YOW2018 Cloud Performance Root Cause Analysis at Netflix

YOW! Conference Australia
Nov-Dec 2018
Brendan Gregg
Senior Performance Architect
Cloud and Platform Engineering
Cloud Performance
Root Cause Analysis
at Netflix

# perf record -F99 -a
# perf script
[…]
java 14327 [022] 252764.179741: cycles: 7f36570a4932 SpinPause (/usr/lib/jvm/java-8
java 14332 [015] 252764.188720: cycles: 7f3658078350 pthread_cond_wait@@GLIBC_2.3.2
java 14341 [019] 252764.191307: cycles: 7f3656d150c8 ClassLoaderDataGraph::do_unloa
java 14341 [019] 252764.198825: cycles: 7f3656d140b8 ClassLoaderData::free_dealloca
java 14341 [019] 252764.207057: cycles: 7f3657192400 nmethod::do_unloading(BoolObje
java 14341 [019] 252764.215962: cycles: 7f3656ba807e Assembler::locate_operand(unsi
java 14341 [019] 252764.225141: cycles: 7f36571922e8 nmethod::do_unloading(BoolObje
java 14341 [019] 252764.234578: cycles: 7f3656ec4960 CodeHeap::block_start(void*) c
[…]

Observability
Methodology
Velocity

Root Cause Analysis at Netflix
…
…
Load
Netflix
Application
ELB ASG Cluster SG
ASG 1
Instances (Linux)Instances (Linux)
JVM
Tomcat
Service
Hystrix
Zuul 2
AZ 1
ASG 2
AZ 2 AZ 3
…
Devices
Eureka
Ribbon
gRPC
Roots
Atlas
Chronos
Zipkin
Vector
sar, *stat
ftrace
bcc/eBPF
bpftrace
PMCs, MSRs

Agenda
1. The Netflix Cloud 2. Methodology
3. Cloud Analysis 4. Instance Analysis

Since 2014
Asgard Spinnaker→ Spinnaker
Salp Zipkin→ Spinnaker
gRPC adoption
New Atlas UI & Lumen
Java frame pointer
eBPF bcc & bpftrace
PMCs in EC2
...
From Clouds to Roots (2014 presentation): Old Atlas UI

>150k AWS EC2 server instances
~34% US Internet traffic at night
>130M members
Performance is customer satisfaction & Netflix cost

Acronyms
AWS: Amazon Web Services
EC2: AWS Elastic Compute 2 (cloud instances)
S3: AWS Simple Storage Service (object store)
ELB: AWS Elastic Load Balancers
SQS: AWS Simple Queue Service
SES: AWS Simple Email Service
CDN: Content Delivery Network
OCA: Netflix Open Connect Appliance (streaming CDN)
QoS: Quality of Service
AMI: Amazon Machine Image (instance image)
ASG: Auto Scaling Group
AZ: Availability Zone
NIWS: Netflix Internal Web Service framework (Ribbon)
gRPC: gRPC Remote Procedure Calls
MSR: Model Specific Register (CPU info register)
PMC: Performance Monitoring Counter (CPU perf counter)
eBPF: extended Berkeley Packet Filter (kernel VM)

S3S3
EC2
CassandraCassandra
EVCacheEVCache
Applications
(Services)
Applications
(Services)
ELBELB
ElasticsearchElasticsearch
SQSSQSSESSES
The Netflix Cloud

Netflix
Microservices User Data
Personalization
Viewing Hist.
Authentication
Web Site API
Streaming API
Client
Devices
DRM
QoS Logging
CDN Steering
Encoding
OCA CDN
…
EC2EC2

Freedom and Responsibility
●
Culture deck memo is true
– https://jobs.netflix.com/culture
●
Deployment freedom
– Purchase and use cloud instances without approvals
– Netflix environment changes fast!

●
Usually open source
●
Linux, Java, Cassandra,
Node.js, …
●
http://netflix.github.io/
Cloud Technologies

Linux (Ubuntu)
Java (JDK 8)
TomcatGC and
thread
dump
logging
GC and
thread
dump
logging
Application war files, base
servlet, platform, hystrix,
health check, metrics (Servo)
Application war files, base
servlet, platform, hystrix,
health check, metrics (Servo)
Optional Apache,
memcached, non-
Java apps (incl.
Node.js, golang)
Optional Apache,
memcached, non-
Java apps (incl.
Node.js, golang)
Atlas monitoring,
S3 log rotation,
ftrace, perf,
bcc/eBPF
Atlas monitoring,
S3 log rotation,
ftrace, perf,
bcc/eBPF
Typical BaseAMI
Cloud Instances

5 Key Issues
And How the Netflix Cloud is
Architected to Solve Them

1. Load Increases → Spinnaker Auto Scaling Groups
– Instances automatically
added or removed by a
custom scaling policy
– Alerts & monitoring used
to check scaling is sane
– Good for customers: Fast workaround
– Good for engineers: Fix later, 9-5
Scaling Policy
loadavg, latency, …
CloudWatch,Servo
ASG
Instance
Instance
Instance
Instance

2. Bad Push → Spinnaker ASG Cluster Rollback
– ASG red black clusters: how code
versions are deployed
– Fast rollback for issues
– Traffic managed by Elastic Load
Balancers (ELBs)
– Automated Canary Analysis (ACA)
for testing
…
ASG-v011
…
ASG-v010
ASG
Cluster
prod1
Canary
ELB
Instance
Instance
Instance
Instance
Instance
Instance

3. Instance Failure → Spinnaker Hystrix Timeouts
– Hystrix: latency and fault tolerance
for dependency services
Fallbacks, degradation, fast fail and rapid
recovery, timeouts, load shedding, circuit
breaker, realtime monitoring
– Plus Ribbon or gRPC for more fault tolerance
Tomcat
Application
Hystrix
get A
Dependency
A1
Dependency
A2
>100ms

4. Region failure → Spinnaker Zuul 2 Reroute Traffic
– All device traffic goes through the Zuul 2 proxy: dynamic routing, monitoring,
resiliency, security
– Region or AZ failure: reroute traffic to another region
Zuul 2, DNS Monitoring
Region 1Region 1 Region 2Region 2 Region 3Region 3

5. Overlooked Issue → Spinnaker Chaos Engineering
lnstances: termination
Availability Zones: artificial failures
Latency: artificial delays by ChAP
Conformity: kills non-best-practices instances
Doctor: health checks
Janitor: kills unused instances
Security: checks violations
10-18: geographic issues
(Resilience)

A Resilient Architecture
…
…
Load
Netflix
Application
ELB ASG Cluster SG
ASG 1
Instances (Linux)Instances (Linux)
JVM
Tomcat
Service
Hystrix
Zuul 2
AZ 1
ASG 2
AZ 2 AZ 3
…
Devices
Chaos Engineering
Some services vary:
- Apache Web Server
- Node.js & Prana
- golang
Eureka
Ribbon
gRPC

2. Methodology
Cloud & Instance

Why Do Root Cause Perf Analysis?
…
…
Netflix
Application
ELBASG ClusterSG
ASG 1
Instances (Linux)
JVM
Tomcat
Service
AZ 1
ASG 2
AZ 2AZ 3
…
Often for:
●
High latency
●
Growth
●
Upgrades

Cloud Methodologies
●
Resource Analysis
●
Metric and event correlations
●
Latency Drilldowns
●
RED Method
Service A
Service C
Service B
Service D
For each microservice, check:
- Rate
- Errors
- Duration

Instance Methodologies
●
Log Analysis
●
Micro-benchmarking
●
Drill-down analysis
●
USE Method CPU Memory
Disk
Controller
Network
Controller
Disk Disk Net Net
For each resource, check:
- Utilization
- Saturation
- Errors

Bad Instance Anti-Method
1. Plot request latency per-instance
2. Find the bad instance
3. Terminate it
4. Someone else’s problem now!
Bad instance latency
Terminate!
Could be an early warning of a bigger issue

3. Cloud Analysis
Atlas, Lumen, Chronos, ...

Netflix Cloud Analysis Process
Instance AnalysisInstance Analysis
Atlas AlertsAtlas Alerts
Atlas/Lumen DashboardsAtlas/Lumen Dashboards
Atlas MetricsAtlas Metrics
ZipkinZipkinSlalomSlalom
PICSOUPICSOU
4. Check Dependencies
Create
New Alert
Cost
3. Drill Down
5. Root Cause
ChronosChronos
2. Check Events
Redirected to
a new Target
1. Check Issue
Example path
enumerated
Plus some other
tools not pictured
SlackSlack
Chat

Atlas: Alerts
Custom alerts on streams per second (SPS) changes, CPU usage, latency, ASG
growth, client errors, …

Winston: Automated Diagnostics & Remediation
Chronos: Possible Related Events
Links to Atlas
Dashboards & Metrics

Atlas: Dashboards
Netflix perf vitals dashboard
1. RPS, CPU
3. Instances
5. CPU/RPS
7. Java heap
9. Latency
2. Volume
6. Load avg
4. Scaling
8. ParNew
10. 99th
tile

Atlas & Lumen: Custom Dashboards
●
Dashboards are a checklist methodology: what to show first,
second, third...
●
Starting point for issues
1. Confirm and quantify issue
2. Check historic trend
3. Atlas metrics to drill down
Lumen: more flexible dashboards
eg, go/burger

Atlas: Metrics
Application
Metrics
Presentation
Interactive
graph
Summary
statistics
Region
Time range

Atlas: Metrics
●
All metrics in one system
– System metrics: CPU usage, disk I/O, memory, …
– Application metrics: latency percentiles, errors, …
●
Filters or breakdowns by region,
application, ASG, metric, instance
●
URL has session state: shareable

Chronos: Change Tracking
Scope Time Range Event Log

Slalom: Dependency Graphing
App
Dependency
Traffic Volume

Zipkin UI: Dependency Tracing
Dependency
Latency

PICSOU: AWS Usage
Cost per hour
Breakdowns
Details (redacted)

Slack: Chat
Latency is high in us-east-1
Sorry
We just did a bad push

Netflix Cloud Analysis Process
Atlas AlertsAtlas Alerts
Atlas MetricsAtlas Metrics
ZipkinZipkinSlalomSlalom
Create
New Alert
Cost
3. Drill Down
5. Root Cause
ChronosChronos
2. Check Events
Redirected to
a new Target
1. Check Issue
Example path
enumerated
Plus some other
tools not pictured
PICSOUPICSOU
Atlas/Lumen DashboardsAtlas/Lumen Dashboards
SlackSlack
Chat

Generic Cloud Analysis Process
AlertsAlerts
Custom DashboardsCustom Dashboards
Metric AnalysisMetric Analysis
Dependency AnalysisDependency Analysis
Usage ReportsUsage Reports
Create
New Alert
Cost
3. Drill Down
5. Root Cause
Change TrackingChange Tracking
2. Check Events
Redirected to
a new Target
1. Check Issue
Example path
enumerated
Plus other tools
as needed
MessagingMessaging
Chat

4. Instance Analysis
1. Statistics
2. Profiling
3. Tracing
4. Processor Analysis

Linux Tools
●
vmstat, pidstat, sar, etc, used mostly normally
●
Micro benchmarking can be used to investigate hypervisor
behavior that can’t be observed directly
$ sar -n TCP,ETCP,DEV 1
Linux 4.15.0-1027-aws (xxx) 12/03/2018 _x86_64_ (48 CPU)
09:43:53 PM IFACE rxpck/s txpck/s rxkB/s txkB/s rxcmp/s txcmp/s rxmcst/s %ifutil
09:43:54 PM lo 15.00 15.00 1.31 1.31 0.00 0.00 0.00 0.00
09:43:54 PM eth0 26392.00 33744.00 19361.43 28065.36 0.00 0.00 0.00 0.00
09:43:53 PM active/s passive/s iseg/s oseg/s
09:43:54 PM 18.00 132.00 17512.00 33760.00
09:43:53 PM atmptf/s estres/s retrans/s isegerr/s orsts/s
09:43:54 PM 0.00 0.00 11.00 0.00 0.00
[…]

Exception: Containers
●
Most Linux tools are still not container aware
– From the container, will show the full host
●
We expose cgroup metrics in our cloud GUIs: Vector

Vector: Instance/Container Analysis

Experience:
“ZFS is eating my CPUs”

Application (truncated)
38% kernel time (why?)
CPU Mixed-Mode Flame Graph

Java Profilers System Profilers
2014: Java Profiling

Java
JVM
Kernel
GC
2018: Java Profiling
CPU Mixed-mode Flame Graph

CPU Flame Graphs
a()
b() h()
c()
d()
e() f()
g()
i()

CPU Flame Graphs
a()
b() h()
c()
d()
e() f()
g()
i()
Top edge:
Who is running on CPU
And how much (width)
Ancestry
●
Y-axis: stack depth
– 0 at bottom
– 0 at top == icicle graph
●
X-axis: alphabet
– Time == flame chart
●
Color: random
– Hues often used for
language types
– Can be a dimension
eg, CPI

Application Profiling
●
Primary approach:
– CPU mixed-mode flame graphs (eg, via Linux perf)
– May need frame pointers (eg, Java -XX:+PreserveFramePointer)
– May need a symbol file (eg, Java perf-map-agent, Node.js --perf-basic-prof)
●
Secondary:
– Application profiler (eg, via Lightweight Java Profiler)
– Application logs

Vector: Push-button Flame Graphs

Future: eBPF-based Profiling
perf record
perf script
stackcollapse-perf.pl
flamegraph.pl
perf.data
flamegraph.pl
profile.py
Linux 4.9Linux 2.6

Core Linux Tracers
Ftrace 2.6.27+ Tracing views
perf 2.6.31+ Official profiler & tracer
eBPF 4.9+ Programmatic engine
bcc - Complex tools
bpftrace - Short scripts
Plus other kernel tech:
kprobes, uprobes

# iostat –x 1
[…]
avg-cpu: %user %nice %system %iowait %steal %idle
5.37 0.00 0.77 0.00 0.00 93.86
Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await r_await w_await svctm %util
xvda 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
xvdb 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
xvdj 0.00 0.00 139.00 0.00 1056.00 0.00 15.19 0.88 6.19 6.19 0.00 6.30 87.60
[…]

# /apps/perf-tools/bin/iolatency 10
Tracing block I/O. Output every 10 seconds. Ctrl-C to end.
>=(ms) .. <(ms) : I/O |Distribution |
0 -> 1 : 421 |######################################|
1 -> 2 : 95 |######### |
2 -> 4 : 48 |##### |
4 -> 8 : 108 |########## |
8 -> 16 : 363 |################################# |
16 -> 32 : 66 |###### |
32 -> 64 : 3 |# |
64 -> 128 : 7 |# |
^C

# /apps/perf-tools/bin/iosnoop
Tracing block I/O. Ctrl-C to end.
COMM PID TYPE DEV BLOCK BYTES LATms
java 30603 RM 202,144 1670768496 8192 0.28
cat 6587 R 202,0 1727096 4096 10.07
cat 6587 R 202,0 1727120 8192 10.21
cat 6587 R 202,0 1727152 8192 10.43
java 30603 RM 202,144 620864512 4096 7.69
java 30603 RM 202,144 584767616 8192 16.12
java 30603 RM 202,144 601721984 8192 9.28
java 30603 RM 202,144 603721568 8192 9.06
java 30603 RM 202,144 61067936 8192 0.97
java 30603 RM 202,144 1678557024 8192 0.34
java 30603 RM 202,144 55299456 8192 0.61
java 30603 RM 202,144 1625084928 4096 12.00
java 30603 RM 202,144 618895408 8192 16.99
java 30603 RM 202,144 581318480 8192 13.39
java 30603 RM 202,144 1167348016 8192 9.92
java 30603 RM 202,144 51561280 8192 22.17
[...]

# /usr/share/bcc/tools/biosnoop
TIME(s) COMM PID DISK T SECTOR BYTES LAT(ms)
0.000000000 tar 8519 xvda R 110824 4096 6.50
0.004183000 tar 8519 xvda R 111672 4096 4.08
0.016195000 tar 8519 xvda R 4198424 4096 11.88
0.018716000 tar 8519 xvda R 4201152 4096 2.43
0.019416000 tar 8519 xvda R 4201160 4096 0.61
0.032645000 tar 8519 xvda R 4207968 4096 13.16
0.033181000 tar 8519 xvda R 4207976 4096 0.47
0.033524000 tar 8519 xvda R 4208000 4096 0.27
0.033876000 tar 8519 xvda R 4207992 4096 0.28
0.034840000 tar 8519 xvda R 4208008 4096 0.89
0.035713000 tar 8519 xvda R 4207984 4096 0.81
0.036165000 tar 8519 xvda R 111720 4096 0.37
0.039969000 tar 8519 xvda R 8427264 4096 3.69
0.051614000 tar 8519 xvda R 8405640 4096 11.44
0.052310000 tar 8519 xvda R 111696 4096 0.55
0.053044000 tar 8519 xvda R 111712 4096 0.56
0.059583000 tar 8519 xvda R 8411032 4096 6.40
0.068278000 tar 8519 xvda R 4218672 4096 8.57
0.076717000 tar 8519 xvda R 4218968 4096 8.33
0.077183000 tar 8519 xvda R 4218984 4096 0.40
0.082188000 tar 8519 xvda R 8393552 4096 4.94
[...]

eBPF: extended Berkeley Packet Filter
Kernel
kprobes
uprobes
tracepoints
sockets
SDN Configuration
User-Defined BPF Programs
…
Event TargetsRuntime
perf_events
BPF
actions
BPF
verifier
DDoS Mitigation
Intrusion Detection
Container Security
Observability
Firewalls (bpfilter)
Device Drivers

bcc
# /usr/share/bcc/tools/tcplife
PID COMM LADDR LPORT RADDR RPORT TX_KB RX_KB MS
2509 java 100.82.34.63 8078 100.82.130.159 12410 0 0 5.44
2509 java 100.82.34.63 8078 100.82.78.215 55564 0 0 135.32
2509 java 100.82.34.63 60778 100.82.207.252 7001 0 13 15126.87
2509 java 100.82.34.63 38884 100.82.208.178 7001 0 0 15568.25
2509 java 127.0.0.1 4243 127.0.0.1 42166 0 0 0.61
12030 upload-mes 127.0.0.1 34020 127.0.0.1 8078 11 0 3.38
12030 upload-mes 127.0.0.1 21196 127.0.0.1 7101 0 0 12.61
3964 mesos-slav 127.0.0.1 7101 127.0.0.1 21196 0 0 12.64
12021 upload-sys 127.0.0.1 34022 127.0.0.1 8078 372 0 15.28
2509 java 127.0.0.1 8078 127.0.0.1 34022 0 372 15.31
2235 dockerd 100.82.34.63 13730 100.82.136.233 7002 0 4 18.50
2235 dockerd 100.82.34.63 34314 100.82.64.53 7002 0 8 56.73
[...]

bpftrace
# biolatency.bt
Attaching 3 probes...
Tracing block device I/O... Hit Ctrl-C to end.
^C
@usecs:
[256, 512) 2 | |
[512, 1K) 10 |@ |
[1K, 2K) 426 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@|
[2K, 4K) 230 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@ |
[4K, 8K) 9 |@ |
[8K, 16K) 128 |@@@@@@@@@@@@@@@ |
[16K, 32K) 68 |@@@@@@@@ |
[32K, 64K) 0 | |
[64K, 128K) 0 | |
[128K, 256K) 10 |@ |

bpftrace: biolatency.bt
#!/usr/local/bin/bpftrace
BEGIN
{
printf("Tracing block device I/O... Hit Ctrl-C to end.n");
}
kprobe:blk_account_io_start
{
@start[arg0] = nsecs;
}
kprobe:blk_account_io_completion
/@start[arg0]/
{
@usecs = hist((nsecs - @start[arg0]) / 1000);
delete(@start[arg0]);
}

What “90% CPU Utilization” might suggest:
What it typically means on the Netflix cloud:

PMCs
●
Performance Monitoring Counters help you analyze stalls
●
Some instances (eg. Xen-based m4.16xl) have the architectural set:

Instructions Per Cycle (IPC)
“bad”
“good*”
<0.2
>2.0 Instruction bound
IPC
Stall-cycle bound
* probably; exception: spin locks

PMCs: EC2 Xen Hypervisor
# perf stat -a -- sleep 30
Performance counter stats for 'system wide':
1921101.773240 task-clock (msec) # 64.034 CPUs utilized (100.00%)
1,103,112 context-switches # 0.574 K/sec (100.00%)
189,173 cpu-migrations # 0.098 K/sec (100.00%)
4,044 page-faults # 0.002 K/sec
2,057,164,531,949 cycles # 1.071 GHz (75.00%)
<not supported> stalled-cycles-frontend
<not supported> stalled-cycles-backend
1,357,979,592,699 instructions # 0.66 insns per cycle (75.01%)
243,244,156,173 branches # 126.617 M/sec (74.99%)
4,391,259,112 branch-misses # 1.81% of all branches (75.00%)
30.001112466 seconds time elapsed
# ./pmcarch 1
CYCLES INSTRUCTIONS IPC BR_RETIRED BR_MISPRED BMR% LLCREF LLCMISS LLC%
38222881237 25412094046 0.66 4692322525 91505748 1.95 780435112 117058225 85.00
40754208291 26308406390 0.65 5286747667 95879771 1.81 751335355 123725560 83.53
35222264860 24681830086 0.70 4616980753 86190754 1.87 709841242 113254573 84.05
38176994942 26317856262 0.69 5055959631 92760370 1.83 787333902 119976728 84.76
[...]

PMCs: EC2 Nitro Hypervisor
●
Some instance types (large, Nitro-based) support most PMCs!
●
Meltdown KPTI patch TLB miss analysis on a c5.9xl:
nopti:
# tlbstat -C0 1
K_CYCLES K_INSTR IPC DTLB_WALKS ITLB_WALKS K_DTLBCYC K_ITLBCYC DTLB% ITLB%
2854768 2455917 0.86 565 2777 50 40 0.00 0.00
2884618 2478929 0.86 950 2756 6 38 0.00 0.00
2847354 2455187 0.86 396 297403 46 40 0.00 0.00
[...]
pti, nopcid:
# tlbstat -C0 1
K_CYCLES K_INSTR IPC DTLB_WALKS ITLB_WALKS K_DTLBCYC K_ITLBCYC DTLB% ITLB%
2875793 276051 0.10 89709496 65862302 787913 650834 27.40 22.63
2860557 273767 0.10 88829158 65213248 780301 644292 27.28 22.52
2885138 276533 0.10 89683045 65813992 787391 650494 27.29 22.55
2532843 243104 0.10 79055465 58023221 693910 573168 27.40 22.63
[...]
worst case

MSRs
●
Model Specific Registers
●
System config info, including current clock rate:
# showboost
Base CPU MHz : 2500
Set CPU MHz : 2500
Turbo MHz(s) : 3100 3200 3300 3500
Turbo Ratios : 124% 128% 132% 140%
CPU 0 summary every 1 seconds...
TIME C0_MCYC C0_ACYC UTIL RATIO MHz
23:39:07 1618910294 89419923 64% 5% 138
23:39:08 1774059258 97132588 70% 5% 136
23:39:09 2476365498 130869241 99% 5% 132
^C

Take Aways
1. Get push-button CPU flame graphs: kernel & user
2. Check out eBPF perf tools: bcc, bpftrace
3. Measure IPC as well as CPU utilization using PMCs
90% CPU busy:
… really means:

Observability
Statistics, Flame Graphs, eBPF Tracing, Cloud PMCs
Methodology
USE method, RED method, Drill-down Analysis, …
Velocity
Self-service GUIs: Vector, FlameScope, …

Resources
●
2014 talk From Clouds to Roots: http://www.slideshare.net/brendangregg/netflix-from-clouds-to-roots
http://www.youtube.com/watch?v=H-E0MQTID0g
●
Chaos: https://medium.com/netflix-techblog/chap-chaos-automation-platform-53e6d528371f https://principlesofchaos.org/
●
Atlas: https://github.com/Netflix/Atlas
●
Atlas: https://medium.com/netflix-techblog/introducing-atlas-netflixs-primary-telemetry-platform-bd31f4d8ed9a
●
RED method: https://thenewstack.io/monitoring-microservices-red-method/
●
USE method: https://queue.acm.org/detail.cfm?id=2413037
●
Winston: https://medium.com/netflix-techblog/introducing-winston-event-driven-diagnostic-and-remediation-platform-46ce39aa81cc
●
Lumen: https://medium.com/netflix-techblog/lumen-custom-self-service-dashboarding-for-netflix-8c56b541548c
●
Flame graphs: http://www.brendangregg.com/flamegraphs.html
●
Java flame graphs: https://medium.com/netflix-techblog/java-in-flames-e763b3d32166
●
Vector: http://vectoross.io https://github.com/Netflix/Vector
●
FlameScope: https://github.com/Netflix/FlameScope
●
Tracing ponies: thanks Deirdré Straughan & General Zoi's Pony Creator
●
ftrace: http://lwn.net/Articles/608497/ - usually already in your kernel
●
perf: http://www.brendangregg.com/perf.html - perf is usually packaged in linux-tools-common
●
tcplife: https://github.com/iovisor/bcc - often available as a bcc or bcc-tools package
●
bpftrace: https://github.com/iovisor/bpftrace
●
pmcarch: https://github.com/brendangregg/pmc-cloud-tools
●
showboost: https://github.com/brendangregg/msr-cloud-tools - also try turbostat

Thank you.
Brendan Gregg
@brendangregg

YOW2018 Cloud Performance Root Cause Analysis at Netflix

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