The Troubleshooting Chart

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The Troubleshooting Chart is a method of structuring an IT troubleshooting process and visualizing the impact and timeline of the incident.

Technology

The Troubleshooting Chart
James Wing
Seattle DevOps Meetup
September 30, 2014

Agenda
• The Troubleshooting Chart
• Process
• Mindset
• Chart Components
– Success
– Timeline
• Communication

Data Feeds
Successful Feeds
Time
100K
0
Break
Fix

The Troubleshooting Chart
Success Rate
Time
100%
0%
Used to Work
Now it’s Busted
Working Again
Break
Fix

Typical Process
① “Try it again”
② Reboot, restart, refresh, etc.
③ Look for obvious errors
④ Search knowledge base, Google
⑤ Check recent release notes
⑥ Draw a chart!

Example: Late Reports
Customers Developers
Reports are not available until
late in the day
Logs show ETL processing
finished at 9 AM. No errors.
It’s 2 PM and still not ready
Aaahh!

Superstition vs. Denial
Customer
• Expectations of how it
should work
• Experience of how it
does or does not work
Developer
• Superstition!
• No repro steps?
• Which component (dev)?
• What is the
impact/priority?

Late Reports
Success Rate
Ready by 10 AM ?
Time
100%
Break?
0% Now it’s Busted

Success Rate
Success Rate
Time
100%
0%
Used to Work
Now it’s Busted
Working Again
Break
Fix

Why Success Rate?
• Customers complain about lack of success
– Not errors
– Not system problems
• Triaging the complaint means
– Understanding what is not succeeding now
– Historical context of success
– Identifying what a fix must do

Partial vs. Complete Failure
Success Rate
Time
100%
0%

Find 100% of Failure
System Status Incident Scope
99% success
All customers
All features
100% failure
Customers X, Y
Feature Z
Success Rate
Time
100%
0%
Success Rate
Time
100%
0%

The Error Chart
Error Rate
Time
100%
0%
Used to Work
Now it’s Busted
Working Again
Break
Fix

Success vs. Errors
Error Rate
Time
100%
0%
Used to Work
Now it’s Busted
Working Again
Break
Fix
Success Rate
Time
100%
0%
Used to Work
Now it’s Busted
Working Again
Break
Fix

Error Data
Good
• Errors (might) indicate root cause
• By Geeks, for Geeks
Bad
• Errors != Absence of Success
• Poor data quality
• No impact
Ugly
• Signal-to-Noise Ratio
• Technical artifacts

Success Data Sources
• Ideal sources
– Transaction or Operational data stores
– Custom data collection
– Logs?
• Ideal form
– 1 record per attempt
– Date, time, duration
– Outcome as success or failure
– Attributes for customer, feature,
process step, etc.

Timeline
Success Rate
Time
100%
0%
Used to Work
Now it’s Busted
Working Again
Break
Fix

You Are Here
Success Rate
Time
100%
0%
Used to Work
Now it’s Busted
Break
?
Working Again
Fix
Complaint

Example: FTP
Problem
• Integration solution using customized FTP
• Intermittent failure reported, “Sometimes it
works!”
Data
• FTP logs
• Application exceptions

FTP – Initial Fix
Success Rate
Time
100%
0%
Working Again!
Fix
Working…Better?

FTP – Starting Over
Success Rate
Time
100%
0%
Used to Work Still Busted

FTP – More Fixes
Success Rate
Time
100%
0%
Working Again!
Fix 1
Fix 2
Fix 3
Fix 4
Used to Work Still Busted

Communication
• Charts make you look smart
• Confirm issue with customer
• Help other departments, customers
• Supervise troubleshooting

Wrapping Up
• Troubleshooting = Success Rate + Timeline
• Success not the same as absence of Errors
• Superstition and Belief
• Charts make you look smart
• Verify fixes

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The Troubleshooting Chart

1. The Troubleshooting Chart James Wing Seattle DevOps Meetup September 30, 2014

2. Agenda • The Troubleshooting Chart • Process • Mindset • Chart Components – Success – Timeline • Communication

3. Data Feeds Successful Feeds Time 100K 0 Break Fix

4. The Troubleshooting Chart Success Rate Time 100% 0% Used to Work Now it’s Busted Working Again Break Fix

5. Typical Process ① “Try it again” ② Reboot, restart, refresh, etc. ③ Look for obvious errors ④ Search knowledge base, Google ⑤ Check recent release notes ⑥ Draw a chart!

6. Example: Late Reports Customers Developers Reports are not available until late in the day Logs show ETL processing finished at 9 AM. No errors. It’s 2 PM and still not ready Aaahh!

7. Superstition

8. Superstition vs. Denial Customer • Expectations of how it should work • Experience of how it does or does not work Developer • Superstition! • No repro steps? • Which component (dev)? • What is the impact/priority?

9. Late Reports Success Rate Ready by 10 AM ? Time 100% Break? 0% Now it’s Busted

10. Belief

11. Success Rate Success Rate Time 100% 0% Used to Work Now it’s Busted Working Again Break Fix

12. Why Success Rate? • Customers complain about lack of success – Not errors – Not system problems • Triaging the complaint means – Understanding what is not succeeding now – Historical context of success – Identifying what a fix must do

13. Partial vs. Complete Failure Success Rate Time 100% 0%

14. Find 100% of Failure System Status Incident Scope 99% success All customers All features 100% failure Customers X, Y Feature Z Success Rate Time 100% 0% Success Rate Time 100% 0%

15. The Error Chart Error Rate Time 100% 0% Used to Work Now it’s Busted Working Again Break Fix

16. Success vs. Errors Error Rate Time 100% 0% Used to Work Now it’s Busted Working Again Break Fix Success Rate Time 100% 0% Used to Work Now it’s Busted Working Again Break Fix

17. Error Data Good • Errors (might) indicate root cause • By Geeks, for Geeks Bad • Errors != Absence of Success • Poor data quality • No impact Ugly • Signal-to-Noise Ratio • Technical artifacts

18. Success Data Sources • Ideal sources – Transaction or Operational data stores – Custom data collection – Logs? • Ideal form – 1 record per attempt – Date, time, duration – Outcome as success or failure – Attributes for customer, feature, process step, etc.

19. Timeline Success Rate Time 100% 0% Used to Work Now it’s Busted Working Again Break Fix

20. You Are Here Success Rate Time 100% 0% Used to Work Now it’s Busted Break ? Working Again Fix Complaint

21. Example: FTP Problem • Integration solution using customized FTP • Intermittent failure reported, “Sometimes it works!” Data • FTP logs • Application exceptions

22. FTP – Initial Fix Success Rate Time 100% 0% Working Again! Fix Working…Better?

23. FTP – Starting Over Success Rate Time 100% 0% Used to Work Still Busted

24. FTP – More Fixes Success Rate Time 100% 0% Working Again! Fix 1 Fix 2 Fix 3 Fix 4 Used to Work Still Busted

25. Communication • Charts make you look smart • Confirm issue with customer • Help other departments, customers • Supervise troubleshooting

26. Blame

27. Wrapping Up • Troubleshooting = Success Rate + Timeline • Success not the same as absence of Errors • Superstition and Belief • Charts make you look smart • Verify fixes

Editor's Notes

Why and how to use data in troubleshooting DevOps – Metrics, Collaboration, Feedback James Monitoring Startup Mercent
Troubleshooting process Using data Mindset Badmouth developers
History Spoiled by data Generalized form
Apply to other issues Success Rate vs. Time Activity and Product, important part of a nutritious process Visualize over calculate (not a math test) Structure troubleshooting Understand and communicate impact
Break/fix issue No guidance for structured path to root cause Escalate? Easy vs. Hard based on these steps Hard = Intermittent, partial, degraded perf, cross-system
Customer says reports not ready Dev says processing complete, has log entry to prove it Recrimination, personal attacks
Temptation to ridicule users User observations without insight to internals Indicator that you need a chart
Lessons – Denial result of Superstition More than just ridicule, barriers to getting Dev help Some denial seems reasonable Hard to get help from Developers
How to chart this? % of compliance with required 10 AM No history, we didn’t measure it SHOCK – users were right? Used to work?
Mindset - How can I find issues Devs cannot? Cannot find problems you KNOW aren’t there At end of issue, seems crazy you didn’t notice problems Devs will fix when they believe in problem CSI vs. X-Files Believe first - build chart for now, go through motions Success Rate…
Axis detail…Success Rate Apply belief in initial stages of investigation Past and present success rate
Customers perception of success Will ignore errors if they can Make errors go away Fix must do what?
Unless it’s BAD, hard to see issue Good news, the rest of your system works fine
Impact assessment Future fix verification Understand scope of the issue Promote urgency / prevent panic
Why not errors?
Aren’t these the same charts, just inverted? Maybe… Yes - only if they are based on the same underlying data set No - if they based on different data, for example Success data from an operational or transactional store Error data from log files
Cultural bias towards geeky error logs Devs don’t do Excel Do invest in error data quality, helps in quick steps above
Where is success stored? “Order Table” HARD to know what success really is Order feed succeeds, but doesn’t contain “all” of the orders
Timeline as key to troubleshooting ‘puzzle’ Start with much less information
Success investigation -> Used to work Why complain now? Error appearance and disappearance Find EXACTLY when it broke – Release, maintenance, partner something Match to possible explanations Who broke something? Vs. Who broke the X feature last night around 11:15 PM? Consistent explanation at END, chart does not stop at fix
Found several problems over 3 months 1 of 3 web servers configured wrong New customer setup improvements Several bug fixes
Classic mistake - announcing ‘fix’ before proof
Time to start over Not sure about “Used to Work” Find 100%? Confusing… Small data set is hard – New customers, debugging, different needs Scripted file upload every 15 minutes – data quality
Many fixes – more like process improvement Never too late to start Always verify fixes with chart
Overestimation of smart Demonstrate control I feel your pain Click tracking example
Supposed to say… Inevitable, sort of. Not you-blame-them, get confession Need root cause, extract confession Developers perfectionist Junior programmers, no points Retrospectives?
Act smart? Superstion as trigger, Belief as prereq. to fix

The Troubleshooting Chart

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Editor's Notes