⏰Alerts
Last updated
Last updated
One of the unique "selling points" of DataFlint is the alerting system. Where in traditional spark monitoring you get a bunch of metrics and needs to figure out what they mean, with DataFlint you have alerts that points you to what is wrong and suggest you on potential fixes.
[Also works for Apache Iceberg tables]
Another type of "alert" is query failure. When hovering on the alert icon, DataFlint extract the error from the scary JVM stack trace and show it in the top of the message.
When you press the query you can see the exact place on the logical plan the query failed, in our case it's in the stage relates to writing files in the end of the query plan
High task error rate
High executors error rate
High disk spill relative to input size and available memory.
Nested for-loop join with high cardinality
repartition before write with low cardinality that causes lack of parallelism or huge files.
Executor memory overhead is too low and causes container failure
