aws loft talk: behind the scenes with signalfx
TRANSCRIPT
SignalFx
Agenda
• Background
• Overview of Key SignalFx Services
• SignalFx infrastructure and operations
• Analytics approach to monitoring
• Code push side effects, an example
• Summary
SignalFx
Background
About Me
[2013 - ] SignalFx - Founder, CTO, Software EngineerMicroservices; Monitoring using Analytics
[2008 - 2012] Facebook - Software Engineer, Software ArchitectHyperscale SOA; Monitoring using Nagios, Ganglia, and in-house Analytics
[2004 - 2008] Opsware - Chief Architect, Software EngineerMonolithic Architecture; Monitoring using Ganglia, Nagios, Splunk
[2000 - 2004] Loudcloud - Software EngineerLAMP, Application Server; Monitoring using SNMP, Ganglia, NetCool
[1998 - 2000] Marimba - Software EngineerClient / Server; Monitoring using SNMP, FreshWater Software
[ … ]
About SignalFx
SignalFx
Overview of SignalFx Services
A Microservices Definition
Loosely coupled service oriented architecture with bounded context.
Adrian Cockcroft
Overview of Key SignalFx Services
Microservice Complexity
More than 15 internal services. Services span hundreds of instances across multiple AZs.
Have dependencies on tens of external services.
SignalFx
SignalFx Infrastructure
Amazon EC2
SignalFx
Operations at SignalFx
Shared Responsibility
• Engineering is organized around services they provide
• No dedicated operations team
• Each service team is responsible for building and operating their services
• Infrastructure team provides IaaS - DNS, LB, Mail, Server, and Network configuration and provisioning
• Ingest team provides Ingest API, Quantization, and TSDB services
Continuous Build and Deployment
• Services are built and tested on each commit
• Each service deploy at their cadence
• Nearly all deployments are non-disruptive
• Push to lab, test; push product canary, test; rest of prod
• Service engineered to be resilient to partial cluster
availability
• Each service is engineered to support +1/-1 upgrades
On-call Rotation
• All dev on weekly on-call rotation (couple of times a year)
• On-call works on operational tools
• On-call rotates from lab -> production
• On-call is the incident manager• Owns driving both black out and brown out incidents to
resolution
Operations Tools
sfhost - CLI for VM configuration and provisioning
sfc - console to access management data for all services
signalscope - deep transactions tracing
maestro - Docker orchestrator
jenkins - continuous build and deployment
Monitoring
• We use SignalFx to monitor SignalFx
• Engineers instrument their code as part of dev process
• Each service provides at least one dashboard
• CollectD for OS and Docker metrics on all VMs
• Yammer metrics for all Java app servers
• Custom logger to count exception types
Monitoring - API Service Dashboard
SignalFx
Analytics Approach to Monitoring
Monitoring Challenges
• High iteration rate leads to shortened test cycles
• Integration test combinations are intractable
• Catch problems during rolling deployments
• Identify upstream/downstream side effects
• e.g. backpressure
• Identify brownouts before the customer
• etc.
Analytics Approach to Monitoring
Measure
Analytics Approach to Monitoring
Analyze
Analytics Approach to Monitoring
Detect
SignalFx
Examples
Code Push Side Effects - Time Series Router
Code Push Side Effects
Push canary instance and Metadata API dashboard shows healthy tier.
Code Push Side Effects
However, upstream UI dashboard showed unusual # of timeouts.
Code Push Side Effects
In search of root cause. Always safe to start by looking at exception counts.Can’t derive much from all the noise.
Code Push Side Effects
Sum the # of exceptions to create a single signal.
Code Push Side Effects
Compare sum with time-shifted sum from a day ago.
Code Push Side Effects
Look at an outlier host - an Analytics service host.
Code Push Side Effects
java.io.InvalidObjectException: enum constant MURMUR128_MITZ_64 does not exist in class com.google.common.hash.BloomFilterStrategies at java.io.ObjectInputStream.readEnum(ObjectInputStream.java:1743) ~[na:1.7.0_79] at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1347) ~[na:1.7.0_79] at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990) ~[na:1.7.0_79] …
Looking at Analytic’s logs revealed source of the problem.
Code Push Side Effects
• Analytics across multiple microservices reduced time to identify problem. From push to resolution was ~15min
• Service instrumentation helped narrowed down root cause
• Discovery allowed us to create a detector using analytics to notify similar problems in the future
Other Examples
• A customer started dropping data because they reverted to an unsupported API• Compare TSDB write throughput of two different write
strategies• Create per-service capacity reports• Identify memory usage patterns across our Analytics
service• Create a detector for every previously uncaught error
conditions - postmortem output
SignalFx
Summary
Summary
• Microservice architecture is inherently complex
• Measure all the things
• Use data analytics techniques to• Identify problems• Chase down root cause
• Use intelligent detectors to catch recurrence
SignalFx
Questions
