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docs: refer to the https://itnext.io/how-do-open-source-solutions-for-logs-work-elasticsearch-loki-and-victorialogs-9f7097ecbc2f in the appropriate places
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@ -119,6 +119,7 @@ See also [case studies](https://docs.victoriametrics.com/casestudies/).
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* [Improving histogram usability for Prometheus and Grafana](https://valyala.medium.com/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350)
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* [Why irate from Prometheus doesn't capture spikes](https://valyala.medium.com/why-irate-from-prometheus-doesnt-capture-spikes-45f9896d7832)
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* [VictoriaMetrics: PromQL compliance](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e)
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* [How do open source solutions for logs work: Elasticsearch, Loki and VictoriaLogs](https://itnext.io/how-do-open-source-solutions-for-logs-work-elasticsearch-loki-and-victorialogs-9f7097ecbc2f)
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### Tutorials, guides and how-to articles
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@ -34,7 +34,7 @@ VictoriaLogs is optimized specifically for logs. So it provides the following fe
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- Easy to setup and operate. There is no need in tuning configuration for optimal performance or in creating any indexes for various log types.
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Just run VictoriaLogs on the most suitable hardware, ingest logs into it via [supported data ingestion protocols](https://docs.victoriametrics.com/victorialogs/data-ingestion/)
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and get the best available performance out of the box.
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- Up to 30x less RAM usage than Elasticsearch for the same workload.
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- Up to 30x less RAM usage than Elasticsearch for the same workload. See [this article](https://itnext.io/how-do-open-source-solutions-for-logs-work-elasticsearch-loki-and-victorialogs-9f7097ecbc2f) for details.
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- Up to 15x less disk space usage than Elasticsearch for the same amounts of stored logs.
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- Ability to work efficiently with hundreds of terabytes of logs on a single node.
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- Easy to use query language optimized for typical log analysis tasks - [LogsQL](https://docs.victoriametrics.com/victorialogs/logsql/).
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@ -15,7 +15,7 @@ VictoriaLogs provides the following features:
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- VictoriaLogs' capacity and performance scales linearly with the available resources (CPU, RAM, disk IO, disk space).
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It runs smoothly on Raspberry PI and on servers with hundreds of CPU cores and terabytes of RAM.
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- It can handle up to 30x bigger data volumes than Elasticsearch and Grafana Loki when running on the same hardware.
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See [these docs](#benchmarks).
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See [these docs](#benchmarks) and [this article](https://itnext.io/how-do-open-source-solutions-for-logs-work-elasticsearch-loki-and-victorialogs-9f7097ecbc2f) for details.
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- It provides fast full-text search out of the box for [log fields](https://docs.victoriametrics.com/victorialogs/keyconcepts/#data-model)
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with high cardinality (e.g. high number of unique values) such as `trace_id`, `user_id` and `ip`.
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- It supports multitenancy - see [these docs](#multitenancy).
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