Prometheus exporter for custom eBPF metrics and OpenTelemetry traces.
- Metrics:
Motivation of this exporter is to allow you to write eBPF code and export metrics that are not otherwise accessible from the Linux kernel.
ebpf.io describes eBPF:
eBPF is a revolutionary technology with origins in the Linux kernel that can run sandboxed programs in a privileged context such as the operating system kernel. It is used to safely and efficiently extend the capabilities of the kernel without requiring to change kernel source code or load kernel modules.
An easy way of thinking about this exporter is bcc tools as prometheus metrics:
We use libbpf rather than legacy bcc driven code, so it's more like libbpf-tools:
Producing OpenTelemetry compatible traces is also supported, see Tracing docs for more information on that.
- https://www.brendangregg.com/ebpf.html
- https://nakryiko.com/posts/bpf-core-reference-guide/
- https://nakryiko.com/posts/bpf-portability-and-co-re/
- https://nakryiko.com/posts/bcc-to-libbpf-howto-guide/
- https://libbpf.readthedocs.io/en/latest/program_types.html
To build a binary, clone the repo and run:
make build
The default build target makes a static binary, but you could also
use the build-dynamic target if you'd like a dynamically linked binary.
In either case libbpf is built from source, but you could override this
behavior with BUILD_LIBBPF=0, if you want to use your system libbpf.
If you're having trouble building on the host, you can try building in Docker:
docker build --tag ebpf_exporter --target ebpf_exporter .
docker cp $(docker create ebpf_exporter):/ebpf_exporter ./
To build examples (see building examples section):
make -C examples clean build
To run with biolatency config:
sudo ./ebpf_exporter --config.dir=examples --config.names=biolatency
If you pass --debug, you can see raw maps at /maps endpoint
and see debug output from libbpf itself.
A docker image can be built from this repo. A prebuilt image with examples included is also available for download from GitHub Container Registry:
To build the image with just the exporter binary, run the following:
docker build --tag ebpf_exporter --target ebpf_exporter .
To run it with the examples, you need to build them first (see above). Then you can run by running a privileged container and bind-mounting:
$(pwd)/examples:/examples:roto allow access to examples on the host/sys/fs/cgroup:/sys/fs/cgroup:roto allow resolving cgroups
You might have to bind-mount additional directories depending on your needs. You might also not need to bind-mount anything for simple kprobe examples.
The actual command to run the docker container (from the repo directory):
docker run --rm -it --privileged -p 9435:9435 \
-v $(pwd)/examples:/examples \
-v /sys/fs/cgroup:/sys/fs/cgroup:ro \
ebpf_exporter --config.dir=examples --config.names=timers
For production use you would either bind-mount your own config and compiled bpf programs corresponding to it, or build your own image based on ours with your own config baked in.
For development use when you don't want or have any dev tools on the host, you can build the docker image with examples bundled:
docker build --tag ebpf_exporter --target ebpf_exporter_with_examples .
Some examples then can run without any bind mounts:
docker run --rm -it --privileged -p 9435:9435 \
ebpf_exporter --config.dir=examples --config.names=timers
Or with the publicly available prebuilt image:
docker run --rm -it --privileged -p 9435:9435 \
ghcr.io/cloudflare/ebpf_exporter --config.dir=examples --config.names=timers
A third party helm chart is available here:
Please note that the helm chart is not provided or supported by Cloudflare, so do your own due diligence and use it at your own risk.
See benchmark directory to get an idea of how low ebpf overhead is.
While you can run ebpf_exporter as root, it is not strictly necessary.
Only the following two capabilities are necessary for normal operation:
CAP_BPF: required for privileged bpf operations and for reading memoryCAP_PERFMON: required to attach bpf programs to kprobes and tracepoints
If you are using systemd, you can use the following configuration to run
as on otherwise unprivileged dynamic user with the needed capabilities:
DynamicUser=true
AmbientCapabilities=CAP_BPF CAP_PERFMON
CapabilityBoundingSet=CAP_BPF CAP_PERFMONPrior to Linux v5.8 there was no dedicated CAP_BPF and CAP_PERFMON,
but you can use CAP_SYS_ADMIN instead of your kernel is older.
If you pass --capabilities.keep=none flag to ebpf_expoter, then it drops
all capabilities after attaching the probes, leaving it fully unprivileged.
The following additional capabilities might be needed:
CAP_SYSLOG: if you useksymdecoder to have access to/proc/kallsyms. Note that you must keep this capability:--capabilities.keep=cap_syslog. See: https://elixir.bootlin.com/linux/v6.4/source/kernel/kallsyms.c#L982CAP_IPC_LOCK: if you useperf_event_arrayfor reading from the kernel. Note that you must keep it:--capabilities.keep=cap_perfmon,cap_ipc_lock.CAP_SYS_ADMIN: if you want BTF information from modules. See: https://github.com/libbpf/libbpf/blob/v1.2.0/src/libbpf.c#L8654-L8666 and https://elixir.bootlin.com/linux/v6.5-rc1/source/kernel/bpf/syscall.c#L3789CAP_NET_ADMIN: if you use net admin related programs like xdp. See: https://elixir.bootlin.com/linux/v6.4/source/kernel/bpf/syscall.c#L3787CAP_SYS_RESOURCE: if you run an older kernel without memcg accounting for bpf memory. Upstream Linux kernel added support for this in v5.11. See: https://github.com/libbpf/libbpf/blob/v1.2.0/src/bpf.c#L98-L106CAP_DAC_READ_SEARCH: if you want to usefanotifyto monitor cgroup changes, which is the preferred way, but only available since Linux v6.6. See: https://github.com/torvalds/linux/commit/0ce7c12e88cf
Execution of eBPF programs requires kernel data types normally available
in /sys/kernel/btf/vmlinux, which is created during kernel build process.
However, on some older kernel configurations, this file might not be available.
If that's the case, an external BTF file can be supplied with --btf.path.
An archive of BTFs for all some older distros and kernel versions can be
found here.
Currently the only supported way of getting data out of the kernel is via maps.
See examples section for real world examples.
If you have examples you want to share, please feel free to open a PR.
Skip to format to see the full specification.
You can find additional examples in examples directory.
Unless otherwise specified, all examples are expected to work on Linux 5.15, which is the latest LTS release at the time of writing. Thanks to CO-RE, examples are also supposed to work on any modern kernel with BTF enabled.
You can find the list of supported distros in libbpf README:
To build examples, run:
make -C examples clean build
This will use clang to build examples with vmlinux.h we provide
in this repo (see include for more on vmlinux.h).
Examples need to be compiled before they can be used.
Note that compiled examples can be used as is on any BTF enabled kernel with no runtime dependencies. Most modern Linux distributions have it enabled.
This config attaches to kernel tracepoints for timers subsystem and counts timers that fire with breakdown by timer name.
Resulting metrics:
# HELP ebpf_exporter_timer_starts_total Timers fired in the kernel
# TYPE ebpf_exporter_timer_starts_total counter
ebpf_exporter_timer_starts_total{function="blk_stat_timer_fn"} 10
ebpf_exporter_timer_starts_total{function="commit_timeout [jbd2]"} 1
ebpf_exporter_timer_starts_total{function="delayed_work_timer_fn"} 25
ebpf_exporter_timer_starts_total{function="dev_watchdog"} 1
ebpf_exporter_timer_starts_total{function="mix_interrupt_randomness"} 3
ebpf_exporter_timer_starts_total{function="neigh_timer_handler"} 1
ebpf_exporter_timer_starts_total{function="process_timeout"} 49
ebpf_exporter_timer_starts_total{function="reqsk_timer_handler"} 2
ebpf_exporter_timer_starts_total{function="tcp_delack_timer"} 5
ebpf_exporter_timer_starts_total{function="tcp_keepalive_timer"} 6
ebpf_exporter_timer_starts_total{function="tcp_orphan_update"} 16
ebpf_exporter_timer_starts_total{function="tcp_write_timer"} 12
ebpf_exporter_timer_starts_total{function="tw_timer_handler"} 1
ebpf_exporter_timer_starts_total{function="writeout_period"} 5
There's config file for it:
metrics:
counters:
- name: timer_starts_total
help: Timers fired in the kernel
labels:
- name: function
size: 8
decoders:
- name: ksymAnd corresponding C code that compiles into an ELF file with eBPF bytecode:
#include <vmlinux.h>
#include <bpf/bpf_tracing.h>
#include "maps.bpf.h"
struct {
__uint(type, BPF_MAP_TYPE_HASH);
__uint(max_entries, 1024);
__type(key, u64);
__type(value, u64);
} timer_starts_total SEC(".maps");
SEC("tp_btf/timer_start")
int BPF_PROG(timer_start, struct timer_list *timer)
{
u64 function = (u64) timer->function;
increment_map(&timer_starts_total, &function, 1);
return 0;
}
char LICENSE[] SEC("license") = "GPL";This config attaches to block io subsystem and reports disk latency as a prometheus histogram, allowing you to compute percentiles.
The following tools are working with similar concepts:
- https://github.com/iovisor/bcc/blob/master/tools/biosnoop_example.txt
- https://github.com/iovisor/bcc/blob/master/tools/biolatency_example.txt
- https://github.com/iovisor/bcc/blob/master/tools/bitesize_example.txt
This program was the initial reason for the exporter and was heavily influenced by the experimental exporter from Daniel Swarbrick:
Resulting metrics:
# HELP ebpf_exporter_bio_latency_seconds Block IO latency histogram
# TYPE ebpf_exporter_bio_latency_seconds histogram
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="1e-06"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="2e-06"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="4e-06"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="8e-06"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="1.6e-05"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="3.2e-05"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="6.4e-05"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.000128"} 22
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.000256"} 36
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.000512"} 40
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.001024"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.002048"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.004096"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.008192"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.016384"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.032768"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.065536"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.131072"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.262144"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="0.524288"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="1.048576"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="2.097152"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="4.194304"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="8.388608"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="16.777216"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="33.554432"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="67.108864"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="134.217728"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme0n1",operation="write",le="+Inf"} 48
ebpf_exporter_bio_latency_seconds_sum{device="nvme0n1",operation="write"} 0.021772
ebpf_exporter_bio_latency_seconds_count{device="nvme0n1",operation="write"} 48
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="1e-06"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="2e-06"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="4e-06"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="8e-06"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="1.6e-05"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="3.2e-05"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="6.4e-05"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.000128"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.000256"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.000512"} 0
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.001024"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.002048"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.004096"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.008192"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.016384"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.032768"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.065536"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.131072"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.262144"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="0.524288"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="1.048576"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="2.097152"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="4.194304"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="8.388608"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="16.777216"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="33.554432"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="67.108864"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="134.217728"} 1
ebpf_exporter_bio_latency_seconds_bucket{device="nvme1n1",operation="write",le="+Inf"} 1
ebpf_exporter_bio_latency_seconds_sum{device="nvme1n1",operation="write"} 0.0018239999999999999
ebpf_exporter_bio_latency_seconds_count{device="nvme1n1",operation="write"} 1
You can nicely plot this with Grafana:
The following concepts exists within ebpf_exporter.
Configs describe how to extract metrics from kernel. Each config has a corresponding eBPF code that runs in kernel to produce these metrics.
Multiple configs can be loaded at the same time.
Metrics define what values we get from eBPF program running in the kernel.
Counters from maps are direct transformations: you pull data out of kernel, transform map keys into sets of labels and export them as prometheus counters.
Histograms from maps are a bit more complex than counters. Maps in the kernel cannot be nested, so we need to pack keys in the kernel and unpack in user space.
We get from this:
sda, read, 1ms -> 10 ops
sda, read, 2ms -> 25 ops
sda, read, 4ms -> 51 ops
To this:
sda, read -> [1ms -> 10 ops, 2ms -> 25 ops, 4ms -> 51 ops]
Prometheus histograms expect to have all buckets when we report a metric, but the kernel creates keys as events occur, which means we need to backfill the missing data.
That's why for histogram configuration we have the following keys:
bucket_type: can be eitherexp2,exp2zero,linear, orfixedbucket_min: minimum bucket key (exp2,exp2zeroandlinearonly)bucket_max: maximum bucket key (exp2,exp2zeroandlinearonly)bucket_keys: maximum bucket key (fixedonly)bucket_multiplier: multiplier for bucket keys (default is1)
For exp2 histograms we expect kernel to provide a map with linear keys that
are log2 of actual values. We then go from bucket_min to bucket_max in
user space and remap keys by exponentiating them:
count = 0
for i = bucket_min; i < bucket_max; i++ {
count += map.get(i, 0)
result[exp2(i) * bucket_multiplier] = count
}
Here map is the map from the kernel and result is what goes to prometheus.
We take cumulative count, because this is what prometheus expects.
These are the same as exp2 histograms, except:
- The first key is for the value
0 - All other keys are
1larger than they should be
This is useful if your actual observed value can be zero, as regular exp2
histograms cannot express this due the the fact that log2(0) is invalid,
and in fact BPF treats log2(0) as 0, and exp2(0) is 1, not 0.
See tcp-syn-backlog-exp2zero.bpf.c
for an example of a config that makes use of this.
For linear histograms we expect kernel to provide a map with linear keys
that are results of integer division of original value by bucket_multiplier.
To reconstruct the histogram in user space we do the following:
count = 0
for i = bucket_min; i < bucket_max; i++ {
count += map.get(i, 0)
result[i * bucket_multiplier] = count
}
For fixed histograms we expect kernel to provide a map with fixed keys
defined by the user.
count = 0
for i = 0; i < len(bucket_keys); i++ {
count += map.get(bucket_keys[i], 0)
result[bucket_keys[i] * multiplier] = count
}
For exp2 and linear histograms, if bucket_max + 1 contains a non-zero
value, it will be used as the sum key in histogram, providing additional
information and allowing richer metrics.
For fixed histograms, if buckets_keys[len(bucket_keys) - 1 ] + 1 contains
a non-zero value, it will be used as the sum key.
For both exp2 and linear histograms it is important that kernel does
not count events into buckets outside of [bucket_min, bucket_max] range.
If you encounter a value above your range, truncate it to be in it. You're
losing +Inf bucket, but usually it's not that big of a deal.
Each kernel map key must count values under that key's value to match
the behavior of prometheus. For example, exp2 histogram key 3 should
count values for (exp2(2), exp2(3)] interval: (4, 8]. To put it simply:
use log2l or integer division and you'll be good.
Labels transform kernel map keys into prometheus labels.
Maps coming from the kernel are binary encoded. Values are always u64, but
keys can be either primitive types like u64 or complex structs.
Each label can be transformed with decoders (see below) according to metric configuration. Generally the number of labels matches the number of elements in the kernel map key.
For map keys that are represented as structs alignment rules apply:
u64must be aligned at 8 byte boundaryu32must be aligned at 4 byte boundaryu16must be aligned at 2 byte boundary
This means that the following struct:
struct disk_latency_key_t {
u32 dev;
u8 op;
u64 slot;
};Is represented as:
- 4 byte
devinteger - 1 byte
opinteger - 3 byte padding to align
slot - 8 byte
slotinteger
When decoding, either specify the padding explicitly with the key padding or
include it in the label size:
- 4 for
dev - 4 for
op(1 byte value + 3 byte padding) - 8 byte
slot
Decoders take a byte slice input of requested length and transform it into
a byte slice representing a string. That byte slice can either be consumed
by another decoder (for example string -> regexp) or or used as the final
label value exporter to Prometheus.
Below are decoders we have built in.
With cgroup decoder you can turn the u64 from bpf_get_current_cgroup_id
into a human readable string representing cgroup path, like:
/sys/fs/cgroup/system.slice/ssh.service
Ifname decoder takes a network interface index and converts it into its
name like eth0.
Dname decoder read DNS qname from string in wire format, then decode
it into '.' notation format. Could be used after string decoder.
E.g.: \x07example\03com\x00 will become example.com. This decoder
could be used after string decode, like the following example:
- name: qname
decoders:
- name: string
- name: dnameErrno decoder converts errno number into a string representation like
EPIPE. It is normally paired with a unit decoder as the first step.
Hex decoder turns bytes into their hex representation.
Network IP decoded can turn byte encoded IPv4 and IPv6 addresses
that kernel operates on into human readable form like 1.1.1.1.
KSym decoder takes kernel address and converts that to the function name.
In your eBPF program you can use PT_REGS_IP_CORE(ctx) to get the address
of the function you attached to as a u64 variable. Note that for kprobes
you need to wrap it with KPROBE_REGS_IP_FIX() from regs-ip.bpf.h.
With major-minor decoder you can turn kernel's combined u32 view
of major and minor device numbers into a device name in /dev.
With pci_vendor decoder you can transform PCI vendor IDs like 0x8086
into human readable vendor names like Intel Corporation.
With pci_vendor decoder you can transform PCI vendor IDs like 0x80861000
into human readable names like 82542 Gigabit Ethernet Controller (Fiber).
Note that the you need to concatenate vendor and device id together for this.
With pci_class decoder you can transform PCI class ID (the lowest byte) into
the class name like Network controller.
With pci_subclass decoder you can transform PCI subclass (two lowest bytes)
into the subclass name like Ethernet controller.
Regexp decoder takes list of strings from regexp configuration key
of the decoder and ties to use each as a pattern in golang.org/pkg/regexp:
If decoder input matches any of the patterns, it is permitted. Otherwise, the whole metric label set is dropped.
An example to report metrics only for systemd-journal and syslog-ng:
- name: command
decoders:
- name: string
- name: regexp
regexps:
- ^(kswapd).*$ # if sub-matches are present, the first one is used for the value
- ^systemd-journal$
- ^syslog-ng$Static map decoder takes input and maps it to another value via static_map
configuration key of the decoder. Values are expected as strings.
An example to match 1 to read and 2 to write:
- name: operation
decoders:
- name:static_map
static_map:
1: read
2: writeUnknown keys will be replaced by "unknown:key_name" unless allow_unknown: true
is specified in the decoder. For example, the above will decode 3 to unknown:3
and the below example will decode 3 to 3:
- name: operation
decoders:
- name:static_map
allow_unknown: true
static_map:
1: read
2: writeString decoder transforms possibly null terminated strings coming from the kernel into string usable for prometheus metrics.
Syscall decoder transforms syscall numbers into syscall names.
The tables can be regenerated by make syscalls. See scripts/mksyscalls.
UInt decoder transforms hex encoded uint values from the kernel
into regular base10 numbers. For example: 0xe -> 14.
Per CPU map reading is fully supported. If the last decoder for a percpu
map is called cpu (use 2 byte uint decoder), then cpu label is
added automatically. If it's not present, then the percpu counters are
aggregated into one global counter.
There is percpu-softirq in examples. See #226 for examples of different modes of operation for it.
Configuration file is defined like this:
# Metrics attached to the program
[ metrics: metrics ]
# Kernel symbol addresses to define as kaddr_{symbol} from /proc/kallsyms (consider CONFIG_KALLSYMS_ALL)
kaddrs:
[ - symbol_to_resolve ]
See Metrics section for more details.
counters:
[ - counter ]
histograms:
[ - histogram ]
See Counters section for more details.
name: <prometheus counter name>
help: <prometheus metric help>
perf_event_array: <whether map is a BPF_MAP_TYPE_PERF_EVENT_ARRAY map: bool>
flush_interval: <how often should we flush metrics from the perf_event_array: time.Duration>
labels:
[ - label ]
An example of perf_map can be found here.
See Histograms section for more details.
name: <prometheus histogram name>
help: <prometheus metric help>
bucket_type: <map bucket type: exp2 or linear>
bucket_multiplier: <map bucket multiplier: float64>
bucket_min: <min bucket value: int>
bucket_max: <max bucket value: int>
labels:
[ - label ]
See Labels section for more details.
name: <prometheus label name>
size: <field size>
padding: <padding size>
decoders:
[ - decoder ]
See Decoders section for more details.
name: <decoder name>
# ... decoder specific configuration
This gauge reports a timeseries for every loaded config:
# HELP ebpf_exporter_enabled_configs The set of enabled configs
# TYPE ebpf_exporter_enabled_configs gauge
ebpf_exporter_enabled_configs{name="cachestat"} 1
This gauge reports information available for every ebpf program:
# HELP ebpf_exporter_ebpf_programs Info about ebpf programs
# TYPE ebpf_exporter_ebpf_programs gauge
ebpf_exporter_ebpf_program_info{config="cachestat",id="545",program="add_to_page_cache_lru",tag="6c007da3187b5b32"} 1
ebpf_exporter_ebpf_program_info{config="cachestat",id="546",program="mark_page_accessed",tag="6c007da3187b5b32"} 1
ebpf_exporter_ebpf_program_info{config="cachestat",id="547",program="folio_account_dirtied",tag="6c007da3187b5b32"} 1
ebpf_exporter_ebpf_program_info{config="cachestat",id="548",program="mark_buffer_dirty",tag="6c007da3187b5b32"} 1
Here tag can be used for tracing and performance analysis with two conditions:
net.core.bpf_jit_kallsyms=1sysctl is set--kallsyms=/proc/kallsymsis passed toperf record
Newer kernels allow --kallsyms to perf top as well,
in the future it may not be required at all:
This gauge reports whether individual programs were successfully attached.
# HELP ebpf_exporter_ebpf_program_attached Whether a program is attached
# TYPE ebpf_exporter_ebpf_program_attached gauge
ebpf_exporter_ebpf_program_attached{id="247"} 1
ebpf_exporter_ebpf_program_attached{id="248"} 1
ebpf_exporter_ebpf_program_attached{id="249"} 0
ebpf_exporter_ebpf_program_attached{id="250"} 1
It needs to be joined by id label with ebpf_exporter_ebpf_program_info
to get more information about the program.
This counter reports how much time individual programs spent running.
# HELP ebpf_exporter_ebpf_program_run_time_seconds How long has the program been executing
# TYPE ebpf_exporter_ebpf_program_run_time_seconds counter
ebpf_exporter_ebpf_program_run_time_seconds{id="247"} 0
ebpf_exporter_ebpf_program_run_time_seconds{id="248"} 0.001252621
ebpf_exporter_ebpf_program_run_time_seconds{id="249"} 0
ebpf_exporter_ebpf_program_run_time_seconds{id="250"} 3.6668e-05
It requires kernel.bpf_stats_enabled sysctl to be enabled.
It needs to be joined by id label with ebpf_exporter_ebpf_program_info
to get more information about the program.
This counter reports how many times individual programs ran.
# HELP ebpf_exporter_ebpf_program_run_count_total How many times has the program been executed
# TYPE ebpf_exporter_ebpf_program_run_count_total counter
ebpf_exporter_ebpf_program_run_count_total{id="247"} 0
ebpf_exporter_ebpf_program_run_count_total{id="248"} 11336
ebpf_exporter_ebpf_program_run_count_total{id="249"} 0
ebpf_exporter_ebpf_program_run_count_total{id="250"} 69
It requires kernel.bpf_stats_enabled sysctl to be enabled.
It needs to be joined by id label with ebpf_exporter_ebpf_program_info
to get more information about the program.
This counter reports the number of times labels failed to be decoded by config.
# HELP ebpf_exporter_decoder_errors_total How many times has decoders encountered errors
# TYPE ebpf_exporter_decoder_errors_total counter
ebpf_exporter_decoder_errors_total{config="kstack"} 0
ebpf_exporter_decoder_errors_total{config="sock-trace"} 4
MIT
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