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Elkhound workflow engine

Project description

Elkhound is an opinionated, data-centric workflow engine. It makes the following assumptions about your project:

  • Project workflow can be split into several tasks, each task has clearly defined input and output data files, ideally with no side effects. Tasks form a directed acyclic graph (i.e., no loops).
  • Each data file has clearly defined format and schema. Information between consecutive tasks is transferred primarily via the files. Preference for CSV or gzipped CSV files.

Elkhound will help you by:

  • Versioning data files by timestamping them (tasks read input files with the latest timestamp, write output files with current timestamp).
  • Supporting big data files by providing convenience schema-aware iterators over rows (using Python generators), lowering memory footprint.
  • Managing workflows of tasks, easily running arbitrary lists of tasks.
  • Automatic checkpointing of intermediate results, thanks to inter-task communication via data files and data file versioning.
  • Managing project’s parameters by injecting contents of configuration files and command line parameters as tasks’ contexts (less temptation to hardcode constants).
  • Logging workflow executions, reporting context and execution progress. Automatically collecting and archiving logs from different runs in one place to facilitate reproducibility.
  • Facilitating unit testing by injecting mockable data file objects as inputs and outputs of task classes.

In order to run Elkhound workflows, you need to create an engine configuration file and implement business logic in Task subclasses.

Engine configuration

Engine configuration file (in our example we’ll name it engine.yaml) has three sections:

  • specs, where you’ll describe data files (names, formats, schemas; these files are outputs for some tasks, inputs for other tasks),
  • tasks, where you’ll point to implementations of business logic. Tasks define transformations of data files (how to build an output file Z given input files X and Y)
  • workflows, where you’ll bundle groups of target data files.

Data files are first class citizens in Elkhound. They are identified by four digit codes (e.g. 1230, 2110, 4315, 5214). Design of a new workflow should begin with registering data files, and optionally defining their schemas (if applicable). Here is an example of data files defined in an engine configuration file:

specs:
  - code: 1230
    name: people
    extension: csv.gz
    flags:
      - gzipped
    schema:
      - name: name
        type: str
      - name: dob
        type: datetime
      - name: is_employee
        type: bool
  - code: 2110
    name: budget
    extension: xlsx
    flags:
      - binary
  - code: 4315
    name: report
    extension: txt
  - code: 5214
    name: plots
    extension: dir
    flags:
      - directory

Tasks are Python classes that take zero or more data files on input and produce zero or more data files on output. Each task class has to implement three methods:

  • get_input_data_file_codes(self) returning a list of input data file codes,
  • get_output_data_file_codes(self) returning a list of output data file codes,
  • run(self, input_files, output_files, context) executing business logic.

Here is an example of tasks registered in an engine configuration file:

tasks:
  - class: myapp.DownloadDataTask
  - class: myapp.GenerateReportTask
  - class: myapp.PlotBudgetTask

In our example we will assume that:

  • DownloadDataTask takes no data files on input, produces 1230 and 2110 on output.
  • GenerateReportTask takes 1230 and 2110 on input, produces 4315 on output.
  • PlotBudgetTask takes 2110 on input, produces 5214 on output.

Workflows are named lists of targets, i.e., data files to be created. Here is an example (excerpt of an engine configuration file):

workflows:
  monthly_briefing:
    - 4315
    - 5214

Business logic implementation

Each task is implemented as a subclass of elkhound.Task. Their task is to read the input files they need and create the output files. Here is a simple example:

class GenerateReportTask(Task):
    def get_input_data_file_codes(self):
        return [1230, 2110]

    def get_output_data_file_codes(self):
        return [4315]

    def run(self, input_files, output_files, context=None):
        with output_files[4315].open() as f:
            for _, input_file in input_files.items():
                f.write('Used input file {}\n'.format(input_file.get_path()))

When method run is called by the engine, the input_files and output_files arguments contain DataFile objects that know the exact path of the files and can assist in opening them in the right mode (read or write, text or binary, gzipped or not). Data file objects have utility methods for specific situations, for example when an input file is in CSV format, the corresponding data file object has methods like read_data_frame() that returns a Pandas data frame, and iterate_records() which returns a generator yielding records one-by-one (useful when scanning huge files that won’t fit into memory).

Running workflows

Here’s an example of how to run a workflow:

python -m elkhound.runner --dir /workspace/foo --engine engine.yaml --targets monthly_briefing --deps

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0.0.4

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