Breaking down the Pillars of Observability: From data to outcomes

Transcript

1. AllDayDevops 2023 Breaking down the Pillars of Observability: From data to outcomes Prathamesh Sonpatki, last9.io 1
3. M.E.L.T. 3
4. Pillars of Obs Pillars of Observability Metrics Events Logs Traces 4
6. Why this matters today? • Workloads have changed • Infra is cattle - ephemeral • Services are dynamic • Cloud Native Environments A 3 node cluster running 10 namespaces with 5 deployments with a replica set of ~3-5 with 10 config maps emits whooping 16566 time series per minute using the popular kube-state-metrics library 6
7. Why this matters today? • Workloads have changed • Infra is cattle - ephemeral • Services are dynamic • Cloud Native Environments • Pod Metrics • Deployment Metrics • ReplicaSet Metrics • StatefulSet Metrics • DaemonSet Metrics • Job Metrics • Service Metrics • Namespace Metrics • Node Metrics 7
8. Why this matters today? • Volume • Velocity • Variety • Complexity 8
9. Why this matters today? • Volume • Velocity • Variety • Complexity •C.O.S.T. - Cardinality - Operations - Scale - Toil 9
11. Outcomes we want • To not have downtimes • To mitigate problems quickly • To debug a failure • To know how the system is behaving in real time • To co-relate an outage to a hardware failure • To fi nd anomalies and patterns • To trace a payment failure • To fi nd out unknown failures before they happen • To prevent hampering customer experience and business impact 11
12. Questions we ask • What is wrong? • Did we change anything? • What do we do so this doesn’t repeat? 12
13. Answers we want • Know • Communicate • Recover • Analyse • Debug • Root cause 13
14. Answers we want • System Health • Quick Decisions • Time • Root Cause • Testing • Correctness 14
15. M.E.L.T. 201 15
16. Logs 16
17. Logs • Can be literally anything -> Unstructured logs • Standard programs -> Structured logs 17
18. • Getting Started ✅ • Adoption ✅ • Debugging ✅ • Relationships 🥲 Logs • Volume 🥲 • Standardisation 🥲 • Health 🥲 • System insights 🥲 18
19. Metrics 19
20. Metrics • Aggregated! • Fastest and Cheapest to understand system health • Dimensions 20
21. • Getting Started 😐 • Adoption ✅ • Debugging 🥲 • Relationships 🥲 Metrics • Volume 😐 • Standardisation ✅ • Health ✅ • System insights ✅ 21
22. Traces 22
23. Traces • Relationships and Directions! • Scoped to a request/work fl ow 23
24. • Getting Started 😐 • Adoption 🥲 • Debugging ✅ • Relationships ✅ Traces • Volume 😐 • Standardisation ✅ • Health 🥲 • System insights 🥲 24
25. Events 25
26. Events • Structured logs? • Schema based? • Domain Events • Easier to adopt? • Can unlock co-relation • Dimensionality 26
27. • Getting Started 😐 • Adoption 🥲 • Debugging ✅ • Relationships 🥲 Events • Volume ✅ • Standardisation 🥲 • Health ✅ • System insights ✅ 27
28. Answers we want • Know • Communicate • Recover • Analyse • Debug • Root cause Real Time Post Factor 28
29. Answers we want • Know • Communicate • Recover • Analyse • Debug • Root cause SRE/DevOps Programmer/Developers 29
30. OpenTelemetry 30 80% of Telemetry data is unused
31. 80% of Telemetry data is unused • Yet, we store it and pay for the data that is unused! • Slow dashboards, concurrent access woes • No real time alerting • Cost vs. Performance vs. Retention tradeo ff s 31
32. For high precision observability, You need high precision control levers
33. Control Levers • Treat workloads di ff erently • Fast vs. Slow Data Tiers • Policies • Declarative Observability 33
34. Levitate - A Managed Time Series Data Warehouse Thank You! 34