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BPF: Tracing and More
--client
lca
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lca2017
--room plenary 12124 --force
Next: 12 A tour of the ARM architecture and its Linux support
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Marks
Author(s):
Brendan Gregg
Location
Plenary Hall
Date
jan Tue 17
Days Raw Files
Start
21:15
First Raw Start
20:57
Duration
0:45:0
Offset
0:17:41
End
22:00
Last Raw End
22:27
Chapters
00:00
0:12:03
0:42:03
Total cuts_time
46 min.
http://linux.conf.au/schedule/presentation/96/
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A world of new capabilities is emerging for the Linux 4.x series, thanks to enhancements that have been included in Linux for the Berkeley Packet Filter (BPF): an in-kernel virtual machine that can execute user space-defined programs. It is finding uses for security auditing and enforcement, enhancing networking (including eXpress Data Path), and performance observability and troubleshooting. Many new open source tools that have been written in the past 12 months for performance analysis that use BPF. Tracing superpowers have finally arrived for Linux! For its use with tracing, BPF provides the programmable capabilities to the existing t racing frameworks: kprobes, uprobes, and tracepoints. In particular, BPF allows timestamps to be recorded and compared from custom events, allowing latency to be studied in many new places: kernel and application internals. It also allows data to be efficiently summarized in-kernel, including as histograms. This has allowed dozens of new observability tools to be developed so far, including measuring latency distributions for file system I/O and run queue latency, printing details of storage device I/O and TCP retransmits, investigating blocked stack traces and memory leaks, and a whole lot more. This talk will summarize BPF capabilities and use cases so far, and then focus on its use to enhance Linux tracing, especially with the open source bcc collection. bcc includes BPF versions of old classics, and many new tools, including execsnoop, opensnoop, funcccount, ext4slower, and more (many of which I developed). Perhaps you'd like to develop new tools, or use the existing tools to find performance wins large and small, especially when instrumenting areas that previously had zero visibility. I'll also summarize how we intend to use these new capabilities to enhance systems analysis at Netflix.
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