Here we’ve configured that we’ll be ingesting data using filebeats. Input :- Defines how logstash can ingest data.
This file has the capacity to perform all kinds of operations on the ingested data. Logstash can be setup using the logstash.yml file located at /etc/logstash/logstash.ymlīelow is the logstash configuration file which is the most important file in the entire FELK stack. The logstash server is located at vops and can be accessed via ssh.I’ve installed logstash using our custom ansible role Logstash can be used for ‘massaging’ data into a particular format by applying various formatting rules like mutate,add,remove etc… and also allows to filter data reaching elasticsearch. We also need to provide the location of the logstash server using the ‘logstash_server’ variable as we are shipping all logs to the centralised logstash server for processing before feeding into elastic search.īelow is the configuration file for filebeats which is generated automatically using the ansible role located at /etc/filebeat/filebeat.ymlĬurrently we are only spooling logs out of the API/backend server. We just need to provide the path of the log files using the ‘log_file_locations’ variable which is an array and can take more than one path. This is how we’ve added filebeats in our staging server deployment
FILEBEATS VS LOGSTASH INSTALL
I’ve created a simple Ansible role to install filebeats on the server from where we need to read the log files. It is extremely important that the application spools out data in JSON format as we’ll understand while discussing the filter module of logstash configuration file.įilebeats is a simple golang binary which reads the log file real-time and has capability to ship to various output sources like Elasticsearch,Logstash etc… for further processing. This filter converts the entire request in JSON format and then logs it.įor creating the log files we are utilising the ‘ch.qos.logback’ library which writes the log files in JSON format as shown below In the backend API application we’ve written a custom class to log each and every request
FILEBEATS VS LOGSTASH HOW TO
Here we’ll discuss how to setup an entire ecosystem for managing,indexing,searching and viewing log files real-time with the help of the FELK stack i.e. This process of viewing the logs is not only cumbersome but also highly inefficient and error-prone.
He might even have to ship the logs to another machine to perform an RCA which is a big security risk ! What kind of data it is processing and how it is processing.A fast and efficient logging and monitoring ‘eco-system’ helps the operations team derive this information ‘real-time’.Īs an Ex: if the application is running slow or has suddenly stopped working then the log files are the best way(and possibly the only way) an operations guys (ops) will be able to perform a root-cause analysis.Now in absence of a logging and monitoring ecosystem, he’ll have to manually log into the system and check the log files in a manner shown below. In a DevOps environment it becomes extremely crucial to see how your application is behaving and how it is running. Practical DevOps - Continous Delivery using Jenkins.
FILEBEATS VS LOGSTASH CODE
Practical DevOps - Infrastructure as Code using Vagrant Ansible and Docker.If you’ve landed to this page directly i recommend reading my previous blogs on a related topic Before We BeginĭevOps Lab which can act as a good reference to reinforce learning for the purpose of this blog. Here we’ll look at the configurations for each of these tools and how application developers can help the operations team to collaborate better by throwing relevant data real-time. In this blog i’ll discuss continuous monitoring using tools like Elasticsearch,logstash,kibana and filebeat.