Monitoring Prometheus

This article describes how to monitor Fluentd via Prometheus.
Since both Prometheus and Fluentd are under CNCF (Cloud Native Computing Foundation), Fluentd project is recommending to use Prometheus by default to monitor Fluentd.
Installation
First of all, please install fluent-plugin-prometheus gem.
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$ fluent-gem install fluent-plugin-prometheus --version=0.4.0
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If you are using td-agent, use td-agent-gem for installation.
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$ sudo td-agent-gem install fluent-plugin-prometheus --version=0.4.0
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Example Fluentd Configuration
To expose the Fluentd metrics to Prometheus, we need to configure 3 parts:
Step 1: Prometheus Filter Plugin to count Incoming Records
Step 2: Prometheus Output Plugin to count Outgoing Records
Step 3: Prometheus Input Plugin to expose metrics via HTTP
Step 1: Counting Incoming Records by Prometheus Filter Plugin
First, please add the <filter> section like below, to count the incoming records per tag. With this configuration, prometheus filter starts adding the internal counter as the record comes in.
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# source
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<source>
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  @type forward
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  bind 0.0.0.0
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  port 24224
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</source>
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​
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# count number of incoming records per tag
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<filter company.*>
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  @type prometheus
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  <metric>
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    name fluentd_input_status_num_records_total
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    type counter
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    desc The total number of incoming records
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    <labels>
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      tag ${tag}
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      hostname ${hostname}
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    </labels>
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  </metric>
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</filter>
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Step 2: Counting Outgoing Records by Prometheus Output Plugin
Second, please use copy plugin with prometheus output plugin, to count the outgoing records per tag. With this configuration, prometheus output starts adding the internal counter as the record goes out.
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# count number of outgoing records per tag
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<match company.*>
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  @type copy
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  <store>
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    @type forward
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    <server>
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      name myserver1
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      hostname 192.168.1.3
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      port 24224
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      weight 60
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    </server>
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  </store>
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  <store>
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    @type prometheus
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    <metric>
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      name fluentd_output_status_num_records_total
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      type counter
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      desc The total number of outgoing records
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      <labels>
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        tag ${tag}
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        hostname ${hostname}
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      </labels>
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    </metric>
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  </store>
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</match>
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Step 3: Expose Metrics by Prometheus Input Plugin via HTTP
Finally, please use prometheus input plugin to expose internal counter information via HTTP.
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# expose metrics in prometheus format
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<source>
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  @type prometheus
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  bind 0.0.0.0
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  port 24231
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  metrics_path /metrics
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</source>
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<source>
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  @type prometheus_output_monitor
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  interval 10
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  <labels>
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    hostname ${hostname}
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  </labels>
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</source>
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Step 4: Check the Configuration
After you have done 3 changes, please restart fluentd.
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# For stand-alone Fluentd installations
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$ fluentd -c fluentd.conf
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# For td-agent users
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$ sudo /etc/init.d/td-agent restart
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Let's send some records.
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$ echo '{"message":"hello"}' | bundle exec fluent-cat company.test1
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$ echo '{"message":"hello"}' | bundle exec fluent-cat company.test1
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$ echo '{"message":"hello"}' | bundle exec fluent-cat company.test1
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$ echo '{"message":"hello"}' | bundle exec fluent-cat company.test2
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Then, please access to http://localhost:24231/metrics, which is the URL to receive metrics in Prometheus format.
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curl http://localhost:24231/metrics
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# TYPE fluentd_input_status_num_records_total counter
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# HELP fluentd_input_status_num_records_total The total number of incoming records
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fluentd_input_status_num_records_total{tag="company.test",host="KZK.local"} 3.0
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fluentd_input_status_num_records_total{tag="company.test2",host="KZK.local"} 1.0
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# TYPE fluentd_output_status_num_records_total counter
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# HELP fluentd_output_status_num_records_total The total number of outgoing records
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fluentd_output_status_num_records_total{tag="company.test",host="KZK.local"} 3.0
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fluentd_output_status_num_records_total{tag="company.test2",host="KZK.local"} 1.0
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# TYPE fluentd_output_status_buffer_queue_length gauge
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# HELP fluentd_output_status_buffer_queue_length Current buffer queue length.
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fluentd_output_status_buffer_queue_length{hostname="KZK.local",plugin_id="object:3fcbccc6d388",type="forward"} 1.0
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....
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Example Prometheus Configuration
Please prepare the file below as prometheus.yml.
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global:
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  scrape_interval: 10s # Set the scrape interval to every 10 seconds. Default is every 1 minute.
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​
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# A scrape configuration containing exactly one endpoint to scrape:
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# Here it's Prometheus itself.
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scrape_configs:
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  - job_name: 'fluentd'
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    static_configs:
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      - targets: ['localhost:24231']
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Then, launch prometheus process.
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$ ./prometheus --config.file="prometheus.yml"
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Now please open your browser and access to http://localhost:9090/.
How to use Prometheus to monitor Fluentd
List of Fluentd nodes
If you go to http://localhost:9090/targets, Prometheus will show you a list of Fluentd nodes and its status.
List of Fluentd metrics
Then, visit http://localhost:9090/graph to explore Fluentd internal metrics. There, you'll see 8 metrics in the metric list:
fluentd_input_status_num_records_total
fluentd_output_status_buffer_queue_length
fluentd_output_status_buffer_total_bytes
fluentd_output_status_emit_count
fluentd_output_status_num_errors
fluentd_output_status_num_records_total
fluentd_output_status_retry_count
fluentd_output_status_retry_wait
Please pick fluentd_input_status_num_records_total, and you'll see the total incoming records per tag.
Example Prometheus Queries
Since fluentd_input_status_num_records_total and fluentd_output_status_num_records_total are monotonically increasing numbers, it requires a little bit of calculation by PromQL (Prometheus Query Language) to make them meaningful.
Here are the example PromQLs for common metrics everyone wants to see.
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# number of available nodes
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up
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​
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# incoming records / sec / host
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sum(rate(fluentd_input_status_num_records_total[1m])) by (hostname)
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​
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# incoming records / sec / tag
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sum(rate(fluentd_input_status_num_records_total[1m])) by (tag)
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​
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# outgoing records / sec / host
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sum(rate(fluentd_output_status_num_records_total[1m])) by (hostname)
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​
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# outgoing records / sec / tag
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sum(rate(fluentd_output_status_num_records_total[1m])) by (tag)
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​
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# emit count / sec
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rate(fluentd_output_status_emit_count[1m])
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Metrics to Monitor
In addition to the traffic metrics introduced above, it is important to monitor the queue length and error count.
If these values are increasing, it means Fluentd cannot flush the buffer to the destination. Thus you will lose the data once the buffer becomes full.
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# maximum buffer length in last 1min
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max_over_time(fluentd_output_status_buffer_queue_length[1m])
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​
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# maximum buffer bytes in last 1min
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max_over_time(fluentd_output_status_buffer_total_bytes[1m])
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​
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# maximum retry wait in last 1min
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max_over_time(fluentd_output_status_retry_wait[1m])
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​
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# retry count / sec
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rate(fluentd_output_status_retry_count[1m])
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Grafana for Advanced Visualization / Alerting
For more advanced visualization and alerting, we recommend to use Grafana as a visualization frontend for Prometheus.
​Grafana Support for Prometheus​
Further Readings
​Prometheus Documentation​
​Grafana Documentation​
If this article is incorrect or outdated, or omits critical information, please let us know. Fluentd is a open source project under Cloud Native Computing Foundation (CNCF). All components are available under the Apache 2 License.

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Contents

Installation

Example Fluentd Configuration

Step 1: Counting Incoming Records by Prometheus Filter Plugin

Step 2: Counting Outgoing Records by Prometheus Output Plugin

Step 3: Expose Metrics by Prometheus Input Plugin via HTTP

Step 4: Check the Configuration

Example Prometheus Configuration

How to use Prometheus to monitor Fluentd

List of Fluentd nodes

List of Fluentd metrics

Example Prometheus Queries

Metrics to Monitor

Grafana for Advanced Visualization / Alerting