Monitoring by 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
Install fluent-plugin-prometheus gem:
$ fluent-gem install fluent-plugin-prometheus
For td-agent, use td-agent-gem for installation:
$ sudo td-agent-gem install fluent-plugin-prometheus
This GitHub repository contains a fully working configuration for this article.
Example Fluentd Configuration
To expose Fluentd metrics to Prometheus, we need to configure three (3) parts:
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 1: Counting Incoming Records by Prometheus Filter Plugin
Configure the <filter> section to count the incoming records per tag:
# source
<source>
  @type forward
  bind 0.0.0.0
  port 24224
</source>
​
# count the number of incoming records per tag
<filter company.*>
  @type prometheus
  <metric>
    name fluentd_input_status_num_records_total
    type counter
    desc The total number of incoming records
    <labels>
      tag ${tag}
      hostname ${hostname}
    </labels>
  </metric>
</filter>
With this configuration, the prometheus filter plugin starts adding the internal counter as the record comes in.
Step 2: Counting Outgoing Records by Prometheus Output Plugin
Configure the copy plugin with prometheus output plugin to count the outgoing records per tag:
# count the number of outgoing records per tag
<match company.*>
  @type copy
​
  <store>
    @type forward
    <server>
      name myserver1
      hostname 192.168.1.3
      port 24224
      weight 60
    </server>
  </store>
​
  <store>
    @type prometheus
    <metric>
      name fluentd_output_status_num_records_total
      type counter
      desc The total number of outgoing records
      <labels>
        tag ${tag}
        hostname ${hostname}
      </labels>
    </metric>
  </store>
​
</match>
With this configuration, the prometheus output plugin starts adding the internal counter as the record goes out.
Step 3: Expose Metrics by Prometheus Input Plugin via HTTP
Configure prometheus input plugin to expose internal counter information via HTTP:
# expose metrics in prometheus format
​
<source>
  @type prometheus
  bind 0.0.0.0
  port 24231
  metrics_path /metrics
</source>
​
<source>
  @type prometheus_output_monitor
  interval 10
  <labels>
    hostname ${hostname}
  </labels>
</source>
Check the Configuration
After you have done these three (3) changes, restart fluentd:
# For stand-alone Fluentd installations
$ fluentd -c fluentd.conf
​
# For td-agent users
$ sudo systemctl restart td-agent
Let's send some records:
$ echo '{"message":"hello"}' | bundle exec fluent-cat company.test1
$ echo '{"message":"hello"}' | bundle exec fluent-cat company.test1
$ echo '{"message":"hello"}' | bundle exec fluent-cat company.test1
$ echo '{"message":"hello"}' | bundle exec fluent-cat company.test2
Access http://localhost:24231/metrics to receive the metrics in Prometheus format:
curl http://localhost:24231/metrics
# TYPE fluentd_input_status_num_records_total counter
# HELP fluentd_input_status_num_records_total The total number of incoming records
fluentd_input_status_num_records_total{tag="company.test",host="KZK.local"} 3.0
fluentd_input_status_num_records_total{tag="company.test2",host="KZK.local"} 1.0
# TYPE fluentd_output_status_num_records_total counter
# HELP fluentd_output_status_num_records_total The total number of outgoing records
fluentd_output_status_num_records_total{tag="company.test",host="KZK.local"} 3.0
fluentd_output_status_num_records_total{tag="company.test2",host="KZK.local"} 1.0
# TYPE fluentd_output_status_buffer_queue_length gauge
# HELP fluentd_output_status_buffer_queue_length Current buffer queue length.
fluentd_output_status_buffer_queue_length{hostname="KZK.local",plugin_id="object:3fcbccc6d388",type="forward"} 1.0
....
Example Prometheus Configuration
Prepare the configuration file (prometheus.yml):
global:
  scrape_interval: 10s # Set the scrape interval to every 10 seconds. Default is every 1 minute.
​
# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
  - job_name: 'fluentd'
    static_configs:
      - targets: ['localhost:24231']
Launch prometheus:
$ ./prometheus --config.file="prometheus.yml"
Now, open this URL http://localhost:9090/ in your browser.
How to use Prometheus to monitor Fluentd?
List of Fluentd Nodes
Go to http://localhost:9090/targets to see the list of Fluentd nodes and their status.
Prometheus Targets
List of Fluentd Metrics
Visit http://localhost:9090/graph to explore Fluentd's internal metrics. You'll see eight (8) metrics in the metric list:
Prometheus Metrics
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
Pick fluentd_input_status_num_records_total and you'll see the total incoming records per tag.
Prometheus Graph
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:
# number of available nodes
up
​
# incoming records / sec / host
sum(rate(fluentd_input_status_num_records_total[1m])) by (hostname)
​
# incoming records / sec / tag
sum(rate(fluentd_input_status_num_records_total[1m])) by (tag)
​
# outgoing records / sec / host
sum(rate(fluentd_output_status_num_records_total[1m])) by (hostname)
​
# outgoing records / sec / tag
sum(rate(fluentd_output_status_num_records_total[1m])) by (tag)
​
# emit count / sec
rate(fluentd_output_status_emit_count[1m])
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.
# maximum buffer length in last 1min
max_over_time(fluentd_output_status_buffer_queue_length[1m])
​
# maximum buffer bytes in last 1min
max_over_time(fluentd_output_status_buffer_total_bytes[1m])
​
# maximum retry wait in last 1min
max_over_time(fluentd_output_status_retry_wait[1m])
​
# retry count / sec
rate(fluentd_output_status_retry_count[1m])
Grafana for Advanced Visualization / Alerting
For more advanced visualization and alerting, we recommend Grafana as a visualization frontend for Prometheus.
​Grafana Support for Prometheus​
Prometheus + Grafana
Further Readings
​Prometheus Documentation​
​Grafana Documentation​
If this article is incorrect or outdated, or omits critical information, please let us know. Fluentd is an open-source project under Cloud Native Computing Foundation (CNCF). All components are available under the Apache 2 License.
Overview
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Last updated 7 months ago