Science and technology

An introduction to bpftrace for Linux

Bpftrace is a brand new open supply tracer for Linux for analyzing manufacturing efficiency issues and troubleshooting software program. Its customers and contributors embrace Netflix, Facebook, Red Hat, Shopify, and others, and it was created by Alastair Robertson, a proficient UK-based developer who has received numerous coding competitions.

Linux already has many efficiency instruments, however they’re usually counter-based and have restricted visibility. For instance, iostat(1) or a monitoring agent could let you know your common disk latency, however not the distribution of this latency. Distributions can reveal a number of modes or outliers, both of which can be the true reason behind your efficiency issues. Bpftrace is suited to this type of evaluation: decomposing metrics into distributions or per-event logs and creating new metrics for visibility into blind spots.

You can use bpftrace by way of one-liners or scripts, and it ships with many prewritten instruments. Here is an instance that traces the distribution of learn latency for PID 181 and exhibits it as a power-of-two histogram:

# bpftrace -e 'kprobe:vfs_read /pid == 30153/
kretprobe:vfs_read /@begin[tid]/ @ns = hist(nsecs - @begin[tid]); delete(@begin[tid]); '
Attaching 2 probes...

[256, 512)         10900 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@                      |
[512, 1k)          18291 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@|
[1k, 2k)            4998 |@@@@@@@@@@@@@@                                      |
[2k, 4k)              57 |                                                    |
[4k, 8k)             117 |                                                    |
[8k, 16okay)             48 |                                                    |
[16okay, 32okay)           109 |                                                    |
[32okay, 64okay)             three |                                                    |

This instance devices one occasion out of 1000’s accessible. If you may have some bizarre efficiency drawback, there’s most likely some bpftrace one-liner that may make clear it. For massive environments, this capability may also help you save tens of millions. For smaller environments, it may be of extra use in serving to to remove latency outliers.

I previously wrote about bpftrace vs. different tracers, together with BCC (BPF Compiler Collection). BCC is nice for canned complicated instruments and brokers. Bpftrace is finest for brief scripts and advert hoc investigations. In this text, I will summarize the bpftrace language, variable varieties, probes, and instruments.

Bpftrace makes use of BPF (Berkeley Packet Filter), an in-kernel execution engine that processes a digital instruction set. BPF has been prolonged (aka eBPF) in recent times for offering a protected approach to prolong kernel performance. It additionally has develop into a sizzling matter in methods engineering, with at the very least 24 talks on BPF on the final Linux Plumber’s Conference. BPF is within the Linux kernel, and bpftrace is one of the best ways to get began utilizing BPF for observability.

See the bpftrace INSTALL information for easy methods to set up it, and get the newest model; 0.9.2 was simply launched. For Kubernetes clusters, there’s additionally kubectl-trace for operating it.


probe[,probe,...] /filter/  motion 

The probe specifies what occasions to instrument. The filter is non-obligatory and might filter down the occasions based mostly on a boolean expression, and the motion is the mini-program that runs.

Here’s hi there world:

# bpftrace -e 'BEGIN '

The probe is BEGIN, a particular probe that runs initially of this system (like awk). There’s no filter. The motion is a printf() assertion.

Now an actual instance:

# bpftrace -e 'kretprobe:sys_read /pid == 181/  @bytes = hist(retval); '

This makes use of a kretprobe to instrument the return of the sys_read() kernel perform. If the PID is 181, a particular map variable @bytes is populated with a log2 histogram perform with the return worth retval of sys_read(). This produces a histogram of the returned learn measurement for PID 181. Is your app doing numerous one byte reads? Maybe that may be optimized.

Probe varieties

These are libraries of associated probes. The at the moment supported varieties are (extra can be added):

Type Description
tracepoint Kernel static instrumentation factors
usdt User-level statically outlined tracing
kprobe Kernel dynamic perform instrumentation
kretprobe Kernel dynamic perform return instrumentation
uprobe User-level dynamic perform instrumentation
uretprobe User-level dynamic perform return instrumentation
software program Kernel software-based occasions
Hardware counter-based instrumentation
watchpoint Memory watchpoint occasions (in growth)
profile Timed sampling throughout all CPUs
interval Timed reporting (from one CPU)
BEGIN Start of bpftrace
END End of bpftrace

Dynamic instrumentation (aka dynamic tracing) is the superpower that allows you to hint any software program perform in a operating binary with out restarting it. This enables you to resolve nearly any drawback. However, the features it exposes should not thought of a secure API, as they will change from one software program model to a different. Hence static instrumentation, the place occasion factors are hard-coded and develop into a secure API. When you write bpftrace applications, attempt to use the static varieties first, earlier than the dynamic ones, so your applications are extra secure.

Variable varieties

Variable Description
@title world
@title[key] hash
@title[tid] thread-local
$title scratch

Variables with an @ prefix use BPF maps, which might behave like associative arrays. They could be populated in considered one of two methods:

  • Variable task: @title = x;
  • Function task: @title = hist(x);

Various map-populating features are in-built to supply fast methods to summarize knowledge.

Built-in variables and features

Here are a few of the built-in variables and features, however there are various extra.

Built-in variables:

Variable Description
pid course of ID
comm Process or command title
nsecs Current time in nanoseconds
kstack Kernel stack hint
ustack User-level stack hint
arg0…argN Function arguments
args Tracepoint arguments
retval Function return worth
title Full probe title

Built-in features:

Function Description
printf(“…”) Print formatted string
time(“…”) Print formatted time
system(“…”) Run shell command
@ = rely() Count occasions
@ = hist(x) Power-of-2 histogram for x
@ = lhist(x, min, max, step) Linear histogram for x

See the reference guide for particulars.

One-liners tutorial

A good way to be taught bpftrace is by way of one-liners, which I changed into a one-liners tutorial that covers the next:

Listing probes bpftrace -l ‘tracepoint:syscalls:sys_enter_*’
Hello world bpftrace -e ‘BEGIN ‘
File opens bpftrace -e ‘tracepoint:syscalls:sys_enter_open ‘
Syscall counts by course of bpftrace -e ‘tracepoint:raw_syscalls:sys_enter ‘
Distribution of learn() bytes bpftrace -e ‘tracepoint:syscalls:sys_exit_read /pid == 18644/ ‘
Kernel dynamic tracing of learn() bytes bpftrace -e ‘kretprobe:vfs_read @bytes = lhist(retval, zero, 2000, 200) ‘
Timing learn()s bpftrace -e ‘kprobe:vfs_read @begin[tid] = nsecs kretprobe:vfs_read /@begin[tid]/ ‘
Count process-level occasions bpftrace -e ‘tracepoint:sched:sched* @[name] = rely() interval:s:5 exit() ‘
Profile on-CPU kernel stacks bpftrace -e ‘profile:hz:99 ‘
Scheduler tracing bpftrace -e ‘tracepoint:sched:sched_switch ‘
Block I/O tracing bpftrace -e ‘tracepoint:block:block_rq_issue @ = hist(args->bytes);
Kernel struct tracing (a script, not a one-liner) Command: bpftrace, the place the file is:

#embrace <linux/path.h>
#embrace <linux/dcache.h>


See the tutorial for an evidence of every.

Apart from one-liners, bpftrace applications could be multi-line scripts. Bpftrace ships with 28 of them as instruments:

These could be discovered within the /tools listing:

instruments# ls *.bt 

Apart from their use in diagnosing efficiency points and normal troubleshooting, additionally they present one other approach to be taught bpftrace. Here are some examples.


Here’s the code to

instruments# cat -n
     1  /*
     2   *    Block I/O latency as a histogram.
     three   *                  For Linux, makes use of bpftrace, eBPF.
     four   *
     5   * This is a bpftrace model of the bcc instrument of the identical title.
     6   *
     7   * Copyright 2018 Netflix, Inc.
     eight   * Licensed below the Apache License, Version (the "License")
     9   *
    10   * 13-Sep-2018  Brendan Gregg   Created this.
    11   */
    13  BEGIN
    18  kprobe:blk_account_io_start
    20          @begin[arg0] = nsecs;
    23  kprobe:blk_account_io_done
    24  /@begin[arg0]/
    27          @usecs = hist((nsecs - @begin[arg0]) / 1000);
    28          delete(@begin[arg0]);
    31  END
    33          clear(@begin);

It’s easy, simple to learn, and quick sufficient to incorporate on a slide. This model makes use of kernel dynamic tracing to instrument the blk_account_io_start() and blk_account_io_done() features, and it passes a timestamp between them keyed on arg0 to every. arg0 on kprobe is the primary argument to that perform, which is the struct request *, and its reminiscence tackle is used as a singular identifier.

Example recordsdata

You can see screenshots and explanations of those instruments within the GitHub repo as *_example.txt recordsdata. For example:

instruments# extra biolatency_example.txt
Demonstrations of biolatency, the Linux BPF/bpftrace model.

This traces block I/O, and exhibits latency as a power-of-2 histogram. For instance:

Attaching three probes...
Tracing block gadget I/O... Hit Ctrl-C to finish.

[256, 512)             2 |                                                    |
[512, 1K)             10 |@                                                   |
[1K, 2K)             426 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@|
[2K, 4K)             230 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@                        |
[4K, 8K)               9 |@                                                   |
[8K, 16Okay)            128 |@@@@@@@@@@@@@@@                                     |
[16Okay, 32Okay)            68 |@@@@@@@@                                            |
[32Okay, 64Okay)             zero |                                                    |
[64Okay, 128Okay)            zero |                                                    |
[128Okay, 256Okay)          10 |@                                                   |

While tracing, this exhibits that 426 block I/O had a latency of between 1K and 2K
usecs (1024 and 2048 microseconds), which is between 1 and a couple of milliseconds.
There are additionally two modes seen, one between 1 and a couple of milliseconds, and one other
between eight and 16 milliseconds: this seems like cache hits and cache misses.
There had been additionally 10 I/O with latency 128 to 256 ms: outliers. Other instruments and
instrumentation, like, can shed extra gentle on these outliers.

Sometimes it may be simplest to modify straight to the instance file when making an attempt to grasp these instruments, for the reason that output could also be self-evident (by design!).

Man pages

There are additionally man pages for each instrument within the GitHub repo below /man/man8. They embrace sections on the output fields and the instrument’s anticipated overhead.

# nroff -man man/man8/biolatency.eight
biolatency(eight)               System Manager's Manual              biolatency(eight)

NAME - Block I/O latency as a histogram. Uses bpftrace/eBPF.


       This  instrument  summarizes  time  (latency) spent in block gadget I/O (disk
       I/O) as a power-of-2 histogram. This  permits  the  distribution  to  be
       studied,  together with  modes and outliers. There are sometimes two modes, one
       for gadget cache hits and one for cache misses, which could be  proven  by
       this instrument. Latency outliers may also be proven.

Writing all these man pages was the least enjoyable a part of creating these instruments, and a few took longer to write down than the instrument took to develop, nevertheless it’s good to see the ultimate end result.

bpftrace vs. BCC

Since eBPF has been merging within the kernel, most effort has been positioned on the BCC frontend, which offers a BPF library and Python, C++, and Lua interfaces for writing applications. I’ve developed a whole lot of tools in BCC/Python; it really works nice, though coding in BCC is verbose. If you are hacking away at a efficiency problem, bpftrace is healthier to your one-off customized queries. If you are writing a instrument with many command-line choices or an agent that makes use of Python libraries, you will need to think about using BCC.

On the Netflix efficiency group, we use each: BCC for creating canned instruments that others can simply use and for creating brokers; and bpftrace for advert hoc evaluation. The community engineering group has been utilizing BCC to develop an agent for its wants. The safety group is most taken with bpftrace for fast advert hoc instrumentation for detecting zero-day vulnerabilities. And I count on the developer groups will use each with out figuring out it, by way of the self-service GUIs we’re constructing (Vector), and sometimes could SSH into an occasion and run a canned instrument or advert hoc bpftrace one-liner.

Learn extra

I even have a e-book popping out this yr that covers bpftrace: BPF Performance Tools: Linux System and Application Observability, to be revealed by Addison Wesley, and which accommodates many new bpftrace instruments.

Thanks to Alastair Robertson for creating bpftrace, and the bpftrace, BCC, and BPF communities for all of the work over the previous 5 years.

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