memory

问题 I understand the difference between new gen / old gen /perm gen, but I don't know what "To Space" and "From Space" are. I am seeing my "From Space" hitting 99.8% used while the "To Space" always seems to stay at 0% used. 回答1: Two regions in the garbage-collection algorithm that is used in the VM. Java specifics are found here: How Garbage Collection works in Java And a general explanation about "from space" and "to space": WP The most straightforward approach is the semi-space collector,

问题 I understand the difference between new gen / old gen /perm gen, but I don't know what "To Space" and "From Space" are. I am seeing my "From Space" hitting 99.8% used while the "To Space" always seems to stay at 0% used. 回答1: Two regions in the garbage-collection algorithm that is used in the VM. Java specifics are found here: How Garbage Collection works in Java And a general explanation about "from space" and "to space": WP The most straightforward approach is the semi-space collector,

问题 I was trying to understand memory barrier and came across the below wikipedia link http://en.wikipedia.org/wiki/Memory_barrier This explain the concept well but had thoughts if this is really helpful in system where we have mutex() locking the memory section. Taking the same code as mentioned in wikipedia, will the below approach solve the problem using mutex? [Note: Function names are not specific to any programming language, just used for simplicity sake] Processor #1 mutex_lock(a) while (f

问题 I was trying to understand memory barrier and came across the below wikipedia link http://en.wikipedia.org/wiki/Memory_barrier This explain the concept well but had thoughts if this is really helpful in system where we have mutex() locking the memory section. Taking the same code as mentioned in wikipedia, will the below approach solve the problem using mutex? [Note: Function names are not specific to any programming language, just used for simplicity sake] Processor #1 mutex_lock(a) while (f

问题 If I am building a graph on AWS Neptune with 20 million Nodes and 100 million edges, How much RAM and Disk space would I require? Can someone give me an rough order of magnitude estimate 回答1: Storage capacity in Amazon Neptune is dynamically allocated as you write data into a Neptune cluster. A new cluster starts out with 10GB allocated and then grows in 10GB segments as your data grows. As such, there's no need to pre-provision or calculate storage capacity prior to use. A Neptune cluster

问题 If I am building a graph on AWS Neptune with 20 million Nodes and 100 million edges, How much RAM and Disk space would I require? Can someone give me an rough order of magnitude estimate 回答1: Storage capacity in Amazon Neptune is dynamically allocated as you write data into a Neptune cluster. A new cluster starts out with 10GB allocated and then grows in 10GB segments as your data grows. As such, there's no need to pre-provision or calculate storage capacity prior to use. A Neptune cluster

问题 I would like to iterate over a vector several times: let my_vector = vec![1, 2, 3, 4, 5]; let mut out_vector = vec![]; for i in my_vector { for j in my_vector { out_vector.push(i * j + i + j); } } The j-loop has a "value used here after move" error. I know that I can place an & before the two my_vector s and borrow the vectors, but it is nice to have more than one way to do things. I would like a little insight as well. Alternatively, I can write the following: let i_vec = vec![1, 2, 3, 4, 5,

问题 I would like to iterate over a vector several times: let my_vector = vec![1, 2, 3, 4, 5]; let mut out_vector = vec![]; for i in my_vector { for j in my_vector { out_vector.push(i * j + i + j); } } The j-loop has a "value used here after move" error. I know that I can place an & before the two my_vector s and borrow the vectors, but it is nice to have more than one way to do things. I would like a little insight as well. Alternatively, I can write the following: let i_vec = vec![1, 2, 3, 4, 5,

问题 Analysing a heap dump I look for instances of java.lang.ref.Finalizer class. java.lang.ref.Finalizer has 'next' and 'prev' member fields for maintaining linked list. I always get FileInputStream as a tail of the list and FileOutputStream as previous entry to it (analysed several heap dumps). File descriptors for FileInputStream and FileOutputStream are always 0 and 1 respectively: +---[Pending Finalization] java.lang.ref.Finalizer | | | +---queue java.lang.ref.ReferenceQueue [Stack Local] | |

问题 I have the following workflow, whereby I append data to an empty pandas Series object. (This empty array could also be a NumPy array, or even a basic list.) in_memory_array = pd.Series([]) for df in list_of_pandas_dataframes: new = df.apply(lambda row: compute_something(row), axis=1) ## new is a pandas.Series in_memory_array = in_memory_array.append(new) My problem is that the resulting array in_memory_array becomes too large for RAM. I don't need to keep all objects in memory for this