> For the complete documentation index, see [llms.txt](https://dataflint.gitbook.io/dataflint-for-spark/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dataflint.gitbook.io/dataflint-for-spark/advanced/alerts.md).
# Alerts
## Summary
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.
## Alerts
### Reading Small Files
\[Also works for Apache Iceberg tables]
### Writing Small Files
### Apache iceberg - inefficient replace of data
### 
### Partition Skew
### Large Number Of Small Tasks
### Memory Over-Provisioning
### Memory Under-Provisioning
### High wasted cores rate
### Large Data Broadcast
### 
## Broadcast small table in Sort Merge Join
## Large Cross Join Scan
## Large Partition Size
## Long Filter Conditions
## Query Failures
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
## Alerts roadmap
1. High task error rate
2. High executors error rate
3. High disk spill relative to input size and available memory.
4. repartition before write with low cardinality that causes lack of parallelism or huge files.
5. Executor memory overhead is too low and causes container failure