SlideShare a Scribd company logo
1 of 10
Maurizio Lopez
18/03/2016
This is not a
SumoLogic training.Monitoring tips.
• Error Messages Examples: how to react to a sudden errors spike or to a
new error message?
Start with New Relic first!
.-'The operation was canceled.'
.-'Session has expired'
.-'Second Last FailerAttempts for Retail users'
.-'Profile name must be unique within an account'
.-‘A task was canceled'
.-'Object reference not set to an instance of an object.'
.-'Missing token.'
.-'Method Not Allowed'
.-'Invalid registration code'
.-'Invalid PercentageCompleted, 0-100 is only valid.‘
"You must provide a email address, emailAddress provided : null"
• Go to INSIGHTS -> Accounts Dashboards -> Counters&Metrics.
• For example: "You must provide a email address, emailAddress provided : null"
Keep and eye on……
• If you don’t like the dashboard …………
• Would you like to get a second opinion?.
• Go to: INSIGHTS -> DATA EXPLORER
Do you fancy a better place to go…?
Set up Data Explorer and TIME.
• Go to: Group by -> Transaction Detail -> (use Error Message or Error Type)
How Did We Get Here?
• PLOT it by STRING ->Hosts
• PLOT it by NUMERIC -> Duration -> Number of Transaction Events.
Available Options
Going back to the original example
"You must provide a email address, emailAddress provided : null"
Let’s Sumo: Start simple…
• Do a first exploratory query of 5 to 15 minutes. Pause is if necessary.
• Introduce a Source_Category or a SourceHost according to the initial
query.
• Start a longer query from 24 hours to 3 days for example.
• Display FIELDS and look for NULL VALUES…Example "deviceTypeName=PS4"
Where to get the juice?
_sourceCategory="soa-prod-aws-us-east-1-gladprod" AND ("deviceTypeName=PS4")
// | where isNull(ssl)
| where isNull(%"sc_win32_status")
| where isNull(sc_status)
| where isNull(sc_substatus)
| where isNull(sc_bytes)
| where isNull(date)
| where isNull(cs_version)
| where isNull(cs_useragent)
// | where isNull(cs_uri_stem)
| where isNull(cs_uri_query)
| where isNull(cs_referer)
| where isNull(cs_method)
| where isNull(cs_host)
| where isNull(cs_cookie)
| where isNull(cs_bytes)
| where isNull(c_ip)
• Once you discover meaningful data then point to the specific field
introducing this sentence in your SumoLogic Query: | fields sc_status
• You can PARCE to catch sensitive information and then COUNT it as
you want to present it in order to highlight the CONTEXT of your query.
• Now you are ready to go to DATADOG……..
How to close the loop?

More Related Content

Similar to This is not a SumoLogic training

Validating big data pipelines - FOSDEM 2019
Validating big data pipelines -  FOSDEM 2019Validating big data pipelines -  FOSDEM 2019
Validating big data pipelines - FOSDEM 2019Holden Karau
 
Adversarial Simulation Nickerson/Gates Wild West Hacking Fest Oct 2017
Adversarial Simulation Nickerson/Gates Wild West Hacking Fest Oct 2017Adversarial Simulation Nickerson/Gates Wild West Hacking Fest Oct 2017
Adversarial Simulation Nickerson/Gates Wild West Hacking Fest Oct 2017Chris Gates
 
Validating spark ml jobs stopping failures before production on Apache Spark ...
Validating spark ml jobs stopping failures before production on Apache Spark ...Validating spark ml jobs stopping failures before production on Apache Spark ...
Validating spark ml jobs stopping failures before production on Apache Spark ...Holden Karau
 
Validating big data pipelines - Scala eXchange 2018
Validating big data pipelines -  Scala eXchange 2018Validating big data pipelines -  Scala eXchange 2018
Validating big data pipelines - Scala eXchange 2018Holden Karau
 
Validating big data jobs - Spark AI Summit EU
Validating big data jobs  - Spark AI Summit EUValidating big data jobs  - Spark AI Summit EU
Validating big data jobs - Spark AI Summit EUHolden Karau
 
Validating Big Data Jobs—Stopping Failures Before Production on Apache Spark...
 Validating Big Data Jobs—Stopping Failures Before Production on Apache Spark... Validating Big Data Jobs—Stopping Failures Before Production on Apache Spark...
Validating Big Data Jobs—Stopping Failures Before Production on Apache Spark...Databricks
 
Acceptance And Story Testing Patterns - By Charles Bradley
Acceptance And Story Testing Patterns - By Charles BradleyAcceptance And Story Testing Patterns - By Charles Bradley
Acceptance And Story Testing Patterns - By Charles BradleySynerzip
 
Validating Big Data Pipelines - Big Data Spain 2018
Validating Big Data Pipelines - Big Data Spain 2018Validating Big Data Pipelines - Big Data Spain 2018
Validating Big Data Pipelines - Big Data Spain 2018Holden Karau
 
Duke at SplunkLive! Charlotte
Duke at SplunkLive! CharlotteDuke at SplunkLive! Charlotte
Duke at SplunkLive! CharlotteSplunk
 
lec_4_data_structures_and_algorithm_analysis.ppt
lec_4_data_structures_and_algorithm_analysis.pptlec_4_data_structures_and_algorithm_analysis.ppt
lec_4_data_structures_and_algorithm_analysis.pptSourabhPal46
 
lec_4_data_structures_and_algorithm_analysis.ppt
lec_4_data_structures_and_algorithm_analysis.pptlec_4_data_structures_and_algorithm_analysis.ppt
lec_4_data_structures_and_algorithm_analysis.pptMard Geer
 
Using Stories to Test Requirements and Systems
Using Stories to Test Requirements and SystemsUsing Stories to Test Requirements and Systems
Using Stories to Test Requirements and SystemsPaul Gerrard
 
What Are We Still Doing Wrong
What Are We Still Doing WrongWhat Are We Still Doing Wrong
What Are We Still Doing Wrongafa reg
 
Don't be a zombie reading your stories - Learn the exciting parts of analysis...
Don't be a zombie reading your stories - Learn the exciting parts of analysis...Don't be a zombie reading your stories - Learn the exciting parts of analysis...
Don't be a zombie reading your stories - Learn the exciting parts of analysis...Ryan McKergow
 
Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem Dataiku
 
Sumo Logic Cert Jam - Search Mastery
Sumo Logic Cert Jam - Search MasterySumo Logic Cert Jam - Search Mastery
Sumo Logic Cert Jam - Search MasterySumo Logic
 

Similar to This is not a SumoLogic training (20)

Validating big data pipelines - FOSDEM 2019
Validating big data pipelines -  FOSDEM 2019Validating big data pipelines -  FOSDEM 2019
Validating big data pipelines - FOSDEM 2019
 
Adversarial Simulation Nickerson/Gates Wild West Hacking Fest Oct 2017
Adversarial Simulation Nickerson/Gates Wild West Hacking Fest Oct 2017Adversarial Simulation Nickerson/Gates Wild West Hacking Fest Oct 2017
Adversarial Simulation Nickerson/Gates Wild West Hacking Fest Oct 2017
 
Validating spark ml jobs stopping failures before production on Apache Spark ...
Validating spark ml jobs stopping failures before production on Apache Spark ...Validating spark ml jobs stopping failures before production on Apache Spark ...
Validating spark ml jobs stopping failures before production on Apache Spark ...
 
Validating big data pipelines - Scala eXchange 2018
Validating big data pipelines -  Scala eXchange 2018Validating big data pipelines -  Scala eXchange 2018
Validating big data pipelines - Scala eXchange 2018
 
Validating big data jobs - Spark AI Summit EU
Validating big data jobs  - Spark AI Summit EUValidating big data jobs  - Spark AI Summit EU
Validating big data jobs - Spark AI Summit EU
 
Validating Big Data Jobs—Stopping Failures Before Production on Apache Spark...
 Validating Big Data Jobs—Stopping Failures Before Production on Apache Spark... Validating Big Data Jobs—Stopping Failures Before Production on Apache Spark...
Validating Big Data Jobs—Stopping Failures Before Production on Apache Spark...
 
Acceptance And Story Testing Patterns - By Charles Bradley
Acceptance And Story Testing Patterns - By Charles BradleyAcceptance And Story Testing Patterns - By Charles Bradley
Acceptance And Story Testing Patterns - By Charles Bradley
 
Validating Big Data Pipelines - Big Data Spain 2018
Validating Big Data Pipelines - Big Data Spain 2018Validating Big Data Pipelines - Big Data Spain 2018
Validating Big Data Pipelines - Big Data Spain 2018
 
Duke at SplunkLive! Charlotte
Duke at SplunkLive! CharlotteDuke at SplunkLive! Charlotte
Duke at SplunkLive! Charlotte
 
lec_4_data_structures_and_algorithm_analysis.ppt
lec_4_data_structures_and_algorithm_analysis.pptlec_4_data_structures_and_algorithm_analysis.ppt
lec_4_data_structures_and_algorithm_analysis.ppt
 
lec_4_data_structures_and_algorithm_analysis.ppt
lec_4_data_structures_and_algorithm_analysis.pptlec_4_data_structures_and_algorithm_analysis.ppt
lec_4_data_structures_and_algorithm_analysis.ppt
 
Wit march 2020
Wit march 2020Wit march 2020
Wit march 2020
 
Breaking, Entering and Pentesting
Breaking, Entering and Pentesting Breaking, Entering and Pentesting
Breaking, Entering and Pentesting
 
Using Stories to Test Requirements and Systems
Using Stories to Test Requirements and SystemsUsing Stories to Test Requirements and Systems
Using Stories to Test Requirements and Systems
 
Feedback Loops
Feedback LoopsFeedback Loops
Feedback Loops
 
What Are We Still Doing Wrong
What Are We Still Doing WrongWhat Are We Still Doing Wrong
What Are We Still Doing Wrong
 
Don't be a zombie reading your stories - Learn the exciting parts of analysis...
Don't be a zombie reading your stories - Learn the exciting parts of analysis...Don't be a zombie reading your stories - Learn the exciting parts of analysis...
Don't be a zombie reading your stories - Learn the exciting parts of analysis...
 
Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem
 
Before Kaggle
Before KaggleBefore Kaggle
Before Kaggle
 
Sumo Logic Cert Jam - Search Mastery
Sumo Logic Cert Jam - Search MasterySumo Logic Cert Jam - Search Mastery
Sumo Logic Cert Jam - Search Mastery
 

This is not a SumoLogic training

  • 1. Maurizio Lopez 18/03/2016 This is not a SumoLogic training.Monitoring tips.
  • 2. • Error Messages Examples: how to react to a sudden errors spike or to a new error message? Start with New Relic first! .-'The operation was canceled.' .-'Session has expired' .-'Second Last FailerAttempts for Retail users' .-'Profile name must be unique within an account' .-‘A task was canceled' .-'Object reference not set to an instance of an object.' .-'Missing token.' .-'Method Not Allowed' .-'Invalid registration code' .-'Invalid PercentageCompleted, 0-100 is only valid.‘ "You must provide a email address, emailAddress provided : null"
  • 3. • Go to INSIGHTS -> Accounts Dashboards -> Counters&Metrics. • For example: "You must provide a email address, emailAddress provided : null" Keep and eye on……
  • 4. • If you don’t like the dashboard ………… • Would you like to get a second opinion?. • Go to: INSIGHTS -> DATA EXPLORER Do you fancy a better place to go…? Set up Data Explorer and TIME.
  • 5. • Go to: Group by -> Transaction Detail -> (use Error Message or Error Type) How Did We Get Here?
  • 6. • PLOT it by STRING ->Hosts • PLOT it by NUMERIC -> Duration -> Number of Transaction Events. Available Options
  • 7. Going back to the original example "You must provide a email address, emailAddress provided : null"
  • 8. Let’s Sumo: Start simple… • Do a first exploratory query of 5 to 15 minutes. Pause is if necessary. • Introduce a Source_Category or a SourceHost according to the initial query. • Start a longer query from 24 hours to 3 days for example.
  • 9. • Display FIELDS and look for NULL VALUES…Example "deviceTypeName=PS4" Where to get the juice? _sourceCategory="soa-prod-aws-us-east-1-gladprod" AND ("deviceTypeName=PS4") // | where isNull(ssl) | where isNull(%"sc_win32_status") | where isNull(sc_status) | where isNull(sc_substatus) | where isNull(sc_bytes) | where isNull(date) | where isNull(cs_version) | where isNull(cs_useragent) // | where isNull(cs_uri_stem) | where isNull(cs_uri_query) | where isNull(cs_referer) | where isNull(cs_method) | where isNull(cs_host) | where isNull(cs_cookie) | where isNull(cs_bytes) | where isNull(c_ip)
  • 10. • Once you discover meaningful data then point to the specific field introducing this sentence in your SumoLogic Query: | fields sc_status • You can PARCE to catch sensitive information and then COUNT it as you want to present it in order to highlight the CONTEXT of your query. • Now you are ready to go to DATADOG…….. How to close the loop?