SemiDBM is an attempt at improving the dumbdbm in the python standard library. It's a slight improvement in both performance and in durability. It can be used anywhere dumbdbm would be appropriate to use, which is basically when you have no other options available. It uses a similar design to dumbdbm which means that it does inherit some of the same problems as dumbdbm, but it also attempts to fix problems in dumbdbm, which makes it only a semi-dumb dbm :) It supports a "dbm like" interface:
import semidbm db = semidbm.open('testdb', 'c') db['foo'] = 'bar' print db['foo'] db.close() # Then at a later time: db = semidbm.open('testdb', 'r') # prints "bar" print db['foo']
A design goal of semidbm is to remain a pure python dbm. This makes installation easy and allows semidbm to be used on any platform that supports python.
Read the semidbm docs for more information.
Below are a list of some of the improvements semidbm makes over dumbdbm.
Single Data File
Instead of an index file and a data file, the index and data have been consolidated into a single file. This single data file is always appended to, data written to the file is never modified.
Data File Compaction
Semidbm uses a similar (but not identical) append only file format. This has
the potential to grow to large sizes as space is never reclaimed. Semidbm
addresses this by adding a
compact() method that will rewrite the
data file to a minimal size.
Semidbm is significantly faster than dumbdbm (keep in mind both are pure python libraries) in just about every way. The documentation shows the results of semidbm vs. other dbms, along with how to run the benchmarking script yourself.
- Not thread safe; can't be accessed by multiple processes.
- The entire index must fit in memory. This essentially means that all of the keys must fit in memory.