Finding Meaning in Operational Data

Overview of what operational data is, where to start, and how to leverage it.

Transcript

1. Finding Meaning in your Operational Data Presented by Brad Lhotsky
2. Who Am I? • 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
3. Expectations ‣Operational Data Types ‣Uses for Operational Data ‣Meaningful Features ‣Data Stores and Brad, A Love Story?
4. Operational Data Types
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. Uses for Operational Data 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 incentives • 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. Automation ‣ Can a Machine read my data? ‣ Autoscale ‣ Trend detection ‣ Service Level Roll Ups ‣ Reporting
14. Capacity Planning ‣ Predicting System Stress Levels ‣ Make Intelligent Projections ‣ Test those predictions
15. Alerting ‣ Disrupting People's Lives at 95% Disk Full ‣ Thresholds -> Change Detection ‣ State Changes
16. Anomaly Detection • Anomalies != Alerts • 1 million metrics per minute • 0.3% at a distance of > 3σ • 4.3 Million Anomalies / day
17. Statistics Sidebar • Your data probably isn't normal • You need to use modeling to perform anomaly detection • The residuals should fit a normal distribution • Modeling is only possible if you can explore and interact with the data • There are algorithms and their parameters matter
18. Exploration
19. R Studio R OpenSource Programming Language IDE
20. R's Tidyverse Common data-cleanup, normalization, manipulation, and display
21. Apache Kafka & Spark Robust data pipeline with interactivity and deployment capabilities
22. Meaningful Features
23. Open and Extensible ‣ Integrations with other projects ‣ Open API ‣ Good Documentation ‣ Modular / Plugin Structure ‣ Community
24. Reliability vs. Performance
25. Retention ‣ How easy is it to age data off? ‣ What regulations of laws apply to the data? ‣ Expectations from: ‣ Customers ‣ Employees ‣ Managers ‣ Peers ‣ Legal
26. 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 RBAC?
27. Places People Stick DevOps Datas
28. ‣ Large Community ‣ Forks and Oracle ‣ Performance First ‣ SQL Interface ‣ Limited Data Types ‣ Web > BI ‣ Suitable for Administrative Data
29. ‣ Large Community ‣ Reliability First ‣ Open and Extensible ‣ PGXN ‣ CitusData ‣ GreenPlum ‣ EnterpriseDB ‣ Native Support for IP Addresses ‣ Extensible Data Types ‣ Suitable for Administrative Data
30. ‣ 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
31. security.logging.indexer.*.total Metrics: Wildcards
32. sumSeries(security.logging.indexer.*.total) Combining Metrics
33. alias(sumSeries(security.logging.indexer.*.total),”Today") alias( timeShift( sumSeries(security.logging.indexer.*.total), “7d"), "Last Week") Comparing Metrics
34. 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
35. ‣ Metrics 2.0 (metrics support tagging) ‣ Hadoop / Hbase backed ‣ SQL-like Language ‣ Zero Data Loss ‣ Compatibility ‣ Carbon, Grafana, Statsd, Riemann, Bosun ‣ Suitable for Time Series Data ‣ Smallest Resolution: milliseconds
36. ‣ Metrics 2.0 (metrics support tagging) ‣ SQL-like Language ‣ Zero Data Loss ‣ Compatibility ‣ Carbon, Grafana, Statsd, Riemann, Bosun ‣ Suitable for Time Series Data ‣ Smallest Resolution: nanoseconds
37. ‣ Well Documented API ‣ Many Open Source Integrations ‣ Lucene backed text search ‣ Scalable ‣ "Jepsen ElasticSearch" re:CAP
38. Recap
39. 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
40. 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
41. 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
42. 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