<div dir="ltr"><div><div><div>Eric,<br><br></div>That's an interesting problem. I would be tempted to wrap one of those three (custom appender, filter, or turbo-filter) around a Metrics object:<br><br><a href="https://dropwizard.github.io/metrics/3.1.0/">https://dropwizard.github.io/metrics/3.1.0/</a><br><br></div>Being that rates are one of the things that Metrics supports out of the box.<br><br></div>Donald<br></div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Nov 13, 2014 at 1:21 PM, Eric Kolotyluk <span dir="ltr"><<a href="mailto:eric.kolotyluk@gmail.com" target="_blank">eric.kolotyluk@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">I found an interesting problem while configuring our Amazon Kinesis Connector. After starting up our service everything looks fine, but after 15 minutes or so, all hell breaks loose.<br>
<br>
The Connector is Amazon's code, and it does not throw any exceptions that our code can catch, rather it just reports a lot of errors in the log stream. This is clearly an insane situation that we need to catch, so I was thinking of writing a custom appender, filter, or turbo-filter to monitor the log stream for insanity. For example, errors per minute over some threshold.<br>
<br>
Does this seem like the right way to address this problem? Have other people used similar techniques, or other techniques to sanity check the log stream?<br>
<br>
Cheers, Eric<br>
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