#Prometheus jmx exporter elasticsearch how to
#Prometheus jmx exporter elasticsearch series
#Prometheus jmx exporter elasticsearch download

:'rver:name=UnderReplicatedPartitions,type=ReplicaManager' Now, when you want to want to lookup specific metric in Kibana to look at in Discovery or Visualize, you run the following filter like to get the data: But if it doesn't, just add another mbean mapping under jmx.mappings and it'll put a great big JSON event for every metric it captures. The example config I gave uses wildcards for the JMX metrics 'rver:type=*,name=*' so it should cover quite a lot of metrics already.

Assuming you got metricbeat pushing to Elasticsearch, it'll do its thing and start pushing to indices of the name pattern metricbeat-*.

Open up the metricbeat config file and add the following section under metricbeat.modules.

export KAFKA_OPTS='-javaagent:/opt/jolokia/jolokia.jar=host=0.0.0.0'.

#Prometheus jmx exporter elasticsearch download

Download the jolokia java agent somewhere and put it where the Kafka process can access it.The folks at Jolokia made a handy java agent, which when configured with Kafka startup, exposes a tiny HTTP server with an JSON (not REST, just in case Roy Fielding's reading this) API exposing whatever JMX metric you want on that process. So how can Metricbeat monitor the JMX metrics in Kafka? Well Metricbeat being written in go and JMX metrics being Java means we need something to bridge the two. Well, it can push to Kafka too (in fact all the beats can) but this article's about monitoring Kafka. So how do we get all these JMX metrics into Elasticsearch so we can make the operations guys giddy with all these dashboards and visualisations? Many of you might have heard of Metricbeat as one of these lightweight agents you can have running on your machines and it can siphon up metrics like CPU, memory, and disk usage and push those to Elasticsearch. They even did this thing where ballerina danced on stage with sensors on her and Kibana tracked all these positions on a fast changing chart without skipping a beat.

#Prometheus jmx exporter elasticsearch series

But do you know you can put in metrics too and do fast aggregations and slicing and dicing with it? Well, you can and it's been getting better and better over years with different data structure improvements like bitsets, roaring bitmaps, and instant aggregations so when you start to have dashboards with histograms and time series charts constantly being updated on them, Elasticsearch doesn't bat an eyelid. Everybody knows Elasticsearch is great for indexing logs and doing advanced text searches on them. And those metrics are constantly being updated with the latest state (sometimes per broker) so it's a bit hard to use as is to get a picture of what's going on in your cluster now or for when you're looking back a few hours or a days. Ok maybe not but there's enough to scroll a few pages and get bleary eyed. In this article, I'll show you the little documented and untouted way of using Metricbeat to do just that.įor those who know Kafka, you know that Kafka exposes its metrics via JMX and there's a million of them.

#Prometheus jmx exporter elasticsearch how to

One of the challenges of managing Kafka is how to go about monitoring or looking at historical behaviour of Apache Kafka when under strain or during outages. I know Prometheus is so hot right now but for those of us that know and implemented the Elastic Stack, we know how well it scales and what it buys you in terms of exploring your metrics, creating beautiful visualisations and dashboards, and then slicing and dicing those for historical analysis or doing forensics on our environments when things go awry.