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UID:pretalx-all-systems-go-2026-XHJX3N@cfp.all-systems-go.io
DTSTART;TZID=CET:20260930T171000
DTEND;TZID=CET:20260930T171500
DESCRIPTION:Observability stacks have gotten good at CPU\, memory\, and net
 work — but storage is still where the picture goes dark. When a workload
  stalls on I/O\, device-level tools like iostat or blktrace tell you the d
 isk was busy\; they don't tell you which service caused it\, or where in t
 he stack the latency actually accrued: a slow fsync\, writeback pressure i
 n the page cache\, a filesystem journal commit\, or a saturated block queu
 e.\n\nThis talk follows a single write from the syscall boundary down to t
 he block layer\, using eBPF to instrument each handoff — VFS\, the files
 ystem\, the page cache\, and block I/O submission and completion — witho
 ut patching applications or running a custom kernel. We'll look at which a
 ttach points (tracepoints\, fentry/fexit\, kprobes) give honest per-layer 
 latency\, how to keep the measurement overhead from biasing the result\, a
 nd how to attribute the latency back to the responsible cgroup or systemd 
 service instead of an aggregate line on a dashboard. Examples range from f
 sync-heavy database commits to large sequential writes that look fine at t
 he device level but stall well above it.\n\nAttendees will leave with a co
 ncrete method for turning storage from a black box into a traceable path 
 — and an honest sense of where eBPF helps\, and where it still can't see
 .
DTSTAMP:20260710T230402Z
LOCATION:Loft
SUMMARY:From fsync to Block I/O: Tracing Storage with eBPF - Kritik Sachdev
 a
URL:https://cfp.all-systems-go.io/all-systems-go-2026/talk/XHJX3N/
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