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File dependency graph evaluator.

Project description

# Python Dagger - The File Dependency Graph Engine

![Banner image](banner.png "Dagger graphs.")

[Dagger]( evaluates file dependencies in a directed-acyclic-graph (DAG)
like GNU make, but timestamps or hashes can be enabled per-file.
This allows you to use fast timestamp comparisons with large files,
and hashing on small files. When hashing is used, it's stored in a
simple 2 column text file with filename,hash per line or in a sqlite
database. Dagger can be used as a building block for a larger build

Dagger is written in Python to make it portable and extensible. It's
graph evaluation engine is non-recursive, so it can handle very deep
dependency paths. A benchmark tool (see below) is available to test and visualize
complex graphs.

- Dependency based on modified time or hash (md5) for individual or all files.
- Force individual files as stale or uptodate.
- All paths computation for every node and discovery order.
- Written in pure Python to be platform agnostic.
- Non-recursive graph algorithm.
- Hash database in text or sqlite file.
- Graphviz dot file export that colors stale file nodes.

Quick Example
import dagger

dag = dagger.dagger()
dag.add('1', ['2','3'])
dag.add('3', ['4','5'])
dag.add('6', ['3','7'])

# Force this node to be old, and all dependent parents.
dag.stale('4') # You can force "freshness" with dag.stale(name, 0).'')

![Example image](dot.png "Example of dot export with color enabled.")

__example.dot__ visualized with kgraphviewer. Old/stale nodes are colored in red by dagger.

[Source code](

make test

sudo python install

There is a helper script in `bench/` to help you see how fast dagger can be.
You can specify how many children and depth the mock graph should use.

# Small trees visualized with dot.
$ python --levels 3 --width 2 --dot
nodes: inner=6 outer=8 total=14
0.0s Run

![dot3x2 image](dot3x2.png "Small trees output by with 1 old node.")

Small 3 level, 2 child wide graph output by with 1 old node ('7').

# Larger test case. Does your project use 56,000 files?
$ python --levels 6 --width 6 --allpaths
nodes: inner=9330 outer=46656 total=55986
0.24s Run

# See if even faster with pypy.
$ pypy --levels 6 --width 6 --allpaths
nodes: inner=9330 outer=46656 total=55986
0.16s Run

# Try extreme case of simulating 1 million files.
$ python --levels 7 --width 7 --allpaths
nodes: inner=137256 outer=823543 total=960799
6.35s Run

# pypy reports a shorter runtime for the 1 million node graph.
$ pypy ...
3.17s Run

Example: Hashing
# Use a text file for file hashes.
# It's ok if it doesn't exist.
dag = dagger.dagger('/home/project/hash.txt')

# dag.add(...)

# Enable hashing for all files.
dag.hashall = 1

# Evaluates the dependencies and computes hashes if none existed.

# Export the file.

Example: Hashing and Sqlite
# This time use sqlite database instead of a text file.
# Ok if it doesn't exist yet. It will be created.
dag = dagger.dagger('hash.sqlite', sqlite=1)

# dag.add(...)

# Export the file, but not needed since all updates are atomically commited.

Example: Control Hashing for Specific Files
dag.hash('myfile.txt', 1) # Enable hashing for file.
dag.hash('myfile.txt', 0) # Turn it off (hashing is off by default).

Example: Using Graph Search Results
# See the depth-first-search node order.
print dag.ordernames()
# 2,4,5,3,1,7,6

# Access the nodes directly.
print dag.order
# [<dagger.node object at ...>, <dagger.node object at ...>, ...]

# Find all possible paths in graph. Path order will be bottom-up.

# Now each node will have list of all possible paths to dependents.
print dag.get('4').paths # Lists will have node references.

# Or get paths as just names. For our quick example graph:
print dag.paths2names('4')
# [['3', '1'], ['3', '6']]

v0.0.0 2012/10/15

- Initial release.

make html
make dist VER=0.0.0

ssh -t rsz, create
scp html/* rsz,
scp ../pythondagger-0.0.0.tar.gz rsz,

Copyright 2012 Remik Ziemlinski under the terms of the GNU General Public License

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