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Ticketmaster confidential. Do not distribute.
Ticketmaster confidential. Do not distribute.
Prometheus +
Thanos
Thanos long term storage for Prometheus

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Ticketmaster confidential. Do not distribute.
About me...
• Rocking it at Ticketmaster
• Student in Master of Business Administration (MBA) Strategic project management
• Bachelor of Applied Science (B.A.Sc.) in Computer Science
• Devops, Cloud, Kubernetes, Kafka, etc.
• Sport lover (Hockey, Dek Hockey, Ultimate Frisbee, Football…)
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SECTION

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Where Prometheus & Thanos are in the CNCF landscape...
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Prometheus

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Insert photography and crop to size of grey box.
(24.25 in. x 7.21 in.)
What is Prometheus?
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Prometheus
Prometheus was the second project to be graduated of the Cloud Native Computing Foundation.

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Prometheus Architecture

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How we are using Prometheus at Ticketmaster?
• We are using the Prometheus-Operator made by CoreOS (RedHat (IBM))
• We have created a Helm chart that created common pulling jobs, settings and exporters
• Scrape EC2 instances based on Tags, Kubernetes
• Exporters like cloudwatch-metrics, kafka, blackbox
• Ingresses
• Thanos
• Federations
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Prometheus

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Why moving away from the federation?
• Calculate the right ingestion rate of our scrape job is not really easy
• When the disk is full
• Prometheus stop working
• We needed to delete the PVC to recreated it bigger.
• Long term storage is costly SSD == $$$$
• Single point of failure
• Availability
• Operator error
• Hardware failure
• Rollout
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Prometheus

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Solution

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Goals
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Thanos
- Easy Deployment model
- Minimal number of dependencies
- Minimal baseline cost
Have a global view Seamless
integration with
Prometheus
Increase retentionHave a HA in place

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Global view
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Thanos

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Global view + HA
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Thanos

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Increase retention (Persist data)
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Thanos

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Increase retention (Querying)
• A series is made up of one or more “chunks”
• A chunk contains ~120 samples each
• Chunks can be retrieved through HTTP byte range queries
Example:
• 1000 series @ 30s scrape interval
• Query 1 year
• 8.7 million chunks/range queries
• Chunks of the same series are aligned
• Similar series are aligned due to same metric name
This reduce request count by 4=6 orders of magnitude.
8.7 million requests turned into O(20) requests
•
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Thanos

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Increase retention (Querying)
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Thanos
group chunk same series group by same metric name

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Compaction
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Thanos

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Full Architecture
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Thanos

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Deployment Model( Example)
• Federation through Store API
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Thanos

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Cost
• Store + Query node + Compaction ~ Savings on Prometheus side (+/- 0)
• Fewer SSD space on Prometheus side (Savings)
• Basically we are only paying for your data stored in S3/GCS/etc + requests
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Thanos

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Cortex
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Alternatives

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Cortex - Reason that we didn’t choose Cortex
• Documentation is not really good
• Need to maintain another dataset (NoSQL DB)
• Interfere with the datapath
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Alternatives

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Question?

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