I've been analyzing our DevOps dashboards, and while they show metrics like CPU use, memory consumption, and execution times, they always tell me my Spark jobs are slow without explaining why. I often find myself deep in logs at odd hours, trying to guess if the issue is due to a skewed join, a shuffle problem, or possibly an underperforming cluster. It feels more like I'm chasing ghosts rather than actually fixing anything. Is there a tool or method out there that can help me dig deeper into Spark's workings and identify the real issues instead of just providing surface-level metrics?
3 Answers
It seems you're relying on metrics for troubleshooting problems that may actually need deeper insights. Have you considered using logs or tracing? They can pinpoint which specific queries are slow and give you more context on what's happening under the hood. If the dashboards aren’t meeting your needs, maybe it's worth building or editing your own dashboards to include the specifics you're after.
I totally get your frustration! Dashboards often fall short once the real issues kick in. We started utilizing Dataflint, and it was a game-changer. It highlighted problems like skewed joins and shuffles quickly, turning what used to be hours of troubleshooting into mere minutes.
I hear you! Discovering the root cause can be tough with just surface metrics. Understanding how information flows in your system better might help. Checking out resources like the strace manpage or diving into systems thinking might give you some new perspectives to tackle the issues. It's frustrating, but a holistic view can lead you to the right solutions.
Related Questions
Can't Load PhpMyadmin On After Server Update
Redirect www to non-www in Apache Conf
How To Check If Your SSL Cert Is SHA 1
Windows TrackPad Gestures