Your job fails with a Python kernel is an unresponsive error message.
Fatal error: The Python kernel is unresponsive.
If the cluster runs out of memory, the Python kernel can crash.
This usually happens when running memory-intensive operations with relatively small instances or when running multiple notebooks or jobs in parallel on the same cluster.
Implement the following strategies to address the unresponsive Python kernel issue:
- Use job clusters for non-interactive jobs instead of all-purpose clusters. Refrain from running batch jobs on an all-purpose cluster.
- Ensure that your cluster configuration employs the appropriate type and size to effectively manage the anticipated workload. Consider increasing the cluster size by adding more worker nodes or augmenting the memory capacity of existing nodes.
- Optimize the data pipeline to decrease the amount of data processed simultaneously.
- Distribute workloads across multiple clusters if multiple notebooks or jobs are running simultaneously on the same cluster. Regardless of the cluster's size, there is only one Apache Spark driver node, which cannot be distributed within the cluster.
- If your operations are memory-intensive, verify that sufficient driver memory is available. Be cautious when using the following:
- The collect() operator, which transfers a large volume of data to the driver.
- Converting a substantial DataFrame to a pandas DataFrame.
- Monitor the cluster's performance using Ganglia metrics to identify potential issues and optimize resource usage.