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ADE can process a large numbers of logs from a large number of Linux systems to
create a compact summary of those logs. The summary identifies and
consolidates similar text strings into a single message and assigns it
a key (message id). The summary determines if the
message id are being issued when expected, are being issued at the expected
rate during a time slice, and how often during the day are the message
or a similar message (same message id) issued.
You can use those results to examine
- A set of logs to find anomalies which may be helpful when attempting to find the root cause of a problem or incident
- The currently generated logs to find anomalies which may be helpful when attempting to find the cause of an on-going problem or incident
More details about ADE are available in Github pages http://openmainframeproject.github.io/ade/ .
- Where information extracted by ADE is stored
- How ADE organize information for analysis
- How does ADE find unusual messages within an interval
- How does ADE find unusual intervals
- Why does ADE use a one hour interval length, but provide ten minute time slices
- Examples of how to update style sheets to improve the visualization of ADE output
- Examining ADE data using R to understand the behavior of your systems
- How to make a contribution to ADE
- How to look at information stored in JDBC compliant database
- [Presentation by James Caffrey at Marist ECC 2016 conference](ADE - finding the unusual in logs.ppt)
Made contain links to external web pages that are not maintained by ADE