Effective log management involves a possibility to instantly draw useful insights from millions of log entries, identify issues as they arise, and visualize/communicate patterns that emerge out of your application logs. Fortunately, ELK stack (Elasticsearch, Logstash, and Kibana) makes it easy to ship logs from your application to ES collections for storage and analysis.
Recently, Elastic infrastructure was extended by useful tools for shipping logs called Beats. Filebeat is a part of Beats tool set that can be configured to send log events either to Logstash (and from there to Elasticsearch), or even directly to the Elasticsearch. The tool turns your logs into searchable and filterable ES documents with fields and properties that can be easily visualized and analyzed.
In a previous post, we discussed how to use Filebeat to ship Linux system logs. Now, it’s time to show how to ship logs from your MySQL database via Filebeat transport to your Elasticsearch cluster. Making MySQL general and slow logs accessible via Kibana and Logstash will radically improve your database management, log analysis and pattern discovery leveraging the full potential of ELK stack.