a MySQL database. It allocates a lot of different buffers for various purposes. Some last only for a short time, others live for as long as the database instance itself. These buffers start empty and only fill up over time, which means it may be very easy to run into these problems as in the beginning, right after a database instance is started, there may not be any signs that anything bad could be going on.
Running into memory problems related to MySQL server usually implies bad database configuration. Whether you run a dedicated database server or share resources between a few larger applications, the memory configuration has to be set correctly to reflect the specific circumstances.
A few common causes of overrunning the available memory:
- database main buffers were set too large, e.g.: key_buffer_size, innodb_buffer_pool
- per-session buffers were set too large given the number of concurrently connected clients or concurrently running queries, e.g.: read_buffer_size or sort_buffer_size
- temporary tables configuration allows very large in-memory tables and some queries take advantage of that, e.g.: max_heap_table_size, tmp_table_size
- database carries very large number of InnoDB tables (e.g. tens of thousands), but data dictionary size limit was not set in innodb_dict_size_limit
In other cases it is also possible that something other than MySQL uses a lot of memory. It could be a web server running in the same system, or a cron job script. Their activity can also eventually cause problems with MySQL.
Example: InnoDB buffer pool size was set too larger
mysql> show global variables like 'innodb_buffer_pool_size'; +-------------------------+------------+ | Variable_name | Value | +-------------------------+------------+ | innodb_buffer_pool_size | 5368709120 | +-------------------------+------------+ mysql> \! free -g total used free shared buffers cached Mem: 3 3 0 0 0 0 -/+ buffers/cache: 2 1 Swap: 3 0 3
MySQL allowed using 5GB for InnoDB buffer pool even though the server only had 3GB of RAM. Why? The system allowed it, while MySQL didn’t check. The example is of course a bit exaggerated and may not even be possible with never MySQL versions, but even if you set it just below the limit, eventually the effect could be similar. If for example many threads connected and started allocating and using their private buffers, the system would likely ran out of memory at some point.
Example: A running cron job
Often cron jobs are written in Perl, PHP, Python or another scripting language that allows implementing a more complex logic. Very frequently I see such jobs running on a database server rather than on a dedicated system for handling internal tasks. Those scripts sometimes load into memory a lot of data pulled out of MySQL tables. They often quickly grow to hundreds of megabytes and beyond, which may significantly affect the global memory usage, causing swapping or even trigger a OOM Killer action.
Analyse and fix MySQL configuration
By far the most important element of preventing memory allocation problems on a database server is creating a correct MySQL configuration. Setting any per-thread buffer to a hundred megabytes or more is a frequent bad practice that can very easily backfire. Global buffer sizes have to be sane as well and combined they must never be allowed to allocate more than approximately 80% of memory. In some cases the value that you specify in the configuration does not include any extra buffers or overhead. For instance InnoDB will typically use up to 5-10% more memory than set in innodb_buffer_pool_size.
Adjust OOM score
But taking care of MySQL configuration does not cover every contingency. There can always be some circumstances, such as a nasty cron job running, under which the system may decide to end some task. Fortunately there is a way to control the behaviour of OOM Killer. You can manually adjust the OOM score of each process independently. This is possible through /proc/[pid]/oom_score_adj (or /proc/[pid]/oom_adj on older systems).
mysql-server-01 ~ # cat /proc/$(pidof mysqld)/oom_score_adj 0
The range of values which oom_score_adj accepts is between -1000 and 1000 (or -17 to 15 for the deprecated interface that relies on oom_adj). Once it is set to any non-zero value, the score that oom_badness() calculates will be either reduced or increased by this manual adjustment. Setting it to the minimum possible value does not disable OOM Killer for the particular process, but simply rather reduces its chances for being chose to a minimum.
Here is the example of how it works:
mysql-server-01 ~ # cat /proc/$(pidof mysqld)/oom_score 24 mysql-server-01 ~ # echo '-20' > /proc/$(pidof mysqld)/oom_score_adj mysql-server-01 ~ # cat /proc/$(pidof mysqld)/oom_score 4
It is a pretty good way to tell the system to avoid killing the MySQL instance whenever there is a pressure to free memory. The problem is of course that if not MySQL, the biggest memory consumer, then what? It is why it may be important to adjust the score not only for mysqld process alone, but also for example for sshd, which often is nearly as critical application as database itself.
Starting from kernel 2.5.30 there is possibility to change the system behaviour during memory allocation. It was implemented to help fighting the issues related to the memory overcommitment. It can be controlled through /proc or sysctl:
mysql-server-01 ~ # ls -l /proc/sys/vm/overcommit_* -rw-r--r-- 1 root root 0 May 17 18:12 /proc/sys/vm/overcommit_memory -rw-r--r-- 1 root root 0 May 17 18:12 /proc/sys/vm/overcommit_ratio
The values /proc/sys/vm/overcommit_memory accepts:
- 0 – The default value. Linux kernel uses predefined heuristics when deciding whether to allow memory overcommit or not.
- 1 – Always allow overcommit. Back to the past when Linux was doing it by default.
- 2 – Prevents overcommit when certain limit is exceeded.
The other parameter – /proc/sys/vm/overcommit_ratio – affects the limit mentioned above. The limit is defined as swap size plus the percentage of memory equal to the value set in overcommit_ratio. For example:
mysql-server-01 ~ # free -g total used free shared buffers cached Mem: 3 3 0 0 0 0 -/+ buffers/cache: 2 1 Swap: 3 0 3 mysql-server-01 ~ # cat /proc/sys/vm/overcommit_ratio 50
The calculation is: 3GB of swap space + 50% of 3GB of RAM = 4.5GB. The system will not permit allocating more than 4.5GB and that’s the global limit (not per process). The limit and the actual usage can be checked in /proc/meminfo by looking at the CommitLimit and Committed_AS values.
Disabling memory overcommit is not really a common practice, because you have to be a lot more careful, while usually simply proper database configuration is enough to address all these problems. It should never be used on servers where MySQL shares the system resources with other applications such as web server software, because the configuration does not play well with applications that strongly rely on fork() call such as Apache or even Java applications.
So why is this important? When systems starts swapping it is usually bad for MySQL performance, but it is a condition that can be addressed relatively quickly and painlessly, with a database restart for example. A database crash caused by OOM Killer or a memory outage, on the other hand, is something that can turn into a nasty problem. First, a crash is not a controlled restart, so you do not have any time to prepare for it. Second, restart after a crash may take a lot longer than after a clean shutdown, because database needs to perform recovery. And finally, a crash can actually cause physical damage to data files, so your database may not be able recover at all.
Building a stable, reliable and highly performing MySQL server requires a lot of attention. You cannot just set the buffer sizes arbitrarily and assume it will do as it often leads to nasty problems. Do not ever trust tools that claim they can configure MySQL for you as they can only work on a very limited input and can only make very trivial assumptions.