Github user Tagar commented on the issue: https://github.com/apache/zeppelin/pull/2802 1) My main point was that this exception should be thrown to the user, so he or she has a chance to increase this limit. Currently if it breaks, only way to find out about this limitation is to enable debugging and not a lot of users can do that. 2) You're right .. it's 200M not sure how that user got that much data. That wasn't from my code, but from a colleague of mine. I guess it was a larger table of data. Would you mine making default somewhere in the range 16-32M? I think a lot of folks would run into the 4M limit. 3) Also, it would be great if IPythonInterpreter would catch exceptions better. Found another problem - https://issues.apache.org/jira/browse/ZEPPELIN-3239 - unrelated to this one, but it also shows the same symptoms to the user - Spark interpreter just becomes irresponsive.
---