garbage-collection

问题 In Python, if you either open a file without calling close() , or close the file but not using try - finally or the " with " statement, is this a problem? Or does it suffice as a coding practice to rely on the Python garbage-collection to close all files? For example, if one does this: for line in open("filename"): # ... do stuff ... ... is this a problem because the file can never be closed and an exception could occur that prevents it from being closed? Or will it definitely be closed at

问题 In Python, if you either open a file without calling close() , or close the file but not using try - finally or the " with " statement, is this a problem? Or does it suffice as a coding practice to rely on the Python garbage-collection to close all files? For example, if one does this: for line in open("filename"): # ... do stuff ... ... is this a problem because the file can never be closed and an exception could occur that prevents it from being closed? Or will it definitely be closed at

问题 In Python, if you either open a file without calling close() , or close the file but not using try - finally or the " with " statement, is this a problem? Or does it suffice as a coding practice to rely on the Python garbage-collection to close all files? For example, if one does this: for line in open("filename"): # ... do stuff ... ... is this a problem because the file can never be closed and an exception could occur that prevents it from being closed? Or will it definitely be closed at

问题 I was doing data wrangling with Rstudio, and while I was doing something with a very large dataset, the process died. I restarted the computer, but ever since Rstudio has not been responsive or slow due to memory limits (currently, it's occupying 8gb of my 16gb RAM). I tried doing all the standard things I found on Stackoverflow. gc() and gc(reset=T) rm(list = ls()) .rs.restartR() Restart my computer. But when I open Rstudio, the memory usage would quickly climb and make the entire thing

问题 I was doing data wrangling with Rstudio, and while I was doing something with a very large dataset, the process died. I restarted the computer, but ever since Rstudio has not been responsive or slow due to memory limits (currently, it's occupying 8gb of my 16gb RAM). I tried doing all the standard things I found on Stackoverflow. gc() and gc(reset=T) rm(list = ls()) .rs.restartR() Restart my computer. But when I open Rstudio, the memory usage would quickly climb and make the entire thing

问题 I was doing data wrangling with Rstudio, and while I was doing something with a very large dataset, the process died. I restarted the computer, but ever since Rstudio has not been responsive or slow due to memory limits (currently, it's occupying 8gb of my 16gb RAM). I tried doing all the standard things I found on Stackoverflow. gc() and gc(reset=T) rm(list = ls()) .rs.restartR() Restart my computer. But when I open Rstudio, the memory usage would quickly climb and make the entire thing

问题 I have a Springboot Maven project that uses a @JmsListener to read messages from a queue. If no events are coming in, the heap memory slowly increases. When messages are coming the heap memory is ramping up fast. But the heap memory never comes down (check image below). If I add System.gc() atthe end of the receiver method The garbage collector is doing its job as expected. But this is definately not good practice. How can I ensure that gc will runn at appropriate times. Any help would be

问题 I have a Springboot Maven project that uses a @JmsListener to read messages from a queue. If no events are coming in, the heap memory slowly increases. When messages are coming the heap memory is ramping up fast. But the heap memory never comes down (check image below). If I add System.gc() atthe end of the receiver method The garbage collector is doing its job as expected. But this is definately not good practice. How can I ensure that gc will runn at appropriate times. Any help would be

问题 I have been running a workflow on some 3 Million records x 15 columns all strings on my 4 cores 16GB machine using pyspark 1.5 in local mode. I have noticed that if I run the same workflow again without first restarting spark, memory runs out and I get Out of Memory Exceptions. Since all my caches sum up to about 1 GB I thought that the problem lies in the garbage collection. I was able to run the python garbage collector manually by calling: import gc collected = gc.collect() print "Garbage

问题 I have been running a workflow on some 3 Million records x 15 columns all strings on my 4 cores 16GB machine using pyspark 1.5 in local mode. I have noticed that if I run the same workflow again without first restarting spark, memory runs out and I get Out of Memory Exceptions. Since all my caches sum up to about 1 GB I thought that the problem lies in the garbage collection. I was able to run the python garbage collector manually by calling: import gc collected = gc.collect() print "Garbage