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Filebeat is a lightweight shipper that enables you to send your Apache Kafka message queue logs to Logstash and Elasticsearch. Configure Filebeat using the pre-defined examples below to start sending and analysing your Apache Kafka message queue logs.
curl -L -O https://artifacts.elastic.co/downloads/beats//-oss-7.15.1-amd64.deb sudo dpkg -i -oss-7.15.1-amd64.deb
curl -L -O https://artifacts.elastic.co/downloads/beats//-oss-7.15.1-x86_64.rpm sudo rpm -vi -oss-7.15.1-x86_64.rpm
curl -L -O https://artifacts.elastic.co/downloads/beats//-oss-7.15.1-darwin-x86_64.tar.gz tar xzvf -oss-7.15.1-darwin-x86_64.tar.gz
- Download and extract the Windows zip file.
- Rename the
-<version>-windowsdirectory to ``.
- Open a PowerShell prompt as an Administrator.
- Run the following to install as a Windows service:
PowerShell.exe -ExecutionPolicy UnRestricted -File .\install-service-.ps1.
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"]
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.
sudo -e -c /etc//.yml
cd <EXTRACTED_ARCHIVE> ./ -e -c .yml
cd <EXTRACTED_ARCHIVE> .\.exe -e -c .yml
Ok, time to start ingesting data!
sudo systemctl enable filebeat sudo systemctl start filebeat
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.