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Add file system benchmarks to the benchmark doc.
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Sebastian Jeltsch
@@ -634,3 +634,167 @@ export function FibonacciPocketBaseAndTrailBaseUsageChart() {
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/>
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);
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}
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const fsColors = {
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ext4: "#008b6dff",
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xfs: "#29c299ff",
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bcachefs: "#47a1cdff",
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btrfsNoComp: "#ba36c8ff",
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btrfsZstd1: "#c865d5ff",
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btrfsLzo: "#db9be3ff",
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zfs: "#e6bb1eff",
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};
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export function TrailBaseFileSystemReadLatency() {
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// i100k i10k ip50 ip75 ip90 ip95 rp50 rp75 rp90 rp95
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// ext4 2.415 0.23942 355 425 476 499 169 196 226 249
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// zfs 3.532 0.35463 535 581 655 730 170 197 229 253
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// xfs 2.3789 0.24695 372 441 481 503 168 195 226 248
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// btrfs no compr 3.2142 0.32212 475 533 646 689 168 195 226 249
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// btrfs zstd:1 3.1774 0.31789 475 523 607 659 167 194 225 249
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// btrfs lzo 3.2673 0.34607 513 609 687 726 167 194 224 247
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// bcachefs 2.6001 0.27165 398 489 547 572 169 195 226 249
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// 2025-02-01
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const readLatenciesMicroSec = {
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ext4: [169, 196, 226, 249],
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zfs: [170, 197, 229, 253],
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xfs: [168, 195, 226, 248],
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btrfsNoComp: [168, 195, 226, 249],
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btrfsZstd1: [167, 194, 225, 249],
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btrfsLzo: [167, 194, 224, 247],
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bcachefs: [169, 195, 226, 249],
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};
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const data: ChartData<"bar"> = {
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labels: ["p50", "p75", "p90", "p95"],
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datasets: [
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{
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label: "ext4",
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data: readLatenciesMicroSec.ext4,
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backgroundColor: fsColors.ext4,
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},
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{
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label: "xfs",
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data: readLatenciesMicroSec.xfs,
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backgroundColor: fsColors.xfs,
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},
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{
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label: "bcachefs",
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data: readLatenciesMicroSec.bcachefs,
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backgroundColor: fsColors.bcachefs,
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},
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{
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label: "btrfs w/o compression",
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data: readLatenciesMicroSec.btrfsNoComp,
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backgroundColor: fsColors.btrfsNoComp,
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},
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{
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label: "btrfs zstd:1",
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data: readLatenciesMicroSec.btrfsZstd1,
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backgroundColor: fsColors.btrfsZstd1,
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},
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{
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label: "btrfs lzo",
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data: readLatenciesMicroSec.btrfsLzo,
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backgroundColor: fsColors.btrfsLzo,
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},
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{
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label: "zfs",
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data: readLatenciesMicroSec.zfs,
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backgroundColor: fsColors.zfs,
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},
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],
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};
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return (
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<BarChart
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data={data}
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scales={{
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y: {
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title: {
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display: true,
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text: "Read Latency [µs]",
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},
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},
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}}
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/>
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);
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}
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export function TrailBaseFileSystemWriteLatency() {
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// i100k i10k ip50 ip75 ip90 ip95 rp50 rp75 rp90 rp95
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// ext4 2.415 0.23942 355 425 476 499 169 196 226 249
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// zfs 3.532 0.35463 535 581 655 730 170 197 229 253
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// xfs 2.3789 0.24695 372 441 481 503 168 195 226 248
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// btrfs no compr 3.2142 0.32212 475 533 646 689 168 195 226 249
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// btrfs zstd:1 3.1774 0.31789 475 523 607 659 167 194 225 249
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// btrfs lzo 3.2673 0.34607 513 609 687 726 167 194 224 247
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// bcachefs 2.6001 0.27165 398 489 547 572 169 195 226 249
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// 2025-02-01
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const writeLatenciesMicroSec = {
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ext4: [355, 425, 476, 499],
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zfs: [535, 581, 655, 730],
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xfs: [372, 441, 481, 503],
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btrfsNoComp: [475, 533, 646, 689],
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btrfsZstd1: [475, 523, 607, 659],
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btrfsLzo: [513, 609, 687, 726],
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bcachefs: [398, 489, 547, 572],
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};
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const data: ChartData<"bar"> = {
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labels: ["p50", "p75", "p90", "p95"],
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datasets: [
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{
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label: "ext4",
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data: writeLatenciesMicroSec.ext4,
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backgroundColor: fsColors.ext4,
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},
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{
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label: "xfs",
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data: writeLatenciesMicroSec.xfs,
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backgroundColor: fsColors.xfs,
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},
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{
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label: "bcachefs",
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data: writeLatenciesMicroSec.bcachefs,
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backgroundColor: fsColors.bcachefs,
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},
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{
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label: "btrfs w/o compression",
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data: writeLatenciesMicroSec.btrfsNoComp,
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backgroundColor: fsColors.btrfsNoComp,
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},
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{
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label: "btrfs zstd:1",
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data: writeLatenciesMicroSec.btrfsZstd1,
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backgroundColor: fsColors.btrfsZstd1,
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},
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{
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label: "btrfs lzo",
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data: writeLatenciesMicroSec.btrfsLzo,
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backgroundColor: fsColors.btrfsLzo,
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},
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{
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label: "zfs",
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data: writeLatenciesMicroSec.zfs,
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backgroundColor: fsColors.zfs,
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},
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],
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};
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return (
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<BarChart
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data={data}
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scales={{
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y: {
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title: {
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display: true,
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text: "Read Latency [µs]",
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},
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},
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}}
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/>
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);
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}
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@@ -12,6 +12,8 @@ import {
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SupaBaseMemoryUsageChart,
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SupaBaseCpuUsageChart,
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PocketBaseAndTrailBaseUsageChart,
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TrailBaseFileSystemReadLatency,
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TrailBaseFileSystemWriteLatency,
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FibonacciPocketBaseAndTrailBaseUsageChart,
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} from "./_benchmarks/benchmarks.tsx";
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@@ -178,6 +180,49 @@ being roughly 5 times slower than p50.
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Slower insertions can take north of 100ms. This may be related to GC pauses,
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scheduling, or more generally the same CPU variability we observed earlier.
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## File System Performance
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File systems play an important role for the performance of storage systems,
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we'll therefore take a quick at their impact on SQLite/TrailBase's performance.
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Note that the numbers aren't directly comparable to the ones above, due to
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being taken on a different machine with more storage options,
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specifically an AMD 8700G with a 2TB WD SN850x NVMe SSD running a 6.12 kernel.
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<div class="flex justify-center h-[340px] w-[90%]">
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<div class="w-[50%]">
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<TrailBaseFileSystemReadLatency client:only="solid-js" />
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</div>
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<div class="w-[50%]">
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<TrailBaseFileSystemWriteLatency client:only="solid-js" />
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</div>
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</div>
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Interestingly, the read performance appears identical suggesting that caching
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is at play w/o much real file system interaction.
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In the future we should rerun with a larger data set to observe the degraded
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performance for when not everything fits into memory anymore.
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Yet, most queries being cached is a not too uncommon reality and it's
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reassuring to see that caches are indeed working independent of the file system
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😅.
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The write latencies are bit more interesting. Unsurprisingly modern
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copy-on-write (CoW) file systems have a bit more overhead. The relative ranking
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is roughly in line with
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[Phoronix'](https://www.phoronix.com/review/linux-611-filesystems/2)
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results[^7], with the added OpenZFS (2.2.7) falling in line with its peers.
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Also, the differences are attenuated due to the constant overhead TrailBase
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adds over vanilla SQLite.
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We won't discuss the specific trade-offs and baggage coming with each file
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system but hope that the numbers can help guide the optimization of your own
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production setup.
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In the end its a trade-off between performance, maturity, reliability, physical
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disc space and feature sets. For example, CoW snapshots may or may not be
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important to you.
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## JavaScript Performance
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The benchmark sets up a custom HTTP endpoint `/fibonacci?n=<N>` using the same
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@@ -224,9 +269,8 @@ threads will be an effective remedy (`--js-runtime-threads`).
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We're very happy to confirm that TrailBase's APIs and JS/ES6/TS runtime are
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quick.
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The significant performance gap we observed, especially for the APIs, is likely
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a consequence of how quick SQLite is making even small overheads matter that
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much more.
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The significant performance gap we observed for API access is a consequence of
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how fast SQLite itself is making even small overheads matter that much more.
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With the numbers fresh off the press, prudence is of the essence and ultimately
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nothing beats benchmarking your own setup and workloads.
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@@ -282,3 +326,8 @@ The benchmarks are available on [GitHub](https://github.com/trailbaseio/trailbas
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Runtime-effects, such as garbage collection, may have an effect, however we
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would have expected these to show on shorter time-scales.
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This could also indicate a contention or thrashing issue 🤷.
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[^7]:
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Despite being close, XFS' and ext4's relative ranking is swapped, which
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could be due to a variety of reason like mount options, kernel version,
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hardware, ...
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