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MySQL 5.5.4 looks awesome.

April 15, 2010 8 comments

Been at the MySQL conference the last few days, and I have to say, I’m really blown away by MySQL 5.5.4‘s improvements.  Last year I keynoted and I begged Oracle on stage to realize that MySQL and InnoDB under one roof represented opportunity.  It’s clear they heard the community – this is some serious progress, and right when we needed it.

Jeremy Zawodny’s blog post covers most of the stuff I’m really excited about, and there are some great detailed technical slides here and here, but I wanted to go into a little more detail on one important improvment:  We’ve been plagued by MySQL’s undo slot limits for an awfully long time.  Basically, you could have 512 INSERT transactions and 512 UPDATE transactions running at once, for a grand total of 1024.  If you use INSERT … ON DUPLICATE KEY UPDATE, though, it takes two of those spots, meaning you get 512 concurrent transactions.  On modern hardware, it’s trivially easy to hit this limit.

I’ve had an Enterprise support ticket open for years on the issue, there’s been a MySQL bug for a long time, and there was basically no movement.  In fact, I’d gotten so frustrated about this issue, I’d basically decided this year was our last year of Enterprise MySQL support.  It was one of the sole reasons we paid for support for the last few years – the promise that a fix was just around the corner.  I felt good about voting with my dollars, and contributing back to a core technology we depend on, but enough was enough.

Lo and behold, it’s fixed!  You can now have a whopping 128K transactions in flight.  Best of all, it’s far more performant than it used to be!  And craziest of all?  If you run 5.5.4 on a database, then roll back to some older release, the change still takes effect.  Backwards bug and performance fixing – that’s a new one on me.

THANK YOU ORACLE!

Shameless plug – we’re hiring. And it’s a blast.

Categories: datacenter, MySQL Tags: , , , ,

My MySQL keynote slides and video

April 15, 2010 4 comments

Been asked a few times in the last few days about where my slides are from my MySQL keynote from *last* year.

Ooops.

Um, yeah.  Sorry about that.  Here’s a link to ‘The SmugMug Tale’ slides, and you can watch the video below:

Sorry for the extreme lag.  I suck.

The important highlights go something like this:

  • Use transactional replication.  Without it, you’re dead in the water. You have no idea where a crashed slave was.
  • Use a filesystem that lets you do snapshots.  Easily the best way to do backups, spin up new slaves, etc. I love ZFS.  You’ll need transactional replication to really make this painless.
  • Use SSDs if you can. We can’t afford to be fully deployed on SSDs (terabytes are expensive), but putting them in the write path to lower latency is awesome.  The read path might help, too, depending on how much caching you’re already doing.  Love hybrid storage pools.
  • Use Fishworks (aka Open Storage) if you can.  The analytics are unbeatable, plus you get SSDs, snapshots, ZFS, and tons of other goodies.
  • Use transactional replication. This is so important I’m repeating it.  Patch it into MySQL (Google, Facebook, and Percona have patches) or use XtraDB if you use replication.  We use the Percona patch.

Holler in the comments if something in the presentation isn’t clear, I’ll answer.  Apologies again.

Shameless plug – we’re hiring. And it’s a blast.

Great things afoot in the MySQL community

December 23, 2008 37 comments

tl;dr: The MySQL community rocks. Percona, XtraDB, Drizzle, SSD storage, InnoDB IO scalability challenges.

For anyone who lives and dies by MySQL and InnoDB, things are finally starting to heat up and get interesting. I’ve been banging the “MySQL/InnoDB scales poorly” drums for years now, and despite having paid Enterprise licenses, I haven’t been able to get anywhere. I was pretty excited when Sun bought MySQL since their future is intrinsically tied to concurrency, but things have been pretty slow going over there this year.

But the community has finally taken up arms and is fighting the good fight. It’s (finally!) a great time to be a MySQL user because there’s been lots of recent progress. Here’re some of my favorites (and highlights of work left to do):

PERCONA

I can’t sing Percona’s praises enough. They’re probably the most knowledgeable MySQL experts out there (possibly even including Sun). Absolutely the best bang for the buck in terms of MySQL service and support – better than MySQL’s own offering. (If I had to guess why that is, I’d bet that MySQL/Sun don’t want to step on Oracle’s toes by fixing InnoDB – but >99% of what we need is related to InnoDB. Percona has no such tip-toeing limitations.) Let me quickly count the ways they’ve helped me in the last few months:

  • They knew of a super obscure configuration setting “back_log“. Have you ever heard of it? I hadn’t. But we started seeing latency on MySQL connections (up to *3 seconds*!) on systems that hadn’t changed recently (exactly 3 seconds sounded awfully suspicious, and sure enough, it was TCP retries). After going through every single kernel, network, and MySQL tuning parameter I know (and I know a lot), I finally called Percona. They dug in, investigated the system, and unearthed ‘back_log’ within an hour or two. Popped that into my configuration and boom, everything was fine again. Whew!
  • We have servers that easily exceed InnoDB’s transaction limits. Did you know InnoDB has a concurrent transaction limit of 1024? (Technically, 1024 INSERTs and 1024 UPDATEs. But INSERT … ON DUPLICATE KEY UPDATE manages to chew up one of each). I know all about it – I’ve had bugs open with MySQL Enterprise for more than 2 years on the issue. What’s more, these are low-end systems – 4 cores, 16GB of RAM – and they’re no-where near CPU or IO bound. It took MySQL months to figure out what the problem was (years, really, to figure out all the final details like the different undo logs for INSERT vs UPDATE). Their final answer? It’ll be fixed in MySQL 6. 😦 Note that 5.1 *just* went GA after years and years. On the other hand, it took Percona one weekend to diagnose the problem, and 13 days to have a preliminary patch ready to extend it to 4072 undo slots. Talk about progress! (And yes, we want Percona to release the patch to the world)
  • Solving the CPU scaling problems. These have been plaguing us for years (we have had some older four-socket systems for awhile … now with quad-core, it’s even worse), and thanks to Google and Percona, this problem is well on its way to being solved. We’re sponsoring this work and can’t wait to see what happens next.
  • XtraDB. This is the biggy. So big it deserves its own heading….

XTRADB

Oracle’s done a terrible job of supporting the community with InnoDB. The conspiracy theorists can all say “I told you so! Oracle bought them to halt MySQL progress” now – history supports them. Which is a shame – Heikki is a great guy and has done amazing work with InnoDB, but the fact remains that it wasn’t moving forward. The InnoDB plugin release was disappointing, to say the least. It addressed none of the CPU or IO scalability issues the community has been crying about for years.

Luckily, Percona finally did what everyone else has been too afraid to do – they forked InnoDB. XtraDB is their storage engine, forked from InnoDB (and then turbocharged!). We’re not running it in production yet, but we are running all of the patches that went into XtraDB and I can tell you they’re great. We’re sponsoring more XtraDB development (and yes, we made sure Percona will be contributing anything they build for us back to the community) with Percona, and I’m sure that’ll continue.

DRIZZLE

I’ve already blogged a bit about Drizzle, but it sure looks like Drizzle + XtraDB might be a match made in heaven. Drizzle can be though of as a MySQL engine re-write with an eye towards web workloads and performance, rather than features. MySQL 4.1, 5.0, and 5.1 added a lot of features that bloated the code without offering anything really useful to web-oriented workloads like ours, so the Drizzle team is ripping all that stuff back out and rethinking the approaches to the things that are being left in. Very exciting.

SSD STORAGE

The advent of “cheap enough” super-fast SSD storage is finally upon us. I’ve got Sun S7410 storage appliances in production and they’re blazingly fast. I have a very thorough review coming, but the short version is that even with NFS latencies, we’re able to do obscene write workloads to these boxes (let alone reads). 10000+ write IOPS to 10TB of mirrored, crazy durable (thanks ZFS!) storage is a dream come true. Once you mix in snapshots, clones, replication, and Analytics – well, it just doesn’t get much better than this.

(Don’t get sticker shock looking at the web pricing – no-one pays anything even remotely like that. Sign up for Startup Essentials if you can, or talk to your Sun sales rep if you can’t, and you can get them much cheaper. I nearly had a heart attack myself until I got “real” pricing. Tell them I sent you – enough Sun people read this blog, it might just help 🙂 ).

STILL NEEDED…

So, all in all, there’s been an awful lot of progress this year, which is great. CPUs are finally scaling under InnoDB, and we finally have storage that isn’t bounded by physical rotation and mechanical arms. Unfortunately, great CPU scaling plus amazing IO capabilities isn’t something InnoDB digests very well. As is common in complicated systems, once you fix one bottleneck, another one elsewhere in the system crops up. This time, it’s IOPS. It was eerie reading Mark Callaghan’s post about this last night – I’d come to the exact same conclusions (from an Operations point of view rather than code-level) just yesterday.

Bottom line: Despite having ample CPU and ample IO, InnoDB isn’t capable of using the IO provided. You can bet we’ll be working with Percona, Google and Sun (read: sitting back and admiring their brilliant work while writing the occasional check and providing production workload information) to look into fixing this.

In the meantime, we’re back to the old standbys: replication and data partitioning. Yes, we’re stacking lots of MySQL instances on each S7410 to maximize both our IOPS and our budget. Fun stuff – more on that later. 🙂

UPDATE: Just occurred to me that there are plenty of *new* readers to my blog who haven’t heard me praise Google and their patches before. Mark Callaghan’s team over at Google definitely deserves a shout-out – they’ve really been a catalyst for much of this work along with Percona.

ZFS & MySQL/InnoDB Compression Update

October 13, 2008 26 comments
Network.com setup in Vegas, Thumper disk bay, green by Shawn Ferry

Network.com setup in Vegas, Thumper disk bay, green by Shawn Ferry

As I expected it would, the fact that I used ZFS compression on our MySQL volume in my little OpenSolaris experiment struck a chord in the comments. I chose gzip-9 for our first pass for a few reasons:

  1. I wanted to see what the “best case” compression ratio was for our dataset (InnoDB tables)
  2. I wanted to see what the “worst case” CPU usage was for our workload
  3. I don’t have a lot of time. I need to try something quick & dirty.

I got both those data points with enough granularity to be useful: a 2.12X compression ratio over a large & varied dataset, and the compression was fast enough to not really be noticeable for my end users. The next step, obviously, is to find out what the best ratio of compression and CPU is for our data. So I spent the morning testing exactly that. Here are the details:

  • Created 11 new ZFS volumes (compression = [none | lzjb | gzip1-9])
  • Grabbed 4 InnoDB tables of varying sizes and compression ratios and loaded them in the disk cache
  • Timed the time (using ‘ptime’) it took to read the file from cache and write it to disk (using ‘cp’), watching CPU utilization (using ‘top’, ‘prstat’, and ‘mpstat’)

It quickly became obvious that there’s relatively little difference in compression between gzip-1 and gzip-9 (and, contrary to what people were saying in the comments, relatively little difference between CPU usage, either, in 3 of the 4 cases. The other case, though… yikes!). So I quickly stopped even doing anything but ‘none’, ‘lzjb’, ‘gzip-1’, and ‘gzip-9’. (LZJB is the default compression for ZFS – gzip-N was added later as an option).

Note that all the files were pre-cached in RAM before doing any of the tests, and ‘iostat’ verified we were doing zero reads. Also note that this is writing to two DAS enclosures with 15 x 15K SCSI disks apiece (28 spindles in a striped+mirrored configuration) with 512MB of write cache apiece. So these tests complete very quickly from an I/O perspective because we’re either writing to cache (for the smaller files) or writing to tons of fast spindles at once (the bigger files). In theory, this should mean we’re testing CPU more than we’re testing our IO – which is the whole point.

I ran each ‘cp’ at least 10 times, letting the write cache subside each time, selecting the fastest one as the shown result. Here they are (and be sure to read the CPU utilization note after the tables):

TABLE1
compression size ratio time
uncompressed 172M 1 0.207s
lzjb 79M 2.18X 0.234s
gzip-1 50M 3.44X 0.24s
gzip-9 46M 3.73X 0.217s

Notes on TABLE1:

  • This dataset seems to be small enough that much of time is probably spent in system internals, rather than actually reading, compressing, and writing data, so I view this as only an interesting size datapoint, rather than size and time. Feel free to correct me, though. 🙂
TABLE2
compression size ratio time ratio
uncompressed 631M 1 1.064s 1
lzjb 358M 1.76X 0.668 1.59X
gzip-1 253M 2.49X 1.302 0.82X
gzip-9 236M 3.73X 11.1s 0.10X

Notes on TABLE2:

  • gzip-9 is massively slower on this particular hunk of data. I’m no expert on gzip, so I have no idea why this would be, but you can see the tradeoff is probably rarely worth it, even if were using precious storage commodities (say, flash or RAM rather than hard disks). I ran this one extra times just to make sure. Seems valid (or a bug).
TABLE3
compression size ratio time ratio
uncompressed 2675M 1 15.041s 1
lzjb 830M 3.22X 5.274 2.85X
gzip-1 246M 10.87X 44.287 0.34X
gzip-9 220M 12.16X 52.475 0.29X

Notes on TABLE3:

  • LZJB really shines here, performance wise. It delivers roughly 3X faster performance while also chewing up roughly 3X less bytes. Awesome.
  • gzip’s compression ratios are crazy great on this hunk of data, but the performance is pretty awful. Definitely CPU-bound, not IO-bound.
TABLE4
compression size ratio time ratio
uncompressed 2828M 1 17.09s 1
lzjb 1814M 1.56X 14.495s 1.18X
gzip-1 1384M 2.04X 48.895s 0.35X
gzip-9 1355M 2.09X 54.672s 0.31X

Notes on TABLE4:

  • Again, LZJB performs quite well. 1.5X bytes saved while remaining faster. Nice!
  • gzip is again very obviously CPU bound, rather than IO-bound. Dang.

There’s one other very important datapoint here that ‘ptime’ itself didn’t show – CPU utilization. On every run with LZJB, both ‘top’ and ‘mpstat’ showed idle CPU. The most I saw it consume was 70% of the aggregate of all 4 CPUs, but the average was typically 30-40%. gzip, on the other hand, pegged all 4 CPUs on each run. Both ‘top’ and ‘mpstat’ verified that 0% CPU was idle, and interactivity on the bash prompt was terrible on gzip runs.

Some other crazy observations that I can’t explain (yet?):

  • After a copy (even to an uncompressed volume), ‘du’ wouldn’t always show the right bytes. It took time (many seconds) before showing the right # of bytes, even after doing things like ‘md5sum’. I have no idea why this might be.
  • gzip-9 made a smaller file (1355M vs 1380M) on this new volume as opposed to my big production volume (which is gzip-9 also). I assume this must be due to a different compression dictionary or something, but it was interesting.
  • Sometimes I’d get strange error messages trying to copy a file over an existing one (removing the existing one and trying again always worked):

    bash-3.2# ptime cp table4.ibd /data/compression/gzip-1
    cp: cannot create /data/compression/gzip-1/table4.ibd: Arg list too long
  • After running lots of these tests, I wasn’t able to start MySQL anymore. It crashed on startup, unable to allocate enough RAM for InnoDB’s buffer pool. (You may recall from my last post that MySQL seems to be more RAM limited under OpenSolaris than Linux). I suspect that ZFS’s ARC might have sucked up all the RAM and was unwilling to relinquish it, but I wasn’t sure. So I rebooted and everything was fine. 😦

Conclusion? Unless you care a great deal about eking out every last byte (using a RAM disk, for example), LZJB seems like a much saner compression choice. Performance seem to improve, rather than degrade, and it doesn’t hog your CPU. I’m switching my ZFS volume to LZJB right now (on-the-fly changes – woo!) and will copy all my data so it gets the new compression settings. I’ll sacrifice some bytes, but that’s ok – performance is king. 🙂

Also, my theory that I’d always have idle CPU with modern multi-core chips so compression wouldn’t be a big deal seems to be false. Clearly, with gzip, it’s possible to hog your entire CPU if you’re doing big long writes. We don’t tend to do high-MB/s reads or writes, but it’s clearly something to think about. LZJB seems to be the right balance.

So, what should I test next? I wouldn’t mind testing compression latencies on very small reads/writes more along the lines of what our DB actually does, but I don’t know how to do that in a quick & dirty way like I was able to here.

Also, I have to admit, I’m curious about the different checksum options. Has anyone played with anything other than the default?

Success with OpenSolaris + ZFS + MySQL in production!

October 10, 2008 82 comments
Pimp My Drive by Richard and Barb

Pimp My Drive by Richard and Barb

There’s remarkably little information online about using MySQL on ZFS, successfully or not, so I did what any enterprising geek would do: Built a box, threw some data on it, and tossed it into production to see if it would sink or swim. 🙂

I’m a Linux geek, have been since 1993 (Slackware!). All of SmugMug’s datacenters (and our EC2 images) are built on Linux. But the current state of filesystems on Linux is awful, and it’s been awful for at least 8 years. As a result, we’ve put our first OpenSolaris box into production at SmugMug and I’ve been pleasantly surprised with the performance (the userland portions of the OS, though, leave a lot to be desired). Why OpenSolaris?

ZFS.

ZFS is the most amazing filesystem I’ve ever come across. Integrated volume management. Copy-on-write. Transactional. End-to-end data integrity. On-the-fly corruption detection and repair. Robust checksums. No RAID-5 write hole. Snapshots. Clones (writable snapshots). Dynamic striping. Open source software. It’s not available on Linux. Ugh. Ok, that sucks. (GPL is a double-edged sword, and this is a perfect example). Since it’s open-source, it’s available on other OSes, like FreeBSD and Mac OS X, but Linux is a no go. *sigh* I have a feeling Sun is working towards GPL’ing ZFS, but these things take time and I’m sick of waiting.

The OpenSolaris project is working towards making Solaris resemble the Linux (GNU) userland plus the Solaris kernel. They’re not there yet, but the goal is commendable and the package management system has taken a few good steps in the right direction. It’s still frustrating, but massively less so. Despite all the rough edges, though, ZFS is just so compelling I basically have no choice. I need end-to-end data integrity. The rest of the stuff is just icing on an already delicious cake.

The obvious first place to use ZFS was for our database boxes, so that’s what I did. I didn’t have the time, knowledge of OpenSolaris, or inclination to do any synthetic benchmarking or attempt to create an apples-to-apples comparison with our current software setup, so I took the quickest route I could to have a MySQL box up and running. I had two immediate performance metrics I cared about:

  • Can a MySQL slave on OpenSolaris with ZFS keep up with the write load with no readers?
  • If yes, can the slave shoulder its fair share of the reads, too?

Simple and to the point. Here’s the system:

  • SunFire X2200 M2 w/64GB of RAM and 2 x dual-core 2.6GHz Opterons
  • Dell MD3000 w/15 x 15K SCSI disks and mirrored 512MB battery-backed write caches (these are really starting to piss us off, but that’s another post…)

The quickest path to getting the system up and running resulted in lots of variables in the equation changing:

  • Linux -> OpenSolaris (snv_95 currently)
  • MySQL 5.0 -> MySQL 5.1
  • LVM2 + ext3 -> ZFS
  • Hardware RAID -> Software RAID
  • No compression -> gzip9 volume compression

Whew! Lots of changes. Let me break them down one by one, skipping the obvious first one:

MySQLMySQL 5.1 is nearing GA, and has a couple of very important bug fixes for us that we’ve been working around for an awfully long time now. When I downloaded the MySQL 5.0 Enterprise Solaris packages and they wouldn’t install properly, that made the decision to dabble with 5.1 even easier – the CoolStack 5.1 binaries from Sun installed just fine. 🙂

Going to MySQL 5.1 on a ~1TB DB is painful, though, I should warn you up front. It forced ‘REPAIR TABLE’ on lots of my tables, so this step took much longer than I expected. Also, we found that the query optimizer in some cases did a poor job of choosing which indexes to use for queries. A few “simple” SELECTs (no JOINs or anything) that would take a few milliseconds on our 5.0 boxes took seconds on our 5.1 boxes. A little bit of code solved the problem and resulted in better efficiency even for the 5.0 boxes, so it was a net win, but painful for a few hours while I tracked it down.

Finally, after running CoolStack for a few days, we switched (on advice from Sun) to the 5.1.28 Community Edition to fix some scalability issues. This made a huge difference so I highly recommend it. (On a side note, I wish MySQL provided Enterprise binaries for 5.1 for their paying customers to test with). The Google & Percona patches should make a monster difference, too.

Volume management and the filesystem – There’s some debate online as to whether ZFS is a “layering violation” or not. I could care less – it’s pure heaven to work with. This is how filesystems should have always been. The commands to create, manage, and extend pools are so simple and logical you basically don’t even need man pages (discovering disk names, on the other hand, isn’t easy. I finally used ‘format’ but even typing it gives me the shivers…). zpool create MYPOOL c0t0d0You just created a ZFS pool. Want a mirror? zpool create MYPOOL mirror c0t0d0 c0t0d1Want a striped mirror (RAID-1+0) w/spare? zpool create MYPOOL mirror c0t0d0 c0t0d1 mirror c0t0d2 c0t0d3 spare c0t0d4Want to add another mirror to an already striped mirror (RAID-1+0) pool? zpool add MYPOOL mirror c0t0d5 c0t0d6Get the idea? Super-easy. Massively easier than LVM2+ext3 where adding a mirror is at least 4 commands: pvcreate, vgextend, lvextend, resize2fs – usually with an fsck in there too.

Software RAID – This is something we’ve been itching for for quite some time. With modern system architectures and modern CPUs, there’s no real reason “storage” should be separate from “servers”. A storage device should be just a server with some open-source software and lots of disks. (The “open source” part is important. I’m sick of relying on closed-source RAID firmware). The amount of flexibility, performance, reliability and operational cost savings you can achieve with software RAID rather than hardware is enormous. With real datacenter-grade flash storage devices just around the corner, this becomes even more vital. ZFS makes all of this stuff Just Work, including properly adjusting the write caches on the disk, eliminating the RAID-5 write hole, etc. Our first box still has a battery-backed write-cache between the disks and the CPU for write performance, but all the disks are just exposed as JBOD and striped + mirrored using ZFS. It rocks.

Compression – Ok, so this is where the geek in me decided to get a little crazy. ZFS allows you to turn on (and off) a variety of compression mechanisms on-the-fly on your pool. This comes with some unknown (depends on lots of factors, including your workload, CPUs, etc) performance penalty (CPU is required to compress/decompress), but can have performance upsides too (smaller reads and writes = less busy disk).

InnoDB is notoriously bad at disk usage (we see 2X+ space usage using InnoDB) and while it’s not an enormous concern, it’d be something nice to curtail. On most of our DB boxes, we have idle CPU around (we’re not really I/O bound either – MySQL is a strange duck in that you can be concurrency bound without being either CPU or I/O bound fairly easily thanks to poor locking), so I figured I’d go wild and give it a shot.

Lo and behold, it worked! We’re getting a 2.12X compression ratio on our DB, and performance is keeping up just fine. I ran some quick performance tests on large linear reads/writes and we were measuring 45.6MB/s sustained uncompression and 39MB/s sustained compression on a single-threaded app on an Opteron CPU. We’ll probably continue to test compression stuff, and of course if we run into performance bottlenecks, we’ll turn it off immediately, but so far the mad science experiment is working.

Configuration

Configuring everything was relatively painless. I bounced a few questions off of Sun (imho, this is where Sun really shines – they listen to their customers and put technical people with real answers within arms reach) and read the Evil Tuning Guide to ZFS. In the end I really only ended up tweaking two things (plus setting compression to gzip-9):

  • I set the recordsize to match InnoDB’s – 16KB. zfs set recordsize=16K MYPOOL
  • I turned off file-level prefetching. See the Evil Tuning Guide. (I’m testing with this on, now, and so far it seems fine).

I believe since ZFS is fully checksummed and transactional (so partial writes never occur) I can disable InnoDB’s doublewrite buffer. I haven’t been brave enough to do this yet, but I plan to. I like performance. 🙂

Performance

This box has been in production in our most important DB cluster for two weeks now. On the metrics I care about (replication lag, query performance, CPU utliization, etc) it’s pulling its fair share of the read load and keeping completely up on replication. Just eyeballing the stats (we haven’t had time to number crunch comparison stats, though we gave some to Sun that I’m hoping they crunch), I can’t tell a difference between this slave and any of the others in the cluster running Linux. I sure feel a lot better about the data integrity, though.

Why not [insert other OS here]?

We could have gone with Nexenta, FreeBSD, Mac OS X, or even *gulp* tried ZFS on FUSE/Linux. To be honest, Nexenta is the most interesting because it actually *is* the Solaris kernel plus Linux userland, exactly what I wanted. I’ve played with it a tiny bit, and plan to play with it more, but this is a mission-critical chunk of data we’re dealing with, so I need a company like Sun in my corner. I find myself wishing Sun had taken the Nexenta route (or offered support for it that I could buy or something). Instead, we’ll be buying software service & support from Sun for this and any other mission-critical OpenSolaris boxes.

FreeBSD also doesn’t have the support I need, Mac OS X wasn’t performant enough the last time I fiddled with it as a server, and most FUSE filesystems are slow so I didn’t even bother.

Gotchas

  • On my 64GB Linux boxes, I give InnoDB 54GB of buffer pool size. With otherwise exactly the same my.cnf settings, MySQL on OpenSolaris crashes with anything more than 40GB. 14GB, or 21.9% of my RAM, that I can’t seem to use effectively. Sun is looking into this, I’ll let you know if I find anything out.
  • For a Linux geek, OpenSolaris userland is still painful. Bear in mind that this is a single-purpose box, so all I really want to do is install and configure MySQL, then monitor the software and hardware. If this were a developer box, I would have already given up. OpenSolaris is still very early, so I’m still hopeful, but be prepared to invest some time. Some of my biggest peeves:
    • Common commands, like ‘ps’, have very different flags.
    • Some GNU bins are provided in /usr/gnu/bin – but a better ‘ps’ is missing, as is ‘top’ (no, ‘prstat’ is *not* the same!), ‘screen’, etc (Can anyone even use remote command-line Unix boxes without ‘screen’? If so, how?)
    • Packages are crazily named, making finding your stuff to install tough. Like instead of Apache being called ‘apache’ or ‘httpd’, it’s called ‘SUNWapch’. What?
    • After finally figuring out how to search for packages to get the names (‘pkg search -r Apache’ – which doesn’t provide pleasant results), I discovered that ‘top’ and ‘screen’ just simply aren’t provided (or they’re named even worse than I thought). Instead, I had to go to a 3rd party repository, BlastWave, to get them. And then, of course, the ‘top’ OpenSolaris package wouldn’t actually install and I had to manually break into the package and extract the binary. Ugh.

Whew! Big post, but there was a lot of ground to cover. I’m sure there are questions, so please post in the comments and I’ll try to do a follow-up. As I fiddle, tweak, and change things I’ll try to post updates, too – but no promises. 🙂

UPDATE: One other gotcha I forgot to mention. When MySQL (or, presumably, anything else running on the box) gets really busy, user interactivity evaporates on OpenSolaris. Just hitting enter or any other key at a bash prompt over SSH can take many seconds to register. I remember when Linux had these sort of issues in the past, but had blissfully forgotten about them.

UPDATE: I went more in depth on ZFS compression testing and blogged the results. Enjoy!

MySQL and the Linux swap problem

Ever since Peter over at Percona wrote about MySQL and swap, I’ve been meaning to write this post. But after I saw Dathan Pattishall’s post on the subject, I knew I’d better actually do it. 🙂

There’s a nasty problem with Linux 2.6 even when you have a ton of RAM. No matter what you do, including setting /proc/sys/vm/swappiness = 0, your OS is going to prefer swapping stuff out rather than freeing up system cache. On a single-use machine, where the application is better at utilizing RAM than the system is, this is incredibly stupid. Our MySQL boxes are a perfect example – they run only MySQL and we want InnoDB to have a lot of RAM (32-64GB … and we’re testing 128GB).

You can’t just not have any swap partitions, though, or kswapd will literally dominate one of your CPU cores doing who-knows-what. But you can’t have it swapping to disk, or your performance goes into the toilet. So what to do?

Our solution is to make swap partitions out of RAM disks. Yes, I realize how insane that sounds, but the Linux kernel’s insanity drove us to it. Best part? It works. Here’s how:

mkdir /mnt/ram0
mkfs.ext3 -m 0 /dev/ram0
mount /dev/ram0 /mnt/ram0
dd bs=1024 count=14634 if=/dev/zero of=/mnt/ram0/swapfile
mkswap /mnt/ram0/swapfile
swapon /mnt/ram0/swapfile

That’ll give you a 14MB swap partition that’s actually in RAM, so it’s super-fast. This assumes your kernel is creating 16MB ramdisk partitions, but you can adjust your kernel paramenters and/or the ‘dd’ line above to suit whatever size you want.

We’ve found that anywhere from 20MB-40MB tends to be enough (so use /dev/ram1, /dev/ram2, etc), depending on load of the box. kswapd no longer uses any noticeable CPU, there’s always a few MB of free “swap”, and life is back in the fast lane. Just add those lines to your relevant startup file, like /etc/rc.d/rc.local, and it’ll persist after reboots.

Some Linux purists will probably hate this approach, others may have more efficient ways of achieving the same thing, but this works for us. Give it a shot. 🙂

Oh, and I hope it goes without saying, but make *darn* sure you know what you’re running on your box and what the maximum RAM footprint will be before you try running with only 20-40MB of swap. We’ve never OOMed (Out-Of-Memory) a production MySQL box – but that’s because we’re careful.

UPDATE: See what happens when I wait to blog? I forget that I read another related post over on Kevin Burton’s blog. Like Kevin, we’re using O_DIRECT, but unlike Kevin, this doesn’t solve the problem for us. Linux still swaps. We use the latest 2.6.18-53.1.14.el5 kernel from CentOS 5, btw. (Sorry, had posted 2.6.9 because I was dumb. We’re fully patched)

Categories: datacenter, MySQL Tags: , , , , , , ,

Death of MySQL read replication highly exaggerated

April 16, 2008 4 comments

I know I’m a little late to the discussion, but Brian Aker posted a thought-provoking piece on the imminent death of MySQL replication to scale reads.  His premise is that memcached is so cool and scales so much better, that read replication scaling is going to become a think of the past.  Other MySQL community people, like Arjen and Farhan, chimed in too.

Now, I love memcached.  We use it as a vital layer in our datacenters, and we couldn’t live without it.  But it’s not a total solution to all reads, so at least for our use case, it’s not going to kill our replica slaves that we use to scale reads.  

Why?  Because we still need to do index lookups to get the keys that we can extract from memcached.  And we have to do lots of those indexed queries.  Most of the row data lives inside of memcached, so this turns out to be a great solution, but we still need read slaves to provide the lists of keys.  Bottom line is that we still use read replication heavily – but we use it for different things that we did in years past.

And then, of course, there’s the issue of memcached failure.  For us, it’s very rare, and thanks to the way memcached works, it rarely hampers system performance, but when a node fails and needs to be re-filled, we have to go back to disk to get it.  And doing that efficiently means read slaves again.

For us, memcached plus MySQL replication is true magic.  Brian’s a very smart guy, and I realize he wrote the post to get people thinking and talking about the issue, but at least for us, read slaves are here to stay. 🙂

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