Archive for the 'direct I/O' Category

Apple OS/X with ZFS Shall Rule the World.

The problem with blogging is there is no way to clearly write something tongue-in-cheek. No facial expressions, no smirking, no hand waving—just plain old black and white. That aside, I can’t resist posting a follow-up to a post on StorageMojo about ZFS performance. I’m not taking a swipe at StorageMojo because it is one of my favorite blogs. However, every now and again third party perspective is a healthy thing. The topic at hand is this post about ZFS performance which is a digest of this post on digitalbadger.net. The original post starts with:

I have seen many benchmarks indicating that for general usage, ZFS should be at least as fast if not faster than UFS (directio not withstanding)[…]

Right off the bat I was scratching my head. I have experience with direct I/O dating back to 1991 and have some direct I/O related posts here. I was trying to figure out what the phrase “directio not withstanding” was supposed to mean. The only performance boost direct I/O can possibly yield is due to the elimination of the write-ordering locks generally imposed by POSIX and the double-buffering/memcopys overhead associated with the DMA into the page cache—and subsequent copy into the user address space buffer. So for a read-intensive benchmark, about the only thing one should expect is less processor overhead, not increased throughput.

Anyway, the post continues:

To give a little background : I have been experiencing really bad throughput on our 3510-based SAN. The hosts are X4100s, 12Gb RAM, 2x dual core 2.6Ghz opterons and Solaris 10 11/06. They are each connected to a 3510FC dual-controller array via a dual-port HBA and 2 Brocade SW200e switches, using MxPIO. All fabric is at 2Gb/s […]

OK, the 4100 is a 2-way Opteron 2000 server which, being a lot like an HP Proliant DL385, should have no problem consuming all the data the 2 x 2Gb FCP paths can deliver—roughly 400MB/s. If the post is about a UFS versus ZFS apples-apples comparison, both results should top out at the maximum theoretical throughput of 2x2Gb FCP. The post continues:

On average, I was seeing some pretty average to poor rates on the non-ZFS volumes depending on how they were configured, on average I was seeing 65MB/s write performance and around 450MB/s read on the SAN using a single drive LUN.

I’d be plenty happy with that 450MB/s given that configuration. The post insinuates this is direct I/O so getting 450MB/s out of 2x2Gb FCP should be the end of the contest. Further, getting 65MB/s written to a single drive LUN is quite snappy! What is left to benchmark? It seems UFS cranks the SAN at full bandwidth.

The post continues:

I then tried ZFS, and immediately started seeing a crazy rate of around 1GB/s read AND write, peaking at close to 2GB/s. Given that this was round 2 to 4 times the capacity of the fabric, it was clear something was going awry.

Hmmm…Opterons with HT 2.0 attaining peaks of 2GB/s write throughput via 2x2Gb FCP? There is nothing awry about this, it is simply not writing the data to the storage.

This is a memory test. ZFS is caching, UFS is not.

The post continues:

Many thanks to Greg Menke and Tim Bradshaw on comp.unix.solaris for their help in unravelling this mystery!

What mystery? I’d recommend testing something that runs longer than 1 second (reading 512MB from memory on any Opteron system should take about .6 seconds. I’d recommend a dataset that is about 2 fold larger than physical memory.

How Does ZFS and UFS Compare to Other Filesystems?
Let’s take a look at an equivalent test running on the HP Cluster Gateway internal filesystem (the former PolyServe PSFS). The following is a screen shot of a dd(1) test using real Oracle datafiles on a Proliant DL585. The first test executes a single thread of dd(1) reading the first 512MB of one of the files using 1M read requests. The throughput is 815MB/s. Next, I ran 2 concurrent dd(1) processes each chomping the first 512MB out of two different Oracle datafiles using 1MB read requests. In the concurrent case, the throughput was 1.46GB/s.

NOTE: You may have to right click->view the image.

zfsthing1.jpg

But wait, in order to prove the vast superiority of the HP Cluster Gateway filesystem over ZFS using similar hardware, I fired off 6 concurrent dd(1) processes each reading the first 512MB out of six different files. Here I measured 2.9GB/s!

zfsthing2.jpg

SAN Array Cache and Filers Hate Sequential Writes

Or at least they should.

Jonathan Lewis has taken on a recent Oracle-l thread about Thinking Big. It’s a good blog entry and I recommend giving it a read. The original Oracle-l post read like this:

We need to store apporx 300 GB of data a month. It will be OLTP system. We want to use 
commodity hardware and open source database. we are willing to sacrifice performance 
for cost. E.g. a single row search from 2 billion rows table should be returned in 2 sec.

I replied to that Oracle-l post with:

Try loading a free Linux distro and typing :
man dbopen
man hash
man btree
man mppo
man recno

Yes, I was being sarcastic, but on the other hand I have been involved with application projects where we actually used these time-tested “database” primitives…and primitive they are! Anyway, Jonathan’s blog entry actually took on the topic and covers some interesting aspects. He ends with some of the physical storage concepts that would likely be involved. He writes:

 

SANs can move large amounts of data around very quickly – but don’t work well with very large numbers of extremely random I/Os. Any cache benefit you might have got from the SAN has already been used by Oracle in caching the branch blocks of the indexes. What the SAN can give you is a write-cache benefit for the log writer, database writer, and any direct path writes and re-reads from sorting and hashing.

Love Your Cache, Hate Large Sequential Writes
OK, this is the part about which I’d like to make a short comment—specifically about Log Writer. It turns out that most SAN arrays don’t handle sequential writes well either. All told, arrays shouldn’t be in the business of caching sequential writes (yes, there needs to be a cut-off there somewhere). I’ve had experiences with some that don’t cache sequential writes and that is generally good. I’ve had experiences with a lot that do and when you have a workload that generates a lot of redo, LGWR I/O can literally swamp an array cache. Sure, the blocks should be cached long enough for the write back to disk, but allowing those blocks to push into the array cache any further than the least of the LRU end makes little sense. Marketing words for arrays that handle these subtleties usually sound like, “Adaptive Array Cache”, or words to that effect.

One trick that can be used to see such potential damage is to run your test workload with concurrent sequential write “noise.” If you create a couple of files the same size as your redo logs and loop a couple of dd(1) processes performing 128K writes—without truncating the files on open—you can drive up this sort of I/O to see what it does to the array performance. If the array handles the caching of sequential writes, without polluting the cache, you shouldn’t get very much damage. An example of such a dd(1) command is:

$ dd if=/dev/zero of=pseudo_redo_file_db1 bs=128k count=8192 conv=notrunc &

$ dd if=/dev/zero of=pseudo_redo_file_db2 bs=128k count=8192 conv=notrunc &

$ wait

Looping this sort of “noise workload” will simulate a lot of LGWR I/O for two databases. Considering the typical revisit rate of the other array cache contents, this sort of dd(1) I/O shouldn’t completely obliterate your cache. If it does, you have an array that is too fond of sequential writes.

What Does This Have To Do With NAS?
This sort of workload can kill a filer. That doesn’t mean I’m any less excited about Oracle over NFS—I just don’t like filers. I recommend my collection of NFS related posts and my Scalable NAS for Oracle paper for background on what sequential writes can do to certain NAS implementations.

I’ll be talking about this topic and more at Utah Oracle User Group on March 21st.

 

Copying Files on Solaris. Slow or Fast, It’s Your Choice. Part III

This is the third installment on this thread. For context, please see:
Copying Files on Solaris. Slow or Fast, It’s Your Choice. Part I
Copying Files on Solaris. Slow or Fast, It’s Your Choice. Part II

What About cp8M Versus Stock cp(1) with Non-forcedirectio?
That is a good question. The saga continues after my post about copying files on Solaris. Once again, Padraig O’Sullivan was kind enough to test cp8M (available here ) versus stock cp(1) using a normal mounted UFS (non-forcedirectio). He reports:

Ok, I ran the benchmark in the same manner as before WITHOUT forcedirectio i.e. I rebooted the machine before each copy of the file.

# time /usr/bin/cp large_file large_file.1
real 2m17.504s
user 0m0.002s
sys 0m14.363s
#
# time /usr/bin/cp8m large_file large_file.
real 1m56.217s
user 0m0.003s
sys 0m14.264s
#

Why?
I don’t know. I certainly did not expect an increase in kernel mode cycles for the mmap-enabled cp(1). Please refer to Part I in this series to see that the comparison here is between 14.363s versus 10.853s of kernel-mode cycles.  We’re not talking a little increase. No, what Padraig’s measurements show is an increase of 32% in kernel mode cycles when copying a 1000MB file using stock cp(1) on a regular mount compared to the same work on a file in a forcedirectio mount. But hey, at least the performance (in MB/s) was consistently 16% less than cp8M. Yes, that was sarcasm.

I haven’t yet gotten my head around why the standard mmap-enabled cp(1) suffers such a jump in kernel mode processor overhead when switching from direct I/O to a normal UFS mount. I need to think about that a bit.

As usual, a picture speaks a thousand words, so I’ll provide two:

cp8m1.jpg

cp8m2.jpg

Remember my rant about the “small test?”

Sharing, and Caring
There was a comment by a reader on Part I of this blog thread that is worthy of discussion. The reader commented:

Perhaps a fairly obvious statement this, but notice the use of MAP_SHARED on the mmap call? – (I suspect you’ve spotted that already). This means that multiple processes can attach to the same memory mapped file simultaneously.

That is a good blog comment and evidence of someone giving it some thought. But, I’d like to comment on the sharable aspect of the 8MB map the reader brought out. I replied:

Your point about the kernel bcopy from UFS read buffers to the heap buffer in the address space of the cp(1) process is a good one, but this is a forcedirectio case.

That means there is no copy from the page cache into the virtual address space of process since it is direct I/O. My reply continued:

Since this is an Oracle blog, I would naturally go with the forcedirectio comparison first. It will be interesting to see with a normal UFS mount.

I’ve got a $2 bet that the MAP_SHARED is only there to facilitate copying an already mmapped file…the odds of a process jumping in and sharing a 8MB map that only lives for the duration of an I/O in and an I/O out seems pretty slim to me…but then that is 8MB twice…hmmm…I guess that 8MB mmap could exist for as much as 2-3 seconds if the I/O is headed for a single, simple drive. Sounds like a race just to share an 8MB map to me.

A Closer Look
Yes, when the stock cp(1) mmaps each 8MB segment of the input file it does so with MAP_SHARED. Like I said, I suspect the only thought behind that flag usage was to ensure there wouldn’t be “twinkling” mmap failures by other processes that could potentially be mmapping parts of that file while cp(1) is walking through it.

The reader’s comment continued:

That’s not to say that they all need to be “cp”’s – anything using mmap() on the same file at about same time will yield a benefit – the 8MB chunk paged in by mmap should only be later reclaimed by the pagescanner (or when the last process detaches?).

I already discussed the odds of another process getting in there and benefiting by that very transient mmap. It is 8MB in size and only valid during the read in, and write out—about 2-3 seconds on a really slow disk subsystem.

Reclaims
What’s this about reclaims? Good topic. When the mmap is dissolved through munmap(), the pages of the file are put on the free list (pagecache). Here is where the non-forcedirectio cp8M and cp(1) have a lot in common. In both cases, the blocks from the input file remain in main memory. Now that is where there is some true opportunity for sharing but only in the non-forcedirectio case. All said, it doesn’t take mmap() to get sharing of file contents being copied when you are using UFS with a normal mount.

So the question remains, what’s up with the mmap()-enabled cp(1)?

Is this thread making you sleepy?

Copying Files on Solaris. Slow or Fast, It’s Your Choice. Part II

 

In my post about copying files on Solaris, I cover a modification that to GNU cp(1) that yields substantial performance improvements over the stock cp(1) when copying files on forcedirectio mounts. See my comments at the end of that post regarding non-forcedirectio mounts.

I’ve gotten a large number of requests for that code. I don’t have Solaris gear around here so I asked Padraig O’Sullivan to do the favor of testing the modified GNU cp(1) versus stock cp(1). Padraig states:

Here is his recipe for making a cp8M for solaris:

I used coreutils version 5.2.1 which can be obtained from here:

http://ftp.gnu.org/pub/gnu/coreutils/

In the coreutils-5.2.1/src directory I modified the copy.c file at line 287 with the following modification:

# diff -b coreutils-5.2.1/src/copy.c cp8m/copy.c

287c315

<   buf_size = ST_BLKSIZE (sb);

>   /* buf_size = ST_BLKSIZE (sb);*/

288a317,319

>      buf_size = 8388608 ;

I then used the Makefile which is supplied with coreutils to build the cp binary.

Motives
The intent of this whole blog thread is really nothing more than diving into filesystem, and to some degree VM, internals topics so I truly hope that others will do this test. It would be interesting to see whether the mmap-enabled stock cp(1) is better than cp8M with, say, normal UFS mounts. The difference is clear to me on forcedirectio and that is, naturally, the type of mount I would take the most interest in.

 

Testing Direct Versus Buffered UFS on Solaris 10 with Swingbench.

Padraig O’Sullivan has a good blog entry on Swingbench. Included is the topic of using Swingbench to test direct versus buffed UFS on Solaris 10. Good post—check it out.

Experience Direct I/O Before Experiencing RAC

Bob Sneed makes a good point about direct I/O with regards to preparation for moving to RAC (should you find yourself in that position). I know exactly what he is talking about as I’ve seen people hit with the rude awakening of switching from buffered to unbuffered I/O while trying to implement RAC. The topic is related to the troubles people see when they migrate to RAC from a non-RAC setup where  regular buffered filesystems are being used. Implementing RAC forces you to use direct I/O (or RAW) so if you’ve never seen your application work without the effect of external caching in the OS page cache, going to RAC will include this dramatic change in I/O dynamic. All this at the same time as experiencing whatever normal RAC phenomenon your application may hit as well.

In this blog entry, Bob says:

If you ever intend to move a workload to RAC, tuning it to an unbuffered concurrent storage stack can be a crucial first step! Since there are no RAC storage options that use OS-level filesystem buffering […]

Direct I/O
Bob stipulates “unbuffered concurrent” since Solaris has a lot of different recipes for direct I/O some of which do not throw in concurrent I/O automatically. If you’ve been following my blog, you’ve detected that I think it is a bit crazy that there are still technology solutions out there that do not automatically include concurrent I/O along with direct I/O.

Here are some links to my recent thread on direct I/O :

Standard File Utilities with Direct I/O

Oracle Direct I/O Brought to You By Deranged Monkeys

Direct I/O Can Crash Dataguard. Tricky ORA-01031.

DBWR with CIO on JFS2. Resource Starvation?

What Performs Better, Direct I/O or Direct I/O? There is No Such Thing As a Stupid Question!

 

 

Standard File Utilities with Direct I/O

In my last blog entry about Direct I/O, I covered the topic of what Direct I/O can mean beyond normal Oracle database files. A reader followed up with a comment based on his experience with Direct I/O via Solaris –forcedirectio mount option:

I’ve noticed that on Solaris filesystems with forcedirectio , a “compress” becomes quite significantly slower. I had a database where I was doing disk-based backups and if I did “cp” and “compress” scripting to a forcedirectio filesystem the database backup would be about twice as long as one on a normally mounted filesystem.

I’m surprised it was only twice as slow. He was not alone in pointing this out. A fellow OakTable Network member who has customers using PolyServe had this to say in a side-channel email discussion:

Whilst I agree with you completely, I can’t help but notice that you ‘forgot’ to mention that all the tools in fileutils use 512-byte I/Os and that the response time to write a file to a dboptimised filesystem is very bad indeed…

I do recall at one point cp(1) used 512byte I/Os by default but that was some time ago and it has changed. I’m not going to name the individual that made this comment because if he wanted to let folks know who he is, he would have made the comment on the blog.  However, I have to respectfully disagree with this comment. It is too broad and a little out of date. Oh, and fileutils have been rolled up into coreutils actually. What tools are those? Wikipedia has a good list.

When it comes to the tools that are used to manipulate unstructured data, I think the ones that matter the most are cp, dd, cat, sort, sum, md5sum, split, uniq and tee. Then, from other packages, there are tar and gzip. There are others, but these seem to be the heavy hitters.

Small Bites
As I pointed out in my last blog entry about DIO, the man page for open(2) on Enterprise Linux distributions quotes Linus Torvalds as saying:

The thing that has always disturbed me about O_DIRECT is that the whole interface is just stupid, and was probably designed by a deranged monkey on some serious mind-controlling substances

I beg to differ. I think he should have given that title to anyone that thinks a program like cp(1) needs to operate with little itsy-bitsy-teenie-weenie I/Os. The following is the current state of affairs (although not exhaustive) as per measurements I just took with strace on RHEL4:

  • tar: 10KB default, override with –blocking-factor
  • gzip: 32KB in/16KB out
  • cat, md5sum, split, uniq, cp: 4KB

So as you can see these tools vary, but the majority do operate with insidiously ridiculous small I/O sizes. And 10KB as the default for tar? Huh? What a weird value to pick out of the air. At least you can override that by supplying an I/O size using the –blocking-factor option. But still, 10KB? Almost seems like the work of “deranged monkeys.” But is all lost? No.

Open Source
See, I just don’t get it. Supposedly Open Source is so cool because you can read and modify source code to make your life easier and yet people are reluctant to actually do that.  As far as that list of coreutils goes, only cp(1) causes a headache on a direct I/O mounted filesystem because you can’t pipeline it. Can you imagine the intrusive changes one would have to make to cp(1) to stop doing these ridiculous 4KB operations? I can, and have. The following is what I do to the coreutils cp(1):

copy.c:copy_reg()
/* buf_size = ST_BLKSIZE (sb);*/
buf_size = 8388608 ;

Eek! Oh the horror. Imagine the testing! Heaven’s sake! But, Kevin, how can you copy a small file with such large I/O requests? The following is a screen shot of two copy operations on a direct I/O mounted filesystem. I copy once with my cp command that will use a 8MB buffer and then again with the shipping cp(1) which uses a 4KB buffer.

fig4.jpg

Folks, in both cases the file is smaller than the buffer size. The custom cp8M will use an 8MB buffer but can safely (and quickly) copy a 41 byte file the same way the shipping cp(1) does with a 4KB buffer. The file is smaller than the buffer in both cases—no big deal.

So then you have to go through and make custom file tools right? No, you don’t. Let’s look at some other tools.

Living Happily With Direct I/O
…and reaping the benefits of not completely smashing your physical memory with junk that should not be cached. In the following screen shot I copy a redo log to get a working copy. My current working directory is a direct I/O mounted PSFS and I’m on RHEL4 x86_64. After copying I used gzip straight out of the box as they say. I then followed that with a pipeline command of dd(1) reading the infile with 8MB reads and writing to the pipe (stdout) with 8MB writes. The gzip command is reading the pipe with 32KB reads and in both cases is writing the compressed output with 16KB writes.

fig5.jpg

It seems gzip was written by monkeys who were apparently not deranged. The effect of using 32KB input and 16KB output is apparent. There was only a 16% speedup when I slammed 8MB chucks into gzip on the pipeline example. Perhaps the sane monkeys that implemented gzip could talk to the deranged monkeys that implemented all those tools that do 4KB operations.

What if I pipeline so that gzip is reading and writing on pipes but dd is adapted on both sides to do large reads and writes? The following screen shot shows that using dd as the reader and writer does pick up another 5%:

fig6.jpg

So, all told, there is 20% speedup to be had going from canned gzip to using dd (with 8MB I/O) on the left and right hand of a pipeline command. To make that simpler one could easily write the following scripts:

#!/bin/bash

dd if=$1 bs=8M

and

#!/bin/bash

dd of=$1 bs=8M

Make these scripts executable and use as follows:

$ large_read.sh file1.dbf | gzip –c -9 | large_write.sh file1.dbf.gz

But why go to that trouble? This is open source and we are all so very excited that we can tweak the code. A simple change to any of these tools that operate with 4KB buffers is very easy as I pointed out above. To demonstrate the benefit of that little tiny tweak I did to coreutils cp(1), I offer the following screen shot. Using cp8M offers a 95% speedup over cp(1) by moving 42MB/sec on the direct I/O mounted filesystem:

fig7.jpg

More About cp8M
Honestly, I think it is a bit absurd that any modern platform would ship a tool like cp(1) that does really small I/Os. If any of you can test cp(1) on, say, AIX, HP-UX or Solaris you might find that it is smart enough to do large I/O requests if is sees the file is large. Then again, since OS page cache also comes with built-in read-ahead, the I/O request size doesn’t really matter since the OS is going to fire off a read-ahead anyway.

Anyway, for what it is worth, here is the README that we give to our customers when we give them cp8M:

$ more README

INTRODUCTION
Files stored on DBOPTIMIZED mounted filesystems do not get accessed with buffered I/O. Therefore, Linux tools that perform small I/O requests will suffer a performance degradation compared to buffered filesystems such as normal mounted PolyServe CFS , Ext3, etc. Operations such as copying a file with cp(1) will be very slow since cp(1) will read and write small amounts of data for every operation.

To alleviate this problem, PolyServe is providing this slightly modified version of the Open Source cp(1) program called cp8M. The seed source for this tool is from the coreutils-5.2.1 package. The modification to the source is limited to changing the I/O size that cp(1) issues from ST_BLOCKSIZE to 8 MB. The following code snippet is from the copy.c source file and depicts the entirety of source changes to cp(1):

copy.c:copy_reg()

/* buf_size = ST_BLKSIZE (sb);*/

buf_size = 8388608 ;

This program is statically linked and has been tested on the following filesystems on RHEL 3.0, SuSE SLES8 and SuSE SLES9:

* Ext3

* Regular mounted PolyServe CFS

* DBOPTIMIZED mounted PSFS

Both large and small files have been tested. The performance improvement to be expected from the tool is best characterized by the following terminal session output where a 1 GB file is copied using /bin/cp and then with cp8M. The source and destination locations were both DBOPTIMIZED.

# ls -l fin01.dbf

-rw-r–r– 1 root root 1073741824 Jul 14 12:37 fin01.dbf

# time /bin/cp fin01.dbf fin01.dbf.bu
real 8m41.054s

user 0m0.304s

sys 0m52.465s

# time /bin/cp8M fin01.dbf fin01.dbf.bu2

real 0m23.947s

user 0m0.003s

sys 0m6.883s


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All content is © Kevin Closson and "Kevin Closson's Blog: Platforms, Databases, and Storage", 2006-2013. Unauthorized use and/or duplication of this material without express and written permission from this blog’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Kevin Closson and Kevin Closson's Blog: Platforms, Databases, and Storage with appropriate and specific direction to the original content.

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