Tag Archives: python

Testing Python and PostgreSQL on Windows, Part 4

At the end of Part 2, I suggested those who were anxious to start testing could try python tests\dbobject\test_schema.py right after installing psycopg2, and implied everything would work just fine by showing the output of a successful run.

That was deceptive because before running the Pyrseas tests you need to create a PostgreSQL user. So you first need to connect using psql or pgAdmin, as the postgres user and issue a command such as the following:


Of course, username and password should be your user name and a suitable password, respectively. While you’re at it, you can also create the two testing roles:


The user username needs the CREATEDB privilege to create the Pyrseas test database, by default, pyrseas_testdb. Alternatively, the test database could be created first, e.g., by postgres and owned by username. Also, if username isn’t granted the SUPERUSER privilege then the tests requiring it will be skipped.

One last detail before being able to run the tests: create a PostgreSQL password file, i.e., %APPDATA%\postgresql\pgpass.conf. The format is straightforward:


Make sure you have the latest pyrseas/testutils.py. It includes a change to make the tests portable to Windows. You’re now ready to give all the tests a whirl:

python tests\dbobject\__init_py

or you may prefer to use test discovery:

python  -m unittest discover -s tests/dbobject

One of the first issues you’ll notice is when running test_tablespace.py, which exercises support for PostgreSQL tablespaces. According to the documentation, “tablespaces can be used only on systems that support symbolic links” (aside: thanks to Andres Freund and Merlin Moncure for answering questions about this on IRC).

It seems Windows Vista (or even XP) may have something akin to symbolic links (junctions?). Nevertheless for Pyrseas, it is sufficient to create directories for the tablespaces, e.g.,

C:\>md c:\somedir\pg\9.1\ts1
C:\>md c:\somedir\pg\9.1\ts2

However, when you try to define the tablespace from psql, you’ll probably see:

postgres=# CREATE TABLESPACE ts1 LOCATION E'C:\\somedir\\pg\\9.1\\ts1';
ERROR:  could not set permissions on directory "C:/somedir/pg/9.1/ts1": Permission denied

The problem is that the directory needs to be owned by the postgres user. Unfortunately, there is nothing equivalent to chown on native Windows XP Home. Andres had mentioned cacls and after some web searching and trying, I came up with the following commands as the nearest approximation to chown:

cacls c:\somedir\pg\9.1\ts1 /E /G postgres:F
cacls c:\somedir\pg\9.1\ts2 /E /G postgres:F

These give the postgres user FULL owner privileges over the directories. If you invoke the CREATE TABLESPACE statements again, then they should succeed … Well, at least on PostgreSQL 8.4, 9.0 and 9.1, the cacls commands allow the tablespaces to be defined. However, under 9.2 the “Permission denied” error persists (still haven’t figured out why—if you know why, do leave a comment).

Update: Thank to alemarko’s comment below, I figured out that for PG 9.2, assuming you installed with the defaults, the correct incantations  to use are:

cacls c:\somedir\pg\9.2\ts1 /E /G networkservice:F
cacls c:\somedir\pg\9.2\ts2 /E /G networkservice:F

Testing Python and PostgreSQL on Windows, Part 3

As a commenter mentioned in response to Part 2, an alternative to using pip install psycopg2, which requires that you first install VC++ 2008 Express, is to download and install the Windows port, aka win-psycopg. Jason Erickson makes these builds available for several versions of Python for both 32- and 64-bit Windows.

If you’re not planning to use virtualenvs or tox (which creates virtualenvs for you), then the win-psycopg installer is the easiest way to satisfy the psycopg2 dependency (in fact, that’s how I started after the initial failure with pip). Simply download the Python 2.7 and 3.2 installers, run them and you’re done. However, the installers don’t work in a virtualenv (there is a workaround to extract the files, but I didn’t explore it because I wanted to use tox).

Another option is to build psycopg2 with the MinGW compiler. Daniele Varrazzo has a post describing this. Daniele used a special MinGW package, but I chose to install the latest (mingw-get-inst-20120426.exe) from MinGW.org (click on the Navigation – Downloads link which will take you to SourceForge).

To get the psycopg2 sources, you can download the .tar.gz package, or, since you have Git, do this from Git Bash:

git clone git://luna.dndg.it/public/psycopg2.git
cd psycopg2
git checkout 2_4_5  # or latest tag

Create a pydistutils.cfg file in your home directory (%USERPROFILE%) with the following (an alternative is to use the --compiler option to the python setup.py command below):


Make sure MinGW, Python, and PostgreSQL are in your PATH, e.g., set PATH=C:\MinGW\bin;C:\Python27;C:\Program Files\PostgreSQL\8.4\bin;%PATH%, and then run:

python setup.py build_ext build

With the latest MinGW and unless Python bug 12641 has been resolved, you’ll see the following error message:

cc1.exe: error: unrecognized command line option '-mno-cygwin'

The workaround is to edit the cygwincompiler.py file in your Python Lib\distutils directory and remove all instances of -mno-cygwin. Hopefully after that, the python setup.py above will work and then you can run the following to install it:

python setup.py install

Rinse and repeat for the Python 3.2 version and you should be ready to test connecting from both Python versions to PostgreSQL.

Aside: To remove the pip-installed psycopg2, you run pip uninstall psycopg2 from the corresponding Python environment. To remove win-psycopg, you use Control Panel’s Add or Remove Programs and click Remove on the desired version. To remove the versions installed with MinGW, I’m afraid you’ll have to resort to deleting the Lib\site-packages\psycopg2 directories and the related psycopg2-*.egg-info files.

Testing Python and PostgreSQL on Windows, Part 2

In the previous post, I covered installation of Git, PostgreSQL and Python under Windows in order to set up a Pyrseas testing and development environment. Today, we’ll explore installation of the Python dependencies.

The Hitchhiker’s Guide to Python recommends first downloading and running the distribute_setup.py script. This gives you the easy_install command but the Guide recommends installing pip (with easy_install pip) and then using pip to install all other modules.

You can use pip to install pyyaml with the following command:

pip install pyyaml

However, if you try pip install psycopg2 (or even easy_install psycopg2), it’s very likely you’ll see the error:

error: Unable to find vcvarsall.bat

As best as I’ve been able to determine the only way to get around this is by installing Microsoft Visual Express Studio. According to this email and this post, for Python 2.7, it must be the 2008 Express Studio which, to make things interesting, is no longer available from the download links given. If you search enough you may find it here (download vcsetup.exe) (Update below). After installing VC++ 2008 Express (and if you haven’t installed Strawberry Perl—a later installment in our saga), the pip install psycopg2 command should succeed.

However, if you try to import psycopg2 at the Python 2.7 prompt you may be surprised with a traceback ending in:

    from psycopg2._psycopg import BINARY, NUMBER, STRING, DATETIME, ROWID
ImportError: DLL load failed: The specified module could not be found.

Ahh … the mysteries of Windows DLLs. Don’t despair: this probably means you don’t have the PostgreSQL DLLs (libpq.dll in particular) in your PATH. Add one of the postgres\x.x\bin directories to your PATH and (hopefully) you should then be able to connect from Python 2.7 to your PostgreSQL installations.

OK, let’s turn our attention to Python 3.2. If you followed the Hitchhiker’s Guide instructions previously and added C:\Python27 to your PATH, you’ll now have to change that to C:\Python32. Suggestion: create a couple of batch scripts, e.g., env27.bat and env32.bat, so you can easily switch between the two Python installations. And don’t forget to add the postgres\x.x\bin directory as well.

For 3.2, once again run the distribute_setup.py script, easy_install pip, and pip install pyyaml, as for 2.7 above. Then you can run pip install psycopg2, and if you installed VC++ previously, the gods may smile upon you and you may see the following message:

Successfully installed psycopg2
Cleaning up...

At this point, if you followed along, you’ll have four versions of PostgreSQL (8.4 through 9.2), two versions of Python (2.7 and 3.2), each with PyYAML and psycopg2, ready for testing. If you’re anxious to check things out, invoke one of the PATH setup scripts and try the following, from the Pyrseas source directory:

C:\...\src\Pyrseas>python tests\dbobject\test_schema.py
Ran 12 tests in 1.452s


There are some alternatives to installing psycopg2 using pip and VC++ 2008.  I’ll cover those in a subsequent post.

Update: Microsoft seems to keep changing download URLs. Your best bet is to search for “Visual C++ 2008 Express download.” Currently, that should lead you to the following download link.

Testing Python and PostgreSQL on Windows – Basics

In my previous post, I wrote:

Although I have not yet personally run the [Pyrseas] unit tests on Windows …, I believe the tox setup should be quite portable …, since the tests only depend on Python and psycopg2 being able to connect to Postgres, i.e., they do not depend on running any PG utilities from the command line.

Several moons ago, I had done a cursory test of the Pyrseas utilities on Windows from a source zip file, but now I wanted to set up a full development environment (well, almost full—I used Notepad for minor editing) and run through all the unit tests on as many Python/PostgreSQL combinations as possible and ideally using tox.

This post describes what I found out during the install/test process. Hopefully others will find it useful.

Operating System

I chose to use Windows XP Home Edition running under VirtualBox. It’s not a professional solution, but I wasn’t prepared to pay for the “privilege” of using Windows and it’s likely others also have a home edition CD or similar media from an earlier hardware purchase.

Version Control

Pyrseas sources are stored on GitHub. Installing Git and cloning the repository was probably the most uneventful step. The Git download page gives you an installer which offers three options. I chose “Use Git Bash only” as this appears to be the most friendly to someone coming from a Linux/Unix environment. It doesn’t change nor does it require you to change the PATH, all you need to do is select “Git Bash” from the Start menu and a Bash shell is opened for you.


Installing PostgreSQL was fairly straightforward. The Windows download page leads to EnterpriseDB one-click installers for multiple platforms and for the more recent versions you have to choose between 32-bit and 64-bit systems. The installer asks for an installation directory, data directory, postgres user password, port number and locale, offering defaults except for the password.

The installer installs both the DBMS and pgAdmin III. If you’re more comfortable with psql, you can select “SQL Shell (psql)” from the Start menu. With either the latter or pgAdmin, you won’t have to change PATH, unless you want to run psql or some other PostgreSQL utility from a Command Prompt window.


Installing Python can be done from Windows MSI installers available from Python.org for the latest releases of Python 2.7 and 3.2. Aside from specifying the installation directory, you’re given the choice of additional components to install, e.g., Tcl/Tk, documentation.

The installers provide a “Python (command line)” option from the Start Menu, but for testing or development, you’ll probably want to open your own Command Prompt window, in order to customize your setup. This requires that you add, e.g., C:\Python27 and C:\Python27\Scripts, to your PATH. Alternatively, you could use the Git Bash window to stay within a Unix-like environment (in which case you’ll still have to add the equivalent directories, e.g., /c/Python27, to PATH).

So far so good. A forthcoming post will cover more, shall we say, entertaining topics.

Testing Python and PostgreSQL on Multiple Platforms

I’m working on making the Pyrseas functional tests portable enough so that they can be submitted to the repository.

Until now, these tests —which exercise dbtoyaml and yamltodb directly— existed as Linux shell scripts. Briefly, each test runs both dbtoyaml and pg_dump -s on a source database creating YAML and SQL dump outputs, respectively. Then it runs yamltodb on a second dabase to recreate the source tables, etc., and finishes by comparing the first pg_dump ouput to that from the target database to verify that all database objects are present and identical.

The Pyrseas unit tests now use tox which makes it fairly easy to add new platforms. For example, on the Python side, the tox.ini configuration includes 2.7 and 3.2, using a single virtualenv for each version.  It would be easy to add 2.6 or 3.3 (when that is released or from a 3.3.0 rc1 install). To test against Postgres 8.4, 9.0, 9.1 and recently 9.2rc1, the only requirement is to define environment variables PG(84|90|91|92)_PORT with the port numbers used for those Postgres installations. Then tox takes care of running the tests eight times, using each Python/Postgres combination.

Although I have not yet personally run the unit tests on Windows or Linux/Unix variants other than Debian, I believe the tox setup should be quite portable (assuming multiple Postgres installations can be present on a given platform), since the tests only depend on Python and psycopg2 being able to connect to Postgres, i.e., they do not depend on running any PG utilities from the command line.

For the functional tests, running the Pyrseas utilities can be done in a fairly portable way thanks to the os.path, tempfile and subprocess modules. Even the diffing of the pg_dump output can be implemented without having to worry about the presence of a diff command, e.g., on Windows.

However, executing pg_dump against multiple Postgres clusters is not so easy. On Debian (and presumably Ubuntu and all other Debian derivatives), if installed from Debian packages, Postgres utilities can be invoked, for example, as

$ pg_dump --cluster 9.1/main -s pyrseas_testdb

The --cluster option causes the correct executable, e.g., /usr/lib/postgresql/9.1/bin/pg_dump to be run and using the correct port. This translates to Python as:

subprocess.call(['pg_dump', '--cluster 9.1/main', '-s',
                targdump, TEST_DBNAME])

The second element in the list could be provided programmatically to run the tests against various Postgres versions, but it would only work on Debian, Ubuntu, etc. (it also assumes the default cluster installation location).

For Red Hat variants, *BSD or Windows, the only solution I could come up with is requiring the existence of a shell script or .bat file with a set name, e.g., pg_dumpXX, where XX is the PG version number, somewhere along the PATH, to point to the right executable. That is not ideal so I’d appreciate hearing from others who may have dealt with similar issues.

Database User Interfaces – Pagination

Since it’s been a while from my last post on this subject, let me recap what we’ve covered:

I also took a detour to explore Python web frameworks. One of the first comments inquired about Tornado. I recently had the opportunity to experiment with it. There’s much to admire, particularly in terms of speed. However, the use of one-or-two-method handler classes essentially for each action (see the blog demo, for example) struck me as counterproductive. It reminded me of Jack Diederich’s “Stop Writing Classes” presentation.

Getting back to database UIs, I have added basic pagination to the listing of films. Here is an example of what it looks like:

The number of lines per page is currently hard-coded (see maxlines in FilmHandler.index), but eventually it should be configurable. The principal additions are the count() and slice() methods in the bl/film.py module. The first does a SELECT COUNT(*) FROM films, so that we can determine how many pages will be needed (the upcoming PostgreSQL 9.2 should improve performance of that query). The slice() method does a SELECT like the all() method but uses LIMIT and OFFSET to retrieve the subset of rows needed for the requested page number.

There are further refinements possible. For example, the list of page numbers/links at the bottom could get very long given enough data in the table. For now, correcting this is left as an exercise for the reader.

1 I’m glad to hear that Kenneth Reitz, Armin Ronacher and others are working on “merging” Werkzeug with Requests.

More Database Tools?

It’s been over year since I started blogging on these pages about Pyrseas and version control. In a month it will also be the first anniversary of the initial commit to GitHub. Much code and many words have flown under these “bridges,” so this seems an appropriate time to reflect.

When I discovered Andromeda, I was looking for a framework to do simple (CRUD-type) database updates

  • with more flexibility (read, programability) than pgAdmin or phpPgAdmin
  • without being tied to an object-relational mapper, either built-in as in Django or external as SQLAlchemy (Pylons/Pyramid)
  • without having to write repetitive code, either SQL or ORM.

Andromeda appeared to satisfy these objectives (although I wasn’t thrilled about having to customize it in PHP).

When I conceived dbtoyaml, I was being lazy: reacting to Andromeda’s requirement to handcraft a YAML description of a database before I could use it to manage SQL changes to it. I thought: why not create the YAML from the database catalogs?

Since my concept for a YAML database specification didn’t match well to Andromeda’s, that led to yamltodb, my attempt to recreate the SQL “diff’ing” features of Andromeda in Python. Andromeda did it using the information_schema catalogs, which made it portable to other DBMSs that had those. Andromeda also did the comparisons by issuing SQL queries (which didn’t perform well). I chose to use the pg_catalog tables and did the comparisons directly on Python structures.

At first, I had intended to only diff schemas and tables and not much more, since that sufficed for my purposes. However, Peter Eisentraut’s comment eventually convinced me that Pyrseas had to support ALL PostgreSQL DDL features1. I’m very pleased with what was accomplished. Pyrseas 0.5, to be released shortly2, will add support for TEXTSEARCH and FOREIGN DATA WRAPPER related objects. The only gaps left are TABLESPACE, GROUP/ROLE and the EXTENSIONs added in PG 9.1.

2012 brought another turn of events. My post on the controversy between Chris Travers and Tony Marston on whether business logic ought to reside in the database led to collaboration with Roger Hunwicks to create dbextend, a tool to automate database augmentation. A first submission was made and work continues on that front.

The latter effort raises other possibilities. For example, since yamltodb already knows how to create nearly all PG objects, it would be trivial to create a schemadump utility (equivalent to pg_dump -s). Another potential tool of interest to PostgreSQL advocates: dbtoyaml for other databases (mytoyaml, oratoyaml anyone?) together with a conversion utility that operates on the YAML specification so it can be accepted by the PG-only yamltodb (the YAML converter seems should be easier than editing SQL statements). The YAML/JSON output from dbtoyaml is amenable to other analysis or automation tasks.

I hope to get back to my database user interface “dream” … one of these days, but in the meantime, I’m glad for having taken these detours. I’d like to thank those who helped along the way: Josh Berkus, Robert Brewer, Adam Cornett, Ronan Dunklau, Peter Eisentraut, David Fetter, Dickson Guedes, Matthias Howell, Roger Hunwicks, Toon Koppelaars, Marko Kreen, Fabrízio Mello, Regina Obe, Filip Rembialkowski, Dariusz Suchojad, Daniele Varrazzo, Evgeni Vasilev, David Wheeler and others I may have missed.

1 Actually, Josh Berkus was the first one who mentioned (in a private email) that I ought to support all PG objects.
2 And just in time for PyCon, I’m happy to announce that it will support Python 3.

Automated Database Augmentation

Suppose you have a PostgreSQL database like the Pagila sample with 14 tables, each with a last_update timestamp column to record the date and time each row was modified, and it is now a requirement to capture which user effected each change. Or perhaps you have several tables without such audit trail columns and need to add them quickly. Or maybe you have decided to denormalize your design by adding a calculated column, e.g., extended price = unit price times quantity ordered, or a derived column, e.g., carrying the customer name in the invoice table.

If you have some experience as a DBA, the word “drudgery” may have come to mind at the prospect of implementing the above features. It’s possible that, after a while, you’ve developed an approach for dealing with some of them but still wish there’d be some way to automate these thankless tasks.

You may have looked at the Andromeda project’s “automations” which provide some of these capabilities. However, in order to take advantage of the automations, you’ll first have to manually describe your database in a YAML format (and you’ll have to install Apache and PHP). Or you could have tried to use the follow-on project, Triangulum, but essentially you’d still have to create a YAML schema (no need for Apache, but you still need PHP).

Some relief is forthcoming. As a result of discussions resulting from my Business Logic in the Database post, I have been collaborating with Roger Hunwicks on a potential solution to these common DBA needs. The new Pyrseas tool is tentatively named dbextend1 and its initial documentation is available in the Pyrseas extender branch. This is how I envision dbextend being used.

Consider the opening example. The DBA would create a simple YAML file such as the (abbreviated) one below, listing the tables and the needed features:

schema public:
  table actor:
    audit_columns: default
  table category:
    audit_columns: default
  table store:
    audit_columns: default

The DBA would then use this file, say audext.yaml, as input to dbextend, e.g.,

dbextend pagiladb audext.yaml

dbextend reads the PostgreSQL catalogs (using code shared with dbtoyaml and yamltodb), building its internal representation. It also reads the YAML extensions file and builds a parallel (albeit much smaller) structure. Thirdly, it reads extension configuration information, e.g., a definition of what columns need to be added for “audit_columns: default“, for example, modified_timestamp and modified_by_user, what trigger(s) to add, and what function(s) to be created.

The output of dbextend is a YAML schema file, just like the one output by dbtoyaml, which can be piped directly to yamltodb to generate SQL to implement the desired features.

In case you’re wondering, dbextend —like other Pyrseas tools— will require Python, psycopg2 and pyyaml.

What features would you like to see automated? What are your suggested best practices for automating these common needs?

Picture credit: Thanks to Mr. O’Brien, a fourth-grade teacher in Minnesota.

1 We’re still receptive to some other suitable name.

The Phantom of the Database – Part 4

At the end of November, I finished the third episode with mild suspense: I suggested that the problem of optimistic “locking” could perhaps be solved in PostgreSQL with something other than extra qualifications, row version numbers or timestamps.

Let’s start this episode with action!

moviesdb=> INSERT INTO film VALUES (47478, 'Seven  Samurai', 1956);
moviesdb=> SELECT xmin, xmax, ctid, title, release_year FROM film;
  xmin  | xmax | ctid  |     title      | release_year
 853969 |    0 | (0,1) | Seven  Samurai |         1956
(1 row)

moviesdb=> UPDATE film SET title = 'Seven Samurai' WHERE id = 47478;
moviesdb=> SELECT xmin, xmax, ctid, title, release_year FROM film;
  xmin  | xmax | ctid  |     title     | release_year
 853970 |    0 | (0,2) | Seven Samurai |         1956
(1 row)

moviesdb=> UPDATE film SET title = 'Sichinin Samurai' WHERE id = 47478;
moviesdb=> SELECT xmin, xmax, ctid, title, release_year FROM film;
  xmin  | xmax | ctid  |      title       | release_year
 853971 |    0 | (0,3) | Sichinin Samurai |         1956
(1 row)

moviesdb=> UPDATE film SET release_year = 1954 WHERE id = 47478;
moviesdb=> SELECT xmin, xmax, ctid, title, release_year FROM film;
  xmin  | xmax | ctid  |      title       | release_year
 853972 |    0 | (0,4) | Sichinin Samurai |         1954
(1 row)

moviesdb=> VACUUM FULL film;
moviesdb=> SELECT xmin, xmax, ctid, title, release_year FROM film;
  xmin  | xmax | ctid  |      title       | release_year
 853972 |    0 | (0,1) | Sichinin Samurai |         1954
(1 row)

moviesdb=> BEGIN; DELETE FROM film WHERE id = 47478; ROLLBACK;
moviesdb=> SELECT xmin, xmax, ctid, title, release_year FROM film;
  xmin  |  xmax  | ctid  |      title       | release_year
 853972 | 853974 | (0,1) | Sichinin Samurai |         1954
(1 row)

moviesdb=> BEGIN; UPDATE film SET release_year = 1956 WHERE id = 47478;
moviesdb=> SELECT xmin, xmax, ctid, title, release_year FROM film;
  xmin  | xmax | ctid  |      title       | release_year
 853975 |    0 | (0,2) | Sichinin Samurai |         1956
(1 row)

moviesdb=> ROLLBACK;
moviesdb=> SELECT xmin, xmax, ctid, title, release_year FROM film;
  xmin  |  xmax  | ctid  |      title       | release_year
 853972 | 853975 | (0,1) | Sichinin Samurai |         1954
(1 row)

What element in the queries above could be used as a surrogate “row version identifier?”  If you examine the changes carefully, you’ll notice that the xmin system column provides that capability. The ctid, a row locator, on the other hand, does not survive the VACUUM operation, and xmax is only used when a row is deleted (or updated, causing it to move).

So my suggestion, in terms of web user interfaces, is that fetching a row for a possible update should include the xmin value, e.g., in the get() method of the Film class, use the following:

    def get(self, db):
            row = db.fetchone(
                "SELECT xmin, title, release_year FROM film WHERE id = %s",

The xmin value can then be sent as a hidden field to the web client, and used in the update() method to implement optimistic concurrency control.

Update: The DB UI Tutorial tag v0.4.3 now has an implementation of this concept for the command line client.

Python Web Frameworks – Application Object

In response to my previous post, someone asked “… what’s the point? How one way is better than the other?” My response was that I’m “not trying to judge these frameworks from the perspective of a developer (at least not completely), but rather from the POV of ‘if I were writing a framework, which features are essential, which nice to have, which I can disregard’” (emphasis added). Of course, I have my own biases as a developer as to what I consider “nice” or essential.

With that clarification out of the way, let’s look at the next feature: the WSGI callable generally known as the application object.

Django doesn’t want developers to be concerned with this artifact. A Django application is the collection of model, views (controllers in standard MVC terminology) and templates (views) that gets run by the server. For those interested, the Django WSGI callable is an instance of WSGIHandler (in django.core.handlers.wsgi).

As best as I can tell, Twisted Web expects the developer to write the application object. It will get called from the WSGIResponse run() method in twisted.web.wsgi. As its documentation says, Twisted Web is “a framework for doing things with the web” but is “not a ‘web framework’ in the same sense” as Django, so it doesn’t fit well in this comparison.

Pyramid‘s Router class (in pyramid.router) is what implements the WSGI callable. The Router instance gets created from the Configurator class’s make_wsgi_app() method. Select Router attributes and methods:

  • logger
  • root_factory
  • routes_mapper
  • request_factory
  • registry (from args)
  • handle_request()

In CherryPy, the Application class is the application object, although it’s not generallly used directly. Instead a user root object is mounted on a Tree which is a registry of these applications. Major attributes and methods:

  • root (from args): root object (application handler)
  • namespaces, config: for configuration
  • wsgiapp(): a CPWSGIApp instance, to handle apps in a pipeline

Being a support library, Werkzeug doesn’t include an application object, but it has a sample in its Shortly example. Major attributes and methods:

  • dispatch_request(): dispatcher
  • jinja_env: template enviroment
  • url_map: Map() taking list of Rule()’s
  • error_404(): error handler
  • render_template(): template renderer

Like Django, Web2py isn’t interested in having developers worry about the application object. Its WSGI callable is the standalone wsgibase() function, in gluon.main.

The web.py application class (yes, not capitalized) implements its WSGI callable. Major attributes and methods:

  • autoreload (from args)
  • init_mapping (from args)
  • request()
  • handle(): matches path to mapping
  • run(): runs the developer server
  • notfound(), internalerror(): error handlers

Flask‘s Flask class is its application object, derived from flask.helpers._PackageBoundObject. Selected attributes and methods (in addition to those in Werkzeug’s example above):

  • static_url_path, static_folder (from args)
  • instance_path (from args or auto-determined)
  • view_functions
  • before/after_xxx_funcs (dicts)
  • logger
  • run()
  • route(): decorator for URL rules
  • error_handler_spec (a dict)
  • errorhandler(): decorator for error handler functions

Like Werkzeug, WebOb doesn’t include an application object, but it has a sample in its wiki example, most of it very specific to the example.

Bottle‘s Bottle class is its WSGI callable. Major attributes and methods:

  • routes (list) and router (class Router instance)
  • config (class ConfigDict instance)
  • mount(): to mount an app at a given point
  • match(): to search for matching routes
  • route(): decorator to bind a function to a URL

Pesto‘s DispatcherApp class is its application object. Major attributes and methods:

  • prefix (from args): a “mount” point
  • matchpattern(), match(): matches URLs (match is decorator)
  • urlfor(): handler/dispatcher to URL converter
  • gettarget(): returns a four-part tuple from the URI
  • status404_application(): error handler

Diva‘s Application class is the object of interest. Select attributes and methods:

  • config (from kwargs), configure()
  • routing_cfg and routing
  • locale_dir (from args)
  • template_dirs (from args)
  • templates(): template loader
  • prepare() and cleanup(): before and after methods

Summary: For developers who care about writing application code, Django and Web2py and to some extent Pyramid and CherryPy hide the existence of the WSGI callable, while other frameworks require that the programmer instantiate it or invoke it. If I were to write a framework, I’d want to make it easy on the developer as the former projects do, but wouldn’t keep it completely out of sight.