A new release of fakeredis is out. This 0.3.0 release adds:

• Support for redis 2.6.
• Improved support for pipelines/watch/multi/exec.
• Full support for variadic commands.
• Better consistency with the actual behavior of redis.

And of course, a handful of bug fixes. This release was tested against:

• redis 2.6.4
• redis-py 2.6.2
• python 2.7.3, 2.6

You can install fakeredis via pip install fakeredis. Also check out:

• pypi
• github repo
• report issues

Semidbm is a pure python dbm. While the docs go into the specifics of how to use the dbm, I’d like to offer a more editorialized view of semidbm (the why of semidbm).

Semidbm is a pure python dbm, which is basically a key value store. Similar python modules in the standard library include gdbm, bsddb, and dumbdbm.

The first question one might ask is:

Another persistent key value store, really?

Fair question.

It all started when I was working on a project where I needed a simple key value store, accessible from python. Technically, I was using the shelve module, and it decided to use the Berkeley DB (via anydbm). So far so good. But there were a few issues:

• Not everyone has the Berkeley DB python bindings installed. Or in general, dbms that are based on C libraries have varying availability on people’s systems.
• Not all dbms perform equally.
• Not all dbms are portable.

C based DBMs and their availability

The first issue is regarding availability. Not all python installations are the same. Just because a user has python installed does not mean they necessarily have all the standard libraries installed. I just checked my python install on my Macbook, and I don’t have the bsddb module available. On my debian system I don’t have the gdbm module installed. Given that these packages are just python bindings to C based dbms, installing these packages involves:

• Install the C libraries and development packages for the appropriate dbm.
• Have a development environment that can build python.
• Rebuild python

None of these steps are that much work, but are there any alternatives?

Not all dbms perform equally

On all of my systems I have the dbm module available. This is a C based DBM that seems to available on most python installations. How fast is it? There’s a scripts/benchmark script available in the semidbm repo that can benchmark any dbm like module. Here’s the results for the dbm module:

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$ scripts/benchmark -d dbm
Generating random data.
Benchmarking: <module 'dbm' from
'/Users/jsaryer/.virtualenvs/semidbm/lib/python2.7/lib-dynload/dbm.so'>
    num_keys  : 1000000
    key_size  : 16
    value_size: 100
HASH: Out of overflow pages.  Increase page size

ERROR: exception caught when benchmarking <module 'dbm' from '/Users/jsaryer/.virtualenvs/semidbm/lib/python2.7/lib-dynload/dbm.so'>: cannot add item to database

Or in other words, it made it to about 450000 keys before this error was generated. So storing a large number of keys doesn’t seem possible with python’s dbm module.

Not all dbms are portable

While some dbms that aren’t available simply require compiling/installing the right packages and files, there are some dbms that just aren’t available on certain platforms (notoriously windows).

Well fortunately, there’s a fallback python module that’s guaranteed to be available on every single python installation: dumbdbm.

Unfortunately, the performance is terrible. There’s also a number of undesirable qualities:

• When a key is added to the DB, the data file is updated, but the index file is not updated, which means the data file and the index file are not in sync. If python crashed, any newly added/updated keys are lost.
• Every deletion writes out the entire index. This makes deletions painfully slow (O(n)).

To be fair, dumbdbm was most likely written as a last resort fallback to the more classic dbms. It’s also really old (written by Guido himself if I remember correctly).

A key value store with modest aspirations

Hopefully the goals of semidbm are becoming clearer. I just wanted a dbm that was:

1. Portable
2. Easily installable
3. Reasonably performance and semantics

The first two points I felt I could achieve by simply using python, and not requiring any C libraries or C extensions.

The third point I felt I could improve by taking dumbdbm and making some minor improvements.

So that’s the background of semidbm.

Can simpler really be better?

I think so. The benchmark page has more details regarding the performance, but as a quick comparison to semidbm:

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$ scripts/benchmark -d semidbm -n 10000
Generating random data.
Benchmarking: <module 'semidbm'>
    num_keys  : 10000
    key_size  : 16
    value_size: 100
fill_sequential     : time:     0.126,   micros/ops:    12.597,   ops/s:  79382.850,  MB/s:      8.782
read_hot            : time:     0.041,   micros/ops:     4.115,   ops/s: 243036.754,  MB/s:     26.886
read_sequential     : time:     0.039,   micros/ops:     3.861,   ops/s: 258973.197,  MB/s:     28.649
read_random         : time:     0.042,   micros/ops:     4.181,   ops/s: 239171.571,  MB/s:     26.459
delete_sequential   : time:     0.058,   micros/ops:     5.819,   ops/s: 171856.854,  MB/s:     19.012

$ scripts/benchmark -d dumbdbm -n 10000
Generating random data.
Benchmarking: <module 'dumbdbm'>
    num_keys  : 10000
    key_size  : 16
    value_size: 100
fill_sequential     : time:     1.824,   micros/ops:   182.400,   ops/s:   5482.447,  MB/s:      0.607
read_hot            : time:     0.165,   micros/ops:    16.543,   ops/s:  60450.332,  MB/s:      6.687
read_sequential     : time:     0.167,   micros/ops:    16.733,   ops/s:  59762.818,  MB/s:      6.611
read_random         : time:     0.175,   micros/ops:    17.505,   ops/s:  57126.529,  MB/s:      6.320
delete_sequential   : time:    99.025,   micros/ops:  9902.522,   ops/s:    100.984,  MB/s:      0.011

From the output above, writes are an order of magnitude faster (and semidbm computes and writes out a checksum for every value) and reads are almost 4 times faster. Deletion performance is much better (0.058 seconds vs. 99.025 seconds for deleting 10000 keys).

Also, every single insertion/update/deletion is immediately written out to disk so if python crashes, at worst you’d lose one key, the key that was being writen out to disk when python crashed.

Why you should use semidbm

I think if you ever need to use a pure python dbm, semidbm is a great choice. Any time you’d otherwise have to use dumbdbm, use semidbm instead.

Future plans for semidbm

There’s a number of things I’d like to investigate in the future:

• Faster db loading. Semidbm needs to read the entire data file to load the db. There’s potential to speed this up.
• Caching reads. Looking at the implementation of other dbms, many of them have some type of in memory cache to improve read performance.
• Support for additional db methods. Semidbm does not support all of the dict methods.
• Batch writes/reads. Due to the append only nature of the file format, this could substantially improve write performance.

For more info, check out the docs and the github repo.

Learning a new programming language can be a daunting task. Even though you start with the basic things like syntax, in order to become productive in the language you must learn things like

• Common coding idioms and patterns
• The standard library
• Best practices (including what frameworks to use, what development tools to use, etc)

But then there’s also the, for lack of a better term, “extra stuff.” The collection of miscellaneous tips and tricks you pick up while coding in the language on a day to day basis. These set of tips end up saving you a lot of time in the long run, but are hard to distinguish how useful a tip really is when you first hear about it.

Well, this is my list of tips. It’s not 100% complete, and focuses mostly on various tidbits of information that, when I think about how I code on a day to day basis, I find myself repeatedly doing.

The _ variable

This tip is useful when you’re in an interactive python shell. The _ variable stores the value of the most recently evaluated expression:

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>>> 1 + 2 + 3
6
>>> _ * 24
144
>>> _ / 12.
12.0
>>> [i for i in range(100) if i < 10]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> len(_)
10

Figuring Out Where to Look

Sometimes if you’re trying to debug a problem you’ll to need to figure out where a module is located. A really easy way to do this is to use the __file__ attribute of a module object:

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>>> import httplib
>>> httplib.__file__
'/usr/local/lib/python2.7/httplib.pyc'

You can also use inspect.getfile(obj) to find where an object is located.

Running Your Module as a Script

Every module will have a __name__ attribute, but the value of that attribute will depend on how the module is executed. Consider a module:

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print __name__

When the module is imported the name will be “foo”.

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>>> import foo
foo
>>>

However, when the module is executed as a script, the name will be __name__:

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$ python foo.py
__main__

It may not be obvious how this is useful. The way that this is typically used is to allow a module to be both imported and used as a script. Sometimes the script is a command line interface to the functionality available in the module. Sometimes the script provides a demo of the capabilities of the module. And sometimes the script runs any tests that live in the module (for example all of the doctests). To use this in your own library you can use something like this:

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def do_something(args):
    # Do something with args.
    pass

def main(argv=None):
    if argv is None:
        argv = sys.argv
    args = parse_args(argv)
    do_something(args)


if __name__ == '__main__':
    sys.exit(main())

The main() function is only called when the module is run directly.

The -m option

Once your module has an if __name__ == '__main__' clause (I usually refer to this as just the ifmain clause), an easy way to invoke the module is to use the -m option of python. This allows you to refer to a module by its import name rather than its specific path. In the previous example the foo.py module could be run using:

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$ python -m foo

One final thing worth pointing out is that many modules in python’s stdlib have useful ifmain functionality. A few notable ones include:

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python -m SimpleHTTPServer

This will serve the current working directory on port 8000. I use this command on almost a daily basis. From quickly downloading files to viewing html files on a remote server, this is one of the most useful ifmain clauses in the entire python standard library.

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python -m pdb myfile.py

Run a python script via pdb (the python debugger).

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python -m trace --trace myfile.py

Print each line to stdout before it’s executed. Be sure to see the help of the trace module, there’s a lot of useful options besides printing each line being executed.

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python -m profile myfile.py

Profile myfile.py and print out a summary.

So there it is. My list of tips. In the future I plan on expanding on some of these tips in more depth (the profiling workflow for python code and how to debug python code stand out), but in the meantime, may these tips be as helpful to you as they are to me.