How to build observability into a serverless application
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The document discusses the challenges of building observability into serverless applications and some approaches to address these challenges. Specifically, it notes that in serverless environments there is nowhere to install agents, no background processing, high concurrency, and risk of data loss if batching logs and metrics. It then outlines approaches for logging, metrics, and tracing using services like CloudWatch Logs, X-Ray, and propagating correlation IDs across functions.
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How to build observability into a serverless application
- 1. how to build Serverless OBSERVABILITY into a application
- 3. Abraham Wald
- 4. Abraham Wald
- 5. Abraham Wald
- 6. Abraham Wald Wald noted that the study only considered the aircraft that had survived their missions—the bombers that had been shot down were not present for the damage assessment. The holes in the returning aircraft, then, represented areas where a bomber could take damage and still return home safely.
- 7. Abraham Wald Wald noted that the study only considered the aircraft that had survived their missions—the bombers that had been shot down were not present for the damage assessment. The holes in the returning aircraft, then, represented areas where a bomber could take damage and still return home safely.
- 8. survivor bias in monitoring
- 9. survivor bias in monitoring Only focus on failure modes that we were able to successfully identify through investigation and postmortem in the past. The bullet holes that shot us down and we couldn’t identify stay invisible, and will continue to shoot us down.
- 10. What do I mean by “observability”?
- 11. Monitoring watching out for known failure modes in the system, e.g. network I/O, CPU, memory usage, …
- 12. Observability being able to debug the system, and gain insights into the system’s behaviour
- 13. In control theory, observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. https://en.wikipedia.org/wiki/Observability
- 14. Known Success
- 16. Known SuccessKnown Errors easy to monitor!
- 17. Known SuccessKnown Errors Known Unknowns
- 18. Known SuccessKnown Errors Known UnknownsUnknown Unknowns
- 19. Known SuccessKnown Errors Known UnknownsUnknown Unknowns invisible bullet holes
- 20. Known SuccessKnown Errors Known UnknownsUnknown Unknowns
- 21. Known SuccessKnown Errors Known UnknownsUnknown Unknowns only alert on this
- 22. Known SuccessKnown Errors Known UnknownsUnknown Unknowns alert on the absence of this!
- 23. Known SuccessKnown Errors Known UnknownsUnknown Unknowns what went wrong?
- 24. These are the four pillars of the Observability Engineering team’s charter: • Monitoring • Alerting/Visualization • Distributed systems tracing infrastructure • Log aggregation/analytics “ ” http://bit.ly/2DnjyuW- Observability Engineering at Twitter
- 25. microservices death stars circa 2015
- 26. microservices death stars circa 2015 mm… I wonder what’s going on here…
- 31. microservices death stars circa 2015 I got this!
- 34. Yan Cui http://theburningmonk.com @theburningmonk Principal Engineer @ Independent Consultant
- 37. available in Austria, Switzerland, Germany, Japan, Canada, Italy, US and Spain
- 38. available on 30+ platforms
- 40. follow @dazneng for updates about the engineering team We’re hiring! Visit engineering.dazn.com to learn more. WE’RE HIRING!
- 42. AWS user since 2009
- 43. AWS user since 2009
- 44. new challenges
- 45. NO ACCESS to underlying OS
- 47. •nowhere to install agents/daemons new challenges
- 48. user request user request user request user request user request user request user request critical paths: minimise user-facing latency handler handler handler handler handler handler handler
- 49. user request user request user request user request user request user request user request critical paths: minimise user-facing latency StatsD handler handler handler handler handler handler handler rsyslog background processing: batched, asynchronous, low overhead
- 50. user request user request user request user request user request user request user request critical paths: minimise user-facing latency StatsD handler handler handler handler handler handler handler rsyslog background processing: batched, asynchronous, low overhead NO background processing except what platform provides
- 51. •no background processing •nowhere to install agents/daemons new challenges
- 52. EC2 concurrency used to be handled by your code
- 53. EC2 Lambda Lambda Lambda Lambda Lambda now, it’s handled by the AWS Lambda platform
- 54. EC2 logs & metrics used to be batched here
- 55. EC2 Lambda Lambda Lambda Lambda Lambda now, they are batched in each concurrent execution, at best…
- 58. HIGHER concurrency to log aggregation/telemetry system
- 59. •higher concurrency to telemetry system •nowhere to install agents/daemons •no background processing new challenges
- 61. Lambda data is batched between invocations
- 62. Lambda idle data is batched between invocations
- 63. Lambda idle garbage collectiondata is batched between invocations
- 64. Lambda idle garbage collectiondata is batched between invocations HIGH chance of data loss
- 65. •high chance of data loss (if batching) •nowhere to install agents/daemons •no background processing •higher concurrency to telemetry system new challenges
- 66. Lambda
- 69. my code send metrics internet internet press button something happens
- 72. ? functions are often chained together via asynchronous invocations
- 74. ? SNS Kinesis CloudWatch Events CloudWatch LogsIoT DynamoDB S3 SES tracing ASYNCHRONOUS invocations through so many different event sources is difficult
- 75. •asynchronous invocations •nowhere to install agents/daemons •no background processing •higher concurrency to telemetry system •high chance of data loss (if batching) new challenges
- 76. These are the four pillars of the Observability Engineering team’s charter: • Monitoring • Alerting/Visualization • Distributed systems tracing infrastructure • Log aggregation/analytics “ ” http://bit.ly/2DnjyuW- Observability Engineering at Twitter
- 77. LOGGING
- 79. 2016-07-12T12:24:37.571Z 994f18f9-482b-11e6-8668-53e4eab441ae GOT is off air, what do I do now?
- 80. 2016-07-12T12:24:37.571Z 994f18f9-482b-11e6-8668-53e4eab441ae GOT is off air, what do I do now? UTC Timestamp Request Id your log message
- 82. one log group per function one log stream for each concurrent invocation
- 83. logs are not easily searchable in CloudWatch Logs me
- 84. CloudWatch Logs
- 86. CloudWatch Logs is an async event source for Lambda
- 87. Concurrent Executions Time regional max concurrency functions that are delivering business value
- 88. Concurrent Executions Time regional max concurrency functions that are delivering business value ship logs
- 89. either set concurrency limit on the log shipping function (and potentially lose logs due to throttling) or…
- 91. 1 shard = 1 concurrent execution i.e. control the no. of concurrent executions with no. of shards
- 92. …
- 93. CloudWatch Logs
- 99. CloudWatch Logs
- 100. use structured logging with JSON
- 104. traditional loggers are too heavy for Lambda
- 108. CloudWatch Logs $0.50 per GB ingested $0.03 per GB archived per month
- 109. CloudWatch Logs $0.50 per GB ingested $0.03 per GB archived per month 1M invocation of a 128MB function = $0.000000208 * 1M + $0.20 = $0.408
- 110. DON’T leave debug logging ON in production
- 112. have to redeploy ALL the functions along the call path to collect all relevant debug logs
- 117. EC2 Lambda Lambda Lambda Lambda Lambda Concurrency is handled by the AWS Lambda platform
- 124. sampling decision has to be followed by an entire call chain
- 125. Initial Request ID User ID Session ID User-Agent Order ID …
- 127. EC2 Lambda Lambda Lambda Lambda Lambda Concurrency is handled by the AWS Lambda platform
- 128. store correlation IDs in global variable
- 131. use middleware to auto-capture incoming correlation IDs
- 132. extract correlation IDs from invocation event, and store them in the correlation-ids module reset
- 134. logger to always include captured correlation IDs
- 135. HTTP and AWS SDK clients to auto-forward correlation IDs on
- 139. { “body”: null, “resource”: “/restaurants”, “headers”: { “x-correlation-id”: “…” }, … } get-index get-restaurants
- 140. get-restaurants global.CONTEXT global.CONTEXT x-correlation-id = … x-correlation-xxx = … get-index headers[“User-Agent”] headers[“Debug-Log-Enabled”] headers[“User-Agent”] headers[“Debug-Log-Enabled”] headers[“x-correlation-id”] capture forward function event log.info(…)
- 144. MONITORING
- 146. •no background processing •nowhere to install agents/daemons new challenges
- 147. my code send metrics internet internet press button something happens
- 148. those extra 10-20ms for sending custom metrics would compound when you have microservices and multiple APIs are called within one slice of user event
- 149. Amazon found every 100ms of latency cost them 1% in sales. http://bit.ly/2EXPfbA
- 150. console.log(“hydrating yubls from db…”); console.log(“fetching user info from user-api”); console.log(“MONITORING|1489795335|27.4|latency|user-api-latency”); console.log(“MONITORING|1489795335|8|count|yubls-served”); timestamp metric value metric type metric namemetrics logs
- 151. CloudWatch Logs AWS Lambda ELK stack logs m etrics CloudWatch
- 155. API Gateway send custom metrics asynchronously
- 156. SNS KinesisS3API Gateway … send custom metrics asynchronously send custom metrics as part of function invocation
- 159. TRACING
- 160. X-Ray
- 164. don’t span over async invocations good for identifying dependencies of a function, but not good enough for tracing the entire call chain as user request/data flows through the system via async event sources.
- 165. don’t span over non-AWS services
- 171. instrument your code
- 172. make it easy to do the right thing
- 174. follow @dazneng for updates about the engineering team We’re hiring! Visit engineering.dazn.com to learn more. WE’RE HIRING!