Using eBPF per-CPU Cgroup local storage for low overhead accounting

Aug 22, 2021   #linux  #cgroup  #ebpf 

Linux eBPF ecosystem is growing fast with new features coming with every release cycle. The spirit remains the same. At the core, eBPF is like a safe way to script the Linux kernel by attaching to various pre-defines hooks and exported functions. For kernel / user spaces communication, the eBPF comes with various “map” types.

Every few kernel versions, new “map” types are introduced or significantly improved and 3 years ago BPF_MAP_TYPE_PERCPU_CGROUP_STORAGE was introduced and is now available in recent distribution kernels.

I was intrigued by it and wanted to learn about it with a simple Cgroup network throughput monitoring application. Typically the kind of real-world use-case when running multiple workload on a resource-constrained machine.

It turns out I had a hard time understand how to use effectively and wrote this article with the hope that it could help other people getting started with per-CPU eBPF Cgroup local storage.

Example presentation

This post, will use Cilium’s ebpf library for the userland part. Similar results could be easily achieved in C using libbpf or in Python using BCC. The kernel side eBPF should work with any other userland with no change.

To monitor the target Cgroup ingress and egress throughput, the demo program will attach to the cgroup_skb/ingress and cgroup_skb/egress Cgroup eBPF hooks. Of course, result will be communicated to userland using a map of type BPF_MAP_TYPE_PERCPU_CGROUP_STORAGE.

To keep this post reasonably simple and focused on the eBPF portions, this post will only monitor one specific hard-coded cgroup path.


This is the eBPF skeleton. It starts with a dummy comment kindly asking the go compiler not to attempt compiling it by itself. This is then followed by Linux kernel and BPF helpers includes and by the license definition.

The license may sound superfluous but eBPF programs are subject to the same rule as plain-c kernel modules. Typically, some symbol exports are subject to “GPL only” (or compatible).

// +build ignore

#include <stdbool.h>
#include <linux/bpf.h>
#include <bpf/bpf_helpers.h>
#include <netinet/ip.h>
#include <netinet/ip6.h>

// ---------------------------------------------
// -- The real program will be somewhere here --
// ---------------------------------------------

char __license[] __attribute__((section("license"), used)) = "MIT";

This program can be compiled with:

clang -g -Wall -Werror -O2 -emit-llvm -c bpf-accounting.bpf.c -o - | llc -march=bpf -filetype=obj -o bpf-accounting.bpf.o

Of course, Clang needs to be installed first.

This is now the Go skeleton. It is built around a setup phase and a monitoring phase that will run once every second until “Ctrl+C” or TERM-inated.

Make sure to adjust TARGET_CGROUP_V2_PATH to target the Cgroup V2 of interest. The “/sys/fs/cgroup/unified” prefix may be different from systems to systems. This one comes from a Ubuntu with Systemd still configured to use v1 Cgroup implementation as the main implementation.

EBPF_PROG_ELF may also need adjustment if the eBPF part was not called bpf-accounting.bpf.c.

package main

import (


const (
	TARGET_CGROUP_V2_PATH = "/sys/fs/cgroup/unified/yadutaf" // <--- Change as needed for your system/use-case
	EBPF_PROG_ELF         = "./bpf-accounting.bpf.o"

func main() {
	log.Printf("Attaching eBPF monitoring programs to cgroup %s\n", TARGET_CGROUP_V2_PATH)

	// ------------------------------------------------------------
	// -- The real program initialization will be somewhere here --
	// ------------------------------------------------------------

	// Wait until signaled
	c := make(chan os.Signal, 1)
	signal.Notify(c, syscall.SIGINT)
	signal.Notify(c, syscall.SIGTERM)

	// Periodically check counters
	ticker := time.NewTicker(1 * time.Second)

	for {
		select {
		case <-ticker.C:

			// ------------------------------------------
			// -- And here will be the counters report --
			// ------------------------------------------

		case <-c:
			break out

Data structures

It is now time to introduce BPF_MAP_TYPE_PERCPU_CGROUP_STORAGE. But first, why not using the most typical and easier to use BPF_MAP_TYPE_HASH map type ?

For this article’s use case, BPF_MAP_TYPE_HASH would be a great fit. However, if a real-world application needs to monitor all cgroups, dynamically attach them as they are created or conversely cleanup associated resources when a Cgroup is terminated then BPF_MAP_TYPE_CGROUP_STORAGE is likely a more suitable choice. If, in addition, collected data may be accessed concurrently from multiple CPUs then the per-CPU variant BPF_MAP_TYPE_PERCPU_CGROUP_STORAGE is the go-to choice to avoid spin-locks.

Technically, BPF_MAP_TYPE_PERCPU_CGROUP_STORAGE is actually a /virtual/ map or more accurately a Cgroup local storage in the kernel’s terminology. Internally, the Cgroup data structure has a member dedicated to cgroup local storage. Every program of given type will therefore share the same storage area for a given Cgroup. By default, programs of different types will have a dedicated storage area in the Cgroup. It is however possible to share this storage area between all program types for a given Cgroup.

This has a couple of practical consequences:

  1. By design, the (virtual) map key is conceptually a (cgroup_id, program_type) tuple. This not be customized. The leaf member type is however flexible.
  2. If multiple programs of the same type need to store data in the same Cgroup, have to cooperate for the leaf member type definition.
  3. Programs may only access the storage area of the cgroup they are attached to. Said otherwise, a program attached to Cgroup foo will only access storage for Cgroup foo even if triggered by events in child Cgroup foo/bar. If child Cgroup events needs to be specifically tracked, the same program needs to be attached to the Cgroup of interest as well.

One last thing regarding BPF_MAP_TYPE_PERCPU_CGROUP_STORAGE: Since this is a per-CPU map type, the storage in the group is not directly the leaf member type but a per-CPU array of this type. This is fully transparent to the eBPF portion of the program which has only a view of the current cgroup + CPU. It will however require specific handling for the Go part which has a full view of data stored for each cgroups + CPUs.

For this article, there are 2 program types (cgroup_skb/ingress and cgroup_skb/egress) to match both incoming and outgoing traffic. Since the goal is to measure the number of bytes transmitted in each direction, the leaf member type can be as simple as a uint64.

On the eBPF side, the structure declaration is as simple as:

struct bpf_map_def SEC("maps") cgroup_counters_map = {
    .key_size = sizeof(struct bpf_cgroup_storage_key),
    .value_size = sizeof(__u64),

This defines eBPF map cgroup_counters_map of type BPF_MAP_TYPE_PERCPU_CGROUP_STORAGE with the imposed key type of struct bpf_cgroup_storage_key as defined in ‘bpf.h’ and a plain unsigned 64 bits integer as map member to hold the bytes counter.

The Go side is a bit more involved, but nothing terrible. First, the programs needs to increase the maximum “locked” memory. While this is not necessary for this example, it is a common pitfall when starting with eBPF and using real-world maps. Thus, here it is:

	// Increase max locked memory (for eBPF maps)
	// For a real program, make sure to adjust to actual needs
	unix.Setrlimit(unix.RLIMIT_MEMLOCK, &unix.Rlimit{
		Cur: unix.RLIM_INFINITY,
		Max: unix.RLIM_INFINITY,

The compiled eBPF ELF file which now contains the map definition must now be loaded. This where Cilium’s ebpf library shines. Under the hod, it parses the ELF file and extracts the map definitions and all program metadata “collection”:

	collec, err := ebpf.LoadCollection(EBPF_PROG_ELF)
	if err != nil {

Getting a handle on the map from the collection is then as trivial as:

	// Get a handle on the statistics map
	cgroup_counters_map := collec.Maps["cgroup_counters_map"]

To actually access the map, the program needs the key and member type definition. For the key, Cilium’s library does not (yet) come with a built-in definition. Since the key format is imposed by the Kernel’s ABI this is only a matter of translating struct bpf_cgroup_storage_key from the kernel cgroup storage documentation to go with a dummy 32 bits field at the end for alignment:

type BpfCgroupStorageKey struct {
	CgroupInodeId uint64
	AttachType    ebpf.AttachType
	_             uint32

The go program only needs a definition of the member type. Since the program uses a per-CPU data structure, this needs to be a slice:

type PerCPUCounters []uint64

And since we are interested in the total number of transmitted bytes regardless of the CPU cores, here is a tiny helper:

func sumPerCpuCounters(perCpuCounters PerCPUCounters) uint64 {
	sum := uint64(0)
	for _, counter := range perCpuCounters {
		sum += counter
	return sum

One last thing: to access a map entry, the map structure definition is not enough. There needs to be some actual values in it. The AttachType was already discussed, this is the type of program (ingress, vs egress). The tricky part is the CgroupInodeId. This field is the Inode number of the cgroup path.

Since this example is voluntarily simplistic, it only considers a single pre-defined hard-coded TARGET_CGROUP_V2_PATH cgroup. The Inode ID can thus be pre-loaded once for all:

	// Get cgroup folder inode number to use as a key in the per-cgroup map
	cgroupFileinfo, err := os.Stat(TARGET_CGROUP_V2_PATH)
	if err != nil {
	cgroupStat, ok := cgroupFileinfo.Sys().(*syscall.Stat_t)
	if !ok {
		log.Fatal("Not a syscall.Stat_t")
	cgroupInodeId := cgroupStat.Ino

With this in place, all the supporting structures are defined and loaded on both the eBPF and Go side. Let’s start to actually store something useful in these structures.

Counting Cgroup ingress / egress bytes

There are 4 cases to consider:

  • The transmitted packet may be on the ingress or the egress path.
  • The transmitted packet may be IPv4 or IPv6.

All these cases can be simply handled in a common function on the eBPF side:

inline int handle_skb(struct __sk_buff *skb)
    __u16 bytes = 0;

    // Extract packet size from IPv4 / IPv6 header
    switch (skb->family)
    case AF_INET:
            struct iphdr iph;
            bpf_skb_load_bytes(skb, 0, &iph, sizeof(struct iphdr));
            bytes = ntohs(iph.tot_len);
    case AF_INET6:
            struct ip6_hdr ip6h;
            bpf_skb_load_bytes(skb, 0, &ip6h, sizeof(struct ip6_hdr));
            bytes = ntohs(ip6h.ip6_plen);
        // This should never be the case as this eBPF hook is called in
        // netfilter context and thus not for AF_PACKET, AF_UNIX nor AF_NETLINK
        // for instance.
        return true;

    // Update counters in the per-cgroup map
    __u64 *bytes_counter = bpf_get_local_storage(&cgroup_counters_map, 0);
    __sync_fetch_and_add(bytes_counter, bytes);

    // Let the packet pass
    return true;

This is surprisingly simple. The biggest complexity here comes from the IPv4 vs IPv6 vs error handling. The main part of interest here are map related lines.

bpf_get_local_storage() encapsulates the complexity of loading the storage area for the current (AttachedCgroup, ProgramType, CPU) tuple.

__sync_fetch_and_add is a helper for atomically incrementing a counter. Since the eBPF program is not supposed to be interrupted and the data is per-CPU, this may not be needed in practice. There is however work underway upstream to support eBPF preemption.

This common function can be inlined from the ingress and egress handlers:

// Ingress hook - handle incoming packets
SEC("cgroup_skb/ingress") int ingress(struct __sk_buff *skb)
    return handle_skb(skb);

// Egress hook - handle outgoing packets
SEC("cgroup_skb/egress") int egress(struct __sk_buff *skb)
    return handle_skb(skb);

The eBPF side is now complete. The next step is to load and attach these programs to the Cgroup of interest and do something useful with the collected data.

Attach the programs and collect data

For monitoring both ingress and egress traffic, the programs needs to be attached twice for the target cgroup. Similarly, produced data will need to be queried for both directions.

To keep the code “DRY”, let’s declare a simple helper structure:

type BPFCgroupNetworkDirection struct {
	Name       string
	AttachType ebpf.AttachType

var BPFCgroupNetworkDirections = []BPFCgroupNetworkDirection{
		Name:       "ingress",
		AttachType: ebpf.AttachCGroupInetIngress,
		Name:       "egress",
		AttachType: ebpf.AttachCGroupInetEgress,

This snippet defines, for each direction, the name of the program to load from the ELF file as well as the attach type which is needed for attaching and querying the Cgroup map.

The programs can now be attached to the target cgroup from the previously loaded collection using a simple loop:

	// Attach program to monitored cgroup
	for _, direction := range BPFCgroupNetworkDirections {
		link, err := link.AttachCgroup(link.CgroupOptions{
			Attach:  direction.AttachType,
			Program: collec.Programs[direction.Name],
		if err != nil {
		defer link.Close()

The placeholder in the periodic loop can then be replaced with map queries and reports:

			for _, direction := range BPFCgroupNetworkDirections {
				var perCPUCounters PerCPUCounters

				mapKey := BpfCgroupStorageKey{
					CgroupInodeId: cgroupInodeId,
					AttachType:    direction.AttachType,

				if err := cgroup_counters_map.Lookup(mapKey, &perCPUCounters); err != nil {
					log.Printf("%s: error reading map (%v)", direction.Name, err)
				} else {
					log.Printf("%s: %d\n", direction.Name, sumPerCpuCounters(perCPUCounters))

Again, this snippet loops over the pre-defined traffic directions. It then builds a key to query the data produced by the program type corresponding to the target direction and Cgroup.

The query itself relies heavily on Cilium’s eBPF library for data marshaling between the kernel and Go worlds. The rest is trivial.


Now that everything is in place, and assuming the target cgroup and file names are left unchanged, the program can now be compiled and run.


clang -g -Wall -Werror -O2 -emit-llvm -c bpf-accounting.bpf.c -o - | llc -march=bpf -filetype=obj -o bpf-accounting.bpf.o
llvm-strip -g bpf-accounting.bpf.o
go build .

To set up a test environment, the easiest is to open a terminal and run:

# Create the Cgroup
sudo mkdir -p /sys/fs/cgroup/unified/yadutaf

# Register the current shell PID in the cgroup
echo $$ | sudo tee /sys/fs/cgroup/unified/yadutaf/cgroup.procs

# Start an advanced networking command
ping -n 2001:4860:4860::8888
# Or, for IPv4: ping -n

And finally:

sudo ./bpf-net-accounting

Which should print something like:

2021/08/22 17:20:50 Attaching eBPF monitoring programs to cgroup /sys/fs/cgroup/unified/yadutaf
2021/08/22 17:20:51 -------------------------------------------------------------
2021/08/22 17:20:51 ingress: 64
2021/08/22 17:20:51 egress: 64
2021/08/22 17:20:52 -------------------------------------------------------------
2021/08/22 17:20:52 ingress: 128
2021/08/22 17:20:52 egress: 128
2021/08/22 17:20:53 -------------------------------------------------------------
2021/08/22 17:20:53 ingress: 192
2021/08/22 17:20:53 egress: 192
2021/08/22 17:20:54 -------------------------------------------------------------
2021/08/22 17:20:54 ingress: 256
2021/08/22 17:20:54 egress: 256
^C2021/08/22 17:20:54 Exiting...

Tadaa !


This post presented the BPF_MAP_TYPE_PERCPU_CGROUP_STORAGE eBPF virtual map for local storage through a trivial per-cgroup throughput monitoring application.

The main takeaways are:

  • Use this storage when storing per-cgroup measures or state in a performance sensitive context.
  • If performance is not an issue, consider using BPF_MAP_TYPE_CGROUP_STORAGE to avoid the added complexity of managing per-cpu data.
  • All eBPF cgroups of a given type attached to a given Cgroup will share the same storage.
  • Attach the eBPF program to each target cgroups: This storage is tied to a specific attachment.

This example could be used as a basis for a real monitoring application. Such an application could for example monitor Cgroup creation and dynamically attach the programs on the fly. It could also expose collected data in OpenMetrics (Prometheus) for integration in a larger system.

Last but not least, I would like to thank Cilium’s ebpf community for helping me fix a tiny but essential bug in the library while working on this post.