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MongoDB covers a wide range of database examples and use cases, supporting key-value pair data concepts. With its flexible schema and rich query language with secondary indexes, MongoDB is a compelling store for “key-value” data.
Key-value stores are not considered suitable for applications requiring frequent updates or for complex queries involving specific data values, or multiple unique keys and relationships between them.
InfluxDB as a key-value store.
LMDB is the most memory-efficient database around http://symas.com/mdb/inmem/
and also proven to be the most reliable - completely crash-proof. http://wisdom.cs.wisc.edu/workshops/spring-14/talks/Thanu.pdf
Of the ones you've mentioned, Tokyo Cabinet has documented corruption issues https://www.google.com/search?q=cfengine+tokyo+cabinet+corruption
BerkeleyDB also has well-documented corruption issues, as does Bitcask. (And bitcask is an in-memory-only DB anyway, so useless for your 1MB RAM requirement.)
LMDB is also well-supported in Python, with a couple different bindings available. https://github.com/dw/py-lmdb/ https://github.com/tspurway/pymdb-lightning
Disclaimer - I am the author of LMDB. But these are documented facts: LMDB is the smallest, most efficient, and most reliable key/value store in the world and nothing else comes anywhere close.
I've had good luck with the Tokyo Cabinet/pytc solution. It's very fast (a bit faster than using the shelve module using anydbm in my implementation), both for reading and writing (though I too do far more reading). The problem for me was the spartan documentation on the python bindings, but there's enough example code around to figure out how to do what you need to do. Additionally, tokyo cabinet is quite easy to install (as are the python bindings), doesn't require a server (as you mention) and seems to be actively supported (stable but no longer under active development). You can open files in read-only mode, allowing concurrent access, or read/write mode, preventing other processes from accessing the database.
I was looking at various options over the summer, and the advice I got then was this: try out the different options and see what works best for you. It'd be nice if there were simply a "best" option, but everyone is looking for slightly different features and are willing to make different trade-offs. You know best.
(That said, it'd be useful to others if you shared what ended up working the best for you, and why you chose that solution over others!)
cdb can handle any database up to 4 GB, making it too small for the 20GB matter at hand.
Riak runs on Linux, and allows you to dynamically add nodes
how about Python 3.0's dbm.ndbm ?
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