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by thedevopsguy
4077 days ago
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Log analytics is a big topic so I'll hit the main points. The approach you take to logging depends on the analysis you want to do after the log event has been recorded. The value of the logs diminishes rapidly as the age of the events get older. Most places want to keep the logs hot for a period ranging from a day to week. After that,the logs are compressed using gzip or Google snappy compression. Even though they are in a compressed form they should still be searchable. The most commont logging formats I've come across in production environments are: 1.log4j(java) or nlog(.NET) 2.json 3.syslog Tools that I've used to search ,visualize and analyse log data have been: 1.Elasticsearch, Logstash and Kibana (ELK) stack 2.splunk (commercial) 3.Logscape (commercial) Changes to the fields representing your data with the database approach is expensive because you are locked in by the schema. The database schema will never fully represent your full understanding of the data. With the tools I've mentioned above you have the option to extract ad-hoc fields at runtime. Hope this helps. |
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Seq is great because you can set up your own instance very near to your servers for low-latency/high-bandwidth logging, which really changes the game in terms of what you can feasibly (perf/financially) log. It also has some decent visualization options, and it's got some great integrations, with a decent plugin architecture to create your own real-time log processing code.
Logg.ly has some amazing GUI/search options.