danielqsj/kafka_exporter

kafka_exporter

Kafka exporter for Prometheus. For other metrics from Kafka, have a look at the JMX exporter.

Table of Contents

• Compatibility
• Dependency
• Download
• Compile
  • Build Binary
  • Build Docker Image
• Run
  • Run Binary
  • Run Docker Image
• Flags
  • Notes
• Metrics
  • Brokers
  • Topics
  • Consumer Groups
• Grafana Dashboard
• Contribute
• Donation
• License

Compatibility

Support Apache Kafka version 0.10.1.0 (and later).

Dependency

• Prometheus
• Sarama
• Golang

Download

Binary can be downloaded from Releases page.

Compile

Build Binary

make

Build Docker Image

make docker

Docker Hub Image

docker pull danielqsj/kafka-exporter:latest

It can be used directly instead of having to build the image yourself. (Docker Hub danielqsj/kafka-exporter)

Run

Run Binary

kafka_exporter --kafka.server=kafka:9092 [--kafka.server=another-server ...]

Run Docker Image

docker run -ti --rm -p 9308:9308 danielqsj/kafka-exporter --kafka.server=kafka:9092 [--kafka.server=another-server ...]

Flags

This image is configurable using different flags

Notes

Boolean values are uniquely managed by Kingpin. Each boolean flag will have a negative complement:
--<name> and --no-<name>.

For example:

If you need to disable sasl.handshake, you could add flag --no-sasl.handshake

Metrics

Documents about exposed Prometheus metrics.

For details on the underlying metrics please see Apache Kafka.

Brokers

Metrics details

Metrics output example

# HELP kafka_brokers Number of Brokers in the Kafka Cluster.
# TYPE kafka_brokers gauge
kafka_brokers 3

Topics

Metrics details

Metrics output example

# HELP kafka_topic_partitions Number of partitions for this Topic
# TYPE kafka_topic_partitions gauge
kafka_topic_partitions{topic="__consumer_offsets"} 50

# HELP kafka_topic_partition_current_offset Current Offset of a Broker at Topic/Partition
# TYPE kafka_topic_partition_current_offset gauge
kafka_topic_partition_current_offset{partition="0",topic="__consumer_offsets"} 0

# HELP kafka_topic_partition_oldest_offset Oldest Offset of a Broker at Topic/Partition
# TYPE kafka_topic_partition_oldest_offset gauge
kafka_topic_partition_oldest_offset{partition="0",topic="__consumer_offsets"} 0

# HELP kafka_topic_partition_in_sync_replica Number of In-Sync Replicas for this Topic/Partition
# TYPE kafka_topic_partition_in_sync_replica gauge
kafka_topic_partition_in_sync_replica{partition="0",topic="__consumer_offsets"} 3

# HELP kafka_topic_partition_leader Leader Broker ID of this Topic/Partition
# TYPE kafka_topic_partition_leader gauge
kafka_topic_partition_leader{partition="0",topic="__consumer_offsets"} 0

# HELP kafka_topic_partition_leader_is_preferred 1 if Topic/Partition is using the Preferred Broker
# TYPE kafka_topic_partition_leader_is_preferred gauge
kafka_topic_partition_leader_is_preferred{partition="0",topic="__consumer_offsets"} 1

# HELP kafka_topic_partition_replicas Number of Replicas for this Topic/Partition
# TYPE kafka_topic_partition_replicas gauge
kafka_topic_partition_replicas{partition="0",topic="__consumer_offsets"} 3

# HELP kafka_topic_partition_under_replicated_partition 1 if Topic/Partition is under Replicated
# TYPE kafka_topic_partition_under_replicated_partition gauge
kafka_topic_partition_under_replicated_partition{partition="0",topic="__consumer_offsets"} 0

Consumer Groups

Metrics details

Metrics output example

# HELP kafka_consumergroup_current_offset Current Offset of a ConsumerGroup at Topic/Partition
# TYPE kafka_consumergroup_current_offset gauge
kafka_consumergroup_current_offset{consumergroup="KMOffsetCache-kafka-manager-3806276532-ml44w",partition="0",topic="__consumer_offsets"} -1

# HELP kafka_consumergroup_lag Current Approximate Lag of a ConsumerGroup at Topic/Partition
# TYPE kafka_consumergroup_lag gauge
kafka_consumergroup_lag{consumergroup="KMOffsetCache-kafka-manager-3806276532-ml44w",partition="0",topic="__consumer_offsets"} 1

Grafana Dashboard

Grafana Dashboard ID: 7589, name: Kafka Exporter Overview.

For details of the dashboard please see Kafka Exporter Overview.

Contribute

If you like Kafka Exporter, please give me a star. This will help more people know Kafka Exporter.

Please feel free to send me pull requests.

Contributors ✨

Thanks goes to these wonderful people:

Donation

Your donation will encourage me to continue to improve Kafka Exporter. Support Alipay donation.

License

Code is licensed under the Apache License 2.0.

Kafka Exporter Helm Chart

The exporter, alert rule, and dashboard can be deployed in Kubernetes using the Helm chart. The Helm chart used for deployment is taken from the Prometheus community, which can be found here.

Installing Kafka Cluster

If your Kafka cluster is not up and ready you can start it using Helm:

$ helm repo add bitnami https://charts.bitnami.com/bitnami
$ helm install my-release bitnami/kafka

Note that bitnami charts allow you to deploy a Kafka exporter as part of the Helm chart. You can enable it by adding “--set metrics.kafka.enabled=true”

Installing Kafka Exporter

helm repo add Prometheus-community https://prometheus-community.github.io/helm-charts

helm repo update
helm install my-release prometheus-community/prometheus-kafka-exporter

Some of the common parameters that must be changed in the values file include: 

kafkaServer: "IP/Hostname:9092"

All these parameters can be tuned via the values.yaml file here.

Scrape the metrics

There are multiple ways to scrape the metrics as discussed above. In addition to the native way of setting up Prometheus monitoring, a service monitor can be deployed (if a Prometheus operator is being used) to scrap the data from the Kafka exporter. With this approach, multiple Kafka servers can be scrapped without altering the Prometheus configuration. Every Kafka exporter comes with its own service monitor.
In the above-mentioned chart, a service monitor can be deployed by turning it on from the values.yaml file here.

prometheus:
  serviceMonitor:
    enabled: true
    namespace: monitoring
    interval: "30s"
    # If serviceMonitor is enabled and you want prometheus to automatically register
    # target using serviceMonitor, add additionalLabels with prometheus release name
    # e.g. If you have deployed kube-prometheus-stack with release name kube-prometheus
    # then additionalLabels will be
    # additionalLabels:
    #   release: kube-prometheus
    additionalLabels: {}
    targetLabels: []

Update the annotation section here if you are not using the Prometheus Operator.

service: 
  annotations:
    prometheus.io/path: /metrics
    prometheus.io/scrape: "true"

And with this, we conclude our discussion of the Kafka exporter. If you have any questions about the content of this article or our other exporter reviews, you can reach our team via support@nexclipper.io. Stay tuned for more useful exporter reviews in the near future!

Kafka Exporter Alerts

After digging into all the valuable metrics, this section explains in detail how we can get critical alerts.

PromQL is a query language for the Prometheus monitoring system. It is designed for building powerful yet simple queries for graphs, alerts, or derived time series (aka recording rules). PromQL is designed from scratch and has zero common grounds with other query languages used in time series databases, such as SQL in TimescaleDB, InfluxQL, or Flux. More details can be found here.

Prometheus comes with a built-in Alert Manager that is responsible for sending alerts (could be email, Slack, or any other supported channel) when any of the trigger conditions is met. Alerting rules allow users to define alerts based on Prometheus query expressions. They are defined based on the available metrics scraped by the exporter. Click here for a good source for community-defined alerts.

A general alert looks as follows:

- alert:(Alert Name)
expr: (Metric exported from exporter) >/</==/<=/=> (Value)
for: (wait for a certain duration between first encountering a new expression output vector element and counting an alert as firing for this element)
labels: (allows specifying a set of additional labels to be attached to the alert)
annotation: (specifies a set of informational labels that can be used to store longer additional information)

Some of the recommended Kafka alerts are:

1. Alert - Kafka topics replicas

- alert: KafkaTopicsReplicas
    expr: sum(kafka_topic_partition_in_sync_replica) by (topic) < 3
    for: 0m
    labels:
      severity: critical
    annotations:
      summary: Kafka topics replicas (instance {{ $labels.instance }})
      description: "Kafka topic in-sync partition\n  VALUE = {{ $value }}\n  LABELS = {{ $labels }}"

2. Alert - Kafka consumers group

- alert: KafkaConsumersGroup
    expr: sum(kafka_consumergroup_lag) by (consumergroup) > 50
    for: 1m
    labels:
      severity: critical
    annotations:
      summary: Kafka consumers group (instance {{ $labels.instance }})
      description: "Kafka consumers group\n  VALUE = {{ $value }}\n  LABELS = {{ $labels }}"

3.  Alert - Kafka broker count

 - alert: KafkaBrokerDown
    expr: kafka_brokers < 3   
    for: 0m
    labels:
      severity: critical
    annotations:
      Summary: "Kafka broker *{{ $labels.instance }}* alert status"
      description: "One of the Kafka broker *{{ $labels.instance }}* is down."

Kafka Exporter Grafana

Graphs are easier to understand and more user-friendly than a row of numbers. For this purpose, users can plot their time series data in visualized format using Grafana.

Grafana is an open-source dashboarding tool used for visualizing metrics with the help of customizable and illustrative charts and graphs. It connects very well with Prometheus and makes monitoring easy and informative. Dashboards in Grafana are made up of panels, with each panel running a PromQL query to fetch metrics from Prometheus.
Grafana supports community-driven graphs for most of the widely used software, which can be directly imported to the Grafana Community.

NexClipper uses the Kafka by the jack chen dashboard, which is widely accepted and has a lot of useful panels.

What is a Panel?

Panels are the most basic component of a dashboard and can display information in various ways, such as gauge, text, bar chart, graph, and so on. They provide information in a very interactive way. Users can view every panel separately and check the value of metrics within a specific time range. 
The values on the panel are queried using PromQL, which is Prometheus Query Language. PromQL is a simple query language used to query metrics within Prometheus. It enables users to query data, aggregate and apply arithmetic functions to the metrics, and then further visualize them on panels.

Here are some examples of panels: