今天在用Streaming-Python处理一个MapReduce程序时，发现reducer失败，原由于耗费内存达到极限了。细致查看代码时，发现有一个集合里保存着URL，而URL长度是比較长的，直接保存确实是耗费内存，于是想到用压缩存储，然后用的时候再解压，尽管处理时间添加。可是耗费内存大大减少！
详细就是使用zlib模块
import zlib
raw_data = "hello,world,ooooooooooooxxxxxxxxxxx"
zb_data = zlib.compress(raw_data)
print "len(raw_data)=%d, len(zb_data)=%d, compression ratio=%.2f"
% (len(raw_data), len(zb_data), float(len(zb_data))/len(raw_data))
# len(raw_data)=35, len(zb_data)=25, compression ratio=0.71
raw_data2 = zlib.decompress(zb_data)
print raw_data2
假设存在网络传输。上面的方法可能失效；比如我跑了一个MapReduce，mapper中压缩，reducer中解压，结果报错：
Traceback (most recent call last):
File "/hadoop/yarn/local/usercache/lming_08/appcache/application_1415110953023_46173/container_1415110953023_46173_01_000018/./build_visitor_company_ulti_info_red.py", line 25, in <module>
urllist += zlib.decompress(urlitem) + ""
zlib.error: Error -3 while decompressing data: incorrect header check
log4j:WARN No appenders could be found for logger (org.apache.hadoop.hdfs.DFSClient).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
临时还没找到有效办法。