Forum Discussion
Py4JJavaError: An error occurred while calling o84455.csv.
Thanks for clarifying! So you’re using coalesce(1) because you need a single CSV file, then you move it to SFTP. That makes sense.
But keep in mind:
- Using coalesce(1) on a big DataFrame forces all the data to one node/worker, which can cause memory issues or serialization errors—especially with large datasets like yours.
- That’s usually why the Spark job fails or throws those message size/serialization errors.
Possible solutions:
If you must have a single file, try to:
- Increase spark.rpc.message.maxSize even more (if you haven’t already).
- Make sure your cluster has enough memory/resources for one node to handle the whole DataFrame.
- If possible, filter or reduce your data before doing coalesce(1) to make the final file smaller.
Alternative approach (if you keep hitting errors):
- Write the CSV without coalesce(1) (so you get multiple part files).
- Combine those part files into one CSV outside of Spark (with a shell script, Python, etc.) before SFTP transfer.
Let me know if you still get errors or want help with merging the part files after export!
It looks like you’re hitting the error when writing out your CSV with:
df_clean.coalesce(1).write.mode("overwrite").option("header", "true").csv(onelake_path)
with a pretty big DataFrame (349,927 rows, 24 columns).
If you’re still facing the same Spark error about message size or task serialization, it’s likely because:
- The data being written is too large for the default Spark RPC message size.
- The config change (spark.rpc.message.maxSize) might not be picked up by all nodes, or it may need to be increased even more.
Things to try:
- Double check you set the config before creating your SparkSession (as in my previous post).
- Try bumping up spark.rpc.message.maxSize higher (4096, 8192, etc).
- If possible, avoid coalesce(1) (writing as a single file) for huge data—it forces all data to one node, which can cause memory or serialization issues.
- Try writing without coalesce(1) and see if it works.
If you can share the exact error message you’re getting here, it may help pinpoint the issue better!
Thanks for clarifying! So you’re using coalesce(1) because you need a single CSV file, then you move it to SFTP. That makes sense.
But keep in mind:
- Using coalesce(1) on a big DataFrame forces all the data to one node/worker, which can cause memory issues or serialization errors—especially with large datasets like yours.
- That’s usually why the Spark job fails or throws those message size/serialization errors.
Possible solutions:
If you must have a single file, try to:
- Increase spark.rpc.message.maxSize even more (if you haven’t already).
- Make sure your cluster has enough memory/resources for one node to handle the whole DataFrame.
- If possible, filter or reduce your data before doing coalesce(1) to make the final file smaller.
Alternative approach (if you keep hitting errors):
- Write the CSV without coalesce(1) (so you get multiple part files).
- Combine those part files into one CSV outside of Spark (with a shell script, Python, etc.) before SFTP transfer.
Let me know if you still get errors or want help with merging the part files after export!
- spark = SparkSession.builder \.appName("Increase RPC Message Size") \.config("spark.rpc.message.maxSize", "8192") \.getOrCreate()what is the maximum size it can be increased? even 8192 fails.
Hi tan_thiamhuat ,
Great follow-up and thanks for sharing your code and error details.
For spark.rpc.message.maxSize, there isn’t a fixed “maximum” documented, but in practice, most Spark clusters won’t let you go much above 2047 MB due to JVM and system-level constraints. Setting it higher (like 8192) almost always fails, and even if it works, handling such a large message in a single node is very risky for memory/serialization.
Best practice:
- Try to avoid coalesce(1) for very large dataframes.
- If you must create a single CSV, write with default partitioning (you’ll get multiple part files), then merge those files outside Spark (e.g., with a shell script or Python).
- If you need to filter, sample, or aggregate your data before exporting, that can help reduce file size and avoid hitting these limits.
If you want, I can share a sample script for merging part files after export. Let me know if you still get errors or if you want to try another approach!
Hope this helps!
Combine those part files into one CSV outside of Spark (with a shell script, Python, etc.) before SFTP transfer --> how can this be achieved? with Python code? I need it to be automated too.