Kafka

Collect and ship Kafka application logs to Logstash and Elasticsearch.

Filebeat is a lightweight shipper that enables you to send your Apache Kafka application logs to Logstash and Elasticsearch. Configure Filebeat using the pre-defined examples below to start sending and analysing your Apache Kafka application logs.

Step 1 - Install Filebeat

deb (Debian/Ubuntu/Mint)

curl -L -O https://artifacts.elastic.co/downloads/beats//-oss-7.8.1-amd64.deb
sudo dpkg -i -oss-7.8.1-amd64.deb

rpm (CentOS/RHEL/Fedora)

curl -L -O https://artifacts.elastic.co/downloads/beats//-oss-7.8.1-x86_64.rpm
sudo rpm -vi -oss-7.8.1-x86_64.rpm

macOS

curl -L -O https://artifacts.elastic.co/downloads/beats//-oss-7.8.1-darwin-x86_64.tar.gz
tar xzvf -oss-7.8.1-darwin-x86_64.tar.gz

Windows

Download the Windows zip file from the official downloads page.
Extract the contents of the zip file into C:\Program Files.
Rename the -<version>-windows directory to ``.
Open a PowerShell prompt as an Administrator (right-click the PowerShell icon and select Run As Administrator). If you are running Windows XP, you may need to download and install PowerShell.
Run the following commands to install as a Windows service:

cd 'C:\Program Files\'
.\install-service-.ps1

If script execution is disabled on your system, you need to set the execution policy for the current session to allow the script to run. For example: PowerShell.exe -ExecutionPolicy UnRestricted -File .\install-service-.ps1.

My OS isn't here! Don't see your system? Check out the official downloads page for more options (including 32-bit versions).

Step 2 - Locate configuration file

deb/rpm /etc/filebeat/filebeat.yml
mac/win <EXTRACTED_ARCHIVE>/filebeat.yml

Step 3 - Enable the Kafka module

deb/rpm

sudo filebeat modules list
sudo filebeat modules enable kafka

macOS

cd <EXTRACTED_ARCHIVE>
./filebeat modules list
./filebeat modules enable kafka

Windows

cd <EXTRACTED_ARCHIVE>
.\filebeat.exe modules list
.\filebeat.exe modules enable kafka

Additional module configuration can be done using the per module config files located in the modules.d folder, most commonly this would be to read logs from a non-default location

deb/rpm /etc/filebeat/modules.d/
mac/win <EXTRACTED_ARCHIVE>/modules.d/

- module: kafka
# All logs
log:
  enabled: true

  # Set custom paths for Kafka. If left empty,
  # Filebeat will look under /opt.
  #var.kafka_home:

  # Set custom paths for the log files. If left empty,
  # Filebeat will choose the paths depending on your OS.
  #var.paths:

Step 4 - Configure Output

We'll be shipping to Logstash so that we have the option to run filters before the data is indexed.
Comment out the elasticsearch output block.

## Comment out elasticsearch output
#output.elasticsearch:
#  hosts: ["localhost:9200"]

No input available! Your stack is missing the required input for this data source Talk to support to add the input

Step 5 - Validate configuration

Let's check the configuration file is syntactically correct by running directly inside the terminal. If the file is invalid, will print an error loading config file error message with details on how to correct the problem.

deb/rpm

sudo  -e -c /etc//.yml

macOS

cd <EXTRACTED_ARCHIVE>
./ -e -c .yml

Windows

cd <EXTRACTED_ARCHIVE>
.\.exe -e -c .yml

Step 6 - Start filebeat

Ok, time to start ingesting data!

deb/rpm

sudo systemctl enable filebeat
sudo systemctl start filebeat

macOS

./filebeat

Windows

PS C:\Program Files\Filebeat> Start-Service filebeat

Step 7 - Apache Kafka Logging Overview

Apache Kafka is a distributed streaming platform written in Scala & Java, that is primarily used for generating low latency real-time data streaming pipelines for apps & data lake engines.

Kafka offers users the ability to publish & subscribe to record streams, decouple data & sort the aggregated data in chronological order for improved real-time processing. The platform is suited to processing many trillions of cross systems events per day making the tool ideal as a big data solution.

Kafka is one of the leading Apache projects and is used by enterprise level businesses globally; including Uber, LinkedIn, Netflix & Twitter. Much of this infrastructure also uses Logstash, which works side by side with the platform as Kafka acts as a buffer between the two for improved resilience.

The combined power of Elasticsearch, Logstash & Kibana form the Elastic Stack which can be used for efficient log analysis as platform & Kafka broker logs contain vital information on the performance & overall health of your systems.

Our hosted Elastic Stack solution can help monitor & visualise Kafka logs and alert you on performance issues & broker degradation in real time. Logit.io’s built in Kibana can easily generate dashboards for capturing various Kafka log messages along with their severity counts.

If you need any assistance with analysing your Kafka logs (no matter if their server, utils or state-change logs) we're here to help. Feel free to get in touch by contacting the Logit.io help team via chat & we'll be happy to help you start analysing your log data.

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