The diagram above shows the elements of the solution: sFlow telemetry streams from hosts and switches to an instance of sFlow-RT. The sFlow-RT analytics software converts the raw measurements into metrics that are accessible through a REST API.
The following prometheus.php script mediates between the Prometheus metrics export protocol and the sFlow-RT REST API. HTTP queries from Prometheus are translated into calls to the sFlow-RT REST API and JSON responses are converted into Prometheus metrics.
<?php
header('Content-Type: text/plain');
if(isset($_GET['labels'])) {
$keys = htmlspecialchars($_GET["labels"]);
}
$vals = htmlspecialchars($_GET["values"]);
if(isset($keys)) {
$cols = $keys.','.$vals;
} else {
$cols = $vals;
}
$key_arr = explode(",",$keys);
$result = file_get_contents('http://localhost:8008/table/ALL/'.$cols.'/json');
$obj = json_decode($result,true);
foreach ($obj as $row) {
unset($labels);
foreach ($row as $cell) {
if(!isset($labels)) {
$labels = 'agent="'.$cell['agent'].'",datasource="'.$cell['dataSource'].'"';
}
$name = $cell['metricName'];
$val = $cell['metricValue'];
if(in_array($name,$key_arr)) {
$labels .= ','.$name.'="'.$val.'"';
} else {
print $name."{".$labels."} ".$val."\n";
}
}
}
?>
Install prometheus.php under the web server home directory (e.g. /var/www/html) on the system running sFlow-RT and verify functionality using cURL:$ curl "http://localhost/prometheus.php?labels=host_name&values=load_one"
load_one{agent="10.0.0.86",datasource="2.1",host_name="server3"} 0.06
load_one{agent="10.0.0.85",datasource="2.1",host_name="server2"} 0
load_one{agent="10.0.0.82",datasource="2.1",host_name="spine1"} 0
load_one{agent="10.0.0.83",datasource="2.1",host_name="spine2"} 0
load_one{agent="10.0.0.80",datasource="2.1",host_name="leaf1"} 0
load_one{agent="10.0.0.81",datasource="2.1",host_name="leaf2"} 0
global:
scrape_interval: 15s
evaluation_interval: 15s
rule_files:
# - "first.rules"
# - "second.rules"
scrape_configs:
- job_name: 'sflow-rt-hosts'
scrape_interval: 30s
metrics_path: /prometheus.php
params:
labels: ['host_name']
values: ['load_one,cpu_utilization,proc_run']
static_configs:
- targets: ['10.0.0.162']
- job_name: 'sflow-rt-ifstats'
scrape_interval: 30s
metrics_path: /prometheus.php
params:
labels: ['host_name,ifname']
values: ['ifinutilization,ifoututilization,ifindiscards,ifoutdiscards']
static_configs:
- targets: ['10.0.0.162']
Start Prometheus. For example, the following command shows how to run Prometheus under Docker:docker run --name prometheus --rm -v $PWD/data:/prometheus \ -v $PWD/prometheus.yml:/etc/prometheus/prometheus.yml \ -p 9090:9090 prom/prometheusThe screen capture above shows the Prometheus web interface, accessible on port 9090.
The following command shows how to run Grafana under Docker:
docker run --name grafana --rm -v $PWD/data:/var/lib/grafana \ -p 3000:3000 grafana/grafanaAccess the Grafana web interface on port 3000, configure a data source for the Prometheus database, and start building dashboards. The screen capture above shows the same chart built earlier using the native Prometheus interface.
Standard sFlow telemetry provides a unified method of monitoring large scale network and compute infrastructure. This example focussed on sFlow counter metrics, but sFlow also provides real-time flow information that can be used to generate flow metrics, for example, reporting on interactions between microservices.
Additional examples on this blog include:
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