代码运行时的内存问题(Python、Networkx) [英] Memory problems while code is running (Python, Networkx)

问题描述

我编写了一个代码来生成一个有 379613734 条边的图.

I made a code for generate a graph with 379613734 edges.

但是由于内存问题，代码无法完成.当它经过6200万行时，它会占用大约97%的服务器内存.所以我杀了它.

But the code couldn't be finished because of memory. It takes about 97% of server memory when it go through 62 million lines. So I killed it.

你有解决这个问题的想法吗?

Do you have any idea to solve this problem?

我的代码是这样的:

import os, sys
import time
import networkx as nx


G = nx.Graph()

ptime = time.time()
j = 1

for line in open("./US_Health_Links.txt", 'r'):
#for line in open("./test_network.txt", 'r'):
    follower = line.strip().split()[0]
    followee = line.strip().split()[1]

    G.add_edge(follower, followee)

    if j%1000000 == 0:
        print j*1.0/1000000, "million lines done", time.time() - ptime
        ptime = time.time()
    j += 1

DG = G.to_directed()
#       P = nx.path_graph(DG)
Nn_G = G.number_of_nodes()
N_CC = nx.number_connected_components(G)
LCC = nx.connected_component_subgraphs(G)[0]
n_LCC = LCC.nodes()
Nn_LCC = LCC.number_of_nodes()
inDegree = DG.in_degree()
outDegree = DG.out_degree()
Density = nx.density(G)
#       Diameter = nx.diameter(G)
#       Centrality = nx.betweenness_centrality(PDG, normalized=True, weighted_edges=False)
#       Clustering = nx.average_clustering(G)

print "number of nodes in G\t" + str(Nn_G) + '\n' + "number of CC in G\t" + str(N_CC) + '\n' + "number of nodes in LCC\t" + str(Nn_LCC) + '\n' + "Density of G\t" + str(Density) + '\n'
#       sys.exit()
#   j += 1

边缘数据是这样的:

1000    1001
1000245    1020191
1000    10267352
1000653    10957902
1000    11039092
1000    1118691
10346    11882
1000    1228281
1000    1247041
1000    12965332
121340    13027572
1000    13075072
1000    13183162
1000    13250162
1214    13326292
1000    13452672
1000    13844892
1000    14061830
12340    1406481
1000    14134703
1000    14216951
1000    14254402
12134   14258044
1000    14270791
1000    14278978
12134    14313332
1000    14392970
1000    14441172
1000    14497568
1000    14502775
1000    14595635
1000    14620544
1000    14632615
10234    14680596
1000    14956164
10230    14998341
112000    15132211
1000    15145450
100    15285998
1000    15288974
1000    15300187
1000    1532061
1000    15326300

最后，有没有人有分析推特链接数据的经验?我很难用有向图计算节点的平均/中值入度和出度.有什么帮助或想法吗?

Lastly, is there anybody who has an experience to analyze Twitter link data? It's quite hard to me to take a directed graph and calculate average/median indegree and outdegree of nodes. Any help or idea?

推荐答案

首先，您应该考虑是否可以添加更多 RAM.对内存使用情况进行一些估计，方法是根据您拥有的数据进行计算，或者通过读取各种大小的数据的子样本来衡量事物的规模.几 GB RAM 的适度成本可能会为您节省大量时间和麻烦.

First, you should consider whether you could just add more RAM. Make some estimates of memory usage, either by calculating based on the data you have or by reading in subsamples of the data of various sizes to measure how things scale. The modest cost of a few GB of RAM might spare you lots of time and trouble.

其次，考虑是否需要实际构建整个图.例如，您可以通过遍历文件并计数来确定顶点的数量及其度数 - 您一次只需要在内存中保留一行，加上计数，这将比图形小很多.知道度数后，您可以在找到最大的连通分量时从图中省略度数为 1 的任何顶点，然后对省略的节点进行校正.您正在进行数据分析，而不是实现一些通用算法:学习有关数据的简单知识以进行更复杂的分析.

Second, consider whether you need to actually build the whole graph. For example, you could determine the number of vertices and their degrees just by iterating through the file and counting - you'd only need to keep one line at a time in memory, plus the counts, which will be a lot smaller than the graph. Knowing the degrees, you could omit any vertices with degree one from the graph when finding the largest connected component, then correct for the omitted nodes afterwards. You're doing data analysis, not implementing some general algorithm: learn simple things about the data to enable more complicated analyses.

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