- Data Sources
- Results
- Technical Details
- Plots
- Cost effectiveness
- Full Data
- Replicating my results
- Erratum
I’m buying a hard drive for backups, and I want to buy a drive that’s not going to fail. I’m going to use data from BackBlaze to assess drive reliability. Backblaze did their own analysis of drive failures, but I don’t like their approach for 2 reasons:
- Their “annualized failure rate”
Drive Failures / (Drive Days / 365)assumes that failure rates are constant over time. E.g. this assumption means that observing 1 drive for 100 days gives you the exact same information as observing 100 drives for 1 day. If drives fail at a constant rate over time, this is fine, but I suspect that drives actually fail at a higher rate early in their lives. So their analysis is biased against newer drives. - I want to compute a confidence interval, so I can select a drive where we have enough observations to be very confident in a low failure rate. For example, if I have a model drive that’s been observed for one drive for 1 day with 0 failures, I probably don’t want to buy it, despite it’s zero percent failure rate. I’d rather buy a drive model thats been observed for 100 drives for 1000 days with one failure. This blog post has some good details on why confidence intervals are useful for sorting things.
I chose to order the drives by their expected 5 year survival rate. I calculated a 95% confidence interval on the 5-year survival rate, and I used that interval to sort the drives. Based on this analysis, the wdc wuh721816ale6l4 is the most reliable model in our data, with an estimated 5-year survival rate that is at least 97.46%. (In other words we are 95% confident that at least 97.46% of the wdc wuh721816ale6l4’s will last at least 5 years).
Here are the top drives from this analysis, by size. (for example, many manufacturers have drives in the 16TB range that are very reliable, but I’m only showing the single best model in this size range).
| model | capacity_tb | N | drive_days | failures | years_97pct | surv_5yr_lo | surv_5yr | surv_5yr_hi |
|---|---|---|---|---|---|---|---|---|
| wdc wuh721816ale6l4 | 16 | 26602 | 11616742 | 102 | 5.7 | 97.46% | 97.91% | 98.28% |
| wdc wuh721414ale6l4 | 14 | 8603 | 10867094 | 113 | 5.6 | 97.43% | 97.86% | 98.22% |
| wdc hms5c4040ale640 | 04 | 8716 | 18224627 | 253 | 5.4 | 97.30% | 97.61% | 97.89% |
| wdc huh721212ale600 | 12 | 2673 | 4483664 | 61 | 4.8 | 96.80% | 97.50% | 98.05% |
| st6000dx000 | 06 | 1939 | 4330559 | 100 | 3.6 | 95.25% | 96.09% | 96.78% |
| wdc hds5c3030ala630 | 03 | 4664 | 6934573 | 150 | 3.5 | 95.18% | 95.88% | 96.48% |
| wdc huh728080ale600 | 08 | 1218 | 2609201 | 73 | 3.0 | 94.18% | 95.34% | 96.28% |
| wdc hds722020ala330 | 02 | 4774 | 5675646 | 235 | 1.9 | 90.53% | 91.62% | 92.60% |
| wdc wuh722222ale6l4 | 22 | 13244 | 1279877 | 42 | 1.8 | 89.85% | 92.40% | 94.36% |
| st10000nm0086 | 10 | 1304 | 2924650 | 202 | 1.5 | 87.58% | 89.09% | 90.44% |
| st18000nm000j | 18 | 70 | 82370 | 10 | 0.5 | 63.65% | 77.91% | 87.67% |
Data details:
- model is the drive model.
- capacity_tb is the size of the drive.
- N is the number of unique drives in the analysis.
- drive_days is the total number of days that we’ve observed for drives of this model in the sample.
- failures is the number of failures observed so far.
- years_97pct Is the 97th percentile survival time for the drives. 97% of the drives will last at least this long.
- surv_5yr_lo is the lower bound of the 95% confidence interval of the 5-year survival rate.
- surv_5yr is the 5-year survival rate.
- surv_5yr_hi is the upper bound of the 95% confidence interval of the 5-year survival rate.
Survival analysis is a little weird, because you don’t observe the full distribution of the data. This makes some traditional statistics impossible to calculate. For example, until you observe every hard drive in the sample fail, you can’t know the mean time to failure: if you have one drive left that hasn’t failed, and becomes an outlier in survival time, that might have a big impact on mean survival time. You won’t know the true mean until that last drive fails.
Similarly, to find the median survival time, you need to wait for half of the drives in your sample fail, which can take a decade or more!
Modern hard drives are so reliable, that even after 5+ years of observation, we’ve barely observed the distribution of failures! (This is a good thing, but it makes it hard to chose between drives!).
To compare models with different observational periods (e.g. 22 TB vs 4TB drives), I fit a Cox Proportional Hazard model. This enabled me to estimate 5 years survival rates for all of the drives, as well as a confidence interval on that rate. The confidence interval narrows as you observe more drives and as you observe those drives for a longer time.
The Cox model is semi-parametric. It assumes a non-parametric, baseline hazard rate that is the same for all drives. It then fits a single parameter for each drive that is a multiple on that baseline hazard rate. So every drive has the same “shape” for its survival curve, but multiplied by a fixed coefficient per model that makes that “shape” steeper or shallower.
Here is a plot of the survival for each of the best drive models. Each curve ends with the oldest drive we’ve observed (these are called Kaplan–Meier curves):
The “proportional hazards” assumption from the Cox model allows us to extend these curves and estimate survival times at 5 years for all of the drives:
Note that the curves all have the same shape, but each model has a different slope. Compare this plot to the Kaplan-Meier plot above: The proportional hazards assumption works pretty well, but isn’t perfect.
I manually gatherted some hard drive prices from ebay and amazon. I limited this search to drives with >70% expected 5 years survival, as I want to buy drives that are unlikely to fail on me. I can then use the price data to calculate the cost to store 1TB of data for 5 years for each drive. Note that these prices could be wrong, and also not that only one drive may be available at the given price.
| model | price | capacity_tb | surv_5yr_lower | cost_per_tb | cost_per_tb_5yr |
|---|---|---|---|---|---|
| st16000nm001g | 165.0 | 16 | 94.9 | 10.3 | 10.9 |
| st12000nm001g | 126.0 | 12 | 94.3 | 10.5 | 11.1 |
| st12000nm0008 | 122.0 | 12 | 90.0 | 10.2 | 11.3 |
| st14000nm001g | 149.9 | 14 | 91.7 | 10.7 | 11.7 |
| wdc huh721212ale600 | 148.8 | 12 | 96.8 | 12.4 | 12.8 |
| wdc wuh721414ale6l4 | 179.0 | 14 | 97.4 | 12.8 | 13.1 |
| wdc wuh721816ale6l4 | 209.0 | 16 | 97.5 | 13.1 | 13.4 |
| wdc huh728080ale600 | 101.2 | 08 | 94.2 | 12.6 | 13.4 |
| st4000dm000 | 48.0 | 04 | 88.5 | 12.0 | 13.6 |
| toshiba mg08aca16te | 200.0 | 16 | 91.8 | 12.5 | 13.6 |
| wdc hds722020ala330 | 25.1 | 02 | 90.5 | 12.6 | 13.9 |
| st6000dx000 | 85.0 | 06 | 95.3 | 14.2 | 14.9 |
| st8000nm0055 | 109.0 | 08 | 91.5 | 13.6 | 14.9 |
| wdc wuh721816ale6l0 | 229.9 | 16 | 96.0 | 14.4 | 15.0 |
| st10000nm0086 | 133.0 | 10 | 87.6 | 13.3 | 15.2 |
| toshiba mg08aca16tey | 229.9 | 16 | 93.5 | 14.4 | 15.4 |
| st12000nm0007 | 162.0 | 12 | 87.5 | 13.5 | 15.4 |
| st16000nm002j | 225.0 | 16 | 90.9 | 14.1 | 15.5 |
| wdc hus726040ale610 | 50.1 | 04 | 79.2 | 12.5 | 15.8 |
| st8000nm000a | 111.8 | 08 | 88.2 | 14.0 | 15.8 |
| wdc hds5c3030ala630 | 45.7 | 03 | 95.2 | 15.2 | 16.0 |
| st8000dm002 | 119.7 | 08 | 93.4 | 15.0 | 16.0 |
| wdc huh721212ale604 | 199.0 | 12 | 94.3 | 16.6 | 17.6 |
| toshiba dt01aca300 | 45.5 | 03 | 70.2 | 15.2 | 21.6 |
| wdc hds724040ale640 | 69.0 | 04 | 79.2 | 17.2 | 21.8 |
| wdc hds723030ala640 | 59.8 | 03 | 88.6 | 19.9 | 22.5 |
| wdc wuh722222ale6l4 | 450.0 | 22 | 89.8 | 20.5 | 22.8 |
| wdc wd30efrx | 54.0 | 03 | 73.5 | 18.0 | 24.5 |
| wdc wd40efrx | 79.5 | 04 | 77.6 | 19.9 | 25.6 |
| wdc hms5c4040ale640 | 102.0 | 04 | 97.3 | 25.5 | 26.2 |
| wdc wd60efrx | 125.0 | 06 | 77.5 | 20.8 | 26.9 |
| toshiba mg08aca16ta | 415.0 | 16 | 91.9 | 25.9 | 28.2 |
| wdc huh721212aln604 | 418.0 | 12 | 92.1 | 34.8 | 37.8 |
| wdc hds5c4040ale630 | 180.0 | 04 | 95.6 | 45.0 | 47.1 |
| st8000dm005 | 603.8 | 08 | 75.0 | 75.5 | 100.6 |
According to this analysis, the most cost effective drive is the st16000nm001g, which costs $165 and has a 5 year survival rate of 94.9%. This drive costs $10.87 to store 1 TB for 5 years (this price includes the probability of failure).
Again, the price data is probably incorrect, but its still an interesting analysis.
Here are the full results for all drives, excluding drives that are less than 2TB:
| model | capacity_tb | N | drive_days | failures | years_97pct | surv_5yr_lo | surv_5yr | surv_5yr_hi |
|---|---|---|---|---|---|---|---|---|
| wdc wuh721816ale6l4 | 16 | 26602 | 11616742 | 102 | 5.7 | 97.46% | 97.91% | 98.28% |
| wdc wuh721414ale6l4 | 14 | 8603 | 10867094 | 113 | 5.6 | 97.43% | 97.86% | 98.22% |
| wdc hms5c4040ale640 | 04 | 8716 | 18224627 | 253 | 5.4 | 97.30% | 97.61% | 97.89% |
| wdc huh721212ale600 | 12 | 2673 | 4483664 | 61 | 4.8 | 96.80% | 97.50% | 98.05% |
| wdc wuh721816ale6l0 | 16 | 3069 | 2772374 | 37 | 4.0 | 96.02% | 97.10% | 97.89% |
| wdc hds5c4040ale630 | 04 | 2837 | 4790383 | 95 | 3.8 | 95.61% | 96.40% | 97.04% |
| st6000dx000 | 06 | 1939 | 4330559 | 100 | 3.6 | 95.25% | 96.09% | 96.78% |
| wdc hds5c3030ala630 | 03 | 4664 | 6934573 | 150 | 3.5 | 95.18% | 95.88% | 96.48% |
| st16000nm001g | 16 | 34293 | 22614411 | 480 | 3.4 | 94.88% | 95.32% | 95.72% |
| toshiba mg07aca14ta | 14 | 39365 | 51123732 | 1376 | 3.1 | 94.40% | 94.69% | 94.96% |
| st12000nm001g | 12 | 13627 | 16705713 | 434 | 3.1 | 94.31% | 94.82% | 95.27% |
| wdc huh721212ale604 | 12 | 13519 | 15607978 | 392 | 3.1 | 94.28% | 94.81% | 95.30% |
| wdc huh728080ale600 | 08 | 1218 | 2609201 | 73 | 3.0 | 94.18% | 95.34% | 96.28% |
| toshiba mg08aca16tey | 16 | 5347 | 4846164 | 125 | 2.7 | 93.48% | 94.51% | 95.37% |
| st8000dm002 | 08 | 10307 | 27580788 | 1111 | 2.7 | 93.41% | 93.79% | 94.15% |
| wdc huh721212aln604 | 12 | 11422 | 20559877 | 882 | 2.3 | 92.06% | 92.55% | 93.01% |
| toshiba mg08aca16ta | 16 | 39184 | 12456523 | 361 | 2.2 | 91.88% | 92.67% | 93.39% |
| toshiba mg08aca16te | 16 | 6130 | 5737006 | 192 | 2.2 | 91.84% | 92.89% | 93.81% |
| toshiba md04aba400v | 04 | 150 | 378365 | 11 | 2.2 | 91.74% | 95.32% | 97.39% |
| st14000nm001g | 14 | 11177 | 13299198 | 504 | 2.2 | 91.68% | 92.36% | 92.98% |
| st8000nm0055 | 08 | 15680 | 36632508 | 1893 | 2.1 | 91.46% | 91.84% | 92.20% |
| st16000nm002j | 16 | 468 | 259866 | 4 | 2.0 | 90.91% | 96.45% | 98.66% |
| wdc hds722020ala330 | 02 | 4774 | 5675646 | 235 | 1.9 | 90.53% | 91.62% | 92.60% |
| st12000nm0008 | 12 | 20955 | 31032423 | 1615 | 1.8 | 89.96% | 90.42% | 90.87% |
| wdc wuh722222ale6l4 | 22 | 13244 | 1279877 | 42 | 1.8 | 89.85% | 92.40% | 94.36% |
| wdc hds723030ala640 | 03 | 1048 | 1495337 | 73 | 1.6 | 88.62% | 90.84% | 92.65% |
| st4000dm000 | 04 | 37040 | 81347421 | 5770 | 1.6 | 88.51% | 88.83% | 89.14% |
| toshiba mg07aca14tey | 14 | 738 | 692480 | 28 | 1.6 | 88.22% | 91.69% | 94.20% |
| st8000nm000a | 08 | 249 | 128292 | 1 | 1.5 | 88.20% | 98.18% | 99.74% |
| st10000nm0086 | 10 | 1304 | 2924650 | 202 | 1.5 | 87.58% | 89.09% | 90.44% |
| st12000nm0007 | 12 | 38842 | 36947060 | 2173 | 1.5 | 87.48% | 88.00% | 88.51% |
| wdc hds724040ale640 | 04 | 45 | 64934 | 2 | 1.0 | 79.20% | 94.08% | 98.51% |
| wdc hus726040ale610 | 04 | 55 | 69213 | 3 | 1.0 | 79.16% | 92.49% | 97.56% |
| wdc wd40efrx | 04 | 50 | 77099 | 4 | 0.9 | 77.59% | 90.65% | 96.44% |
| wdc wd60efrx | 06 | 499 | 692834 | 72 | 0.9 | 77.55% | 81.68% | 85.20% |
| st8000dm005 | 08 | 27 | 53730 | 3 | 0.8 | 75.01% | 90.77% | 96.99% |
| wdc wd30efrx | 03 | 1335 | 1365902 | 174 | 0.8 | 73.54% | 76.71% | 79.61% |
| toshiba dt01aca300 | 03 | 60 | 78820 | 7 | 0.6 | 70.16% | 84.05% | 92.19% |
| st4000dm005 | 04 | 90 | 95987 | 11 | 0.6 | 69.16% | 81.19% | 89.25% |
| st14000nm0138 | 14 | 1690 | 1951778 | 317 | 0.6 | 68.21% | 70.99% | 73.62% |
| st33000651as | 03 | 351 | 241851 | 31 | 0.6 | 66.53% | 74.90% | 81.75% |
| st18000nm000j | 18 | 70 | 82370 | 10 | 0.5 | 63.65% | 77.91% | 87.67% |
| toshiba hdwf180 | 08 | 69 | 65254 | 9 | 0.4 | 60.88% | 76.61% | 87.33% |
| st12000nm000j | 12 | 482 | 74238 | 7 | 0.4 | 60.47% | 77.96% | 89.10% |
| wdc huh728080ale604 | 08 | 98 | 77317 | 10 | 0.4 | 59.18% | 74.76% | 85.82% |
| wdc wd30ezrx | 03 | 500 | 149891 | 22 | 0.3 | 54.14% | 66.31% | 76.65% |
| st4000dx000 | 04 | 222 | 305172 | 81 | 0.3 | 52.70% | 59.57% | 66.08% |
| wdc wd20efrx | 02 | 167 | 88330 | 15 | 0.3 | 52.05% | 66.77% | 78.81% |
| st10000nm001g | 10 | 29 | 23056 | 2 | 0.3 | 50.56% | 82.44% | 95.57% |
| st32000542as | 02 | 385 | 147014 | 33 | 0.2 | 46.35% | 57.43% | 67.81% |
| wdc hds723030ble640 | 03 | 10 | 14225 | 1 | 0.2 | 45.21% | 87.06% | 98.21% |
| st14000nm0018 | 14 | 80 | 52551 | 12 | 0.2 | 41.31% | 59.29% | 75.08% |
| st4000dm004 | 04 | 20 | 13505 | 1 | 0.1 | 38.97% | 84.33% | 97.84% |
| wdc hus728t8tale6l4 | 08 | 20 | 13248 | 1 | 0.1 | 38.39% | 84.05% | 97.81% |
| st14000nm000j | 14 | 143 | 26784 | 5 | 0.1 | 33.15% | 60.27% | 82.27% |
| st4000dm001 | 04 | 425 | 95868 | 34 | 0.1 | 25.86% | 37.26% | 50.29% |
| seagate barracuda ssd za2000cm10002 | 02 | 4 | 6645 | 1 | 0.1 | 24.80% | 75.70% | 96.71% |
| wdc hds723020bla642 | 02 | 11 | 9985 | 3 | 0.1 | 18.89% | 52.09% | 83.54% |
| st3000dm001 | 03 | 4707 | 2463925 | 1708 | 0.1 | 17.25% | 18.88% | 20.62% |
| st12000nm0117 | 12 | 30 | 13805 | 7 | 0.1 | 14.78% | 36.11% | 64.81% |
| st2000vn000 | 02 | 10 | 4920 | 2 | 0.0 | 6.48% | 37.76% | 84.15% |
| wdc wd30ezrs | 03 | 18 | 5421 | 2 | 0.0 | 5.53% | 35.29% | 83.55% |
| st2000dm001 | 02 | 8 | 2510 | 1 | 0.0 | 1.92% | 33.21% | 92.67% |
| st320005xxxx | 02 | 18 | 6256 | 7 | 0.0 | 0.50% | 4.96% | 35.21% |
| st4000dx002 | 04 | 7 | 2337 | 4 | 0.0 | 0.02% | 1.53% | 49.92% |
| st8000dm004 | 08 | 7 | 2823 | 7 | 0.0 | 0.01% | 0.35% | 19.01% |
| st2000dl001 | 02 | 12 | 1556 | 5 | 0.0 | 0.00% | 0.09% | 29.71% |
| st2000dl003 | 02 | 17 | 1278 | 8 | 0.0 | 0.00% | 0.00% | 1.24% |
| wdc hus726040aln610 | 04 | 19 | 4483 | 0 | 0.0 | 0.00% | 100.00% | 100.00% |
| st16000nm000j | 16 | 62 | 15848 | 0 | 0.0 | 0.00% | 100.00% | 100.00% |
| st12000nm003g | 12 | 5 | 2031 | 0 | 0.0 | 0.00% | 100.00% | 100.00% |
| st16000nm005g | 16 | 26 | 33182 | 0 | 0.0 | 0.00% | 100.00% | 100.00% |
| wdc hds5c3030ble630 | 03 | 1 | 1477 | 0 | 0.0 | 0.00% | 100.00% | 100.00% |
| st6000dm001 | 06 | 15 | 17510 | 0 | 0.0 | 0.00% | 100.00% | 100.00% |
| st6000dm004 | 06 | 3 | 2761 | 0 | 0.0 | 0.00% | 100.00% | 100.00% |
| toshiba hdwe160 | 06 | 10 | 10437 | 0 | 0.0 | 0.00% | 100.00% | 100.00% |
| wdc huh721010ale600 | 10 | 20 | 40104 | 0 | 0.0 | 0.00% | 100.00% | 100.00% |
| wdc hms5c4040ble641 | 04 | 1 | 2070 | 0 | 0.0 | 0.00% | 100.00% | 100.00% |
Note that some drives have a very low sample size, which gives them a very wide confidence interval. More data are needed for these drives to draw conclusions about their survival rates.
results/drive_dates.csv has the cleaned up data from backblaze, with each drive by serial number, model, when it was installed, when it was last observed, and whether it failed.
| serial_number | capacity_tb | model | min_date | max_date | failed |
|---|---|---|---|---|---|
| PL1311LAG2AULH | 4 | wdc hds5c4040ale630 | 2013-04-10 | 2024-06-30 | 0 |
| W300B5H1 | 4 | st4000dm000 | 2013-06-27 | 2024-06-30 | 0 |
| W300CK7H | 4 | st4000dm000 | 2013-06-27 | 2024-06-30 | 0 |
| W300BA76 | 4 | st4000dm000 | 2013-06-28 | 2024-06-30 | 0 |
| Z300GPBY | 4 | st4000dm000 | 2013-07-23 | 2024-06-30 | 0 |
| Z300GYP2 | 4 | st4000dm000 | 2013-07-23 | 2024-06-30 | 0 |
| W300B2WJ | 4 | st4000dm000 | 2013-07-25 | 2024-06-30 | 0 |
| W300B37Q | 4 | st4000dm000 | 2013-07-25 | 2024-06-30 | 0 |
| PK1331PAJ2V6WS | 4 | wdc hds724040ale640 | 2013-10-15 | 2024-06-30 | 0 |
| W300460J | 4 | st4000dm000 | 2013-10-15 | 2024-06-30 | 0 |
I use a Makefile to automate the analysis. Run make help
for more info, or just run make all to download the data, unzip and
combine it, run the survival analysis and generate the
README.md. The download is 50+ GB, so it takes a while, but
you only need to do it once.
An interesting note about this data: It’s 55GB uncompressed, and contains a whole bunch of irrelevant information. It was very interesting to me that I could compress a 55GB dataset to 22 Mb, while still keeping all of the relevant information for modeling. (In other words, this dataset was thousands of times larger than it needed to be!). I think this is another example of how good data structures are essential for data science.
I’m probably way over-thinking this, but it was fun to analyze the data.
There are some drives in this data I plan to avoid. For example, the st3000dm001 has a 5 year survival of 18.9%. I’d be a little nervous to buy this drive. Backblaze has a good analysis of issues with 3TB drives on their blog.
