Performance comparison: key/value stores for language model counts | AI and Social Science – Brendan O'Connor
225 tweets/sec Eventually, I’ll want a purely in-memory, distributed table. That’s why I was interested in Memcached. But for development purposes, it’s very convenient to use an on-disk database. In the past I’ve used BerkeleyDB for this. (An SQL database is also possible but seems like overkill.) Ideally it would be nice to have a distributed key-value store with a heavy caching layer. Check out
python-tokyotyrant - スコトプリゴニエフスク通信
http://code.google.com/p/python-tokyotyrant/ from tokyotyrant import open tt = open("127.0.0.1", 1978) tt.put("spam", "SPAM") assert tt.get("spam") == "SPAM" for key in ("spam egg", "spam spam egg", "ham egg"): tt.put(key, "YUMMY!") tt.fwmkeys("spam") # ["spam egg", "spam spam egg"] tt.get_multi(["spam egg", "ham egg"]) # {'spam egg': 'YUMMY!', 'ham egg': 'YUMMY!'}
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