Bazaar Bundles

This article follows on from the series of tutorials on using Bazaar that I have neglected for a while. This article is about the bundle feature of Bazaar. Bundles are to Bazaar branches what patches are to tarballs or plain source trees.

Context/unified diffs and the patch utility are arguably one of most important inventions that enable distributed development:

  • The patch is a self contained text file, making it easy to send as an email attachment or attach to a bug report.
  • The size of the patch is proportional to the size of the changes rather than the size of the source tree. So submitting a one line fix to the Linux kernel is as easy as a one line fix for a small one person project.
  • Even if the destination source tree has moved forward since the patch was created, the patch utility does a decent job of applying the changes using heuristics to match the surrounding context. Human intervention is only needed if the edits are to the same section of code.
  • As patches are human readable text files, they are a convenient form to review the code changes.

Of course, patches do have their limitations:

  • The unified diff format doesn’t convey file moves, instead showing the entire file content being removed and then added again. If the file was changed in addition to being moved, the change can easily be missed when reviewing the patch.
  • Changes to binary files are omitted from the patch. While we can’t expect such changes to be represented in a human readable form, it’d be nice for them to be represented in a way that they can be applied at the other end.
  • The patch doesn’t record any intermediate steps in the creation of the change. This can be worked around by sending a sequence of patches that each build on the previous one, but this requires a fair bit of attentiveness on the part of the patch creator.
  • If the project in question is using some form of version control, the changes in the patch will likely be attributed to the person who applied the patch rather than the person who made the patch.

Using distributed version control solves these limitations, but simply publishing a branch and telling someone to pull from it does not provide all the benefits of a patch. For one, the person reviewing the changes needs to be online to merge the branch and evaluate the changes.

Second, the contributor of the change needs somewhere to host the branch. Even though finding a place to host the branch may not be difficult (for example, anyone can host their branches on Launchpad), uploading the branch may be more effort than the contributor cares for (uploading a branch the size of the Linux kernel will take a while, for instance). That branch would need to remain available until the changes were accepted.

For Bazaar, bundles provide a solution to this problem. A bundle is effectively a “branch diff”, which can then be used to integrate a set of revisions into a repository assuming it contains the revisions from the target branch. At this point, those changes can be merged or pulled.

So how do we produce a bundle? Lets start by creating a branch of the project we want to contribute to. For this example, we’ll create a branch of Mailman to make our changes. As Mailman is using Launchpad to host its branches, I can use the shorthand implemented by the Launchpad Bazaar plugin to create my branch:

bzr branch lp:mailman mailman.jamesh
cd mailman.jamesh
# make my changes here
bzr commit

After I am happy with my changes, I can create a bundle of those changes:

bzr bundle > my-changes.diff

As mentioned earlier, a bundle is essentially a diff between two branches. As I did not specify any branch in the above command, Bazaar uses the parent branch, which in this case will be the upstream Mailman branch. If we look at my-changes.diff, we will see a text file with three general sections:

  1. A short header identifying the file as a bundle and giving the last commit message, author and date
  2. A unified diff made between the last common revision with the parent and the head of our branch (this bit is also convenient to review).
  3. Some extra book keeping data. If I’d made multiple commits, this would include data needed to reconstruct the other revisions in the bundle.

I can now submit this bundle in the same way that I’d submit a patch: as an email attachment or in the bug tracker.

To merge the bundle, a developer simply needs to save the bundle to disk and use “bzr merge” on it:

bzr merge my-changes.diff
bzr commit

This will have the same effect as if they merged a branch with those changes. The “bzr log” output will show the merged revisions and “bzr annotate” will credit the changes to the person who made them rather than the person who merged it.

So next time you want to submit a patch to a project that uses Bazaar, consider submitting a bundle instead.

Storm Released

This week at the EuroPython conference, Gustavo Niemeyer announced the release of Storm and gave a tutorial on using it.

Storm is a new object relational mapper for Python that was developed for use in some Canonical projects, and we’ve been working on moving Launchpad over to it. I’ll discuss a few of the nice features of the package:

Loose Binding Between Database Connections and Classes

Storm has a much looser binding between database connections and the classes used to represent records in particular tables. The standard way of querying the database uses a store object:

for obj in store.find(SomeClass, conditions):
    # do something with obj (which will be a SomeClass instance)

Some things to note about this syntax:

  • The class used to represent rows in the table is passed to find(), so it is possible to have multiple classes representing a single table. This can be useful with large tables where you are only interested in a few columns in some cases.
  • The class used to represent the table is not bound to a particular connection. So instances of it can come from different stores.

Lockstep Iteration

As well as iterating over a single table, a Storm result set can iterate over multiple tables together. For instance, if we have a table representing people and a table representing email addresses (where each person can have multiple email addresses), it is possible to iterate over them in lockstep:

for person, email in store.find((Person, Email), == Email.person):
    print, email.address

Automatic Flushing Before Queries

One of the gotchas when using SQLObject was the way it locally cached updates to tables. This is a great way to reduce the number of updates sent to the database, but could result in unexpected results when performing subsequent SELECT queries. It was up to the programmer to remember to flush changes before doing a query.

With Storm, the store will flush pending changes automatically before performing the query.

Easy To Execute Raw SQL

An ORM can really help when developing a database driven application, but sometimes plain old SQL is a better fit. Storm makes it easy to execute raw SQL against a particular store with the store.execute() method. This method returns an object that you can iterate over to get the tuples from the result set. It also makes sure that any local changes have been flushed before executing the query.

Nice Clean Code

After working with SQLObject for a while, Storm has been a breath of fresh air. The internals are clean and nicely laid out, which makes hacking on it very easy. It was developed using test-driven development methodology, so there is an extensive test suite that makes it easy to validate changes.