We built this city: Behind the scenes of Apigee Edge
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Check out these slides from I Love APIs 2014 to learn how architects and engineers are building products at Apigee, and how we incorporate open source technology for enterprise grade apps: Cassandra, Postgres, Netty, Jersey, jQuery, Bootstrap, Tomcat, Puppet, Hadoop, Couchbase and more.
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We built this city: Behind the scenes of Apigee Edge
- 1. We Built this City (Although not this one in particular…) Greg Brail, Gary Forrest, and Paul Kim
- 2. Overview • A little bit about architecture • How we monitor at Apigee • Some real data 2
- 3. A Little Bit About Architecture
- 4. We try to stick to our principles within reason 4
- 5. Principle: Eventual Consistency 5 Consistency Availability Partition Tolerance Zookeeper
- 6. Eventual Consistency Leads to… • Better availability – Support for multiple regions and data centers – No downtime due to hardware or network failures – No downtime to replace a component • Better scalability – Add nodes without downtime – Scale horizontally 6
- 7. Single- versus Multi-Tenant 7 Single Tenant Multitenant Tenant A Tenant B Tenant C Tenant A Tenant B Tenant C
- 8. A Little of Each 8 Inbound Routing API Call Processing Data Storage Tenant A Tenant B Tenant C Tenant A Tenant B Tenant C Tenant D Tenant E Tenant D Tenant E Tenant A Tenant B Tenant C Tenant D Tenant E
- 9. Everything Should Have an API 9
- 10. Everything Gets Deployed to the Cloud; Most Things Get Deployed On- Premises 10
- 11. How We Monitor at Apigee
- 12. Traditional Monitoring – information silos 12 Alerts Custom Checks * Product names for illustrative purposes only. No endorsement by Apigee is intended or implied.
- 13. A different approach – standard alerts 13 standard alert Custom Checks Event Processor * Product names for illustrative purposes only. No endorsement by Apigee is intended or implied.
- 14. A different approach – standard alerts 14 standard alert Problem Details Host, service, metric Actions-to-take Graph Playbook Escalation contact Custom Checks Event Processor * Product names for illustrative purposes only. No endorsement by Apigee is intended or implied.
- 15. Monitoring Architecture 15 System Being Monitored Monitoring Agent Event Stream Processing Server (Real Time Alerting) Notification IM chat, ticketing system, email groups, phone Visualization Queryable Metrics (CloudWatch, API, Logs) Monitoring Agents cloud-lb-metrics api-curl-agent
- 16. Support Ticket History 16
- 17. P1 Incidents Caught by Monitoring 17
- 18. Some Real Data
- 19. Sports News API 19 When did the 2014 NFL season start? 08/29 08/30 08/31 09/01 09/02 09/03 09/04
- 20. Online Ticket Business 20 Traffic spikes before events
- 21. US Retailer 21 Labor Day Weekend Daytime spikes and higher on weekends
- 22. Mobile Application Monitoring 22 5-day traffic view 12-hour traffic view
- 23. Thank you
Notes de l'éditeur
- When we say “standard alert”… 2 pieces of info. 99% disk example. Not enough context. about the standardize alert context. Describe briefly support flow. So how do we turn events into a standard alert – using Reimann.
- Context. How does Riemann help us create standard alerts? Center – all monitoring data goes to central hub. One-directional arrows - Asynchronous event processing. Central location for processing monitoring data streams, which allows us to maximize kinds of alerts. Arbitrary Grouping by tags, advanced alerting criteria. Separate alerting from notification (separation of metric reporting and alert criteria
- Going beyond thresholds to windows, thresholds wont catch all anomalous events. Correlation of application and system metrics (ELB and App server metrics) . Additional context to “see” what is behind major traffic changes
- Can’t always predict anomalous events.
- Short lived spiky traffic.
- Daily cycle. Human eating/sleeping patterns.
- Context, solving problems. Stakeholders come out.