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Datasets
Research tables with explicit methods, sources and download rights.
62 datasets · Page 2 of 3
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IOPS, bandwidth, queue depth and latency: a worked table from Little's law
How IOPS, bandwidth, queue depth and mean latency relate, as a worked table of 100 rows: four block sizes (4 KiB to 1 MiB), queue depths of 1 to 128 and mean latencies of 100 microseconds to 10 milliseconds. The rules are IOPS = queue depth / mean latency (Little's law) and bandwidth = IOPS x block size, in decimal MB/s and in MiB/s as fio prints them. Pure arithmetic, no measurement. For example, queue depth 1 at 100 microseconds is 10,000 IOPS and 40.96 MB/s at 4 KiB, and queue depth 32 at 1 millisecond is 32,000 IOPS.
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Key-value separation: cleaning-cost sensitivity
Thirty deterministic byte-accounting cases compare inline records with separated values, including relocation-driven index writes. Invented inputs, not engine measurements.
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Linux names 4 blk-mq schedulers; I/O priority lists 8 RT levels for bfq and mq-deadline
Linux switching-sched names 4 per-device blk-mq schedulers: mq-deadline, none, bfq, and kyber. I/O priority is named for bfq and mq-deadline, with 3 classes, 8 RT class-data levels, and best-effort levels 0 (highest) through 7 (lowest); none and kyber are not named. Kyber has 2 tunables, read and synchronous-write targets in nanoseconds, which are throttle targets rather than a measured latency. The none scheduler does not reorder, and completion order is not guaranteed. No throughput was measured.
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Linux zero-copy source lifetime: eight file-to-pipe checks
40 file-to-pipe observations, plus 10 separate tee(2) checks, distinguish source stability from enqueue and consumer completion.
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Low-end NetApp interconnect events exceed disk events, 4,338 to 3,230
In Jiang et al., FAST 2008, Table 1, low-end NetApp systems recorded 4,338 physical-interconnect failure events and 3,230 disk failure events from January 2004 through August 2007 (22,031 systems and 264,983 FC disks ever installed). For Figure 4(b), which excludes subsystems that used Disk H, that class has a stated disk AFR of 0.9% and a storage-subsystem AFR of 4.6%. In Maneas et al., FAST 2020, Table 2, SCSI error is 32.78% of SSD replacements (ARR 0.055%) on almost 1.4 million NetApp SSDs; that signal is a hardware error the SSD reports (the example is drive DRAM ECC), while aborted commands are 13.56% (ARR 0.023%) and include a host or connection case where write data never reaches the device.
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LRC repair costs: explicit denominators
Five deterministic whole-fragment examples distinguish data repair, all-fragment averages and parity-inclusive accounting. Not cluster measurements.
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LTO generations: native and compressed capacity and throughput, LTO-1 to LTO-10
Native and advertised compressed capacity, release year and maximum native throughput of LTO Ultrium generations 1 to 10 (11 rows, with the 30 TB and 40 TB LTO-10 cartridges), taken from the LTO Program pages for generations 8, 9 and 10 and the Wikipedia generations table for the rest, plus arithmetic: the hours to write one full cartridge at the maximum native rate and the capacity step from one generation to the next. Native capacity goes from 0.1 TB in LTO-1 to 40 TB in LTO-10, and the 40 TB cartridge takes 27.78 hours to fill at 400 MB/s. Two published speed figures do not match their own arithmetic and are flagged.
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NAND geometry and timing: historical source values
Selected chip parameters and simulation results from 2008-2018, with explicit source scope. These are not current SSD specifications or a controlled comparison of NAND generations.
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Over 56% of failed drives had no count on four strong SMART signals
Over 56% of failed drives in the Google fleet studied by Pinheiro, Weber, and Barroso (FAST '07; more than 100,000 consumer PATA and SATA drives; SMART counts from December 2005 through August 2006) had no count on scan errors, reallocation count, offline reallocation, or probational count. Adding every other SMART parameter except temperature still left over 36% of failed drives at zero on all of those variables. That 56% figure is an upper bound on coverage for the four signals, not a false-positive-aware accuracy.
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Parity declustering: survivor-read accounting
Twenty-one exact survivor rows compare fixed and balanced width-four groups with a width-eight control. Synthetic accounting, not storage measurements.
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Parquet checksum and read-coverage observations
9 read cases across 3 repetitions show how checksum presence, verification and selected columns or row groups change detection of one deliberate payload bit flip.
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Payload ceilings of SATA, SAS and PCIe x4 links by generation
Theoretical one-direction payload ceilings for 14 storage-link generations: SATA 1.0 to 3.0, SAS-1 to SAS-4 (one lane), and PCIe 1.0 to 7.0 at four lanes. Each value is the published line rate times the line-code efficiency, and nothing is measured. SATA 3.0 gives 600 MB/s, SAS-4 gives 2,400 MB/s, PCIe 4.0 x4 gives 7,877 MB/s and PCIe 7.0 x4 gives 60,500 MB/s. Real drives and protocols stay below these ceilings.
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Perseus fail-slow drives: reported counts and benchmark limits
Reported Perseus drive counts and benchmark scores, with population limits and a separate comparison to operator incident reports.
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Power-on age of failed drives versus the fleet, Backblaze Q4 2025
Per-model power-on age of the hard drives that failed in Backblaze's Q4 2025 (October 1 to December 31, 2025) against the age of the fleet on December 31, computed from Backblaze's raw daily Drive Stats files. Sample: 34 hard-drive models, 334,392 drives on December 31, 30,534,025 drive days, 943 failures (1.13% pooled AFR), of which 924 carry a power-on-hours value. Pooled medians are 4.65 years for failed drives and 3.38 years for the fleet, but within single models the two medians are close.
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RAID write costs: reads, writes and payload
56 deterministic RAID5/P+Q examples compare read-modify-write and reconstruct-write with explicit byte denominators. No device measurements or timing predictions.
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RAID write-hole states: an exhaustive one-bit XOR model
32 synthetic states distinguish inconsistent parity, recovery of an untouched missing chunk and preservation of an interrupted update. Not hardware measurements or failure probabilities.
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RAIDZ expansion: ideal retained parity overhead
Five deterministic mixtures of old and new full-width parity geometry for the same 6 TiB payload. Not measured storage capacity or ZFS space-report output.
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Read repair: three constructed state traces
A finite example contrasts a narrow blocking repair, response-only reconciliation and a full-partition read, without running a Cassandra cluster.
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Read-only SSD intervals: when byte WAF is undefined
Four constructed counter-delta cases preserve zero-host-write intervals, distinguish ratios from rates, and show why sequential windows must be combined before division.
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Replacing disks at 200 reallocated sectors removed 88% of triple-disk RAID failures at one vendor
Ma et al., FAST 2015, analysed about 1 million SATA disks in 6 models in one vendor's backup systems (logs of 21 to 60 months from June 2008). Replacing a disk once its reallocated-sector count passed 200 avoided about 88% of triple-disk RAID failures, which were 80% of RAID failures, or about 70% of all disk-related incidents. In simulation, thresholds below 200 caught 52% to 70% of failures within 60 days with 0.8% to 4.5% false positives. Cross-check rows come from Google (FAST 2007) and Backblaze (2016).
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Reported SSD failure-group measurements (FAST 2021)
Sixteen attributed measurements from published storage studies, with explicit populations and units. The Alibaba grouping shares use all recorded failures as their denominator; the adjacent-size group ratio is not a next-drive forecast. This CSV is a corrected transcription, not raw telemetry or a reproduced experiment.
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Sector-error prediction and adaptive scrubbing: reported figures
Selected published figures for drive-week prediction, simulated detection time and two other fleets; these are different endpoints, not a pooled estimate.
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Silent corruption: reading a 1.53-million-drive field study
FAST 2008 recorded checksum mismatches on 0.86% of nearline and 0.065% of enterprise disks. Unequal observation times and detection coverage limit what these shares mean.
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Slow RAID hours are not request tail latency
A slow-drive hour, a slow-RAID hour and a request percentile count different events. This source review separates them and shows why fan-out estimates need explicit dependence and workload assumptions.
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SMART 5, 187, 188, 197 and 198 in failed versus surviving drives, Backblaze Q4 2025
Failure-day SMART association in Backblaze Q4 2025: 690 of 926 failed drives reporting at least one selected attribute have a positive raw value (74.5%), versus 8.1% in the December 31 nonfailed snapshot. Of 236 failed rows with reported values but no positive, 66 have all five zero and 170 have partial coverage; another 19 have none. Capacity filter: at least 3 TB. Not a prediction test.
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SMART replacement prediction: recall, F-score and random splits
Botezatu et al. report 98% Seagate recall; their Hitachi table gives 79%, while the prose says 81%. These random-split results are not a prospective deployment estimate.
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SSD mapping-table footprint: exact byte accounting
Twelve constructed flat-map cases distinguish TB from TiB, mapping granularity from host sectors, and cache address coverage from cached user data.
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SSD over-provisioning: analytical model values
Seven computed reserve-to-write-amplification cases with explicit logical and physical capacity denominators. Analytical model evaluation, not SSD measurements.
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SSD steady-state window counterexamples
Six synthetic five-round IOPS examples separate variation, drift and local stability. These are arithmetic counterexamples, not SSD benchmark measurements.
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Storage latency: synthetic arrival cohorts
390 synthetic request records compare independent and completion-driven arrivals during one pause; downloadable timestamps reproduce the p99 calculation.