Most recently, I’m working on Redis which is a key-value datastore with interesting characteristics. It’s ultra fast and has built in atomic operations to handle concurrent usage. Although everything lives in-memory, Redis syncs with hard disk time to time to serve as permanent storage. Most impressively, downloading Redis and making a working build doesn’t take more than a few minutes.
Redis explains itself as a non-volatile memcached with various in-built data structures. Instead of only key and string value pairs, you can have lists and sets as values. There are atomic pop/push operations to work on these structures and increment/decrement functionality to work on numeric values.
Several client libraries are available including Perl, Python, Erlang, C++, Ruby, Scala and PHP. To write a more meaningful post, I’d like to add lines from a simple Python script.
import redis
storage = redis.Redis()
storage.keys("a*") # returns keys starting with a
storage.get("key1") # returns the value of "key1"
storage.set("key1", "hello world") # setting the value of "key1"
storage.delete("key1") # deletes the pair with key1.
# working on lists
storage.push('key2', 'This is the first value', tail=True)
storage.push('key2', 'This is the second value', tail=True)
print storage.pop('key2')
Most of these methods are ported to client libraries and are available in downloadable Redis archive.
Although Redis can not be distributed, it’s easy to set up a slave node to replicate the master. Since it syncs with hard-disk in certain intervals, there might be data-loss in possible system crashes. So, setting up a slave may decrease the risks. It’s also advised to use Redis on a central server and manage sharding in the application level.
Burcu Dogan 