USENIX LISA 2017: Container Performance Analysis
Talk by Brendan Gregg for USENIX LISA17Video: https://www.youtube.com/watch?v=NYLXZ58EboM
Description: "Containers pose interesting challenges for performance monitoring and analysis, requiring new analysis methodologies and tooling. Resource-oriented analysis, as is common with systems performance tools and GUIs, must now account for both hardware limits and soft limits, as implemented using cgroups. A reverse diagnosis methodology can be applied to identify whether a container is resource constrained, and by which hard or soft resource. The interaction between the host and containers can also be examined, and noisy neighbors identified or exonerated. Performance tooling can need special usage or workarounds to function properly from within a container or on the host, to deal with different privilege levels and name spaces. At Netflix, we're using containers for some microservices, and care very much about analyzing and tuning our containers to be as fast and efficient as possible. This talk will show you how to identify bottlenecks in the host or container configuration, in the applications by profiling in a container environment, and how to dig deeper into kernel and container internals."
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PDF: LISA2017_Container_Performance_Analysis.pdf
Keywords (from pdftotext):
slide 1:
Container Performance Analysis Brendan Gregg bgregg@neIlix.com October 29–November 3, 2017 | San Francisco, CA www.usenix.org/lisa17 #lisa17slide 2:
Take Aways IdenNfy boPlenecks: 1. In the host vs container, using system metrics 2. In applicaNon code on containers, using CPU flame graphs 3. Deeper in the kernel, using tracing tools Focus of this talk is how containers work in Linux (will demo on Linux 4.9)slide 3:
slide 4:
Containers at NeIlix: summary slides from the Titus team. 1. TITUSslide 5:
Titus • Cloud runNme plaIorm for container jobs • Scheduling – Service & batch job management – Advanced resource management across elasNc shared resource pool • Container ExecuNon Service Batch Job Management Resource Management & OpNmizaNon – Docker and AWS EC2 IntegraNon • Adds VPC, security groups, EC2 metadata, IAM roles, S3 logs, … Container ExecuNon IntegraNon – IntegraNon with NeIlix infrastructure • In depth: hPp://techblog.neIlix.com/2017/04/the-evoluNon-of-container-usage-at.htmlslide 6:
Current Titus Scale • Used for ad hoc reporNng, media encoding, stream processing, … • Over 2,500 instances (Mostly m4.16xls & r3.8xls) across three regions • Over a week period launched over 1,000,000 containersslide 7:
Container Performance @NeIlix • Ability to scale and balance workloads with EC2 and Titus • Performance needs: – ApplicaNon analysis: using CPU flame graphs with containers – Host tuning: file system, networking, sysctl's, … – Container analysis and tuning: cgroups, GPUs, … – Capacity planning: reduce over provisioningslide 8:
And Strategy 2. CONTAINER BACKGROUNDslide 9:
Namespaces: RestricNng Visibility Current Namespaces: • cgroup • ipc • mnt • net • pid • user • uts PID namespaces Host PID 1 PID namespace 1 1 (1238) 2 (1241) Kernelslide 10:
Control Groups: RestricNng Usage Current cgroups: • blkio • cpu,cpuacct • cpuset • devices • hugetlb • memory • net_cls,net_prio • pids • … CPU cgroups container container container cpu cgroup 1 CPUsslide 11:
Linux Containers Container = combinaNon of namespaces & cgroups Host Container 1 Container 2 Container 3 (namespaces) (namespaces) (namespaces) cgroups cgroups cgroups Kernelslide 12:
cgroup v1 cpu,cpuacct: cap CPU usage (hard limit). e.g. 1.5 CPUs. CPU shares. e.g. 100 shares. usage staNsNcs (cpuacct) Docker: --cpus (1.13) --cpu-shares memory: limit and kmem limit (maximum bytes) OOM control: enable/disable usage staNsNcs blkio (block I/O): weights (like shares) IOPS/tput caps per storage device staNsNcs --memory --kernel-memory --oom-kill-disableslide 13:
CPU Shares Container's CPU limit = 100% x container's shares total busy shares This lets a container use other tenant's idle CPU (aka "bursNng"), when available. Container's minimum CPU limit = 100% x container's shares total allocated shares Can make analysis tricky. Why did perf regress? Less bursNng available?slide 14:
cgroup v2 • Major rewrite has been happening: cgroups v2 – Supports nested groups, bePer organizaNon and consistency – Some already merged, some not yet (e.g. CPU) • See docs/talks by maintainer Tejun Heo (Facebook) • References: – hPps://www.kernel.org/doc/DocumentaNon/cgroup-v2.txt – hPps://lwn.net/ArNcles/679786/slide 15:
Container OS ConfiguraNon File systems Containers may be setup with aufs/overlay on top of another FS See "in pracNce" pages and their performance secNons from hPps://docs.docker.com/engine/userguide/storagedriver/ Networking With Docker, can be bridge, host, or overlay networks Overlay networks have come with significant performance costslide 16:
Analysis Strategy Performance analysis with containers: • One kernel • Two perspecNves • Namespaces • cgroups Methodologies: • USE Method • Workload characterizaNon • Checklists • Event tracingslide 17:
USE Method For every resource, check: 1. UNlizaNon 2. SaturaNon 3. Errors Resource Utilization (%) For example, CPUs: • UNlizaNon: Nme busy • SaturaNon: run queue length or latency • Errors: ECC errors, etc. Can be applied to hardware resources and sotware resources (cgroups)slide 18:
And Container Awareness 3. HOST TOOLSslide 19:
Host Analysis Challenges • PIDs in host don't match those seen in containers • Symbol files aren't where tools expect them • The kernel currently doesn't have a container IDslide 20:
3.1. Host Physical Resources A refresher of basics... Not container specific. This will, however, solve many issues! Containers are oten not the problem. I will demo CLI tools. GUIs source the same metrics.slide 21:
Linux Perf Tools Where can we begin?slide 22:
Host Perf Analysis in 60s uptime dmesg | tail vmstat 1 mpstat -P ALL 1 pidstat 1 iostat -xz 1 free -m sar -n DEV 1 sar -n TCP,ETCP 1 top load averages kernel errors overall stats by Nme CPU balance process usage disk I/O memory usage network I/O TCP stats check overview hPp://techblog.neIlix.com/2015/11/linux-performance-analysis-in-60s.htmlslide 23:
USE Method: Host Resources Resource Utilization Saturation Errors CPU mpstat -P ALL 1, sum non-idle fields vmstat 1, "r" perf Memory Capacity free –m, "used"/"total" vmstat 1, "si"+"so"; demsg | grep killed dmesg Storage I/O iostat –xz 1, "%util" iostat –xnz 1, "avgqu-sz" >gt; 1 /sys/…/ioerr_cnt; smartctl Network nicstat, "%Util" ifconfig, "overrunns"; netstat –s "retrans…" ifconfig, "errors" These should be in your monitoring GUI. Can do other resources too (busses, ...)slide 24:
Event Tracing: e.g. iosnoop Disk I/O events with latency (from perf-tools; also in bcc/BPF as biosnoop) # ./iosnoop Tracing block I/O... Ctrl-C to end. COMM PID TYPE DEV supervise 202,1 supervise 202,1 tar 14794 RM 202,1 tar 14794 RM 202,1 tar 14794 RM 202,1 tar 14794 RM 202,1 tar 14794 RM 202,1 tar 14794 RM 202,1 tar 14794 RM 202,1 tar 14794 RM 202,1 BLOCK BYTES LATmsslide 25:
Event Tracing: e.g. zfsslower # /usr/share/bcc/tools/zfsslower 1 Tracing ZFS operations slower than 1 ms TIME COMM PID T BYTES 23:44:40 java 31386 O 0 23:44:53 java 31386 W 8190 23:44:59 java 31386 W 8192 23:44:59 java 31386 W 8191 23:45:00 java 31386 W 8192 23:45:15 java 31386 O 0 23:45:56 dockerd S 0 23:46:16 java 31386 W 31 OFF_KB LAT(ms) FILENAME 8.02 solrFeatures.txt 36.24 solrFeatures.txt 20.28 solrFeatures.txt 28.15 solrFeatures.txt 32.17 solrFeatures.txt 27.44 solrFeatures.txt 1.03 .tmp-a66ce9aad… 36.28 solrFeatures.txt • This is from our producNon Titus system (Docker). • File system latency is a bePer pain indicator than disk latency. • zfsslower (and btrfs*, etc) are in bcc/BPF. Can exonerate FS/disks.slide 26:
Latency Histogram: e.g. btrfsdist # ./btrfsdist Tracing btrfs operation latency... Hit Ctrl-C to end. operation = 'read' usecs : count distribution 0 ->gt; 1 : 192529 |****************************************| 2 ->gt; 3 : 72337 |*************** 4 ->gt; 7 : 5620 probably 8 ->gt; 15 : 1026 cache reads 16 ->gt; 31 : 369 32 ->gt; 63 : 239 64 ->gt; 127 : 53 128 ->gt; 255 : 975 256 ->gt; 511 : 524 probably cache misses 512 ->gt; 1023 : 128 (flash reads) 1024 ->gt; 2047 : 16 2048 ->gt; 4095 : 7 […] From a test Titus system • Histograms show modes, outliers. Also in bcc/BPF (with other FSes). • Latency heat maps: hPp://queue.acm.org/detail.cfm?id=1809426slide 27:
3.2. Host Containers & cgroups InspecNng containers from the hostslide 28:
Namespaces Worth checking namespace config before analysis: # ./dockerpsns.sh CONTAINER NAME host titusagent-mainvpc-m b27909cd6dd1 Titus-1435830-worker dcf3a506de45 Titus-1392192-worker 370a3f041f36 Titus-1243558-worker af7549c76d9a Titus-1243553-worker dc27769a9b9c Titus-1243546-worker e18bd6189dcd Titus-1243517-worker ab45227dcea9 Titus-1243516-worker PID PATH CGROUP IPC MNT NET PID USER UTS 1 systemd 4026531835 4026531839 4026531840 4026532533 4026531836 4026531837 4026531838 37280 svscanboot 4026531835 4026533387 4026533385 4026532931 4026533388 4026531837 4026533386 27992 /apps/spaas/spaa 4026531835 4026533354 4026533352 4026532991 4026533355 4026531837 4026533353 98602 /apps/spaas/spaa 4026531835 4026533290 4026533288 4026533223 4026533291 4026531837 4026533289 97972 /apps/spaas/spaa 4026531835 4026533216 4026533214 4026533149 4026533217 4026531837 4026533215 97356 /apps/spaas/spaa 4026531835 4026533142 4026533140 4026533075 4026533143 4026531837 4026533141 96733 /apps/spaas/spaa 4026531835 4026533068 4026533066 4026533001 4026533069 4026531837 4026533067 96173 /apps/spaas/spaa 4026531835 4026532920 4026532918 4026532830 4026532921 4026531837 4026532919 A POC "docker ps --namespaces" tool. NS shared with root in red. hPps://github.com/docker/docker/issues/32501 hPps://github.com/kubernetes-incubator/cri-o/issues/868slide 29:
systemd-cgtop A "top" for cgroups: # systemd-cgtop Control Group /docker /docker/dcf3a...9d28fc4a1c72bbaff4a24834 /docker/370a3...e64ca01198f1e843ade7ce21 /system.slice /system.slice/daemontools.service /docker/dc277...42ab0603bbda2ac8af67996b /user.slice /user.slice/user-0.slice /user.slice/u....slice/session-c26.scope /docker/ab452...c946f8447f2a4184f3ccff2a /docker/e18bd...26ffdd7368b870aa3d1deb7a [...] Tasks %CPU Memory 45.9G 42.1G 24.0G 3.0G 4.1G 2.8G 2.3G 34.5M 15.7M 13.3M 6.3G 2.9G Input/s Output/sslide 30:
docker stats A "top" for containers. Resource uNlizaNon. Workload characterizaNon. # docker stats CONTAINER CPU % 353426a09db1 526.81% 6bf166a66e08 303.82% 58dcf8aed0a7 41.01% 61061566ffe5 85.92% bdc721460293 2.69% 6c80ed61ae63 477.45% 337292fb5b64 89.05% b652ede9a605 173.50% d7cd2599291f 504.28% 05bf9f3e0d13 314.46% 09082f005755 142.04% bd45a3e1ce16 190.26% [...] MEM USAGE / LIMIT 4.061 GiB / 8.5 GiB 3.448 GiB / 8.5 GiB 1.322 GiB / 2.5 GiB 220.9 MiB / 3.023 GiB 1.204 GiB / 3.906 GiB 557.7 MiB / 8 GiB 766.2 MiB / 8 GiB 689.2 MiB / 8 GiB 673.2 MiB / 8 GiB 711.6 MiB / 8 GiB 693.9 MiB / 8 GiB 538.3 MiB / 8 GiB MEM % 47.78% 40.57% 52.89% 7.14% 30.82% 6.81% 9.35% 8.41% 8.22% 8.69% 8.47% 6.57% NET I/O 0 B / 0 B 0 B / 0 B 0 B / 0 B 0 B / 0 B 0 B / 0 B 0 B / 0 B 0 B / 0 B 0 B / 0 B 0 B / 0 B 0 B / 0 B 0 B / 0 B 0 B / 0 B BLOCK I/O 2.818 MB / 0 B 2.032 MB / 0 B 0 B / 0 B 43.4 MB / 0 B 4.35 MB / 0 B 9.257 MB / 0 B 5.493 MB / 0 B 6.48 MB / 0 B 12.58 MB / 0 B 7.942 MB / 0 B 8.081 MB / 0 B 10.6 MB / 0 B PIDSslide 31:
top In the host, top shows all processes, but currently no container IDs. # top - 22:46:53 up 36 days, 59 min, 1 user, load average: 5.77, 5.61, 5.63 Tasks: 1067 total, 1 running, 1046 sleeping, 0 stopped, 20 zombie %Cpu(s): 34.8 us, 1.8 sy, 0.0 ni, 61.3 id, 0.0 wa, 0.0 hi, 1.9 si, 0.1 st KiB Mem : 65958552 total, 12418448 free, 49247988 used, 4292116 buff/cache KiB Swap: 0 total, 0 free, 0 used. 13101316 avail Mem PID USER 28321 root 97712 root 98306 root 96511 root 5283 root 2079 root 5272 titusag+ […] VIRT RES 0 33.126g 0.023t 0 11.445g 2.333g 0 12.149g 3.060g 0 15.567g 6.313g 0 1643676 100092 0 10.473g 1.611g SHR S %CPU %MEM TIME+ COMMAND 37564 S 621.1 38.2 35184:09 java 37084 S 3.1 3.7 404:27.90 java 36996 S 2.0 4.9 194:21.10 java 37112 S 1.7 10.0 168:07.44 java 94184 S 1.0 0.2 401:36.16 mesos-slave 12 S 0.7 0.0 220:07.75 rngd 23488 S 0.7 2.6 1934:44 java Can fix, but that would be Docker + cgroup-v1 specific. SNll need a kernel CID.slide 32:
htop htop can add a CGROUP field, but, can truncate important info: CGROUP PID USER PRI NI VIRT RES SHR S CPU% MEM% TIME+ Command :pids:/docker/ 28321 root 0 33.1G 24.0G 37564 S 524. 38.2 672h /apps/java :pids:/docker/ 9982 root 0 33.1G 24.0G 37564 S 44.4 38.2 17h00:41 /apps/java :pids:/docker/ 9985 root 0 33.1G 24.0G 37564 R 41.9 38.2 16h44:51 /apps/java :pids:/docker/ 9979 root 0 33.1G 24.0G 37564 S 41.2 38.2 17h01:35 /apps/java :pids:/docker/ 9980 root 0 33.1G 24.0G 37564 S 39.3 38.2 16h59:17 /apps/java :pids:/docker/ 9981 root 0 33.1G 24.0G 37564 S 39.3 38.2 17h01:32 /apps/java :pids:/docker/ 9984 root 0 33.1G 24.0G 37564 S 37.3 38.2 16h49:03 /apps/java :pids:/docker/ 9983 root 0 33.1G 24.0G 37564 R 35.4 38.2 16h54:31 /apps/java :pids:/docker/ 9986 root 0 33.1G 24.0G 37564 S 35.4 38.2 17h05:30 /apps/java :name=systemd:/user.slice/user-0.slice/session-c31.scope? 74066 root 0 27620 :pids:/docker/ 9998 root 0 33.1G 24.0G 37564 R 28.3 38.2 11h38:03 /apps/java :pids:/docker/ 10001 root 0 33.1G 24.0G 37564 S 27.7 38.2 11h38:59 /apps/java :name=systemd:/system.slice/daemontools.service? 5272 titusagen 20 0 10.5G 1650M 23 :pids:/docker/ 10002 root 0 33.1G 24.0G 37564 S 25.1 38.2 11h40:37 /apps/java Can fix, but that would be Docker + cgroup-v1 specific. SNll need a kernel CID.slide 33:
Host PID ->gt; Container ID … who does that (CPU busy) PID 28321 belong to? # grep 28321 /sys/fs/cgroup/cpu,cpuacct/docker/*/tasks | cut -d/ -f7 dcf3a506de453107715362f6c9ba9056fcfc6e769d28fc4a1c72bbaff4a24834 • Only works for Docker, and that cgroup v1 layout. Some Linux commands: # ls -l /proc/27992/ns/* lrwxrwxrwx 1 root root 0 Apr 13 20:49 cgroup ->gt; cgroup:[4026531835] lrwxrwxrwx 1 root root 0 Apr 13 20:49 ipc ->gt; ipc:[4026533354] lrwxrwxrwx 1 root root 0 Apr 13 20:49 mnt ->gt; mnt:[4026533352] […] # cat /proc/27992/cgroup 11:freezer:/docker/dcf3a506de453107715362f6c9ba9056fcfc6e769d28fc4a1c72bbaff4a24834 10:blkio:/docker/dcf3a506de453107715362f6c9ba9056fcfc6e769d28fc4a1c72bbaff4a24834 9:perf_event:/docker/dcf3a506de453107715362f6c9ba9056fcfc6e769d28fc4a1c72bbaff4a24834 […]slide 34:
nsenter Wrapping … what hostname is PID 28321 running on? # nsenter -t 28321 -u hostname titus-1392192-worker-14-16 Can namespace enter: – -m: mount -u: uts -i: ipc -n: net -p: pid -U: user Bypasses cgroup limits, and seccomp profile (allowing syscalls) – For Docker, enter the container more completely with: docker exec -it CID command Handy nsenter one-liners: nsenter -t PID -u hostname nsenter -t PID -n netstat -i nsenter -t PID –m -p df -h nsenter -t PID -p top container hostname container netstat container file system usage container topslide 35:
nsenter: Host ->gt; Container top … Given PID 28321, running top for its container by entering its namespaces: # nsenter -t 28321 -m -p top top - 18:16:13 up 36 days, 20:28, 0 users, load average: 5.66, 5.29, 5.28 Tasks: 6 total, 1 running, 5 sleeping, 0 stopped, 0 zombie %Cpu(s): 30.5 us, 1.7 sy, 0.0 ni, 65.9 id, 0.0 wa, 0.0 hi, 1.8 si, 0.1 st KiB Mem: 65958552 total, 54664124 used, 11294428 free, 164232 buffers KiB Swap: 0 total, 0 used, 0 free. 1592372 cached Mem PID USER 301 root 1 root 87888 root VIRT RES 0 33.127g 0.023t SHR S %CPU %MEM 37564 S 537.3 38.2 1812 S 0.0 0.0 1348 R 0.0 0.0 TIME+ COMMAND 40269:41 java 4:15.11 bash 0:00.00 top Note that it is PID 301 in the container. Can also see this using: # grep NSpid /proc/28321/status NSpid:slide 36:
perf: CPU Profiling Can run system-wide (-a), match a pid (-p), or cgroup (-G, if it works) # perf record -F 49 -a -g -- sleep 30 # perf script Failed to open /lib/x86_64-linux-gnu/libc-2.19.so, continuing without symbols Failed to open /tmp/perf-28321.map, continuing without symbols • Symbol translaNon gotchas on Linux 4.13 and earlier – perf can't find /tmp/perf-PID.map files in the host, and the PID is different – perf can't find container binaries under host paths (what /usr/bin/java?) – Can copy files to the host, map PIDs, then run perf script/report: • hPp://blog.alicegoldfuss.com/making-flamegraphs-with-containerized-java/ • hPp://batey.info/docker-jvm-flamegraphs.html – Can nsenter (-m -u -i -n -p) a "power" shell, and then run "perf -p PID" • Linux 4.14 perf checks namespaces for symbol files Thanks Krister Johansenslide 37:
CPU Flame Graphs git clone --depth 1 https://github.com/brendangregg/FlameGraph cd FlameGraph perf record –F 49 -a –g -- sleep 30 perf script | ./stackcollapse-perf.pl | ./flamegraph.pl >gt; perf.svg • See previous slide for ge{ng perf symbols to work • From the host, can study all containers, as well as container overheads Kernel TCP/IP stack Look in areas like this to find and quantify overhead (cgroup throttles, FS layers, networking, etc). It's likely small and hard to find. Java, missing stacks (need -XX:+PreserveFramePointer)slide 38:
/sys/fs/cgroups (raw) The best source for per-cgroup metrics. e.g. CPU: # cd /sys/fs/cgroup/cpu,cpuacct/docker/02a7cf65f82e3f3e75283944caa4462e82f8f6ff5a7c9a... # ls cgroup.clone_children cpuacct.usage_all cpuacct.usage_sys cpu.shares cgroup.procs cpuacct.usage_percpu cpuacct.usage_user cpu.stat cpuacct.stat cpuacct.usage_percpu_sys cpu.cfs_period_us notify_on_release cpuacct.usage cpuacct.usage_percpu_user cpu.cfs_quota_us tasks # cat cpuacct.usage # cat cpu.stat total time throttled (nanoseconds). saturation metric. nr_periods 507 average throttle time = throttled_time / nr_throttled nr_throttled 74 throttled_time 3816445175 hPps://www.kernel.org/doc/DocumentaNon/cgroup-v1/, ../scheduler/sched-bwc.txt hPps://blog.docker.com/2013/10/gathering-lxc-docker-containers-metrics/ Note: grep cgroup /proc/mounts to check where these are mounted These metrics should be included in performance monitoring GUIsslide 39:
NeOlix Atlas Cloud-wide monitoring of containers (and instances) Fetches cgroup metrics via Intel snap hPps://github.com/neIlix/Atlasslide 40:
NeOlix Vector Our per-instance analyzer Has per-container metrics hPps://github.com/NeIlix/vectorslide 41:
Intel snap A metric collector used by monitoring GUIs hPps://github.com/intelsdi-x/snap Has a Docker plugin to read cgroup stats There's also a collectd plugin: hPps://github.com/bobrik/collectddockerslide 42:
3.3. Let's Play a Game Host or Container? (or Neither?)slide 43:
Game Scenario 1 Container user claims they have a CPU performance issue Container has a CPU cap and CPU shares configured There is idle CPU on the host Other tenants are CPU busy /sys/fs/cgroup/.../cpu.stat ->gt; throPled_Nme is increasing /proc/PID/status nonvoluntary_ctxt_switches is increasing Container CPU usage equals its cap (clue: this is not really a clue)slide 44:
Game Scenario 2 Container user claims they have a CPU performance issue Container has a CPU cap and CPU shares configured There is no idle CPU on the host Other tenants are CPU busy /sys/fs/cgroup/.../cpu.stat ->gt; throPled_Nme is not increasing /proc/PID/status nonvoluntary_ctxt_switches is increasingslide 45:
Game Scenario 3 Container user claims they have a CPU performance issue Container has CPU shares configured There is no idle CPU on the host Other tenants are CPU busy /sys/fs/cgroup/.../cpu.stat ->gt; throPled_Nme is not increasing /proc/PID/status nonvoluntary_ctxt_switches is not increasing much Experiments to confirm conclusion?slide 46:
Methodology: Reverse Diagnosis Enumerate possible outcomes, and work backwards to the metrics needed for diagnosis. For example, CPU performance outcomes: A. physical CPU throPled B. cap throPled C. shares throPled (assumes physical CPU limited as well) D. not throPled Game answers: 1. B, 2. C, 3. Dslide 47:
CPU BoRleneck IdenTficaTon throttled_time increasing? DifferenNal Diagnosis cap throttled nonvol…switches increasing? (but dig further) not throttled share throttled host has idle CPU? all other tenants idle? physical CPU throttledslide 48:
And Container Awareness 4. GUEST TOOLS … if you only have guest accessslide 49:
Guest Analysis Challenges • Some resource metrics are for the container, some for the host. Confusing! • May lack system capabiliNes or syscalls to run profilers and tracersslide 50:
CPU Can see host's CPU devices, but only container (pid namespace) processes: container# uptime 20:17:19 up 45 days, 21:21, container# mpstat 1 Linux 4.9.0 (02a7cf65f82e) 0 users, 04/14/17 load average: 5.08, 3.69, 2.22 _x86_64_ 20:17:26 CPU %usr %nice %sys %iowait 20:17:27 all 20:17:28 all Average: all container# pidstat 1 Linux 4.9.0 (02a7cf65f82e) 04/14/17 _x86_64_ load! (8 CPU) busy CPUs %irq %soft %steal %guest %gnice %idle (8 CPU) 20:17:33 UID PID %usr %system %guest %CPU CPU 20:17:34 UID PID %usr %system %guest %CPU CPU 20:17:35 [...] UID PID %usr %system %guest %CPU CPU but this container is running nothing Command (we saw CPU usage Command from neighbors) Commandslide 51:
Memory Can see host's memory: container# free -m total Mem: Swap: used free container# perl -e '$a = "A" x 1_000_000_000' Killed shared buff/cache available host memory (this container is --memory=1g) tries to consume ~2 Gbytesslide 52:
Disks Can see host's disk devices: container# iostat -xz 1 avg-cpu: %user %nice %system %iowait %steal 0.00 16.94 %idle host disk I/O Device: rrqm/s wrqm/s r/s xvdap1 xvdb 0.00 200.00 xvdc 0.00 185.00 md0 0.00 385.00 [...] container# pidstat -d 1 Linux 4.9.0 (02a7cf65f82e) w/s rkB/s 0.00 3080.00 0.00 2840.00 0.00 5920.00 22:41:13 UID PID kB_rd/s kB_wr/s kB_ccwr/s iodelay Command 22:41:14 UID PID kB_rd/s kB_wr/s kB_ccwr/s iodelay Command 22:41:15 [...] UID PID kB_rd/s kB_wr/s kB_ccwr/s iodelay Command 04/18/17 wkB/s avgrq-sz avgqu-sz _x86_64_ await r_await w_await svctm %util 2.00 2.00 0.40 0.00 0.20 4.00 0.00 0.24 4.40 0.00 0.00 0.00 (8 CPU) but no container I/Oslide 53:
Network Can't see host's network interfaces (network namespace): container# sar -n DEV,TCP 1 Linux 4.9.0 (02a7cf65f82e) 04/14/17 21:45:07 21:45:08 21:45:08 21:45:07 21:45:08 21:45:08 21:45:09 21:45:09 21:45:08 21:45:09 [...] IFACE eth0 _x86_64_ (8 CPU) rxpck/s txpck/s rxkB/s active/s passive/s iseg/s oseg/s rxpck/s txpck/s rxkB/s active/s passive/s iseg/s oseg/s IFACE eth0 txkB/s rxcmp/s txcmp/s rxmcst/s %ifutil txkB/s rxcmp/s txcmp/s rxmcst/s %ifutil host has heavy network I/O, container sees itself (idle)slide 54:
Metrics Namespace This confuses apps too: trying to bind on all CPUs, or using 25% of memory Including the JDK, which is unaware of container limits We could add a "metrics" namespace so the container only sees itself Or enhance exisNng namespaces to do this If you add a metrics namespace, please consider adding an opNon for: • /proc/host/stats: maps to host's /proc/stats, for CPU stats • /proc/host/diskstats: maps to host's /proc/diskstats, for disk stats As those host metrics can be useful, to idenNfy/exonerate neighbor issuesslide 55:
perf: CPU Profiling Needs capabiliNes to run from a container: container# ./perf record -F 99 -a -g -- sleep 10 perf_event_open(..., PERF_FLAG_FD_CLOEXEC) failed with unexpected error 1 (Operation not permitted) perf_event_open(..., 0) failed unexpectedly with error 1 (Operation not permitted) Error: You may not have permission to collect system-wide stats. Consider tweaking /proc/sys/kernel/perf_event_paranoid, [...] Although tweaking perf_event_paranoid (to -1) doesn't fix it. The real problem is: hPps://docs.docker.com/engine/security/seccomp/#significant-syscalls-blocked-by-the-default-profile:slide 56:
perf, cont. Can enable perf_event_open() with: docker run --cap-add sys_admin – Also need (for kernel symbols): echo 0 >gt; /proc/sys/kernel/kptr_restrict perf then "works", and you can make flame graphs. But it sees all CPUs!? – perf needs to be "container aware", and only see the container's tasks. patch pending: hPps://lkml.org/lkml/2017/1/12/308 Currently easier to run perf from the host (or secure "monitoring" container) – e.g. NeIlix Vector ->gt; CPU Flame Graphslide 57:
Advanced Analysis 5. TRACING … a few more examples (iosnoop, zfsslower, and btrfsdist shown earlier)slide 58:
Built-in Linux Tracers trace (2008+) perf_events (2009+) eBPF (2014+) Some front-ends: • trace: hPps://github.com/brendangregg/perf-tools • perf_events: used for CPU flame graphs • eBPF (aka BPF): hPps://github.com/iovisor/bcc (Linux 4.4+)slide 59:
trace: Overlay FS FuncNon Calls Using trace via my perf-tools to count funcNon calls in-kernel context: # funccount '*ovl*' Tracing "*ovl*"... Ctrl-C to end. FUNC COUNT ovl_cache_free ovl_xattr_get [...] ovl_fill_merge ovl_path_real ovl_path_upper ovl_update_time ovl_permission ovl_d_real ovl_override_creds Ending tracing... Each can be a target for further study with kprobesslide 60:
trace: Overlay FS FuncNon Tracing Using kprobe (perf-tools) to trace ovl_fill_merg() args and stack trace: # kprobe -s 'p:ovl_fill_merge ctx=%di name=+0(%si):string' Tracing kprobe ovl_fill_merge. Ctrl-C to end. bash-16633 [000] d... 14390771.218973: ovl_fill_merge: (ovl_fill_merge+0x0/0x1f0 [overlay]) ctx=0xffffc90042477db0 name="iostat" bash-16633 [000] d... 14390771.218981:slide 61:gt; =>gt; ovl_fill_merge =>gt; ext4_readdir =>gt; iterate_dir =>gt; ovl_dir_read_merged =>gt; ovl_iterate =>gt; iterate_dir =>gt; SyS_getdents =>gt; do_syscall_64 =>gt; return_from_SYSCALL_64 […] Good for debugging, although dumping all events can cost too much overhead. trace has some soluNons to this, BPF has more…
Enhanced BPF Tracing Internals Observability Program BPF bytecode BPF program event config output per-event data staNsNcs Kernel load verifier staNc tracing tracepoints aPach dynamic tracing BPF kprobes uprobes async copy sampling, PMCs maps perf_eventsslide 62:
bcc/BPF Perf Toolsslide 63:
BPF: Scheduler Latency host# runqlat --pidnss -m summarized in-kernel Tracing run queue latency... Hit Ctrl-C to end. for efficiency pidns = 4026532382 msecs : count distribution 0 ->gt; 1 : 646 |****************************************| 2 ->gt; 3 : 18 4 ->gt; 7 : 48 |** Per-PID namespace histograms | 8 ->gt; 15 : 17 16 ->gt; 31 : 150 |********* 32 ->gt; 63 : 134 |******** […] pidns = 4026532870 msecs 0 ->gt; 1 2 ->gt; 3 [...] : count : 264 : 0 distribution |****************************************| Shows CPU share throPling when present (eg, 8 - 65 ms) Currently using task_struct->gt;nsproxy->gt;pid_ns_for_children->gt;ns.inum for pidns. We could add a stable bpf_get_current_pidns() call to BPF.slide 64:
Docker Analysis & Debugging If needed, dockerd can also be analyzed using: go execuNon tracer GODEBUG with gctrace and schedtrace gdb and Go runNme support perf profiling bcc/BPF and uprobes Each has pros/cons. bcc/BPF can trace user & kernel events.slide 65:
BPF: dockerd Go FuncNon CounNng CounNng dockerd Go calls in-kernel using BPF that match "*docker*get": # funccount '/usr/bin/dockerd:*docker*get*' Tracing 463 functions for "/usr/bin/dockerd:*docker*get*"... Hit Ctrl-C to end. FUNC COUNT github.com/docker/docker/daemon.(*statsCollector).getSystemCPUUsage github.com/docker/docker/daemon.(*Daemon).getNetworkSandboxID github.com/docker/docker/daemon.(*Daemon).getNetworkStats github.com/docker/docker/daemon.(*statsCollector).getSystemCPUUsage.func1 github.com/docker/docker/pkg/ioutils.getBuffer github.com/docker/docker/vendor/golang.org/x/net/trace.getFamily github.com/docker/docker/vendor/google.golang.org/grpc.(*ClientConn).getTransport github.com/docker/docker/vendor/github.com/golang/protobuf/proto.getbase github.com/docker/docker/vendor/google.golang.org/grpc/transport.(*http2Client).getStream Detaching... # objdump -tTj .text /usr/bin/dockerd | wc -l 35,859 functions can be traced! Uses uprobes, and needs newer kernels. Warning: will cost overhead at high funcNon rates.slide 66:
BPF: dockerd Go Stack Tracing CounNng stack traces that led to this iouNls.getBuffer() call: # stackcount 'p:/usr/bin/dockerd:*/ioutils.getBuffer' Tracing 1 functions for "p:/usr/bin/dockerd:*/ioutils.getBuffer"... Hit Ctrl-C to end. github.com/docker/docker/pkg/ioutils.getBuffer github.com/docker/docker/pkg/broadcaster.(*Unbuffered).Write bufio.(*Reader).writeBuf bufio.(*Reader).WriteTo io.copyBuffer io.Copy github.com/docker/docker/pkg/pools.Copy github.com/docker/docker/container/stream.(*Config).CopyToPipe.func1.1 runtime.goexit dockerd [18176] means this stack was seen 110 times Detaching... Can also trace funcNon arguments, and latency (with some work) hPp://www.brendangregg.com/blog/2017-01-31/golang-bcc-bpf-funcNon-tracing.htmlslide 67:
Summary IdenNfy boPlenecks: 1. In the host vs container, using system metrics 2. In applicaNon code on containers, using CPU flame graphs 3. Deeper in the kernel, using tracing toolsslide 68:
References hPp://techblog.neIlix.com/2017/04/the-evoluNon-of-container-usage-at.html hPp://techblog.neIlix.com/2016/07/distributed-resource-scheduling-with.html hPps://www.slideshare.net/aspyker/neIlix-and-containers-Ntus hPps://docs.docker.com/engine/admin/runmetrics/#Nps-for-high-performance-metric-collecNon hPps://blog.docker.com/2013/10/gathering-lxc-docker-containers-metrics/ hPps://www.slideshare.net/jpetazzo/anatomy-of-a-container-namespaces-cgroups-some-filesystem-magic-linuxcon hPps://www.youtube.com/watch?v=sK5i-N34im8 Cgroups, namespaces, and beyond hPps://jvns.ca/blog/2016/10/10/what-even-is-a-container/ hPps://blog.jessfraz.com/post/containers-zones-jails-vms/ hPp://blog.alicegoldfuss.com/making-flamegraphs-with-containerized-java/ hPp://www.brendangregg.com/USEmethod/use-linux.html full USE method list hPp://www.brendangregg.com/blog/2017-01-31/golang-bcc-bpf-funcNon-tracing.html hPp://techblog.neIlix.com/2015/11/linux-performance-analysis-in-60s.html hPp://queue.acm.org/detail.cfm?id=1809426 latency heat maps hPps://github.com/brendangregg/perf-tools trace tools, hPps://github.com/iovisor/bcc BPF toolsslide 69:
Thank You! hPp://techblog.neIlix.com hPp://slideshare.net/brendangregg hPp://www.brendangregg.com bgregg@neIlix.com @brendangregg Titus team: @aspyker @anwleung @fabiokung @tomaszbak1974 @amit_joshee @sargun @corindwyer … October 29–November 3, 2017 | San Francisco, CA www.usenix.org/lisa17 #lisa17