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Comment by shifty3 | Hacker News Reader

Can anybody speak to the difference between Graphite+Grafana and the ELK stack? Are there any fundamental differences in analysis capabilities between these?

We are currently using ELK for doing some lightweight analysis of user behavior (we are using Elasticsearch anyway, so this was the easiest way to get up and running), but as far as I can tell there are some limits to the Elasticsearch/Kibana combination. For example, there seems to be no way to correlate separate events, e.g. how many users that performed action A then performed action B within some time window (or even as the next action). Is this possible with Graphite?

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For correlation between distinct events Graphite or any Time series database is not the right tool.

Time series are usually more about being able to collect huge amount of metrics. Metrics that can then be combined, averaged, filtered, put through a processing pipeline (analytical functions), summarized by different intervals. All in order to visualize (usually through graphs) recent live trends or long term trends and statistics.

Grafana is all about maximizing the power and ease of use of the underlying time series store so the user can focus om on building informative and nice looking dashboards. It is also about letting users define generic dashboards through variables that can be used in metric queries, this allows users to reuse the same dashboard for different servers, apps or experiments as long as the metric naming follows a consistent pattern.

Grafana also uses Elasticsearch but not for log analytics, but for annotating graphs with event/log information.

At some point in in the coming 1-3 years log analytics and metric analytics & visualization is going to converge and be solved/addressed by the same piece of software. But that is tricky right now without sacrificing either domain.

Thanks for the insight, that's kind of what I expected. Do you know of any tools/products that can perform this kind of analysis? I looked at the whole range of web analytics tools/platforms (Google Analytics, Mixpanel, Piwiki and others), but these are very much focused on web analytics, not so much user behavior inside a (web) application and offer very limited customization. And they usually do not work offline anyway.

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