mdbrtools 0.1.1

Collection of tools for schema parsing and workload generation used by MongoDB Research

mdbrtools

This package contains experimental tools for schema analysis and query workload generation used by MongoDB Research (MDBR).

Disclaimer

This tool is not officially supported or endorsed by MongoDB Inc. The code is released for use "AS IS" without any warranties of any kind, including, but not limited to its installation, use, or performance. Do not run this tool in critical production systems.

Installation

Installation with pip

This tool requires python 3.x and pip on your system. To install mdbrtools, run the following command:

pip install mdbrtools

Installation from source

Clone the respository from github. From the top-level directory, run:

pip install -e .

This installs an editable development version of mdbrtools in your current Python environment.

Usage

See the ./notebooks directory for more detailed examples for schema parsing and workload generation.

Schema Parsing

Schema parsing operates on a list of Python dictionaries.

from mdbrtools.schema import parse_schema
from pprint import pprint

docs = [
    {"_id": 1, "mixed_field": "world", "missing_field": False},
    {"_id": 2, "mixed_field": 123},
    {"_id": 3, "mixed_field": False, "missing_field": True},
]

schema = parse_schema(docs)
pprint(dict(schema))

Converting the schema object to a dictionary will output some general information about the schema:

{'_id': [{'counter': 3, 'type': 'int'}],
 'missing_field': [{'counter': 2, 'type': 'bool'}],
 'mixed_field': [{'counter': 1, 'type': 'str'},
                 {'counter': 1, 'type': 'int'},
                 {'counter': 1, 'type': 'bool'}]}

For access to types, values and uniqueness information, see the examples in ./notebooks/schema_parsing.ipynb.

Workload Generation

Workload generation takes either a list of Python dictionaries, or a MongoCollection object as input.

from mdbrtools.workload import Workload

docs = [
    {"_id": 1, "mixed_field": "world", "missing_field": False},
    {"_id": 2, "mixed_field": 123},
    {"_id": 3, "mixed_field": False, "missing_field": True},
]

workload = Workload()
workload.generate(docs, num_queries=5)

for query in workload:
    print(query.to_mql())

The generated MQL queries are:

{'missing_field': True}
{'missing_field': {'$exists': False}, '_id': {'$gte': 3}}
{'_id': {'$gt': 3}, 'mixed_field': False, 'missing_field': {'$exists': False}}
{'mixed_field': {'$gte': 'world'}, '_id': 3, 'missing_field': {'$ne': False}}
{'mixed_field': 'world'}

The workload generator supports a number of different constraints on the queries:

• min. and max. number of predicates per query
• allowing only certain fields
• which query operators are allowed for which data types
• control over the weights by which operators are randomly chosen
• min. and max. query selectivity constraints

See the notebook under ./notebooks/workload_generation.ipynb for examples.

Tests

To execute the unit tests, run from the top-level directory:

python -m unittest discover ./tests

License

MIT, see LICENSE.