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Fix images not display on key concepts document (#3266)

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docs/keyConcepts.md
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@ -93,7 +93,7 @@ So, the `counter` metric shows the number of observed events since the service s
In programming, `counter` is a variable that you **increment** each time something happens. In programming, `counter` is a variable that you **increment** each time something happens.
{% include img.html href="keyConcepts_counter.png" %} <img src="keyConcepts_counter.png">
`vm_http_requests_total` is a typical example of a counter. The interpretation of a graph `vm_http_requests_total` is a typical example of a counter. The interpretation of a graph
above is that time series `vm_http_requests_total{instance="localhost:8428", job="victoriametrics", path="api/v1/query_range"}` above is that time series `vm_http_requests_total{instance="localhost:8428", job="victoriametrics", path="api/v1/query_range"}`
@ -119,7 +119,7 @@ by humans from other metric types.
Gauge is used for measuring a value that can go up and down: Gauge is used for measuring a value that can go up and down:
{% include img.html href="keyConcepts_gauge.png" %} <img src="keyConcepts_gauge.png">
The metric `process_resident_memory_anon_bytes` on the graph shows the memory usage of the application at every given time. The metric `process_resident_memory_anon_bytes` on the graph shows the memory usage of the application at every given time.
It is changing frequently, going up and down showing how the process allocates and frees the memory. It is changing frequently, going up and down showing how the process allocates and frees the memory.
@ -219,7 +219,7 @@ Such a combination of `counter` metrics allows
plotting [Heatmaps in Grafana](https://grafana.com/docs/grafana/latest/visualizations/heatmap/) plotting [Heatmaps in Grafana](https://grafana.com/docs/grafana/latest/visualizations/heatmap/)
and calculating [quantiles](https://prometheus.io/docs/practices/histograms/#quantiles): and calculating [quantiles](https://prometheus.io/docs/practices/histograms/#quantiles):
{% include img.html href="keyConcepts_histogram.png" %} <img src="keyConcepts_histogram.png">
Grafana doesn't understand buckets with `vmrange` labels, so the [prometheus_buckets](https://docs.victoriametrics.com/MetricsQL.html#prometheus_buckets) Grafana doesn't understand buckets with `vmrange` labels, so the [prometheus_buckets](https://docs.victoriametrics.com/MetricsQL.html#prometheus_buckets)
function must be used for converting buckets with `vmrange` labels to buckets with `le` labels before building heatmaps in Grafana. function must be used for converting buckets with `vmrange` labels to buckets with `le` labels before building heatmaps in Grafana.
@ -261,7 +261,7 @@ go_gc_duration_seconds_count 83
The visualisation of summaries is pretty straightforward: The visualisation of summaries is pretty straightforward:
{% include img.html href="keyConcepts_summary.png" %} <img src="keyConcepts_summary.png">
Such an approach makes summaries easier to use but also puts significant limitations compared to [histograms](#histogram): Such an approach makes summaries easier to use but also puts significant limitations compared to [histograms](#histogram):
@ -320,7 +320,7 @@ VictoriaMetrics supports both models used in modern monitoring applications: [pu
Client regularly sends the collected metrics to the server in the push model: Client regularly sends the collected metrics to the server in the push model:
{% include img.html href="keyConcepts_push_model.png" %} <img src="keyConcepts_push_model.png">
The client (application) decides when and where to send its metrics. VictoriaMetrics supports the following protocols The client (application) decides when and where to send its metrics. VictoriaMetrics supports the following protocols
for data ingestion (aka `push protocols`): for data ingestion (aka `push protocols`):
@ -378,7 +378,7 @@ The cons of push protocol:
Pull model is an approach popularized by [Prometheus](https://prometheus.io/), where the monitoring system decides when Pull model is an approach popularized by [Prometheus](https://prometheus.io/), where the monitoring system decides when
and where to pull metrics from: and where to pull metrics from:
{% include img.html href="keyConcepts_pull_model.png" %} <img src="keyConcepts_pull_model.png">
In pull model, the monitoring system needs to be aware of all the applications it needs to monitor. The metrics are In pull model, the monitoring system needs to be aware of all the applications it needs to monitor. The metrics are
scraped (pulled) from the known applications (aka `scrape targets`) via HTTP protocol on a regular basis (aka `scrape_interval`). scraped (pulled) from the known applications (aka `scrape targets`) via HTTP protocol on a regular basis (aka `scrape_interval`).
@ -409,7 +409,7 @@ models for data collection. Many installations use exclusively one of these mode
The most common approach for data collection is using both models: The most common approach for data collection is using both models:
{% include img.html href="keyConcepts_data_collection.png" %} <img src="keyConcepts_data_collection.png">
In this approach the additional component is used - [vmagent](https://docs.victoriametrics.com/vmagent.html). Vmagent is In this approach the additional component is used - [vmagent](https://docs.victoriametrics.com/vmagent.html). Vmagent is
a lightweight agent whose main purpose is to collect, filter, relabel and deliver metrics to VictoriaMetrics. a lightweight agent whose main purpose is to collect, filter, relabel and deliver metrics to VictoriaMetrics.
@ -424,7 +424,7 @@ installation for querying collected data.
VictoriaMetrics components allow building more advanced topologies. For example, vmagents can push metrics from separate datacenters to the central VictoriaMetrics: VictoriaMetrics components allow building more advanced topologies. For example, vmagents can push metrics from separate datacenters to the central VictoriaMetrics:
{% include img.html href="keyConcepts_two_dcs.png" %} <img src="keyConcepts_two_dcs.png">
VictoriaMetrics in this example may be either [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html) VictoriaMetrics in this example may be either [single-node VictoriaMetrics](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html)
or [VictoriaMetrics Cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html). Vmagent also allows or [VictoriaMetrics Cluster](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html). Vmagent also allows
@ -854,7 +854,7 @@ VictoriaMetrics has a built-in graphical User Interface for querying and visuali
[VMUI](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#vmui). [VMUI](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#vmui).
Open `http://victoriametrics:8428/vmui` page, type the query and see the results: Open `http://victoriametrics:8428/vmui` page, type the query and see the results:
{% include img.html href="keyConcepts_vmui.png" %} <img src="keyConcepts_vmui.png">
VictoriaMetrics supports [Prometheus HTTP API](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#prometheus-querying-api-usage) VictoriaMetrics supports [Prometheus HTTP API](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#prometheus-querying-api-usage)
which makes it possible to [query it with Grafana](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#grafana-setup) which makes it possible to [query it with Grafana](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#grafana-setup)