Kafka MQ

To get started first follow the steps below:

• Install
• Root access
• Verify the required port is open

Older versions can be found here 7, 6, 5

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

We need to specify the Kafka input details. In the configuration file locate the filebeat.inputs section and edit your config to look similar to the below.

filebeat.inputs:
- type: kafka
  hosts:
    - name-of-your-broker:your-broker-port
  # - localhost:9092        (Example)
  topics: ["name-of-your-topic", "name-of-another-topic"]
  group_id: "filebeat"

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

If you have issues starting in the next step, you can use these commands below to troubleshoot.

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>
sudo ./ -e -c .yml

Windows

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

Start or restart to apply the configuration changes.

Now you should view your data:

Launch Dashboard

If you don't see logs take a look at How to diagnose no data in Stack below for how to diagnose common issues.

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.