I have never tried this but it's something I was wondering about.
If I am downloading a very big file (say 200 GB) using very fast links (1 Gbps or even 10 Gbps), how does the SO (or whoever do this) writes the downloaded file at the same time in the disk, since disks have very slow write speed compared to my link speed?
Would in this case, the hard drive become a bottleneck?
If I run iostat in my PC it shows 1027 KBps ~ 1 MBps write speed, which is very slow compared with the link stated before.
Yes, it's certainly possible for almost any part of your hardware chain, from the incoming link to your hard drive to become the bottleneck, depending on your hardware.
In the case that you are actually sustaining a download speed faster than the linear write speed of your hard drive, it could certainly become a bottleneck.
Note, however, that even most budget hard drives today1 have a linear write speed of at least 50 MB/s, which is 400 Mbps, and often closer to 100 MB/s (i.e., 800 Mbps). At an effective write speed of 800 Mbps, your drive should be able to keep up with a saturated 1 Gbps link2, at least approximately, but would certainly fall behind against a 10 Gbps link.
Now, what you were measuring with iostat isn't any kind of useful benchmark - it's telling you the actual throughput for either the entire uptime of your host of active requests over some interval: unless are doing a large transfer during the interval, the speeds reported there have little relationship to your actual disk write speeds. There are plenty of benchmarking tools that will measure directly your read and write speeds.
A final think to keep in mind is that most modern operating systems use a "write-back" strategy for storage writes - the writes are first sent to RAM (i.e., the file cache on Windows or page cache on Linux), and then streamed out to the disk asynchronously. This helps hide the throughput of the actual disk for relatively short bursts of writes. For example, your disk may appear to have a very high throughput of 5 GB/s or more if writes are of a few hundred MBs or a few GBs3, but then will approach the true disk speed as they get larger, since the buffering ability of your OS disk cache will be exhausted. Clearly for a 200 GB transfer, the disk cache isn't going to be able to hide the disk speed, unless you have 100s of GBs of RAM.
So all that said, yes, your harddrive can certainly become a bottleneck,
but likely at high higher throughputs than what you measured with iostat.
1 This increase in linear read and write speeds is mostly an artifact of increasing areal density of storage, which directly translates to increased linear read/write speeds at the same RPM. Random read/write has no such relationship, however. SSDs of reasonable capacity usually increase this by another order of magnitude, to around 1 GB/s (8 Gbps) or more.
2 Mostly because these network links have overhead which for fast links often reduces the actual payload to less than 80% of the theoretical link speed.
3 The exact values depend on your total RAM, available RAM and OS configuration.
Related
I'm writing real-time data to an empty spinning disk sequentially. (EDIT: It doesn't have to be sequential, as long as I can read it back as if it was sequential.) The data arrives at a rate of 100 MB/s and the disks have an average write speed of 120 MB/s.
Sometimes (especially as free space starts to decrease) the disk speed goes under 100 MB/s depending on where on the platter the disk is writing, and I have to drop vital data.
Is there any way to write to disk in a pattern (or some other way) to ensure a constant write speed close to the average rate? Regardless of how much data there currently is on the disk.
EDIT:
Some notes on why I think this should be possible.
When usually writing to the disk, it starts in the fast portion of the platter and then writes towards the slower parts. However, if I could write half the data to the fast part and half the data to the slow part (i.e. for 1 second it could write 50MB to the fast part and 50MB to the slow part), they should meet in the middle. I could possibly achieve a constant rate?
As a programmer, I am not sure how I can decide where on the platter the data is written or even if the OS can achieve something similar.
If I had to do this on a regular Windows system, I would use a device with a higher average write speed to give me more headroom. Expecting 100MB/s average write speed over the entire disk that is rated for 120MB/s is going to cause you trouble. Spinning hard disks don't have a constant write speed over the whole disk.
The usual solution to this problem is to buffer in RAM to cover up infrequent slow downs. The more RAM you use as a buffer, the longer the span of slowness you can handle. These are tradeoffs you have to make. If your problem is the known slowdown on the inside sectors of a rotating disk, then your device just isn't fast enough.
Another thing that might help is to access the disk as directly as possible and ensure it isn't being shared by other parts of the system. Use a separate physical device, don't format it with a filesystem, write directly to the partitioned space. Yes, you'll have to deal with some of the issues a filesystem solves for you, but you also skip a bunch of code you can't control. Even then, your app could run into scheduling issues with Windows. Windows is not a RTOS, there are not guarantees as far as timing. Again this would help more with temporary slowdowns from filesystem cleanup, flushing dirty pages, etc. It probably won't help much with the "last 100GB writes at 80MB/s" problem.
If you really are stuck with a disk that goes from 120MB/s -> 80MB/s outside-to-inside (you should test with your own code and not trust the specs from the manufacture so you know what you're dealing with), then you're going to have to play partitioning games like others have suggested. On a mechanical disk, that will introduce some serious head seeking, which may eat up your improvement. To minimize seeks, it would be even more important to ensure it's a dedicated disk the OS isn't using for anything else. Also, use large buffers and write many megabytes at a time before seeking to the end of the disk. Instead of partitioning, you could write directly to the block device and control which blocks you write to. I don't know how to do this in Windows.
To solve this on Linux, I would be tempted to test mdadm's raid0 across two partitions on the same drive and see if that works. If so, then the work is done and you don't have to write and test some complicated write mechanism.
Partition the disk into two equally sized partitions. Write a few seconds worth of data alternating between the partitions. That way you get almost all of the usual sequential speed, nicely averaged. One disk seek every few seconds eats up almost no time. One seek per second reduces the usable time from 1000ms to ~990ms which is a ~1% reduction in throughput. The more RAM you can dedicate to buffering the less you have to seek.
Use more partitions to increase the averaging effect.
I fear this may be more difficult than you realize:
If your average 120 MB/s write speed is the manufacturer's value then it is most likely "optimistic" at best.
Even a benchmarked write speed is usually done on a non-partitioned/formatted drive and will be higher than what you'd typically see in actual use (how much higher is a good question).
A more important value is the drive's minimum write speed. For example, from Tom's Hardware 2013 HDD Benchmarks a drive with a 120 MB/s average has a 76 MB/s minimum.
A drive that is being used by other applications at the same time (e.g., Windows) will have a much lower write speed.
An even more important value is the drives actual measured performance. I would make a simple application similar to your use case that writes data to the drive as fast as possible until it fills the drive. Do this a few (dozen) times to get a more realistic average/minimum/maximum write speed value...it will likely be lower than you'd expect.
As you noted, even if your "real" average write speed is higher than 100 MB/s you run into issues if you run into slow write speeds just before the disk fills up, assuming you don't have somewhere else to write the data to. Using a buffer doesn't help in this case.
I'm not sure if you can actually specify a physical location to write to on the hard drive these days without getting into the drive's firmware. Even if you could this would be my last choice for a solution.
A few specific things I would look at to solve your problem:
Measure the "real" write performance of the drive to see if its fast enough. This gives you an idea of how far behind you actually are.
Put the OS on a separate drive to ensure the data drive is not being used by anything other than your application.
Get faster drives (either HDD or SDD). It is fine to use the manufacturer's write speeds as an initial guide but test them thoroughly as well.
Get more drives and put them into a RAID0 (or similar) configuration for faster write access. You'll again want to actually test this to confirm it works for you.
You could implement the strategy of alternating writes bewteen the inside and the outside by directly controlling the disk write locations. Under Windows you can open a disk like "\.\PhysicalDriveX" and control where it writes. For more info see
http://msdn.microsoft.com/en-us/library/windows/desktop/aa363858(v=vs.85).aspx
First of all, I hope you are using raw disks and not a filesystem. If you're using a filesystem, you must:
Create an empty, non-sparse file that's as large as the filesystem will fit.
Obtain a mapping from the logical file positions to disk blocks.
Reverse this mapping, so that you can map from disk blocks to logical file positions. Of course some blocks are unavailable due to filesystem's own use.
At this point, the disk looks like a raw disk that you access by disk block. It's a valid assumption that this block addressing is mostly monotonous to the physical cylinder number. IOW if you increase the disk block number, the cylinder number will never decrease (or never increase -- depending on the drive's LBA to physical mapping order).
Also, note that a disk's average write speed may be given per cylinder or per unit of storage. How would you know? You need the latter number, and the only sure way to get it is to benchmark it yourself. You need to fill the entire disk with data, by repeatedly writing a zero page to the disk, going block by block, and divide the total amount of data written by the amount it took. You need to be accessing the disk or the file in the direct mode. This should disable the OS buffering for the file data, and not for the filesystem metadata (if not using a raw disk).
At this point, all you need to do is to write data blocks of sensible sizes at the two extremes of the block numbers: you need to fill the disk from both ends inwards. The size of the data blocks depends on the bandwidth wastage you can allow for seeks. You should also assume that the hard drive might seek once in a while to update its housekeeping data. Assuming a worst-case seek taking 15ms, you waste 1.5% of per-second bandwidth for each seek. Assuming you can spare no more than 5% of bandwidth, with 1 seek/s on average for the drive itself, you can seek twice per second. Thus your block size needs to be your_bandwith_per_second/2. This bandwidth is not the disk bandwidth, but the bandwidth of your data source.
Alas, if only things where this easy. It generally turns out that the bandwidth at the middle of the disk is not the average bandwidth. During your benchmark you must also take a note of write speed over smaller sections of the disk, say every 1% of the disk. This way, when writing into each section of the disk, you can figure out how to split the data between the "low" and the "high" section that you're writing to. Suppose that you're starting out at 0% and 99% positions on the disk, and the low position has a bandwidth of mean*1.5, and the high position has a bandwidth of mean*0.8, where mean is your desired mean bandwidth. You'll then need to write 100% * 1.5/(0.8+1.5) of the data into the low position, and the remainder (100% * 0.8/(0.8+1.5)) into the slower high position.
The size of your buffer needs to be larger than just the block size, since you must assume some worst-case latency for the hard drive if it hits bad blocks and needs to relocate data, etc. I'd say a 3 second buffer may be reasonable. Optionally it can grow by itself if latencies you measure while your software runs turn out higher. This buffer must be locked ("pinned") to physical memory so that it's not subject to swapping.
Another possible option is to destroke (or short stroke) a hard drive. If you start with a 4TB or larger drive and destroke it to 2TB, only the outer portions of the platters will be used, resulting in a faster throughput rate. The issue would be getting the software that issues vendor unique commands to a hard drive to destroke it.
This is very interesting topic, they use following formula to compute access interval time:
BreakEvenIntervalinSeconds = (PagesPerMBofRAM / AccessesPerSecondPerDisk) × (PricePerDiskDrive / PricePerMBofRAM).
It is derived using formulas for the cost of RAM to hold a page in the buffer pool and the cost of a (fractional) disk to perform I/O every time a page is needed, equating these two costs, and solving the equation for the interval between accesses.
so the cost of disc I/O per access is PricePerDiskDrive / AccessesPerSecondPerDisk, My question is why disc I/O cost per access is computed like this?
The underlying assumption is that the limit to the life of a disk is how many disk seeks there are, while RAM has a fixed cost for its size, and a fixed lifetime regardless of how often it is accessed. This is reasonable because seeking to disk causes physical wear and tear, and when the disk goes, you lose the whole disk. By contrast RAM has no physical moving parts, and so does not wear out with use.
With that assumption, the cost of keeping data on disk depends on the frequency of access and the cost of the disk. The cost of keeping data in RAM depends on how much RAM you're using. What they are trying to find is the break even point between where it is cheaper to keep data on disk or in RAM.
However the equation as given is incomplete. While that equation identifies relevant factors, there is an important constant of proportionality missing. How many accesses can the average hard drive sustain? How long does RAM last on average? Those enter into the costs for keeping data on hard drives and RAM, and without them you are comparing apples and oranges.
This is indicative of my impression of the whole paper. It says a lot at great length, about an important topic, but the analysis is sloppy. They are slopping and leave critical things out, and don't do enough to help people understand what they are thinking and when their analysis is appropriate what you are doing. For instance if you are trying to maintain a low latency system, you have to keep all of your data in RAM. Period. If you're processing large data sets and don't want to pay to keep it all in RAM, then you will be streaming data to/from disk. If you're keeping data in a redundant format, for instance RAID, you are doing more seeks per read than they admit.
A python program I created is IO bounded. The majority of the time (over 90%) is spent in a single loop which repeats ~10,000 times. In this loop, ~100KB data is generated and written to a temporary file; it is then read back out by another program and statistics about that data collected. This is the only way to pass data into the second program.
Due to this being the main bottleneck, I thought that moving the location of the temporary file from my main HDD to a (~40MB) RAMdisk (inside of over 2GB of free RAM) would greatly increase the IO speed for this file and so reduce the run-time. However, I obtained the following results (each averaged over 20 runs):
Test data 1: Without RAMdisk - 72.7s, With RAMdisk - 78.6s
Test data 2: Without RAMdisk - 223.0s, With RAMdisk - 235.1s
It would appear that the RAMdisk is slower that my HDD.
What could be causing this?
Are there any other alternative to using a RAMdisk in order to get faster file IO?
Your operating system is almost certainly buffering/caching disk writes already. It's not surprising the RAM disk is so close in performance.
Without knowing exactly what you're writing or how, we can only offer general suggestions. Some ideas:
If you have 2 GB RAM you probably have a decent processor, so you could write this data to a filesystem that has compression. That would trade I/O operations for CPU time, assuming your data is amenable to that.
If you're doing many small writes, combine them to write larger pieces at once. (Can we see the source code?)
Are you removing the 100 KB file after use? If you don't need it, then delete it. Otherwise the OS may be forced to flush it to disk.
Can you write the data out in batches rather than one item at a time? Are you caching resources like open file handles etc or cleaning those up? Are your disk writes blocking, can you use background threads to saturate IO while not affecting compute performance.
I would look at optimising the disk writes first, and then look at faster disks when that is complete.
I know that Windows is very aggressive about caching disk data in RAM, and 100K would fit easily. The writes are going directly to cache and then perhaps being written to disk via a non-blocking write, which allows the program to continue. The RAM disk probably wouldn't support non-blocking operations because it expects those operations to be quick and not worth the bother.
By reducing the amount of memory available to programs and caching, you're going to increase the amount of disk I/O for paging even if only slightly.
This is all speculation on my part, since I'm not familiar with the kernel or drivers. I also speculate that Linux would operate similarly.
In my tests I've found that not only batch size affects overall performance, but also the nature of data itself. I've managed to get 5 times better write times compared to SSD in only one scenario: writing a 100MB chunk of pre-cooked random byte array to RAM drive. Writing more "predictable" data like letters "aaa" or current datetime yields quite opposite results - SSD is always faster or equal. So my guess is that opertating system (Win 7 in my case) does lots of caching and optimizations.
Looks like the most hindering case for RAM-drive is when you perform lots of small writes instead of a few big ones, and RAM drive shines at writing large amounts of hard-to-compress data.
I had the same mind boggling experience, and after many tries I figured it out.
When ramdisk is formatted as FAT32, then even though benchmarks shows high values, real world use is actually slower than NTFS formatted SSD.
But NTFS formatted ramdisk is faster in real life than SSD.
I join the people having problems with RAM disk speeds (only on Windows).
The SSD i have can write 30 GiB (in one big block, dump a 30GiB RAM ARRAY) with a speed of 550 MiB/s (arround 56 seconds to write 30 GiB) ... this is if the write is asked in one source code sentence.
The RAM Disk (imDisk) i have can write 30 GiB write (in one big block, dump a 30GiB RAM ARRAY) with a speed of a bit less than 100 MiB/s (arround 5 minutes and 13 seconds to write 30 GiB) ... this is if the write is asked in one source code sentence.
I had also done another RAM test: from source code do a sequential direct write (one byte per source code loop pass) to a 30GiB RAM ARRAY (i have 64GiB of RAM) and i get a speed of near 1.3GiB/s (1298 MiB per second).
Why on the hell (on Windows) RAM Disk is so slow for one BIG secuential write?
Of course that low write speed happens on RAM disks on Windows, since i tested the same 'concept' on Linux with Linux native ram disk and Linux ram disk can write at near one gigabyte per second.
Please note that i had also tested SoftPerfect and other RAM disks on Windows, RAM Disk speeds are near the same, can not write at more than one hundred megabytes per second.
Actual Windows tested: 10 & 11 (on both HOME & PRO, on 64 bits), RAM Disk format (exFAT & NTFS); since RAM disk speed was too slow i was trying to find one Windows version where RAM disk speed be normal, but found no one.
Actual Linux Kernel tested: Only 5.15.11, since Linux native RAM disk speed was normal i do not test on any other kernel.
Hope this help other people, since knowledge is the base to solve a problem.
It used to be that disk compression was used to increase storage space at the expense of efficiency but we were all on single processor systems back then.
These days there are extra cores around to potentially do the decompression work in parallel with processing the data.
For I/O bound applications (particularly read heavy sequential data processing) it might be possible to increase throughput by only reading and writing compressed data to disk.
Does anyone have any experience to support or reject this conjecture?
Take care not to confuse disk seek times and disk read rates. It takes millions of CPU cycles (5–10 milliseconds or 5–10 million nanoseconds) to seek to the right track on a hard drive (HDD). Once you're there, you can read tens of megabytes of data per second, assuming low fragmentation. For solid-state drives (SSD), seek times are lower (35,000–100,000ns) than HDDs.
Whether or not the data is compressed on the disk, you still have to seek. The question becomes, is (disk read time for compressed data + the decompression time) < (disk read time for uncompressed data). Decompression is relatively fast, since it amounts to replacing a short token with a longer one. In the end, it probably boils down to how well the data was compressed and how big it was in the first place. If you're reading a 2KB compressed file instead of a 5KB original, it's probably not worth it. If you're reading a 2MB compressed file instead of a 25MB original, it likely is.
Measure with a reasonable workload.
Yes! In fact, processors are so ridiculously fast now that it even makes sense for memory. (IBM does this, I believe.) I believe, some of the current big iron machines even do compression on the CPU cache.
Yes, this makes perfect sense. On NT-based Windows OS's it's widely accepted that sometimes enabling NTFS compression can be faster than disabling it for precisely this reason. This has been true for years and multicore should only make it more true.
I think it also depends on how aggressive your compression is vs how IO bound you are.
For example, DB2's row compression feature is targeted for IO bound application: data warehouses, reporting systems, etc. It uses a dictionary-based algorithm and isn't very aggressive - resulting in 50-80% compression of data (tables, indexes in storage as well as when in memory). However - it also tends to speed queries up by around 10%.
They could have gone with much more aggressive compression, but then would have taken a performance hit.
On a modern system can local hard disk write speeds be improved by compressing the output stream?
This question derives from a case I'm working with where a program serially generates and dumps around 1-2GB of text logging data to a raw text file on the hard disk and I think it is IO bound. Would I expect to be able to decrease runtimes by compressing the data before it goes to disk or would the overhead of compression eat up any gain I could get? Would having an idle second core affect this?
I know this would be affected by how much CPU is being used to generate the data so rules of thumb on how much idle CPU time would be needed would be good.
I recall a video talk where someone used compression to improve read speeds for a database but IIRC compressing is a lot more CPU intensive than decompressing.
Yes, yes, yes, absolutely.
Look at it this way: take your maximum contiguous disk write speed in megabytes per second. (Go ahead and measure it, time a huge fwrite or something.) Let's say 100mb/s. Now take your CPU speed in megahertz; let's say 3Ghz = 3000mhz. Divide the CPU speed by the disk write speed. That's the number of cycles that the CPU is spending idle, that you can spend per byte on compression. In this case 3000/100 = 30 cycles per byte.
If you had an algorithm that could compress your data by 25% for an effective 125mb/s write speed, you would have 24 cycles per byte to run it in and it would basically be free because the CPU wouldn't be doing anything else anyway while waiting for the disk to churn. 24 cycles per byte = 3072 cycles per 128-byte cache line, easily achieved.
We do this all the time when reading optical media.
If you have an idle second core it's even easier. Just hand off the log buffer to that core's thread and it can take as long as it likes to compress the data since it's not doing anything else! The only tricky bit is you want to actually have a ring of buffers so that you don't have the producer thread (the one making the log) waiting on a mutex for a buffer that the consumer thread (the one writing it to disk) is holding.
Yes, this has been true for at least 10 years. There are operating-systems papers about it. I think Chris Small may have worked on some of them.
For speed, gzip/zlib compression on lower quality levels is pretty fast; if that's not fast enough you can try FastLZ. A quick way to use an extra core is just to use popen(3) to send output through gzip.
For what it is worth Sun's filesystem ZFS has the ability to have on the fly compression enabled to decrease the amount of disk IO without a significant increase in overhead as an example of this in practice.
The Filesystems and storage lab from Stony Brook published a rather extensive performance (and energy) evaluation on file data compression on server systems at IBM's SYSTOR systems research conference this year: paper at ACM Digital Library, presentation.
The results depend on the
used compression algorithm and settings,
the file workload and
the characteristics of your machine.
For example, in the measurements from the paper, using a textual workload and a server environment using lzop with low compression effort are faster than plain write, but bzip and gz aren't.
In your specific setting, you should try it out and measure. It really might improve performance, but it is not always the case.
CPUs have grown faster at a faster rate than hard drive access. Even back in the 80's a many compressed files could be read off the disk and uncompressed in less time than it took to read the original (uncompressed) file. That will not have changed.
Generally though, these days the compression/de-compression is handled at a lower level than you would be writing, for example in a database I/O layer.
As to the usefulness of a second core only counts if the system will be also doing a significant number of other things - and your program would have to be multi-threaded to take advantage of the additional CPU.
Logging the data in binary form may be a quick improvement. You'll write less to the disk and the CPU will spend less time converting numbers to text. It may not be useful if people are going to be reading the logs, but they won't be able to read compressed logs either.
Windows already supports File Compression in NTFS, so all you have to do is to set the "Compressed" flag in the file attributes.
You can then measure if it was worth it or not.
This depends on lots of factors and I don't think there is one correct answer. It comes down to this:
Can you compress the raw data faster than the raw write performance of your disk times the compression ratio you are achieving (or the multiple in speed you are trying to get) given the CPU bandwidth you have available to dedicate to this purpose?
Given today's relatively high data write rates in the 10's of MBytes/second this is a pretty high hurdle to get over. To the point of some of the other answers, you would likely have to have easily compressible data and would just have to benchmark it with some test of reasonableness type experiments and find out.
Relative to a specific opinion (guess!?) to the point about additional cores. If you thread up the compression of the data and keep the core(s) fed - with the high compression ratio of text, it is likely such a technique would bear some fruit. But this is just a guess. In a single threaded application alternating between disk writes and compression operations, it seems much less likely to me.
If it's just text, then compression could definitely help. Just choose an compression algorithm and settings that make the compression cheap. "gzip" is cheaper than "bzip2" and both have parameters that you can tweak to favor speed or compression ratio.
If you are I/O bound saving human-readable text to the hard drive, I expect compression to reduce your total runtime.
If you have an idle 2 GHz core, and a relatively fast 100 MB/s streaming hard drive,
halving the net logging time requires at least 2:1 compression and no more than roughly 10 CPU cycles per uncompressed byte for the compressor to ponder the data.
With a dual-pipe processor, that's (very roughly) 20 instructions per byte.
I see that LZRW1-A (one of the fastest compression algorithms) uses 10 to 20 instructions per byte, and compresses typical English text about 2:1.
At the upper end (20 instructions per byte), you're right on the edge between IO bound and CPU bound. At the middle and lower end, you're still IO bound, so there is a a few cycles available (not much) for a slightly more sophisticated compressor to ponder the data a little longer.
If you have a more typical non-top-of-the-line hard drive, or the hard drive is slower for some other reason (fragmentation, other multitasking processes using the disk, etc.)
then you have even more time for a more sophisticated compressor to ponder the data.
You might consider setting up a compressed partition, saving the data to that partition (letting the device driver compress it), and comparing the speed to your original speed.
That may take less time and be less likely to introduce new bugs than changing your program and linking in a compression algorithm.
I see a list of compressed file systems based on FUSE, and I hear that NTFS also supports compressed partitions.
If this particular machine is often IO bound,
another way to speed it up is to install a RAID array.
That would give a speedup to every program and every kind of data (even incompressible data).
For example, the popular RAID 1+0 configuration with 4 total disks gives a speedup of nearly 2x.
The nearly as popular RAID 5 configuration, with same 4 total disks, gives all a speedup of nearly 3x.
It is relatively straightforward to set up a RAID array with a speed 8x the speed of a single drive.
High compression ratios, on the other hand, are apparently not so straightforward. Compression of "merely" 6.30 to one would give you a cash prize for breaking the current world record for compression (Hutter Prize).
This used to be something that could improve performance in quite a few applications way back when. I'd guess that today it's less likely to pay off, but it might in your specific circumstance, particularly if the data you're logging is easily compressible,
However, as Shog9 commented:
Rules of thumb aren't going to help
you here. It's your disk, your CPU,
and your data. Set up a test case and
measure throughput and CPU load with
and without compression - see if it's
worth the tradeoff.