Backblaze hard disk drive failure data: Update to Q2 2016

Ross Lazarus, September 2016



 This is a Kaplan Meier analysis of the BackBlaze hard drive reliability data, using all available data to end second quarter of 2016 from https://www.backblaze.com/b2/hard-drive-test-data.html . 

 Previous posts are at http://bioinformare.blogspot.com.au/2016/05/survival-analysis-of-hard-disk-drive.html and http://bioinformare.blogspot.com.au/2016/02/survival-analysis-of-hard-disk-drive.html .

 I reran my scripts and got the plots shown below. It's taking a while to read all the data as there are now a very large number of drives spinning. A total of 41740623 rows were processed in about 35 minutes on my home desktop by the python script in the github repository.

The new 8TB drives are performing the best of all - even better than the HGST and Hitachis - and way better than any of the earlier seagates. Hard to miss here - not so obvious in the report at Backblaze https://www.backblaze.com/blog/hard-drive-failure-rates-q2-2016/

 Updated curves:







and
By Manufacturer:
Add caption

Once again for me, little change is seen in the KM curves and statistics with a lot more drives and a lot more observaton time, suggesting that this statistical approach is reliable and robust, although in general we expect that more data provides better resolution. 


In terms of the KM statistical tests, additional data confirms the earlier inference that there are significant differences between the manufacturer and model risk profiles over time.

 Call:
survdiff(formula = sm ~ model, data = dm, rho = 0)

                                   N Observed Expected (O-E)^2/E (O-E)^2/V
model=HGST HMS5C4040ALE640     7168       85   505.51   349.800   406.826
model=HGST HMS5C4040BLE640     8505       29   269.99   215.103   231.736
model=Hitachi HDS5C3030ALA630  4664      117   466.48   261.826   302.989
model=Hitachi HDS5C4040ALE630  2719       71   268.60   145.365   157.458
model=Hitachi HDS722020ALA330  4774      215   472.27   140.149   161.908
model=Hitachi HDS723030ALA640  1048       55   103.54    22.753    23.459
model=ST3000DM001              4707     1705   246.40  8634.322  9272.385
model=ST31500341AS              787      216    35.74   909.141   917.789
model=ST31500541AS             2188      392   157.42   349.574   363.940
model=ST4000DM000             36089     1123  1500.66    95.042   151.313
model=ST500LM012 HN             801       26    22.42     0.573     0.577
model=ST6000DX000              1915       31    77.14    27.601    28.497
model=ST8000DM002              2754        3     3.74     0.146     0.149
model=WDC WD10EADS              550       60    46.72     3.773     3.818
model=WDC WD30EFRX             1289      136    87.38    27.053    27.637

  Chisq= 11353  on 14 degrees of freedom, p= 0 
Call:
survdiff(formula = s ~ manufact, data = ds, rho = 0)

                      N Observed Expected (O-E)^2/E (O-E)^2/V
manufact=HGST    15840      120    821.8   599.348   744.193
manufact=Hitachi 13246      462   1433.5   658.440  1046.810
manufact=HN        801       26     23.6     0.242     0.243
manufact=ST      49900     3792   2255.7  1046.249  2067.849
manufact=TOSHIBA   279       12     13.6     0.181     0.182
manufact=WDC      3920      385    248.7    74.701    78.874
 Chisq= 2493  on 5 degrees of freedom, p= 0 

Comments

  1. UnknownJanuary 21, 2017 at 9:41 AM

    This comment has been removed by the author.

    ReplyDelete
    Replies
    1. UnknownJanuary 21, 2017 at 9:43 AM

      Second attempt. Thanks for the update. I asked for this one in the original publication before looking more carefully :-) .
      One question though. Do think it was wise to draw conclusions about the 8 tb Seagates considering the lowly number of only 3 samples ?

      Delete
    2. Ross LazarusJanuary 21, 2017 at 1:46 PM

      Thanks for the comment. I assume you're referring to:

      model=ST8000DM002 2754 3 3.74 0.146 0.149

      The row shows that 3 samples failed of 2754 units observed so it's not as bad as it sounds although more is usually better. All graphs are trimmed of any row with fewer than 500 observations for this reason.

      What's more important is that the period to the last observation for those drives is only 6 weeks or so. KM assumes all units are under observation for the same duration so the curves are misleading here.

      The perfect is the enemy of the good I guess. I've plotted the curves for periods from a few days to the maximum and they remain fairly stable after a week or so. I'll post those soon.

      Delete
  2. AlexFebruary 2, 2017 at 12:41 PM

    Could you explain what N, Observed and Expected refer to. Thanks.

    ReplyDelete
  3. AlexFebruary 2, 2017 at 12:42 PM

    One other comment if I may, the colors are so similar it is hard to keep track. Can you consider making changes to that for future posts? Thank you.

    ReplyDelete
    Replies
    1. AnonymousFebruary 5, 2017 at 2:40 PM

      Amen to that! A couple of ways to improve that:

      1- Choose a colorblind-friendly color palette. (Info on that available across the web, including here: http://bconnelly.net/2013/10/creating-colorblind-friendly-figures/).

      2- Make the lines a bit thicker so the color is readily discernable on a high-resolution screen.

      3- Consider using various dash patterns for the lines as well.

      4- If nothing else, labeling the right side of each line with what it represents would make it much easier to see which line is what.

      Thanks for your in-depth analyses!

      Delete
  4. IaninKLFebruary 5, 2017 at 4:33 PM

    I'm a couple of weeks off retirement and might try this out once I've time on my hands - make the lines a little thicker AND set the marker colour to the same as its line's colour.
    I do find the KM displays much more informative - thanks very much for doing this.

    ReplyDelete
  5. น้องส้มส้มOctober 6, 2018 at 7:59 PM

    The graph makes me see more things. Many people can do this graph. But for me, I have to hire someone to make me. gclub

    ReplyDelete
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