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Small dependency resolution library for scientific datasets

Project Description

Travis status

depgraph is a tiny Python library for expressing networks of dependencies required to construct datasets. Networks are declared in terms of the relationships (graph edges) between source and target datasets (graph nodes). Target datasets can then report sets of precursor datasets in the correct order. This makes it simple to throw together build script and construct dependencies in parallel.

Traditionally, each Dataset is designed to correspond to a file. A DatasetGroup class handles cases where multiple files can be considered a single file (e.g. a binary data file and its XML metadata).

When a Dataset requires a different dataset to be built to satisfy its dependencies, it provides a reason, such as:

  • the dependency is missing
  • the dependency is out of date

depgraph is intended to be a reusable component for constructing scientific dataset build tools. Important considerations for such a build tool are that it must:

Beyond the Python standard library, depgraph has no dependencies of its own, so it is easy to include in projects running on a laptop, on a large cluster, or in the cloud. depgraph supports modern Python implementations (Python 2, Python 3, PyPy).


Declare a set of dependencies resembling the graph below:

R0      R1      R2      R3         [raw data]
  \     /       |       |
    DA0         DA1    /
        \      /  \   /
           DB0     DB1
            \     / |  \
             \   /  |   \
              DC0  DC1  DC2        [products]
from depgraph import Dataset, buildmanager

# Define Datasets
# use an optional keyword `tool` to provide a key instructing our build tool
# how to assemble this product
R0 = Dataset("data/raw0", tool="read_csv")
R1 = Dataset("data/raw1", tool="read_csv")
R2 = Dataset("data/raw2", tool="database_query")
R3 = Dataset("data/raw3", tool="read_hdf")

DA0 = Dataset("step1/da0", tool="merge_fish_counts")
DA1 = Dataset("step1/da1", tool="process_filter")

DB0 = Dataset("step2/db0", tool="join_counts")
DB1 = Dataset("step2/db1", tool="join_by_date")

DC0 = Dataset("results/dc0", tool="merge_model_obs")
DC1 = Dataset("results/dc1", tool="compute_uncertainty")
DC2 = Dataset("results/dc2", tool="make_plots")

# Declare relationships
DA0.dependson(R0, R1)
DB0.dependson(DA0, DA1)
DB1.dependson(DA1, R3)
DC0.dependson(DB0, DB1)

# Define a function that builds individual dependencies
# The *buildmanager* decorator transforms it into a loop that builds all
# dependencies below a target
def batchbuilder(dependency):
    # [....]
    return exitcode


# Alternatively, implement the build loop manually:
def build(dependency):
    # This may have the same logic as `batchbuilder` above, but we
    # will call it directly rather than wrapping it in @buildmanager
    # [....]
    return exitcode

for group in buildall(DC1):

    for dep, reason in group:
        # Each target is a dataset with a 'name' attribute and whatever
        # additional keyword arguments where defined with it.
        # The 'reason' is a depgraph.Reason object that codifies why a
        # particular target is necessary (e.g. it's out of date, it's missing,
        # and required by a subsequent target, etc.)
        print("Building {0} with {1} because {2}".format(, dep.tool,
        # Call a function or start a subprocess that will result in the
        # target being built and saved to a file
        return_val = built(dep)
        # Optionally, perform logging, clean-up, or error handling operations
        # [....]



  • Performance improvements
  • buildall generator function, which is more efficient than repeatedly calling Dataset.buildnext()


  • Cyclic graph detection
  • Graphviz export


  • Rewrite, dropping DependencyGraph and making Dataset the primary class


  • First version, copied from depchain module of asputil package
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data-depgraph-0.4.1.tar.gz (9.1 kB) Copy SHA256 Checksum SHA256 Source Jun 22, 2017

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