pydatatask 0.1.4

Library for constructing data-centric processing pipelines with few invariants

pydatatask

pydatatask is a library for building data pipelines. Sounds familiar? The cool part here is that you are not restricted in the way your data is stored or the way your tasks are executed.

Installing

pip install pydatatask

Nomenclature

A task is one phase of computation. It is parameterized (instantiated) by a single job that it is currently working on. A pipeline is a collection of tasks. Tasks read and write data from repositories, which are arbitrary key-value stores.

The way your data is stored

Repositories are the core of pydatatask. You can store your data in any way you desire and as long as you can write a Repository class to describe it, it can be used to drive a pipeline.

The notion of the "value" part of the key-value store abstraction is defined very, very loosely. The repository base class doesn't have an interface to get or store values, only to query for and delete keys. Instead, you have to know which repository subclass you're working with, and use its interfaces. For example, MetadataRepository assumes that its values are structured objects and loads them fully into memory, and BlobRepository provides a streaming interface to a flat address space.

Current in-tree repositories:

• In-memory dicts
• Files or directories on the local filesystem
• S3 or compatible buckets
• MongoDB collections
• Docker repositories
• Various combinators

The way your tasks are executed

A task is connected to repositories through links. A link is a repository plus a collection of properties describing the repository's relationship to the task - i.e. whether it is input or output, whether it should inhibit dependent tasks from starting, etc.

Current in-tree task types:

• In-process python function execution
• Python function execution with the help of a concurrent.futures Executor
• Python function execution on a kubernetes cluster
• Script execution on a kubernetes cluster

Most tasks define the notion of an environment which is used to template the task for the particular job that is being run.

Putting it together: the Pipeline object

A pipeline is just an unordered collection of tasks. Relationships between the tasks are implicit, defined by which repositories they share.

Management of resources: the Session

TODO

Example

import os
import aiobotocore.session
import pydatatask

session = pydatatask.Session()

@session.resource
async def bucket():
    bucket_session = aiobotocore.session.get_session()
    async with bucket_session.create_client(
        's3',
        endpoint_url=os.getenv('BUCKET_ENDPOINT'),
        aws_access_key_id=os.getenv("BUCKET_USERNAME"),
        aws_secret_access_key=os.getenv("BUCKET_PASSWORD"),
    ) as client:
        yield client

books_repo = pydatatask.S3BucketRepository(bucket, "books/", '.txt')
done_repo = pydatatask.YamlMetadataFileRepository('./results/')
reports_repo = pydatatask.FileRepository('./reports', '.txt')

@pydatatask.InProcessSyncTask('summary', done_repo)
async def summary(job: str, books: pydatatask.S3BucketRepository, reports: pydatatask.FileRepository):
    paragraphs, lines, words, chars = 0, 0, 0, 0
    async with await books.open(job, 'r') as fp:
        data = await fp.read()
    for line in data.splitlines():
        if line.strip() == '':
            paragraphs += 1
        lines += 1
        words += len(line.split())
        chars += len(line)
    async with await reports.open(job, 'w') as fp:
        await fp.write(f'The book "{job}" has {paragraphs} paragraphs, {lines} lines, {words} words, and {chars} characters.\n')

summary.link('books', books_repo, is_input=True)
summary.link('reports', reports_repo, is_output=True)

pipeline = pydatatask.Pipeline([summary], session)

if __name__ == '__main__':
    pydatatask.main(pipeline)