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POWhat's the most efficient way to process massive amounts of data from a disk using python?
 

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  1. POWhat's the most efficient way to process massive amounts of data from a disk using python?
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    <p>I was writing a simple python script to read from and reconstruct data from a failed RAID5 array that I've been unable to rebuild in any other way. My script is running but slowly. My original script ran at about 80MB/min. I've since improved the script and it's running at 550MB/min but that still seems a bit low. The python script is sitting at 100% CPU, so it seems to be CPU rather than disk limited, which means I have opportunity for optimization. Because the script isn't very long at all I am unable to profile it effectively, so I don't know what's eating it all up. Here's my script as it stands right now (or at least, the important bits)</p> <pre><code>disk0chunk = disk0.read(chunkSize) #disk1 is missing, bad firmware disk2chunk = disk2.read(chunkSize) disk3chunk = disk3.read(chunkSize) if (parityDisk % 4 == 1): #if the parity stripe is on the missing drive output.write(disk0chunk + disk2chunk + disk3chunk) else: #we need to rebuild the data in disk1 # disk0num = map(ord, disk0chunk) #inefficient, old code # disk2num = map(ord, disk2chunk) #inefficient, old code # disk3num = map(ord, disk3chunk) #inefficient, old code disk0num = struct.depack("16384l", disk0chunk) #more efficient new code disk2num = struct.depack("16384l", disk2chunk) #more efficient new code disk3num = struct.depack("16384l", disk3chunk) #more efficient new code magicpotato = zip(disk0num,disk2num,disk3num) disk1num = map(takexor, magicpotato) # disk1bytes = map(chr, disk1num) #inefficient, old code # disk1chunk = ''.join(disk1bytes) #inefficient, old code disk1chunk = struct.pack("16384l", *disk1num) #more efficient new code #output nonparity to based on parityDisk def takexor(magicpotato): return magicpotato[0]^magicpotato[1]^magicpotato[2] </code></pre> <p>Bolding to denote the actual questions inside this giant block of text:</p> <p><strong>Is there anything I can be doing to make this faster/better? If nothing comes to mind, is there anything I can do to better research into what is making this go slowly? (Is there even a way to profile python at a per line level?) Am I even handling this the right way, or is there a better way to handle massive amounts of binary data?</strong></p> <p>The reason I ask is I have a 3TB drive rebuilding and even though it's working correctly (I can mount the image ro,loop and browse files fine) it's taking a long time. I measured it as taking until mid-January with the old code, now it's going to take until Christmas (so it's <em>way</em> better but it's still slower than I expected it to be.)</p> <p>Before you ask, this is an mdadm RAID5 (64kb blocksize, left symmetric) but the mdadm metadata is missing somehow and mdadm does not allow you to reconfigure a RAID5 without rewriting the metadata to the disk, which I am trying to avoid at all costs, I don't want to risk screwing something up and losing data, however remote the possibility may be.</p>
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    1. CODo you profile you code? I don't know about per line profiling, but you can do per function profiling.
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    2. COI only have two functions. :) I haven't yet profiled it because it seems a bit useless, I'll have two data points, code in main and code in takexor. Unless, of course, I'm misremembering python's profiling functions (which is possible)
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      1. POWhat's the most efficient way to process massive amounts of data from a disk using python?
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    3. COHow big is chunkSize? I don't see an `else: output.write`. This does seem incredibly slow. Are the disk devs buffered or unbuffered?
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      1. POWhat's the most efficient way to process massive amounts of data from a disk using python?
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