The source code for the RedisCache class is available at https://github.com/kjam/wswp/blob/master/code/chp3/rediscache.py and, as with DiskCache, the cache can be tested with the link crawler in any Python interpreter. Here, we use IPython to employ the %time command:

In [1]: from chp3.advanced_link_crawler import link_crawler

In [2]: from chp3.rediscache import RedisCache

In [3]: %time link_crawler('http://example.webscraping.com/', '/(index|view)', cache=RedisCache())
Downloading: http://example.webscraping.com/
Downloading: http://example.webscraping.com/index/1
Downloading: http://example.webscraping.com/index/2
...
Downloading: http://example.webscraping.com/view/Afghanistan-1
CPU times: user 352 ms, sys: 32 ms, total: 384 ms
Wall time: 1min 42s

In [4]: %time link_crawler('http://example.webscraping.com/', '/(index|view)', cache=RedisCache())
Loaded from cache: http://example.webscraping.com/
Loaded from cache: http://example.webscraping.com/index/1
Loaded from cache: http://example.webscraping.com/index/2
...
Loaded from cache: http://example.webscraping.com/view/Afghanistan-1
CPU times: user 24 ms, sys: 8 ms, total: 32 ms
Wall time: 282 ms

The time taken here is about the same as our DiskCache for the first iteration. However, the speed of Redis is really seen once the cache is loaded, with a more than 3X speed increase versus our non-compressed disk cache system. The increased readability of our caching code and the ability to scale our Redis cluster to a high availability big data solution is just the icing on the cake!