Splunk All the Things: Our First 3 Months Monitoring Web Service APIs - Splunk .conf2012
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A presentation titled "Splunk All the Things: Our First 3 Months Monitoring Web Service APIs" that Dan Cundiff and Eric Helgeson from Target Corporation gave at Splunk .conf2012.
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Splunk All the Things: Our First 3 Months Monitoring Web Service APIs - Splunk .conf2012
- 1. Splunk All the Things: Our First 3 Months Monitoring Web Service APIs Dan Cundiff (@pmotch) and Eric Helgeson (@nulleric) Target Corporation Copyright © 2012 Splunk Inc.
- 2. 2
- 3. Agenda Context Problem Solution Examples In progress and future stuff Lessons and challenges 3
- 4. Context: Enterprise Services @ Target Data and transactional APIs for all the domains in our business – Products (inventory, price, description, etc) – Locations – Coupons – etc APIs exposed inside and outside Mostly RESTful APIs, some pub sub/messaging Used by mobile devices, applications, partners on the outside, etc. Constantly evolving, rapidly improving, all the time 4
- 5. Problem First API go-live: – Millions of log events per day (grep/cut/sed/awk not cutting it) – Logs scattered everywhere – Limited access to logs – Needed end to end visibility of web services – Needed ability to discover information in logs – Can we be pro-active? Faster reactive? Looming horizon: – BILLIONS of log events coming – Questions changing everyday from business, support, execs, developers 5
- 6. Solution: Gave Splunk a try Installed Splunk on a lab server Hooked up Splunk to the logs Quickly created 15+ searches and reports Generated a dashboard for visibility and trending Total time to do all this in Splunk: ~4 hours 6
- 7. Why Splunk Understanding what’s “normal” – Identify tolerances – Identify actionable events vs. anomalies You don’t know what you don’t know – …but Splunk can tell you what you don’t know 7
- 8. Why Splunk, part 2 Indicators when are things trending badly – Proactive monitoring and recovery – Standard deviations, percentage changes over time, outliers Full stack visibility – API gateway – Network (load balancers, firewalls) – Web/app – OS 8
- 9. Why Splunk, part 3 Quick and flexible dashboards Drill down Community (Splunkbase, blogs, etc) Google-able™ App store! 9
- 11. What is “normal”? Volume 11
- 12. What is “normal”?, part 2 API response time SLAs 12
- 13. What is “normal”?, part 3 Errors happen, but what is acceptable? 13
- 14. 404s ~1700 errors once a day every week 404s for stores that don’t exist Bot? – Who are they? – Malicious? Competitor? Individual? – Reach out to understand why 14
- 15. Understanding consumers Who and how is it being used? What’s their experience? 15
- 16. Understanding consumers, part 2 Load testing in production? 16
- 17. Understanding infrastructure Expected design vs actual implementation Not balancing workload as expected 17
- 18. Understanding providers How are providers responding? Is overhead added to the API response? 18
- 19. Requirements feedback loop Requirement: 200 tps Actual: ~20 tps 19
- 20. Business intelligence from APIs Where are people searching? Where should we build our next store? How far are people traveling? What time of day? Mobile vs website? iOS vs Android? International? 20
- 21. Metrics for APIs (source: http://blog.programmableweb.com/2012/08/02/the-api-measurement-secret-know-what-metrics-matter/) Traffic Metrics Service Metrics Support Metrics – Total calls – Performance – Support tickets – Top methods – Availability – Response time – Call chains – Error rates – Community metrics – Quota faults – Code defects Business Metrics Developer Metrics Marketing Metrics – Direct revenue – Total developer count – Developer registrations – Indirect revenue – Number – Developer portal funnel – Market share of active developers – Traffic sources – Costs – Top developers – Event metrics – Trending apps – Retention 21
- 22. In progress and future stuff
- 23. Splunk all the things Consumer apps Provider systems OS, firewalls, proxies External API gateway logs Anything in between (middleware, integrations, etc) Correlate with logs from apps degrees away (e.g. .com web logs) Development (perf test results, git, Jenkins/CI, wiki, etc)
- 24. Dashboards Global dashboard summarizing all APIs BI dashboards Executive dashboards 24
- 25. Dashboards, part 2 Environment dashboards for each API – CI – Test – Stage – Prod 25
- 26. Dashboards, part 3 Alert trending dashboards for each API 26
- 27. Splunking Continuous Integration Drill down into CI results linked straight from Jenkins – Filtered by date OR transaction GUID 27
- 28. Splunking Continuous Integration, part 2 We practice code as documentation Every commit, Jenkins runs, extracts documentation from code, puts it in the respective wiki pages (pretty cool! – automated / no humans) Splunk monitors wiki changes using the MediaWiki API Monitor CI + human wiki changes https://github.com/pmotch/wikislurp 28
- 29. Common Logging Service CLS is our strategy for getting logs from all places into Splunk How – Use UFs on end points everywhere – Else, consolidate and mount Splunk – Else, use CLS RESTful API Enables end-to-end visibility – Insert GUIDs across all the hops in the transaction Use out of the box log formats (e.g. Log4j) 29
- 31. Lessons RTFM – Keep logs flat – Keep timestamp (ISO8601) at the beginning – k=v Iterate quick, push to prod; minimal tweaks to Splunk Flatten out of box audit events (XML) – Toggle at runtime Don’t re-invent the wheel, use what your system provides, Splunk can handle it! 31
- 32. Lessons, part 2 Don’t pre-optimize up front – Governance – Standards – Alerting – Access controls Optimize as needed 32
- 33. Lessons, part 3 Create a community 33
- 34. Lessons, part 4 Create best practices, standards, etc in a wiki 34
- 35. Challenges: Organizational “Stop. We already have tools that do this. Use those.” – tgtMAKE saves the day – tgtMAKE = R&D – R&D = $, servers, flak shelter, people network Make it real strategy – Demo to as many key players as possible – Drum up interested – Show actual value 35
- 36. Challenges: Organizational, part 2 http://knowyourmeme.com/photos/361379-shut-up-and-take-my-money 36
- 37. Challenges: Organizational, part 3 The data can’t be trusted? 37
- 38. Challenges: OS RHEL 6 SELinux Ipfw Install notes: http://nulleric.tumblr.com/post/13855621770/splunk-on- redhat-6-install-notes 38
- 39. Challenges: Infrastructure VM requirement Adhering to MDHA requirements Universal Forwarder skepticism 39
- 40. Challenges: Logs on the outside Universal Forwarders on servers that we don’t manage Firewalls Multi-layered DMZs 40
- 41. Challenges: Splunk … 41
- 42. Challenges: Splunk (err, improvements) Index improvements – Cheap servers, can fail, can expand – Replication, N=3 – Replicas on N-1 subsequent nodes – Data is always available, smooth out across servers if they go down or expand – Multi-tenant – Think OpenStack Swift “Ring” concept or Cassandra – There’s that CAP Theorem thing; they say it’s a big deal. GUI for deployment client configurations (lazy and for n00bs, we know) Ability to extend charts with other libraries (like D3 or something) 42
- 43. Recap Be bold. Tooling matters. Sell it. Splunk all the things! Iterate, adapt, change quickly. 43
- 44. We’re hiring (come talk to us) 44
- 45. Questions? 45
Notas del editor
- Abig story to draw you in!Anonymizedlat/long data of guest searching for stores in the last 15 minutes.If a store wasn’t nearby those 61 people in Idaho, did they go somewhere else to by Tide, diapers, or socks?Conceptually, maybe we should build a store there (we don’t actually plan our stores with a sole data point like that, but it gives you an idea)?
- Here’s the context for all the material that follows. “Enterprise Services” program is all about…
- Logsscatted everywhere = complex ecosystemLooming horizon = data explosionStory: going live, millions of hits start coming in, try to figure out what is actually happening
- 4 hours. No joke.Wewere drawn to innovate; just try something new and see what happens.
- “You don’t know what you don’t know, but Splunk knows what you don’t know.” – that is, Splunk can help by telling you and helping discover what you don’t know.
- Drill down: filter to the essential places across logs to troubleshoot or discover business intelCommunity and Google-able: Splunkbase, documentation rules!, lots of Google results = good
- HTTP errors in a 24 period; what is normal? 500s are bad. Many 500s early on, but corrected, and much lower now.
- A list of consumers of the Locations service over a 24 hour period.Story:Identify bad API key before the developer knew what was wrong.
- We’re taking a look at our infrastructure design because of this.
- Able to report on non-functional requirements.Goingforward we can do a better job of not over-estimating infrastructure needs; thus saving a lot more money, not wasting idle inventory on the shelf, and open the door to putting the right money in the right places then.
- You saw the original map at the beginning of our presentation; aswe expose more APIs, what can we learn from them?
- How are we adhering to this advice? We have accomplished many of these metrics already. Most of these are achievable with Splunk.
- The more you have in Splunk, the more complete the monitoring picture can be.
- Great for perf/load testing; see all the errors in one place.Youcan even put the Jenkins logs in Splunk and show the results across all APIs being developed.
- Allow apps to have multiple ways to get logs into SplunkNo UF on consumer devicesBuild transactions across multiple layers of the infraUse UFs on end points everywhere = FASTESTElse, consolidate and mount Splunk = FASTElse, use CLS RESTful API = SLOW
- A flattering meme, but at that point, after the demos and the successful research, Splunk sells itself, and honestly at that point everyone is happy to move on, buy what’s needed, and get down to Splunking.
- Nothing is wrong. Your data is wrong. Getting people to trust what Splunk is telling us.Storyabout 1 of the nodes being down and initially people didn’t believe it was right.
- If developers bring Splunk in, take time and educate ops people on how it all works so they understand how the infrastructure is different and how it should be built. We suspect, normally it’s the other way around.
- Get those indexers behaving like Swift or Cassandra: multi-tenant, N-3 replication of the data so cheap servers can fail, scale, etc.