39 lines
1.6 KiB
Markdown
39 lines
1.6 KiB
Markdown
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# Victoria k8s Stack
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We use [Victoria Metrics k8s stack](https://docs.victoriametrics.com/helm/victoriametrics-k8s-stack/) and [Vector](https://docs.victoriametrics.com/helm/victorialogs-single/#sending-logs-to-external-victorialogs).
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## Why do we use it?
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* compare it with competitors like ELK, Loki, Prometheus
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* it delivers logging and metrics
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* in ELK we replaced the 'E'(ElasticSearch) by VictoriaLogs and VictoriaMetrics, L(Logstash) is replaced by Vector, 'K'(Kibana) by Grafana
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* Loki (also the 'E'): has 5 components (like distrubutor, querier, querier-frontend....), VM
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* ELK is hard to manage
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* Durability: We need to store logs for years, there should be a 'shrink' process
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* Challenge: Scaling, there are huge amounts of data (like TB/d)
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* we urde for simplicity, cost, scalability
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## Big Picture
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### Architecture
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The high level deployment picture of VictoriaMetrics k8 s Stack looks like this:
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### Deployment
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In detail, after having deployed it, we see the following components:
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1. vector-vector: the log shipper to victorialogs, twice because it is a daemon-set and thus deploed on each node in the cluster
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2. prometheus-node-exporter: a metrics generator and metrics endpoint of node metrics, also deployed on each node
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3. vmagent: the central agent scraping data from the metrics collectors
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4. vmalert: not used yet
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5. vmsingle-victoria-metrics: the metrics server, getting the data from vmagent
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6. vlogs: the logging server, getting the data from vector
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7. victoria-metrics-operator: the operator providing and managing the custom resources we deploy
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