With all the hype around AI/ML in observability, it's more likely than ever that companies benefit from storing and viewing data in one system and training ML models in another.
Organizations with complex distributed systems that span dozens of teams can have a hard time following such practice without burning out the teams owning the client-facing services. A typical solution is to have alerts on all the layers of their distributed systems. This approach almost always leads to an excessive number of alerts and results in alert fatigue.
Adaptive Paging is an alert handler that leverages the causality from tracing and OpenTracing's semantic conventions to page the team closest the problem. From a single alerting rule, a set of heuristics can be applied to identify the most probable cause, paging the respective team instead of the alert owner.
append - to append the search result of one search with another (new search with/without same number/name of fields) search.
Usually to append final result of two searches using different method to arrive to the result (which can't be merged into one search)
appendpipe - to append the search results of post process (subpipeline) of the current resultset to current result set.
Typically to add summary of the current result set.
appendcols - to append the fields of one search result with other search result. Fields are added row-wise, 1st row of first search will be merged with 1st row of 2nd search.
Value of common fields between results will be overwritten by 2nd search result values.
Typically to show comparitive analysis of two search results in same table/chart.
Bookmarks on this page are managed by an admin user. 