DevOps Data Storage

What data do we need to store to be more devopsy? How can we use the data? What data stores are out there, where and why might we use them?

Transcript

1. DevOps Data Storage Presented by Brad Lhotsky
2. Brad Lhotsky • Systems and Security
 at Craigslist • Infrastructure Monitoring & Security
 at Booking.com • Recovering • Perl Programmer • Linux/BSD Systems Admin • Network Security Specialist • PostgreSQL Administrator • ElasticSearch Janitor • DNS Voyeur • OSSEC Core Team Member https://github.com/reyjrar https://twitter.com/reyjrar
3. Expectations ‣Common Data Types ‣Using your Data ‣Features of Data Stores ‣Popular Data Stores
4. Types of DevOps-y Data
5. Administrative & Meta-Data ‣ Inventory ‣ Hardware ‣ Software ‣ Builds or Roles ‣ Services ‣ Users and Groups ‣ Employee/Contractor ‣ Managers ‣ ACLs
6. Monitoring ‣ State ‣ OK / NOT OK ‣ UP / DOWN ‣ Package Version ‣ Time Series ‣ Counter ‣ Rate ‣ Statistical Summaries
7. Events ‣ State Changes ‣ Package Updated ‣ Service Stopped ‣ System Events ‣ Syslog Message ‣ SNMP Traps ‣ Application Events ‣ Access ‣ Errors ‣ Traces
8. Deving and Oping Your Data
9. Monitoring and Metrics
10. -Nicole Forsgren - Monitorama PDX 2016 How Metrics Shape Your Culture “Metrics are your culture.”
11. "How Metrics Shape Your Culture" • You can't improve what you don't measure • Always measure things that matter • Things measured are things managed • Metrics can be gamed • Metrics inform incenticves • Not everything that can be counted counts • Hard to measure doesn't mean it isn't worth measuring
12. ... and that's probably O.K. All of your monitoring is probably wrong.
13. Alerting ‣ Disrupting People's Lives at 95% Disk Full ‣ Thresholds -> Change Detection ‣ State Change Thresholds
14. Automation ‣ Can a Machine read my data? ‣ Autoscale ‣ Trend detection ‣ Service Level Roll Ups in Alerting ‣ Reporting
15. Capacity Planning ‣ Predicting System Stress Levels ‣ Make Intelligent Projections ‣ Test those predictions
16. Exploration
17. Attractive Features
18. Open and Extensible ‣ Integrations with other projects ‣ Open API ‣ Good Documentation ‣ Modular / Plugin Structute ‣ Community
19. Reliability vs. Performance
20. Retention ‣ How easy is it to age data off? ‣ What regulations of laws apply to the data? ‣ Expectations from: ‣ Customers ‣ Employees ‣ Managers ‣ Peers ‣ Legal
21. Privacy and Security ‣ What do you keep on your users in your ops data? ‣ Who might come calling for it? ‣ How comfortable are you handing it over to Trump? ‣ Anyone hear about MongoDB? ‣ Can the store provide security?
22. Places People Stick DevOps Datas
23. ‣ Large Community ‣ Forks and Oracle ‣ Performance First ‣ SQL Interface ‣ Limited Data Types ‣ Web > BI ‣ Suitable for Administrative Data
24. ‣ Large Community ‣ Reliability First ‣ Open and Extensible ‣ PGXN ‣ CitusData ‣ GreenPlum ‣ EnterpriseDB ‣ Native Support for IP Addresses ‣ Extensible Data Types ‣ Suitable for Administrative Data
25. ‣ Large Community ‣ Interchangeable Components, ala, MicroServices ‣ Simple API ‣ Rampant Open Source Adoption ‣ Scalable ‣ Compatibility ‣ Grafana, Statsd, Riemann, Bosun, Cabot, Seyren ‣ etc., etc., ‣ Suitable for Time Series Data ‣ Smallest Resolution: seconds
26. security.logging.indexer.*.total Metrics: Wildcards
27. sumSeries(security.logging.indexer.*.total) Combining Metrics
28. alias(sumSeries(security.logging.indexer.*.total),”Today") alias( timeShift( sumSeries(security.logging.indexer.*.total), “7d"), "Last Week") Comparing Metrics
29. alias(alpha(color(areaBetween( holtWintersConfidenceBands( maxSeries(general.es.*.jvm.mem.heap_used_bytes) ) ),“gray"),0.1),"Hot Winter Confidence Bands”) color(alias( maxSeries(general.es.*.jvm.mem.heap_used_bytes), "Max Heap Size"),"red") Advanced Tricks
30. ‣ Metrics 2.0 ‣ Hadoop / Hbase backed ‣ SQL-like Language ‣ Zero Data Loss ‣ Compatibility ‣ Carbon, Grafana, Statsd, Riemann, Bosun ‣ Suitable for Time Series Data ‣ Smallest Resolution: milliseconds
31. ‣ Metrics 2.0 ‣ SQL-like Language ‣ Zero Data Loss ‣ Compatibility ‣ Carbon, Grafana, Statsd, Riemann, Bosun ‣ Suitable for Time Series Data ‣ Smallest Resolution: nanoseconds
32. ‣ Well Documented API ‣ Many Open Source Integrations ‣ Lucene backed text search ‣ Scalable ‣ "Jepsen ElasticSearch" re:CAP
33. Web Attacks Scanners
34. Slow Pages
35. App::ElasticSearch::Utilities Search Stuff! = Querying Indexes: lhr4-access-2015.06.03,ams4-access-2015.06.03 @timestamp src_ip src_ip_country file 2015-06-03T11:39:27+0200 217.36.201.217 GB /B1D671CF- E532-4481-99AA-19F420D90332/netdefender/hui/ndhui.css 2015-06-03T11:39:26+0200 92.56.217.84 ES /hotel/es/ null.es.html # Search Parameters: # {"query_string":{"query":"dst:www.booking.com AND crit:404"}} # Displaying 3 of (CENSORED) in 0 seconds. # Indexes (2 of 4) searched: ams4-access-2015.06.03,lhr4-access-2015.06.03 https://github.com/reyjrar/es-utils $ es-search.pl --base access dst:www.booking.com and crit:404 \ --show src_ip,src_ip_country,file --size 2
36. Aggregate Stuff https://github.com/reyjrar/es-utils $ es-search.pl --base access --days 1 dst:www.booking.com \ --top src_ip --size 3 = Querying Indexes: ams4-access-2015.06.03,lhr4-access-2015.06.03 count src_ip (CENSORED) 66.249.92.71 (CENSORED) 66.249.92.59 (CENSORED) 66.249.92.65 # Search Parameters: # {"query_string":{"query":"dst:www.booking.com"}} # Displaying 3 of (CENSORED) in 5 seconds. # Indexes (2 of 2) searched: ams4-access-2015.06.03,lhr4-access-2015.06.03 # # Totals across batch # count src_ip (CENSORED) 66.249.92.71 (CENSORED) 66.249.92.59 (CENSORED) 66.249.92.65
37. Find Pages Viewed by Most Countries https://github.com/reyjrar/es-utils $ es-search.pl --base access --days 1 dst:www.booking.com \ --top file --by cardinality:src_ip_country --size 3 = Querying Indexes: lhr4-access-2015.06.03,ams4-access-2015.06.03 cardinality:src_ip_country count file 239 (CENSORED) / 236 (CENSORED) /rt_data/city_bookings 234 (CENSORED) /wishlist/get # Search Parameters: # {"query_string":{"query":"dst:www.booking.com"}} # Displaying 3 of (CENSORED) in 21 seconds. # Indexes (2 of 2) searched: ams4-access-2015.06.03,lhr4-access-2015.06.03
38. Pipeline Queries https://github.com/reyjrar/es-utils $ es-search.pl --base access --days 1 dst:www.booking.com \ --top src_ip --by sum:attack_score --size 3 \ --data-file top_attackers.dat $ es-search.pl --base access --days 1 dst:www.booking.com \ src_ip:top_attackers.dat[-1] --size 3\ --show attack_score,src_ip,crit,dst,method,resource \ --sort attack_score:desc
39. Pipeline Queries https://github.com/reyjrar/es-utils = Querying Indexes: lhr4-access-2015.06.03,ams4-access-2015.06.03 @timestamp attack_score src_ip crit dst method resource 2015-06-03T04:20:59+0200 340 107.150.42.90 404 www.booking.com GET /plus/ search.php?keyword=as&typeArr[111%3D@`%5C'`)+/*!50000And*/+(/*!50000SeLECT*/+1+/*!50000frOM*/+(/*! 50000SeLECT*/+/*!50000Count(*)*/,concat(floor(rand(0)*2),(substring((/*!50000SeLECT*/ +CONCAT(0x40,userid,0x7c,substring(pwd,4,16))+from+`%23@__admin`+limit+0,1),1,62)))a+/*! 50000fRom*/+information_schema.tables+/*!50000gROUP*/+by+a)b)%23@`%5C'`+]=a 2015-06-03T00:50:43+0200 340 107.150.42.90 404 www.booking.com GET /plus/ search.php?keyword=as&typeArr[111%3D@`%5C'`)+/*!50000And*/+(/*!50000SeLECT*/+1+/*!50000frOM*/+(/*! 50000SeLECT*/+/*!50000Count(*)*/,concat(floor(rand(0)*2),(substring((/*!50000SeLECT*/ +CONCAT(0x40,userid,0x7c,substring(pwd,4,16))+from+`%23@__admin`+limit+0,1),1,62)))a+/*! 50000fRom*/+information_schema.tables+/*!50000gROUP*/+by+a)b)%23@`%5C'`+]=a 2015-06-03T05:18:19+0200 340 107.150.42.90 404 www.booking.com GET /plus/ search.php?keyword=as&typeArr[111%3D@`%5C'`)+/*!50000And*/+(/*!50000SeLECT*/+1+/*!50000frOM*/+(/*! 50000SeLECT*/+/*!50000Count(*)*/,concat(floor(rand(0)*2),(substring((/*!50000SeLECT*/ +CONCAT(0x40,userid,0x7c,substring(pwd,4,16))+from+`%23@__admin`+limit+0,1),1,62)))a+/*! 50000fRom*/+information_schema.tables+/*!50000gROUP*/+by+a)b)%23@`%5C'`+]=a # Search Parameters: # {"terms":{"src_ip":["107.150.42.90","37.59.7.157","74.84.138.120"]}} # {"query_string":{"query":"dst:www.booking.com"}} # Displaying 3 of 793 in 0 seconds. # Indexes (2 of 2) searched: ams4-access-2015.06.03,lhr4-access-2015.06.03
40. Recap
41. Data Types Meta-Data State Time Series Events Graphite No No Yes Kinda InfluxDB Kinda Yes Yes Kinda OpenTSDB Kinda Yes Yes Kinda MySQL Yes Yes No Kinda PostgreSQL Yes Yes No Kinda ElasticSearch No Kinda Kinda Yes
42. Features of Your Data Interval Cardinality Data Type Aging Graphite Fixed, Regular Low Numeric Roll up InfluxDB Fixed (best) Any High Any Configurable OpenTSDB Any High Numeric n/a MySQL Any Keys: Low Values: High Structured* None PostgreSQL Any Keys: Low Values: High Structured* None ElasticSearch Any Keys: Low Values: High Any None
43. Features of the Store Security Scalability Performance Reliability Graphite Low High High* Medium InfluxDB Low High Medium High OpenTSDB Low High Low High MySQL Medium Medium Medium* High* PostgreSQL High Medium Medium* High ElasticSearch Low High High Low
44. Thank you! brad.lhotsky@gmail.com https://twitter.com/reyjrar https://github.com/reyjrar https://speakerdeck.com/reyjrar https://www.craigslist.org/about/craigslist_is_hiring