At Days of Wonder we are huge fans of MySQL (and since about a year of the various Open Query, Percona, Google or other community patches), up to the point we’re using MySQL for about everything in production.

But since we moved to 5.0, back 3 years ago our production databases which hold our website and online game systems has a unique issue: the mysqld process uses more and more RAM, up to the point where the kernel OOM decide to kill the process.

You’d certainly think we are complete morons because we didn’t do anything in the last 3 years to fix the issue

Unfortunately, I never couldn’t replicate the issue in the lab, mainly because it is difficult to replicate the exact same load the production server sees (mainly because of the online games activity).

During those 3 years, I tried everything I could, from using other allocators, valgrind, debug builds and so on, without any success.

What is nice, is that we moved to an OurDelta build about a year ago, where InnoDB is able to print more memory statistics than the default MySQL version.

For instance it shows

Internal hash tables (constant factor + variable factor)
    Adaptive hash index 1455381240      (118999688 + 1336381552)
    Page hash           7438328
    Dictionary cache    281544240       (89251896 + 192292344)
    File system         254712  (82672 + 172040)
    Lock system         18597112        (18594536 + 2576)
    Recovery system     0       (0 + 0)
    Threads             408056  (406936 + 1120)
    innodb_io_pattern   0       (0 + 0)

Back several month ago, I analyzed this output just to see what figures were growing, and found that the Dictionary Cache variable part was increasing (slowly but definitely).

Sure fine MySQL experts would have been able to tell me exactly what, when and where the problem was, but since I’m not familiar with the code-base, I looked up what this number was and where it was increased (all in dict0dict.c) and added some logs each time it was increased.

I then installed this version for a quite long time (just to check it wouldn’t crash on production) on a slave server. But this server didn’t print anything interesting because it doesn’t see the exact same load the production masters.

A couple of months after that, I moved this code to one of the master and bingo! I found the operation and the tables exhibiting an increase:

mysqld[8131]: InnoDB: dict_table_rename_in_cache production/rank_tmp2 193330680 + 8112
mysqld[8131]: InnoDB: dict_table_rename_in_cache production/rank 193338792 + 8112

As soon as I saw the operation and table (ie rank), I found what the culprit is. We have a daemon that every 10s computes the player ranks for our online games.

To do this, we’re using the following pattern:

-- compute the ranks
SELECT NULL, playerID
FROM game_score as g
ORDER BY g.rankscore DESC
INTO OUTFILE "/tmp/rank_tmp.tmp"

-- load back the scores
LOAD DATA INFILE "/tmp/rank_tmp.tmp" INTO TABLE rank_tmp

-- swap tables so that clients see new ranks atomatically
RENAME TABLE rank TO rank_tmp2 , rank_tmp TO rank, rank_tmp2 TO rank_tmp

-- truncate the old ranks for a new pass
TRUNCATE TABLE rank_tmp

-- go back to the select above

You might ask why I’m doing a so much convoluted system, especially the SELECT INTO OUTFILE and the LOAD DATA. It’s just because INSERT … SELECT with innodb and binlog enabled can produce transactions abort (which we were getting tons of).

Back to the original issue, apparently the issue lies in the RENAME part of the daemon.

Looking at the dict0dict.c dict_table_rename_in_cache function we see:

ibool
dict_table_rename_in_cache(...)
...
  old_name = mem_heap_strdup(table->heap, table->name);
  table->name = mem_heap_strdup(table->heap, new_name);
...
}

Looking to mem_heap stuff, I discovered that each table has a heap associated in which InnoDB allocates various things. This heap can only grow (by block of 8112 bytes it seems), since the allocator is not a real one. This is done for performance reasons.

So each time we rename a table, the old name (why? since it is already allocated) is duplicated, along with the new name. Each time.

This heap is freed when the table is dropped, so there is a possibility to reclaim the used memory. That means this issue is not a memory leak per-se.

By the way, I’ve filed this bug on mysql bug system.

One work-around, beside fixing the code itself, would be to drop the rank table instead of truncating it. The issue with dropping/creating InnoDB table on a fast pace is that the dictionary cache itself will grow, because it can only grow as there is no way to purge it from old tables (except running one of the Percona patches). So the more tables we create the more we’ll use memory – back to square 0, but worst.

So right now, I don’t really have any idea on how to really fix the issue. Anyone having an idea, please do not hesitate to comment on this blog post

And please, don’t tell me to move to MyISAM…

When I wrote my previous post titled all about storedconfigs, I was pretty confident I explained everything I could about storedconfigs… I was wrong of course

A couple of days ago, I was helping some USG admins who were facing an interesting issue. Interesting for me, but I don’t think they’d share my views on this, as their servers were melting down under the database load.

But first let me explain the issue.

The issue

The thing is that when a client checks in to get its configuration, the puppetmaster compiles its configuration to a digestible format and returns it. This operation is the process of transforming the AST built by parsing the manifests to what is called the catalog in Puppet. This is this catalog (which in fact is a graph of resources) which is later played by the client.

When the compilation process is over, and if storedconfigs is enabled on the master, the master connects to the RDBMS, and retrieves all the resources, parameters, tags and facts. Those, if any, are compared to what has just been compiled, and if some resources differs (by value/content, or if there are some missing or new ones), they get written to the database.

Pretty straightforward, isn’t it?

As you can see, this process is synchronous and while the master processes the storedconfigs operations, it doesn’t serve anybody else.

Now, imagine you have a large site (ie hundreds of puppetd clients), and you decide to turn on storedconfigs. All the clients checking in will see their current configuration stored in the database.

Unfortunately the first run of storedconfigs for a client, the database is empty, so the puppetmaster has to send all the information to the RDBMS which in turns as to write it to the disks. Of course on subsequent runs only what is modified needs to reach the RDBMS which is much less than the first time (provided you are running 0.24.8 or applied my patch).

But if your RDBMS is not correctly setup or not sized for so much concurrent write load, the storedconfigs process will take time. During this time this master is pinned to the database and can’t serve clients. So the immediate effect is that new clients checking in will see timeouts, load will rise, and so on.

The database

If you are in the aforementioned scenario you must be sure your RDBMS hardware is properly sized for this peak load, and that your database is properly tuned.

I’ll soon give some generic MySQL tuning advices to let MySQL handle the load, but remember those are generic so YMMV.

Size the I/O subsystem

What people usually forget is that disk (ie those with rotating plates, not SSDs) have a maximum number of I/O operations per seconds. This value is for professional high-end disks about 250 IOP/s.

Now, to simplify, let’s say your average puppet client has 500 resources with an average of 4 parameters each. That means the master will have to perform at least 500 * 4 + 500 = 2500 writes to the database (that’s naive since there are indices to modify, and transactions can be grouped, etc.. but you see the point).

Add to this the tags, hmm let’s say an average of 4 tags per resources, and we have 500 * 4 + 500 + 500 * 4 = 4500 writes to perform to store the configuration of a given host.

Now remember our 250 IOP/s, how many seconds does the disk need to performs 4500 writes?

The answer is 18s!! Which is a high value. During this time you can’t do anything else. Now add concurrency to the mix, and imagine what that means.

Of course this supposes we have to wait for the disk to have finished (ie synchronous writing), but in fact that’s pretty how RDBMS are working if you really want to trust your data.

So the result is that if you want a fast RDBMS you must be ready to pay for an expensive I/O subsystem.

Size the I/O subsystem

That’s certainly the most important part of your server.

You need:

• fast disks (15k RPM, because they is a real latency benefit compared to 10k )
• the more spindle possible grouped in a sane RAID array like RAID10. Please forget RAID5 if you want your data to be safe (and fast writes). I saw too much horror stories with RAID5. I should really join the BAARF.
• a Battery Backed RAID Cache unit (that will absorb the fsyncs gracefully).
• Tune the RAID for the largest stripe size. Remove the RAID read cache if possible (innodb will take care of the READ cache with the innodb buffer pool).

If you don’t have this, do not even think turning on storedconfigs for a large site.

Size the RDBMS server

Of course other things matters. If the database can fit in RAM (the best if you don’t want to be I/O bound), then you obviously need RAM. Preferably ECC Registered RAM. Use 64 bits hardware with a 64 bits OS.

Then you need some CPU. Nowadays they’re cheap, but beware of InnoDB scaling issues on multi-core/multi-CPU systems (see below).

Tune the database configuration

Here is a checklist on how to tune MySQL for a mostly write load:

InnoDB of course

For concurrency, stability and durability reasons InnoDB is mandatory. MyISAM is at best usable for READ workload but suffers concurrency issues so it is a no-no for our topic

Tuned InnoDB

The default InnoDB settings are tailored to very small 10 years old servers…

Things to look to:

• innodb_buffer_pool_size. Usual advice says 70% to 80% of physical RAM of the server if MySQL is the only running application. I’d say that it depends on the size of the database. If you know you’ll store only a few MiB, no need to allocate 2 GiB . More information with this useful and intersting blog post from Percona guys.
• innodb_log_file_size. We want those to be the largest we can to ease the mostly write log we have. Once all the clients will be stored in the database we’ll reduce this to a something lower. The trade-off with large logs is the recovery time in case of crash. It isn’t uncommon to see several hundreds of MiB, or even GiB.
• innodb_flush_method = O_DIRECT on Linux. This is to prevent the OS to cache the innodb_buffer_pool content (thus ending with a double cache).
• innodb_flush_log_at_trx_commit=2. If your MySQL server doesn’t have any other use than for storedconfigs or you don’t care about the D in ACID. Otherwise use 0. It is also possible to temporarily change it to 2, and then move back to 0 when all clients have their configs stored.
• transaction-isolation=READ-COMMITTED. This one can help also, although I never tested it myself

Patch MySQL

The fine people at Percona or Ourdelta produces some patched builds of MySQL that removes some of the MySQL InnoDB scalability issues. This is more important on high concurrency workload on multi-core/multi-cpu systems.

It can also be good to run MySQL with Google’s perftools TCMalloc. TCMalloc is a memory allocator which scales way better than the Glibc one.

On the Puppet side

The immediate and most straightforward idea is to limit the number of clients that can check in at the same time. This can be done by disabling puppetd on each client (puppetd –disable), blocking network access, or any other creative mean…

When all the active hosts have checked in, you can then enable the other ones. This can be done hundreds of hosts at a time, until all hosts have a configuration stored.

Another solution is to direct some hosts to a special puppetmaster with storedconfigs on (the regular one still has storedconfigs disabled), by playing with DNS or by configuration, whatever is simplest in your environment. Once those hosts have their config stored, move them back to their regular puppetmaster and move newer hosts there.

Since that’s completely manual, it might be unpractical for you, but that’s the simplest method.

And after that?

As long as your manifests are only slightly changing, subsequent runs will see only a really limited database activity (if you run a puppetmaster >= 0.24.8). That means the tuning we did earlier can be undone (for instance you can lower the innodb_log_file_size for instance, and adjust the innodb_buffer_pool_size to the size of the hot set).

But still storedconfigs can double your compilation time. If you are already at the limit compared to the number of hosts, you might see some client timeouts.

The Future

Today Luke announced on the puppet-dev list that they were working on a queuing system to defer storedconfigs and smooth out the load by spreading it on a longer time. But still, tuning the database is important.

The idea is to offload the storedconfigs to another daemon which is hooked behind a queuing system. After the compilation the puppetmaster queues the catalog, where it will be unqueued by the puppet queue daemon which will in turn execute the storedconfigs process.

I don’t know the ETA for this interesting feature, but meanwhile I hope the tips I provided here can be of any help to anyone

Stay tuned for more puppet stories!