Maggma is a framework to build scientific data processing pipelines from data stored in a variety of formats -- databases, Azure Blobs, files on disk, etc., all the way to a REST API. The rest of this README contains a brief, high-level overview of what maggma can do. For more, please refer to the documentation.

Maggma is published on the Python Package Index. The preferred tool for installing packages from PyPi is pip. This tool is provided with all modern versions of Python.

Open your terminal and run the following command.

pip install --upgrade maggma

maggma's core classes -- Store and Builder -- provide building blocks for modular data pipelines. Data resides in one or more Store and is processed by a Builder. The results of the processing are saved in another Store, and so on:

flowchart LR
    s1(Store 1) --Builder 1--> s2(Store 2) --Builder 2--> s3(Store 3)
s2 -- Builder 3-->s4(Store 4)

A major challenge in building scalable data pipelines is dealing with all the different types of data sources out there. Maggma's Store class provides a consistent, unified interface for querying data from arbitrary data sources. It was originally built around MongoDB, so it's interface closely resembles PyMongo syntax. However, Maggma makes it possible to use that same syntax to query other types of databases, such as Amazon S3, GridFS, or files on disk, and many others. Stores implement methods to connect, query, find distinct values, groupby fields, update documents, and remove documents.

The example below demonstrates inserting 4 documents (python dicts) into a MongoStore with update, then accessing the data using count, query, and distinct.

>>> turtles = [{"name": "Leonardo", "color": "blue", "tool": "sword"},
               {"name": "Donatello","color": "purple", "tool": "staff"},
               {"name": "Michelangelo", "color": "orange", "tool": "nunchuks"},
               {"name":"Raphael", "color": "red", "tool": "sai"}
            ]
>>> store = MongoStore(database="my_db_name",
                       collection_name="my_collection_name",
                       username="my_username",
                       password="my_password",
                       host="my_hostname",
                       port=27017,
                       key="name",
                    )
>>> with store:
        store.update(turtles)
>>> store.count()
4
>>> store.query_one({})
{'_id': ObjectId('66746d29a78e8431daa3463a'), 'name': 'Leonardo', 'color': 'blue', 'tool': 'sword'}
>>> store.distinct('color')
['purple', 'orange', 'blue', 'red']

Builders represent a data processing step, analogous to an extract-transform-load (ETL) operation in a data warehouse model. Much like Store provides a consistent interface for accessing data, the Builder classes provide a consistent interface for transforming it. Builder transformation are each broken into 3 phases: get_items, process_item, and update_targets:

1. get_items: Retrieve items from the source Store(s) for processing by the next phase
2. process_item: Manipulate the input item and create an output document that is sent to the next phase for storage.
3. update_target: Add the processed item to the target Store(s).

Both get_items and update_targets can perform IO (input/output) to the data stores. process_item is expected to not perform any IO so that it can be parallelized by Maggma. Builders can be chained together into an array and then saved as a JSON file to be run on a production system.

Maggma has been developed and is maintained by the Materials Project team at Lawrence Berkeley National Laboratory and the Materials Project Software Foundation.

Maggma is written in Python and supports Python 3.9+.