Pipewelder 0.1.4

Description:

pipewelder 0.1.4

Pipewelder is a framework that provides a command-line tool and Python
API to manage AWS Data
Pipeline jobs from flat files.
Simple uses it as a cron-like job scheduler.

Source
https://github.com/SimpleFinance/pipewelder

Documentation
http://pipewelder.readthedocs.org

PyPI
https://pypi.python.org/pypi/pipewelder

Overview
Pipewelder aims to ease the task of scheduling jobs by defining very
simple pipelines which are little more than an execution schedule,
offloading most of the execution logic to files in S3. Pipewelder uses
Data Pipeline’s concept of data
staging
to pull input files from S3 at the beginning of execution and to upload
output files back to S3 at the end of execution.
If you follow Pipewelder’s directory structure, all of your pipeline
logic can live in version-controlled flat files. The included
command-line interface gives you simple commands to validate your
pipeline definitions, upload task definitions to S3, and activate your
pipelines.

Installation
Pipewelder is available from PyPI via
pip and is compatible with Python 2.6, 2.7, 3.3, and 3.4:
pip install pipewelder
The easiest way to get started is to clone the project from GitHub, copy
the example project from Pipewelder’s tests, and then modify to suit:
git clone https://github.com/SimpleFinance/pipewelder.git
cp -r pipewelder/tests/test_data my-pipewelder-project
If you’re setting up Pipewelder and need help, feel free to email the
author.

Development
To do development on Pipewelder, clone the repository and run make
to install dependencies and run tests.

Directory Structure
To use Pipewelder, you provide a template pipeline definition along with
one or more directories that correspond to particular pipeline
instances. The directory structure looks like this (see
test_data for a working example):
pipeline_definition.json
pipewelder.json <- optional configuration file
my_first_pipeline/
run
values.json
tasks/
task1.sh
task2.sh
my_second_pipeline/
...
The values.json file in each pipeline directory specifies parameter
values that are used modify the template definition including the S3
paths for inputs, outputs, and logs. Some of these values are used
directly by Pipewelder as well.
A
`ShellCommandActivity <http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-object-shellcommandactivity.html>`__
in the template definition simply looks for an executable file named
run and executes it. run is the entry point for whatever work
you want your pipeline to do.
Often, your run executable will be a wrapper script to execute a
variety of similar tasks. When that’s the case, use the tasks
subdirectory to hold these definitions. These tasks could be text files,
shell scripts, SQL code, or whatever else your run file expects.
Pipewelder gives tasks folder special treatment in that the CLI will
make sure to remove existing task definitions when uploading files.

Using the Command-Line Interface
The Pipewelder CLI should always be invoked from the top-level directory
of your definitions (the directory where pipeline_definition.json
lives). If your directory structure matches Pipewelder’s expectations,
it should work without further configuration.
As you make changes to your template definition or values.json
files, it can be useful to check whether AWS considers your definitions
valid:
$ pipewelder validate
Once you’ve defined your pipelines, you’ll need to upload the files to
S3:
$ pipewelder upload
Finally, activate your pipelines:
$ pipewelder activate
Any time you change the values.json or pipeline_definition.json,
you’ll need to run the activate subcommand again. Because active
pipelines can’t be modified, the activate command will delete the
existing pipeline and create a new one in its place. The run history for
the previous pipeline will be discarded.

Acknowledgments
Pipewelder’s package structure is based on
python-project-template.