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1
Why you need one, and why you should
stop rolling your own.
By Sean Porter (@PorterTech)
Monitoring Event
Pipelines
● Sean Porter
● Creator of Sensu
● Co-founder & CTO
● @PorterTech
Who am I?
2
Overview
3
● Our shared reality
● My experience in building Sensu
● What is a monitoring pipeline?
● Attributes of an effective pipeline
● Demo time
● The future of pipelines
4
OUR SHARED
REALITY
5
COMPLEXITY
TIME
6
#OFTHINGS
TIME
Containers
Servers
VMs
Functions
Ephemeral compute!
● Host-based -> role-based monitoring
● Polling -> publish-subscribe & push APIs
● Point-and-click -> Infrastructure as Code
The paradigm shift
7
“We gotta find a way to make [this] fit into
the hole for (this), using nothing but {that}.”
8
9
AMOUNTOFDATA
TIME
10
11
THE TOOLS YOU KNOW
12
INTEGRATE
NEED TO BUILD
SCALE
13
14
NEED TO MAINTAIN
15
HOLD THAT THOUGHT
16
BUILDING
SENSU
Sensu origin story
17
I joined Sonian in 2010 as an “Automation Engineer”
● Early adopters
● High rate of change
○ Growing team
○ Evolving software stack
18
Sensu origin story
19
I started a project with some goals (July 2011)
● Handle ephemeral compute
● Leverage existing and familiar technologies
● Easy to drive with config management
● Easy to scale horizontally
● APIs!!!
Sensu origin story
20
● Agent based system with auto-discovery
● Message bus for communication
● Simple key-value data store for state
● Central service check scheduler (pub-sub)
● JSON configuration
● REST APIs
21
22
Sensu origin story
23
● Designed for the cloud
○ Proved to handle ephemeral compute
○ Operated securely on public net (AWS)
● Focused on composability & extensibility
○ Reusable components / building blocks
Sensu origin story
24
Sensu origin story
25
● Named it Sensu
● Sonian sponsored development!
○ Deployed to production after 2 months
○ Replaced a number of tools!
● Open sourced (MIT) on November 1st, 2011
26
27
FRAMEWORK
THE MONITORING ROUTER
BUS
28
THE MONITORING PIPELINE
29
THE MONITORING PIPELINE
OBSERVABILITY
30
WHAT IS A
MONITORING PIPELINE?
Unified data collection and processing for all
types of monitoring events:
● Service checks
● Metrics
● Traces
● Logs
● Inventory
What is a monitoring pipeline?
31
There are two critical layers:
● Data plane
● Control plane
What is a monitoring pipeline?
32
● Data input
● Data transportation & routing
● Load balancing & failover
● The layer developers interact with (APIs)
The data plane
33
● Central management unit
○ Orchestrator
○ Configuration
○ Security (auth)
● APIs, agents, data processors, etc.
● The layer operators interact with
The control plane
34
35
https://www.youtube.com/watch?v=CM2Y6B1yuDg
36
37
https://bravenewgeek.com/the-observability-pipeline/
38
WAIT… BUT WHY?
39
CHANGE YOUR MIND
40
CHANGE DATASTORES
41
CHANGE FORMATS
42
CHANGE VISUALIZATION
43
CHANGE SAMPLING
44
CHANGE PLATFORMS
45
MAKE CHANGE INEXPENSIVE
46
MAKE IT FUTURE PROOF
● Fewer agents
○ Less “edge” service to support
○ Resource utilization (i.e. fewer sidecars)
● Cost savings*
Wait… But why?
47
48
ATTRIBUTES OF AN
EFFECTIVE PIPELINE
● Unified data format(s)
● Unique IDs
● Capture context at collection time
● Support additional metadata
● Support efficient debugging
Event payload(s)
49
Event payload(s)
50
{
metadata: {
annotations: { … }
},
entity: {
name: “osmc.de”
},
timestamp: now()
}
Event payload(s)
51
{
metadata: {
annotations: { … }
},
check: {
output: “the system is down”
},
timestamp: now()
}
Event payload(s)
52
{
metadata: {
annotations: { … }
},
metrics: {
points: [ … ]
},
timestamp: now()
}
Event payload(s)
53
● Lightweight (think sidecars)
● Multi-platform support
● Initiates connections to backends
● Bi-directional communication
Collection Agent
54
● Auto-registration with the backend
● Auto-discovery (context)
○ Platform info
○ System details
○ Roles / responsibilities
Collection Agent
55
● Keepalive / heartbeat mechanism
● Service check execution support
○ Commonly overlooked / underappreciated
○ Leverage over a decade of investment
● Durable outbound data queue
Collection Agent
56
● Several data inputs (APIs)
○ Industry standards*
■ Metrics (StatsD, OpenTelemetry, Graphite)
■ Trace (OpenTracing)
■ Structured log (JSON)
Collection Agent
57
● Standard cryptography (TLS v1.2)
○ mTLS verification & authentication
● Standard protocol (HTTP)
● Agent initiated connections
○ Traverse complex networks
Data Transport
58
● Scale horizontally
● Little coordination with peers
● Concurrency & parallelism
○ “Concurrency is about dealing with lots
of things at once. Parallelism is about
doing lots of things at once.” — Rob Pike
Data Processor
59
● Easy to extend and integrate!
○ Simple APIs and clear specs
● Multi-tenancy to enable self-service
○ Namespaces
○ RBAC
Data Processor
60
● The “secret sauce”
● Granular routing
○ Is it an incident?
○ Is it a resolution?
○ Metric data?
○ Production? Office hours?
Filtering
61
Transformation
62
Actions
63
● Alert notifications
● Incident management
● Metric & event storage
● Inventory
● Auto-remediation
64
Monitoring Pipeline
Filter
Only incidents
Filter
Only logs
Filter
Only production
Transform
Transform
-
-
Action
Action
Action
Action
Action
Action
Events
Service Checks
Metrics
Trace
Log
Transform
Redact Sensitive
Transform
Nagios => InfluxDB
Filter
Only once every 30 min
Filter
Only metrics
Filter
Only incidents
Transform
Annotate
Transform
-
65
https://bravenewgeek.com/the-observability-pipeline/
66
DEMO TIME
67
THE FUTURE OF
PIPELINES
● Sean Porter
● @PorterTech
● https://sensu.io
Thank you!
68

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OSMC 2019 | Monitoring Event Pipelines: Why you need one, and why you should stop rolling your own by Sean Porter