Apache Spark "Py4Jerror: Answer from Java Side Is Empty"
I Get This Error Every Time. .. I Use Sparkling Water. .. My Conf-File: ***"Spark. Driver. Memory 65G Spark. Python. Worker. Memory 65G Spark. Master...
I get this error every time... I use sparkling water... My conf-file:
***"spark.driver.memory 65g
spark.python.worker.memory 65g
spark.master local[*]"***
The amount of data is about 5 Gb. There is no another information about this error... Does anybody know why it happens? Thank you!
***"ERROR:py4j.java_gateway:Error while sending or receiving.
Traceback (most recent call last):
File "/data/analytics/Spark1.6.1/python/lib/", line 746, in send_command
raise Py4JError("Answer from Java side is empty")
Py4JError: Answer from Java side is empty
ERROR:py4j.java_gateway:An error occurred while trying to connect to the Java server
Traceback (most recent call last):
File "/data/analytics/Spark1.6.1/python/lib/", line 690, in start
self.socket.connect((self.address, self.port))
File "/usr/local/anaconda/lib/python2.7/socket.py", line 228, in meth
return getattr(self._sock,name)(*args)
error: [Errno 111] Connection refused
ERROR:py4j.java_gateway:An error occurred while trying to connect to the Java server
Traceback (most recent call last):
File "/data/analytics/Spark1.6.1/python/lib/", line 690, in start
self.socket.connect((self.address, self.port))
File "/usr/local/anaconda/lib/python2.7/socket.py", line 228, in meth
return getattr(self._sock,name)(*args)
error: [Errno 111] Connection refused
ERROR:py4j.java_gateway:An error occurred while trying to connect to the Java server
Traceback (most recent call last):
File "/data/analytics/Spark1.6.1/python/lib/", line 690, in start
self.socket.connect((self.address, self.port))
File "/usr/local/anaconda/lib/python2.7/socket.py", line 228, in meth
return getattr(self._sock,name)(*args)
error: [Errno 111] Connection refused"***
3 Answers
Usually, you'll see this error when the Java process get silently killed by the OOM Killer.
The OOM Killer (Out of Memory Killer) is a Linux process that kicks in when the system becomes critically low on memory. It selects a process based on its "badness" score and kills it to reclaim memory. Read more on OOM Killer here.
Increasing spark.executor.memory and/or spark.driver.memory values will only make things worse in this case, i.e. you may want to do the opposite!
Other options would be to:
- increase the number of partitions if you're working with very big data sources;
- increase the number of worker nodes;
- add more physical memory to worker/driver nodes;
Or, if you're running your driver/workers using docker:
- increase docker memory limit;
- set
--oom-kill-disableon your containers, but make sure you understand possible consequences!
Read more on --oom-kill-disable and other docker memory settings here.
Have you tried setting spark.executor.memory and spark.driver.memory in your Spark configuration file?
See for more info.
Another point to note if you are on wsl2 using pyspark. Ensure that your wsl2 config file has an increased memory.
# Settings apply across all Linux distros running on WSL 2
[wsl2]
# Limits VM memory to use no more than 4 GB, this can be set as whole numbers using GB or MB
memory=12GB # This was originally set to 3gb which caused me to fail since spark.executor.memory and spark.driver.memory was only able to MAX of 3gb regardless of how high i set it.
# Sets the VM to use eight virtual processors
processors=8
for reference. your .wslconfig config file should be located in C:\Users\USERNAME
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