This was written by Bryan Horstmann-Allen
An Intermediate Guide to Saving Your Butt
ZFS is a pooled storage filesystem with many advanced features. This article will describe several circumstances where trivial ZFS usage can aid you, as a systems administrator or developer, immensely.
(There has been a fair amount of branching between Oracle ZFS and the open version of ZFS curated by illumos, but none of those changes will be relevant in this article.)
In ZFS, we refer to a single set of storage as a "pool." The pool can be one disk, or a group of disks, or several groups of disks in any number of configurations.
A single group of disks is referred to as a "vdev."
The pool contains "datasets." A dataset may be a native ZFS filesystem or it may be a block device (referred to as a "zvol.")
ZFS supports mirrors and various RAID levels. The latter are referred to as "RAIDZ1", "RAIDZ2", and RAIDZ3." The number denotes how many disks the vdev can lose before the pool becomes corrupt.
If you're more familiar with Linux filesystems, ZFS condenses the facilities offered by standard filesystems like
XFS, and so on,
md, and LVM into a single package. However, it also contains many features simply not found in that stack. On Linux,
btrfs has been trying to catch up, but ZFS has been around for coming up on a decade, so they have a long way to go yet.
By default, ZFS enables block-level checksumming: each block in the pool has an associated checksum. If you have data silently corrupted by disk firmware, neutrons moseying by, co-workers playing with
dd, you’ll hear about it.
If you are working in a redundant pool configuration (and in production, you will be) a
zpool scrub will auto-heal any corrupted data.
If an application tries to read a corrupted block and you’re running redundant, it will go read a good block instead. And because ZFS loves you, it will then quietly repair the bad block.
ZFS was developed at Sun Microsystems, where engineers had a deep and somewhat disturbing love of numbers. They loved keeping metrics and stats for everything, and then giving you, the administrator, access to them. Typically these are exposed via the generic
# kstat -l | wc -l 42690
ZFS is no different. It exposes several hundred metrics to
arc_summary.pl is something I keep handy on all my Solarish systems. There is an overview of the ARC below.
You can also use command-line utilities on Solarish derivatives like
fsstat zfs and, more recently,
arcstat to get live usage information.
For other versions of ZFS, look to the locally preferred method of exposing kernel stats to userland for your ARC stats (
sysctl, for instance.)
When New Users Say ZFS Sucks
You may notice some other behaviors with ZFS that you may not find with other filesystems: it tends to expose poorly behaving HBAs, flapping disks, bad RAM, and subtly busted firmware. Other filesystems will not surface these problems. They way these issues are exposed tends to be in hung disks, or pools whose files regularly become corrupted (even if ZFS recovers from those issues.)
Some new users complain about this ("It works fine with
foofs!"), but it is far better to be aware that your hardware is having problems. Run
zpool scrub periodically. Get your due diligence in, and sleep more soundly as a result.
Blissful ignorance stops being so blissful when you're blissfully losing customer data.
Bottlenecking on Disk I/O
In serverland, you’ll find you tend to have more CPU than I/O if your application is
iops heavy. Your applications will end up waiting on disk operations instead of doing useful work, and your CPU will sit around watching Vampire Diaries in its free cycles rather than crunching numbers for your customers.
When your applications and users are suffering, it's good to have options. Depending on your workload, ZFS gives you several easy performance wins.
ZFS provides a Separate Log Device (the “slog” or “write log”) to offload the ZFS Intent Log to a device separate from your ZFS pool.
zpool add tank log c1t7d0p1
In effect, this allows you to ship all your synchronous writes to a very fast storage device (SSD), rather than waiting for your I/O to come back from a slower backing store (SATA or SAS). The slog tends to not get very full (a few dozens of megabytes, at most) before it flushes itself to the backing store, but your customers won’t feel that. Once the data hits the slog, the application returns, and the customer doesn’t feel the latency of your slower but much larger SATA disks.
ZFS also batches async writes together into an atomic Transaction Group every 5 or 30 seconds, depending on your version. This not only ensures data should always be consistant on disk (though perhaps not immediately up to date!), but it gives you a heavy performance boost for applications not calling
If the txg fails to write due to a power outage or the system panicking or so forth, you’ll get the most recently known-good transaction. Thus, no
fsck in ZFS.
ZFS also has a main memory filesystem cache called the Adaptive Replacement Cache. The ARC both stores recently accessed data in main memory, and also looks at disk usage pattern and prefetches data into RAM for you. The ARC will use all available memory on the system (it's not doing anything else anyway), but will shrink when applications start allocating memory.
You can also add extra cache devices to a ZFS pool, creating a Layer 2 ARC (L2ARC):
zpool add tank cache c2d0
The caveat for the L2ARC is it consumes main memory for housekeeping. So even if you attach a very fast, battery-backed Flash device as an L2ARC, you may still lose out if L2ARC consumes too many blocks of main memory as L2ARC pointers. (My rule of thumb, which may be out of date, is that each 100GB of L2ARC will utilize 1GB of ARC. So keep that in mind.)
Much like the main ARC, L2ARC is volatile cache: It's lost on reboot, and will take some time to re-warm.
You can view both slog and L2ARC usage in the output of
zpool iostat -v.
However, there is an even simpler way to get more performance out of your disks:
zfs compression=on tank
You can enable compression on a per-dataset level, or at the pool level. The latter will cause all child datasets to inherit the value.
ZFS supports two compression algorithms:
lzjb, which is a light-weight but very fast streaming block compression algorithm, and
gzip. I enable
lzjb on all my pools by default, and have since 2007.
Modern CPUs are ridiculously fast, and disks (even 6Gb/s 15k SAS) are rather slow comparatively. If you’re doing a lot of I/O, you can get a simple but impressive performance win here.
You also get a nice bonus: More disk utilization. On a simple RAIDZ1 SmartOS compute node storing mostly VM block devices, I’m getting 1.48x compression ratio using
lzjb. So out of my 667GB SAS pool, I’m actually going to get around a terabyte of actual capacity.
gzip level is 6 (as you’d get by running the command itself). For my logserver datasets, I enable
gzip and get an impressive compression ratio of 9.59x. The stored value? 1TB. Actual uncompressed? Almost 10TB. I could enable gzip-9 there and get more disk space at the cost of CPU time.
A couple years ago at a previous gig, we were rewriting 30,000
sqlite files as quickly as possible from a relatively random queue. For each write, you read the whole file into memory, modify it, and then write the whole thing out.
As initially deployed, this process was taking 30-60m to do a complete run. Users were not too happy to have their data be be so far out of date, as when they actually needed something it tended to be an item less than a few minutes old.
Once we enabled compression, well, you can see here.
A job going from around an hour to a minute or less by running one command? Not bad. We also minimized the I/O workload for this job, which was very helpful for a highly multi-tenant system.
We later parallelized the process, so it now it takes only a few seconds to complete a run.
The caveat with compression is what when you send a compressed stream (described below), you lose compression. You can compress inline through a pipe, but the blocks will be written uncompressed on the other side. Something to keep in mind when moving large datasets around!
Compression only works on new writes. If you want old data to be compressed, you'll need to move the files around yourself.
ZFS gives you an unlimited number of atomic dataset-level snapshots. You can also do atomic recursive snapshots for a parent dataset and all its children.
zfs snapshot tank/kwatz@snapshot
For application data, I tend to take snapshots every five minutes via a
cron job. Depending on the backing disk space and how often the data changes, this means I can keep snapshots -- local to the application -- around for a few hours, or days, or months.
For simple centralized host backups, I tend to use something like this:
#!/bin/bash source /sw/rc/backups.common now=`/bin/date +%Y%m%d-%H%M` HOSTS=" host1 host2 host3 ... " DIRS="/etc /root /home /export/home /mnt/home /var/spool/cron /opt" for HOST in $HOSTS ; do echo "==> $HOST" /sbin/zfs create -p $BACKUP_POOL/backups/hosts/$HOST /sbin/zfs snapshot $BACKUP_POOL/backups/hosts/$HOST@$now for DIR in $DIRS; do rsync $RSYNC_OPTIONS --delete root@$HOST:$DIR /var/backups/hosts/$HOST/ done /sw/bin/print_epoch > /var/run/backups/host-$HOST.timestamp done /sw/bin/print_epoch > /var/run/backups/hosts.timestamp
So the root of your backups is always the most recent version (note
rsync --delete). Not only are we only transferring the changed files, we're only storing the changed blocks in each snapshot.
We also touch some local files when the backup completes, so we can both graph backups latency and alert on hung or stale backup jobs.
Getting access to the snapshots is trivial as well: There is a hidden
.zfs/snapshot/ directory at the root of every dataset. If you go looking in there, you’ll find all your snapshots and the state of your files at that snapshot.
# cd /var/backups/hosts/lab-int # ls -l .zfs/snapshot | head total 644 drwxr-xr-x 7 root root 7 Aug 31 22:04 20120901-2200/ drwxr-xr-x 7 root root 7 Sep 1 22:03 20120902-2200/ drwxr-xr-x 7 root root 7 Sep 2 22:03 20120903-2200/ ... # ls -l etc/shadow ---------- 1 root root 2043 Oct 12 00:22 etc/shadow # ls -l .zfs/snapshot/20120901-2200/etc/shadow ---------- 1 root root 1947 Jul 30 13:13 .zfs/snapshot/20120901-2200/etc/shadow
It makes building recovery processes rather painless. If you have customers who often delete files they’d rather not, for instance, this is a very simple win for both you (whose mandate as the administrator is to never lose customer data) and the customer (whose mandate is to lose data that is most valuable to them at the least opportune moment).
Make sure you set up your purging scripts, however, or months down the line you might find you've used up all your disk space with snapshots. They're both additive and addictive.
So local snapshots are awesome, but ZFS does you one better:
zfs send tank/kwatz@snapshot | ssh backups1 zfs recv -vdF tank
That will send that one snapshot to another system. That particular command will overwrite any datasets named
kwatz on the target.
However, why only keep one snapshot, when you can ship every snapshot you’ve taken of a dataset and all of its children, off-system or off-site entirely?
And you don’t actually want to send the entire dataset every time, for obvious reasons, so ZFS handily provides deltas in the form of ZFS incremental sends:
#!/bin/bash -e REMOTE_POOL=tank2 LOCAL_POOL=tank TARGET_HOST=foo LAST_SYNCED=$( ssh $TARGET_HOST zfs list -t snapshot -o name -r $REMOTE_POOL/zones/icg_db/mysql | tail -1 ) echo "r: $LAST_SYNCED" LAST_SNAPSHOT=$( zfs list -t snapshot -o name -r tank/zones/icg_db/mysql | tail -1 ) echo "l: $LAST_SNAPSHOT" # In case the target/source pool names are different. RENAMED_STREAM=$( echo $LAST_SYNCED | sed -e "s/$REMOTE_POOL/$LOCAL_POOL/" ) echo "s: $RENAMED_STREAM : $LAST_SNAPSHOT -> $REMOTE_POOL" zfs send -vI $RENAMED_STREAM $LAST_SNAPSHOT | ssh $TARGET_HOST zfs recv -vdF $REMOTE_POOL
I tend to ship all my snapshots to a backup host. Mail stores, databases, user home directories, everything. It all constantly streams somewhere. The blocks tend to already be hot in the ARC, so performance impact is generally very light.
It’s also trivial to write a rolling replication script that constantly sends data to another host. You might use this technique when your data changes so often (I have one application that writes about 30GB of data every run) you can’t actually store incremental snapshots.
Here’s a very naive example that has served me pretty well over the years.
Finally, need full offsite backups? Recursive incremental sends from your backup host.
By this point I hope you’re getting the idea that ZFS provides many facilities -- all of them easy to understand, use, and expand upon -- for saving your butt, and your customers data.
In addition to snapshots, ZFS lets you create a clone of a snapshot. In version control terminology, a clone is a branch. You still have your original dataset, and you’re still writing data to it. And you have a snapshot -- a set of blocks frozen in time -- and now you can create a clone of those frozen blocks, modify them, destroy them.
This gives you a way of taking live data and easily testing against it. You can perform destructive or time-consuming actions, without impacting production. You can time how long a database schema change might take, or you ship a snapshot of your data to another system, clone it, and perform analysis without impacting performance on your production systems.
Eric Sproul gave a talk at ZFS Days this year about just that topic.
Database Snapshots and Cloning
In the same vein, one of my favorite things is taking five minute snapshots of MySQL and Postgres, shipping all those snapshots off-system, and keeping them forever.
For most production databases, I can also keep about a days worth of snapshots locally to the master... so if someone does a “DROP DATABASE” or something, I can very quickly revert to the most recent snapshot on the system, and get the database back up.
We only lose a few minutes of data, someone has to buy some new undies, and you don’t have to spend hours (or days) reimporting from your most recent dump.
The best part about this bacon-saving process is how trivial it is. Here’s a production MySQL master:
# zfs list tank/zones/icg_db/mysql NAME USED AVAIL REFER MOUNTPOINT tank/zones/icg_db/mysql 54.8G 184G 46.8G /var/mysql # zfs list -t snapshot -r tank/zones/icg_db/mysql | tail -1 tank/zones/icg_db/mysql@20121202-1005 15.6M - 46.8G - # zfs clone tank/zones/icg_db/mysql@20121202-1005 tank/database # zfs list tank/database NAME USED AVAIL REFER MOUNTPOINT tank/database 1K 184G 46.8G /tank/database # zfs set mountpoint=/var/mysql tank/database
So we've got our data cloned and mounted. Now we need to start MySQL. Once we do so, InnoDB will run through its crash recovery and replay from its journal.
# ./bin/mysqld_safe --defaults-file=/etc/my.cnf # tail -f /var/log/mysql/error.log 121202 10:11:37 mysqld_safe Starting mysqld daemon with databases from /var/mysql ... 121202 10:11:41 InnoDB: Database was not shut down normally! InnoDB: Starting crash recovery. ... 121202 10:11:50 [Note] /opt/mysql/bin/mysqld: ready for connections.
MySQL is now running with the most recent snapshot of the database we have.
# mysql Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 3 to server version: 5.5.27-log Type 'help;' or '\h' for help. Type '\c' to clear the buffer. mysql>
This entire process took under a minute by hand.
Being able to very quickly spin up snapshots of any journaled datastore has been incredibly helpful over the last few years, for accident remediation, troubleshooting, and performance analysis.
My Next Projects
At work, we’re building a malware lab on top of Joyent’s SmartDatacenter (which runs SmartOS). There are many pieces involved here, but one of the biggest ones is the ability to take a Windows VM image, install software on it, and then clone it 20 times and run various malware through it.
With ZFS, this is as trivial as taking a snapshot of the dataset, cloning it 20 times, and booting the VMs stored on those volumes.
(There are many other facilities that aid in this process in SmartOS, namely the way they’ve wrapped Solaris Zones with vmadm, but that’s perhaps for another article!)
This facility would also make it trivial for us to implement something a lot like AWS’s Elastic MapReduce:
Spin a master node, 20 slaves, and just keep track of which job(s) they’re working on. When its done, terminate the VMs the ZFS datasets are backing, and destroy the clones.
Wash, rinse, repeat, and all the lower-level heavy lifting is done with a handful of ZFS commands.
These processes have all saved multiple butts. More importantly, they have helped us ensure services customers rely upon are not impacted by system failure or accidents.
ZFS’s power lies not only in its many features, safety mechanisms or technical correctness, but in the ways it exposes its features to you.
ZFS is a UNIX tool in the truest sense. It allows you to build powerful and flexible solutions on top of it, without the gnashing of teeth and tedium you might find in other solutions.
(Much thanks to @horstm22, @rjbs, @jmclulow, and @richlowe for help with this article.)