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Adam Kariv
Announcing datapackage-pipelines version 2.0
akariv

Today we’re releasing a major version for datapackage-pipelines, version 2.0.0.

This new version marks a big step forward in realizing the Data Factory concept and framework. We integrated datapackage-pipelines with its younger sister dataflows, and created a set of common building blocks you can now use interchangeably between the two frameworks.

diagram showing the relationship between dataflows and datapackage-pipelines
figure 1: diagram showing the relationship between dataflows and datapackage-pipelines

It’s now possible to bootstrap and develop flows using dataflows, and then run these flows as-is on a datapackage-pipelines server - or effortlessly convert them to the declarative yaml syntax.

Install datapackage-pipelines using pip:

pip install datapackage-pipelines

What Changed?

New Low-level API and stdout Redirect

One big change (and a long time request) is that processors are now allowed to print from inside their processing code, without interfering with the correct operation of the pipeline. All prints are automatically converted to logging.info(...) calls.This behaviour is enabled when using the new low-level API. The main change we've introduced is that ingest() is now a context manager. This means that you now should run:

# New style for ingest and spew
with ingest() as ctx:
 # Do stuff with datapackage and resource_iterator
 spew(ctx.datapackage,
 ctx.resource_iterator,
 ctx.stats)

Backward compatibility is maintained for the old way of using ingest(), so you don’t have to update all your code immediately.

# This still works, but won’t handle print()s
parameters, datapackage, resource_iterator = ingest()
spew(datapackage, resource_iterator)

Dataflows integration

There’s a new integration with dataflows which allows running Flows directly from the pipeline-spec.yaml file. You can integrate dataflows within pipeline specs using the flow attribute instead of run. For example, given the following flow file, saved under my-flow.py:

from dataflows import Flow, dump_to_path, load, update_package
​
def flow(parameters, datapackage, resources, stats):
  stats[‘multiplied_fields’] = 0
 ​
  def multiply(field, n):
    def step(row):
      row[field] = row[field] * n
      stats[‘multiplied_fields’] += 1
      return step
​
    return Flow(update_package(name=’my-datapackage’),
                load((datapackage, resources),
                multiply(‘my-field’, 2))

And a pipeline-spec.yaml in the same directory:

my-flow:
 pipeline:
   — run: load_resource
 parameters:
   url: http://example.com/my-datapackage/datapackage.json
   resource: my-resource
     — flow: my-flow
     — run: dump.to_path

You can run the pipeline using dpp run my-flow.

If you want to wrap a flow inside a processor, you can use the spew_flow helper function:

from dataflows import Flow
from datapackage_pipelines.wrapper import ingest
from datapackage_pipelines.utilities.flow_utils import spew_flow
​
def flow(parameters):
 return Flow(
 # Flow processing comes here
 )
​
​
if __name__ == ‘__main__’:
 with ingest() as ctx:
 spew_flow(flow(ctx.parameters), ctx)

Standard Processor Refactoring

We refactored all standard processors to use their counterparts from dataflows, thus removing code duplication and allowing us to move forward quicker. As a result, we’re also introducing a couple of new processors:

  • load - Loads and streams a new resource (or resources) into the data package. It's based on the dataflows processor with the same name, so it supports loading from local files, remote URL, data packages, locations in environment variables etc. For more information, consult the dataflows documentation.

  • printer - Smart printing processor for displaying the contents of the stream - comes in handy for development or monitoring a pipeline.It will not print all rows, but an logarithmically sparse sample - in other words, it will print rows 1-20, 100-110, 1000-1010 etc. It also prints the last 10 rows of the dataset.

Deprecations

We are deprecating a few processors — you can still use them as usual but they will be removed in the next major version (3.0):

  • add_metadata - was renamed to update_package for consistency
  • add_resource and stream_remote_resources - are being replaced by the load
  • dump.to_path, dump.to_zip, dump.to_sql - are being deprecated - you should use dump_to_path, dump_to_zip and dump_to_sql instead. Note that dump_to_path and dump_to_zip lack some features that exist in the current processors — for example, custom file formatters and non-tabular file support. We might introduce some of that functionality into the new processors as well in the next versions - in the meantime, please let us know what you think about these features and how badly you need them.

The Road Ahead

In the next versions we’re planning to further the integration of dataflows and datapackage-pipelines. We’re going to work on streamlining development and deployment as well as taking care of naming and documentation to harmonize all aspects of the dataflows ecosystem. We’re also working on de-composing datapackage-pipelines into smaller, self contained components. In this version we took apart the standard processor code and some supporting libraries (e.g. kvstore) and delegated it to external libraries.

Links and References

Contributors

Thanks to Ori Hoch for contributing code and other invaluable assistance with this release.