Distributed bpftrace Deployment

Pixie can deploy bpftrace programs to your cluster, collect the resulting data, and display it in the Live UI. This tutorial will demonstrate how to run a bpftrace program using a PxL script and discuss the guidelines for running arbitrary bpftrace code using Pixie.

Most of the data in Pixie's no-instrumentation monitoring platform is collected by the Pixie Edge Modules (PEMs), which are deployed as a daemonset onto every node in your cluster. These PEMs use eBPF based tracing to collect network transactions without any code changes.

One increasingly popular way to write eBPF programs is to use bpftrace, an open source high-level tracing language for Linux. bpftrace provides a simplified front-end language that makes it easier to write BPF programs when compared to frameworks such as BCC. Many bpftrace programs are written as one-liners or stand-alone scripts.

Now, using Pixie, developers can dynamically run their own bpftrace programs on their cluster. Pixie will handle:

• deploying the bpftrace program to all of the nodes in your cluster.
• capturing the output of the bpftrace program's printf statements into a table.
• making the data available to be queried and visualized in the Pixie UI.
• removing the probe(s) after a set expiration time.

bpftrace programs output data through a variety of built-in functions. Examples include printf for general purpose printing, print for printing map contents, and time for printing the current time. bpftrace also automatically prints all maps on termination, which many bpftrace programs rely on.

Pixie's distributed bpftrace deployment feature captures outputs made through bpftrace printf statements, and pushes the arguments into an automatically created table, as shown below.

There are some requirements for bpftrace programs you wish to deploy with Pixie, all of which concern the output mechanism:

• The program must have at least 1 printf statement.
• If the program has more than 1 printf statement, the format string of all printfs must be exactly the same, as it defines the table output columns.
• There should be no printf statements in the BEGIN or END blocks.
• If wishing to specify column names, they must be done by prepending the column name to the format specifier with a colon (example: name:%d). The column names cannot contain any whitespaces.
• To output time in a manner that is recognizable by Pixie, label the column time_ and pass the argument nsecs.

Note that not all programs in the bpftrace repository meet these requirements, but most can be easily adapted to be compatible. For example, in programs with multiple printfs, the extraneous printfs can be removed. Also, programs that output data on termination instead of through printf statements can be converted to instead print the data on a regular interval using an interval block. pidpersec.bt is a good example of this design pattern.

This beta feature has limitations:

• Support for bpftrace kprobes only. Other types of probes will be supported in the future.

In this demo, we'll deploy Dale Hamel's bpftrace TCP retransmit tool using Pixie. TCP retransmits are usually a sign of poor network health and this open-source tool will help us discover if any connections in our cluster are experiencing a high number of retransmits.

We've incorporated this trace into a PxL script called px/tcp_retransmits. To run this script:

• Open up Pixie's Live View and select your cluster.
• Select the px/tcp_retransmits script using the drop down script menu or with Pixie Command. Pixie Command can be opened with the ctrl/cmd+k keyboard shortcut.
• Run the script using the Run button in the top right, or with the ctrl/cmd+enter keyboard shortcut.

Once the probe is deployed to all the nodes in the cluster, the probes will begin to push out data into tables. The PxL script queries this data and the Vis Spec defines how this data will be displayed.

In the Live View, we'll see a graph of the pods (hexagonal grey box icons) and the services (hexagonal grey tree icons) who are are experiencing TCP retransmits.

The color and weight of the arrows between these entities indicates the number of retransmits. Hovering over an arrow will display the number of retransmits for a particular connection. The data displayed in this graph can also be seen in the Data Drawer (use the ctrl/cmd+d keyboard shortcut to open and close this table).

In this particular example, the 3 pods experiencing high levels of retransmits are located on the same node, perhaps indicating an issue with that particular node.

1# Copyright (c) Pixie Labs, Inc.
2# Licensed under the Apache License, Version 2.0 (the "License")
3
4import pxtrace
5import px
6
7# Adapted from https://github.com/iovisor/bpftrace/blob/master/tools/tcpretrans.bt
8program = """
9// tcpretrans.bt Trace or count TCP retransmits
10//               For Linux, uses bpftrace and eBPF.
11//
12// Copyright (c) 2018 Dale Hamel.
13// Licensed under the Apache License, Version 2.0 (the "License")
14
15#include <linux/socket.h>
16#include <net/sock.h>
17
18kprobe:tcp_retransmit_skb
19{
20  $sk = (struct sock *)arg0;
21  $inet_family = $sk->__sk_common.skc_family;
22  $AF_INET = (uint16) 2;
23  $AF_INET6 = (uint16) 10;
24  if ($inet_family == $AF_INET || $inet_family == $AF_INET6) {
25    if ($inet_family == $AF_INET) {
26      $daddr = ntop($sk->__sk_common.skc_daddr);
27      $saddr = ntop($sk->__sk_common.skc_rcv_saddr);
28    } else {
29      $daddr = ntop($sk->__sk_common.skc_v6_daddr.in6_u.u6_addr8);
30      $saddr = ntop($sk->__sk_common.skc_v6_rcv_saddr.in6_u.u6_addr8);
31    }
32    $sport = $sk->__sk_common.skc_num;
33    $dport = $sk->__sk_common.skc_dport;
34    // Destination port is big endian, it must be flipped
35    $dport = ($dport >> 8) | (($dport << 8) & 0x00FF00);
36
37    printf(\"time_:%llu src_ip:%s src_port:%d dst_ip:%s dst_port:%d\",
38      nsecs,
39      $saddr,
40      $sport,
41      $daddr,
42      $dport);
43  }
44}
45"""
46
47
48def demo_func():
49    table_name = 'tcp_retransmits_table'
50    pxtrace.UpsertTracepoint('tcp_retranmits_probe',
51                             table_name,
52                             program,
53                             pxtrace.kprobe(),
54                             "10m")
55    # Rename columns
56    df = px.DataFrame(table=table_name,
57                      select=['time_', 'src_ip', 'src_port', 'dst_ip', 'dst_port'])
58
59    # Convert IPs to domain names.
60    df.src = px.pod_id_to_pod_name(px.ip_to_pod_id(df.src_ip))
61    df.dst = px.Service(px.nslookup(df.dst_ip))
62
63    # Count retransmits.
64    df = df.groupby(['src', 'dst']).agg(retransmits=('ns_src', px.count))
65
66    # Filter for a particular service, if desired.
67    df = df[px.contains(df['dst'], '')]
68
69    return df

Pixie's scripts are written using the Pixie Language (PxL), a domain-specific language that is heavily influenced by the popular Python data processing library Pandas.

On line 8, we've included Dale Hamel's tcpretrans.bt bpftrace tool from the iovisor/bpftrace repo as a string. We've tweaked the original trace in order to work with Pixie's bpftrace rules (seen in the "Output" section above):

• removed the informational print statements on lines 25-26 of tcpretrans.bt so that the program contains a single printf statement.
• modified the printf statement on line 72 of tcpretrans.bt to name the output columns (no whitespaces)
• modified the printf statement on line 72 of tcpretrans.bt to output time using the reserved column name time_ and passing it the nsecs argument.

Some further modifications were made to simplify the program for the purposes of this tutorial (for example, removing the TCP state), but those are not required changes.

On line 50, we call UpsertTracepoint with the following arguments:

• the name of the tracepoint
• the name of the table to push data into
• the type of the trace probe
• the expiration time for the tracepoint

Lines 55-69 query the collected data, convert known IPs to domain names, and group the retransmits by source and destination IPs tallying the number of retransmits.

If you'd like to filter the results to a particular service, modify line 67 to include the namespace:

df = df[px.contains(df['dst'], 'sock-shop')]

Run px/tracepoint_status to see the information about all of the tracepoints running on your cluster. The STATUS column can be used to debug why a tracepoint fails to deploy.

The following bpftrace programs are available today for use in Pixie:

• capable.bt: use the bpftrace/capable script
• dcsnoop.bt: use the bpftrace/dc_snoop script.
• mdflush.bt: use the bpftrace/md_flush script.
• naptime.bt: use the bpftrace/nap_time script.
• oomkill.bt: use the bpftrace/oom_kill script.
• syncsnoop.bt: use the bpftrace/sync_snoop script.
• tcpdrop.bt: use the bpftrace/tcp_drops script.
• tcpretrans.bt: use the bpftrace/tcp_retransmits script.

Many other bpftrace programs can work with Pixie. Some may require a few modifications to obey the rules listed above.

If you have any questions about this feature or how to incorporate your own bpftrace code, we'd be happy to help out over on our Slack.

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