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Centralized vs
Decentralized
Centralized vs Decentralized
Prometheus Scraping Architecture
with DoorDash
Eric D. Schabell, Chronosphere
Ales Koprivnikar, Chronosphere
PromCon EU 2022
Life of a data point…
2
Life of a data point…
3
Life of a data point…
4
Scraping architectures
5
Centralized
Scraping architectures
6
Centralized Decentralized
Hybrid scraping architecture?
7
Centralized Decentralized
You CAN have all the cookies!
8
ABOUT
9
Metrics collection architecture
10
K8S
Node
K8S
Chronocollector
(daemonset)
Microservice
(container)
Microservice
(container)
Microservice
(container)
Statsd
Exporter
Chronocollector
Custom Log
Sources
Custom Log
Sources
Non-K8S
workloads
Custom
Exporter
/
EC2
Process
Process
Chronocollector
(systemd service)
Discovery
file
Models
11
• DaemonSet
‒ For microservices
‒ Standard config, rewrite rules
‒ Standard labels attached to each metric
• Sidecar (k8s)
‒ Suitable for noisy neighbors (can be
allocated more resources)
‒ Custom config
• Unix service
‒ For services on standalone machines
‒ Use file based discovery
‒ Drift possible
• Deployment (k8s or any
containerized env)
‒ Mainly external facing scraping
(Outside DD infra)
‒ Cloudwatch or vendor metrics
‒ Highly customized config (TLS auth,
rewrite rules)
What’s really important
12
• Stability of resource usage of
the scraper
‒ Every change impacts the whole cluster
• Discovery
‒ Performance in a big cluster
• Enriching metadata on metrics
in real-time
‒ Standardization of some labels
• Rewrite rules
‒ Performance penalties under multiple
rules
• A lot of recording rules
‒ SLO tracking
Decentralized scraping architecture
13
Pros:
• No single point of failure
• Scalable
• Flexible
• Negligible performance penalty for
rewrite rules
‒ we have a lot of them
• Noisy neighbor issues limited to local
machines
Decentralized scraping architecture
14
Pros:
• No single point of failure
• Scalable
‒ on our side!!
• Flexible
• Negligible performance penalty for
rewrite rules
‒ we have a lot of them
• Noisy neighbor issues limited to local
machines
Cons:
• Heavy resource usage
‒ Due to daemonset approach
‒ Most of the time, scale resources to the max -> lot
of resource waste
• Every change is a daemonset deploy in
multiple clusters
• Hard to debug issues
Some final thoughts…
15
• Distributed architecture is a must after a certain scale
‒ Plays very nicely with k8s
• There will be many, many exceptions
‒ A lot of helm charts, systemd service config
• Very stable (but resource heavy)
Questions
Thank You

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