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Docker image for pymongo-schema, a schema analyser for MongoDB, written in Python.
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A schema analyser for MongoDB, written in Python.

This tools is inspired by variety, with the following enhancements :

  • extract the hierarchical structure of the schema
  • Several output options : python dictionnary, json, yaml or text
  • finer grained types. ex: INTEGER, DOUBLE rather than NUMBER
  • ways to filter and transform the output schema


Before distribution of a stable distribution on PyPi, you can install pymongo-schema from github :

pip install --upgrade git+


    pymongo-schema  -h | --help
    pymongo-schema  extract [--database=DB --collection=COLLECTION... --port=PORT --host=HOST --output=FILENAME --format=FORMAT... --quiet]
    pymongo-schema  transform [--input=FILENAME --filter=FILENAME --output=FILENAME --format=FORMAT... --columns=COLUMNS  --without-counts --quiet]
    pymongo-schema  tosql [--input=FILENAME --output=FILENAME --quiet]

    extract                     Extract schema from a MongoDB instance
    transform                   Transform a json schema to another format, eventually filtering or changing columns outputs
    tosql                       Create a mapping from mongo schema to relational schema (json input and output)

To display full usage, with options description, run:

pymongo-schema -h

TODO : add examples


We define 'schema' as a dictionnary describing the structure of MongoDB component, being either a MongoDB instances, a database, a collection, an objects or a field.

Schema are hierarchically nested, with the following structure :

# mongo_schema : A MongoDB instance contains databases
    "database_name_1": {}, #database_schema,
    "database_name_2": # A database contains collections
        "collection_name_1": {}, # collection_schema,
        "collection_name_2": # A collection maintains a 'count' and contains 1 object
            "count" : int, 
            "object":  # object_schema : An object contains fields.            
                "field_name_1" : {}, # field_schema, 
                "field_name_2": # A field maintains 'types_count_information
                                # An optional 'array_types_count' field maintains 'types_count' information for values encountered in arrays 
                                # An 'OBJECT' or 'ARRAY(OBJECT)' field recursively contains 1 'object'
                    'count': int,
                    'prop_in_object': float,
                    'type': 'type_str', 
                    'types_count': {  # count for each encountered type  
                        'type_str' : 13,
                        'Null' : 3
                    'array_type': 'type_str',
                    'array_types_count': {  # (optional) count for each type encountered  in arrays
                        'type_str' : 7,
                        'Null' : 3
                    'object': {}, # (optional) object_schema 

Contributing - Limitations - TODO

The code base should be easy to read and improve upon. Contributions are welcomed.

Mixed types handling

pymongo-schema handles mixed types by looking for the lowest common parent type in the following tree.

<img src="type_tree.png" alt="type_tree" width=700/>

If a field contains both arrays and scalars, it is considered as an array. The 'array_type' is defined as the common parent type of scalars and array_types encountered in this field.


  • Improve mapping from Python type to name (TYPE_TO_STR dict)

  • Check a mongo scheme for compatibility to an sql mapping

  • Handle incompatibilities

Support Python 3 version

  • fix encoding issues when exporting manually added non-ascii characters
  • test for support of multiple Python versions

Diff between schemas

A way to compare the schema dictionaries and highlights the differences.

Test if a mongo schema can be mapped tosql

  • test for the presence of mongo types in the mapping
  • look for mixes of list and scalar, that are currently not supported by mongo-connector-postgresql
  • look for the presence of an '_id'

=> It may be donne directly in mongo-connector-postgresql doc_manager

Adding fields in json/yaml outputs

  • for example to add comments

Other option to sort text outputs

  • It is currently based on counts

Tackle bigger databases

This code has been only used on a relatively small sized Mongo database, on which it was faster than Variety.

To tackle bigger databases, it certainly would be usefull to implement the following variety's features :

  • Analyze subsets of documents, most recent documents, or documents to a maximum depth.


The codebase is not tested. It should not be trusted blindly.


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