README.md

Aggify is a Python library to generate MongoDB aggregation pipelines

Aggify

Aggify is a Python library for generating MongoDB aggregation pipelines, designed to work seamlessly with Mongoengine. This library simplifies the process of constructing complex MongoDB queries and aggregations using an intuitive and organized interface.

Features

• Programmatically build MongoDB aggregation pipelines.
• Filter, project, group, and perform various aggregation operations with ease.
• Supports querying nested documents and relationships defined using Mongoengine.
• Encapsulates aggregation stages for a more organized and maintainable codebase.
• Designed to simplify the process of constructing complex MongoDB queries.

TODO

Installation

You can install Aggify using pip:

pip install aggify

Sample Usage

Here's a code snippet that demonstrates how to use Aggify to construct a MongoDB aggregation pipeline:

from mongoengine import Document, fields

class AccountDocument(Document):
    username = fields.StringField()
    display_name = fields.StringField()
    phone = fields.StringField()
    is_verified = fields.BooleanField()
    disabled_at = fields.LongField()
    deleted_at = fields.LongField()
    banned_at = fields.LongField()

class FollowAccountEdge(Document):
    start = fields.ReferenceField("AccountDocument")
    end = fields.ReferenceField("AccountDocument")
    accepted = fields.BooleanField()
    meta = {
        "collection": "edge.follow.account",
    }

class BlockEdge(Document):
    start = fields.ObjectIdField()
    end = fields.ObjectIdField()
    meta = {
        "collection": "edge.block",
    }

Aggify query:

from models import *
from aggify import Aggify, F, Q
from bson import ObjectId

aggify = Aggify(AccountDocument)

pipelines = list(
    (
        aggify.filter(
            phone__in=[],
            id__ne=ObjectId(),
            disabled_at=None,
            banned_at=None,
            deleted_at=None,
            network_id=ObjectId(),
        )
        .lookup(
            FollowAccountEdge,
            let=["id"],
            query=[Q(start__exact=ObjectId()) & Q(end__exact="id")],
            as_name="followed",
        )
        .lookup(
            BlockEdge,
            let=["id"],
            as_name="blocked",
            query=[
                (Q(start__exact=ObjectId()) & Q(end__exact="id"))
                | (Q(end__exact=ObjectId()) & Q(start__exact="id"))
            ],
        )
        .filter(followed=[], blocked=[])
        .group("username")
        .annotate(annotate_name="phone", accumulator="first", f=F("phone") + 10)
        .redact(
            value1="phone",
            condition="==",
            value2="132",
            then_value="keep",
            else_value="prune",
        )
        .project(username=0)[5:10]
        .out(coll="account")
    )
)

Mongoengine equivalent query:

[
    {
        "$match": {
            "phone": {"$in": []},
            "_id": {"$ne": ObjectId("65486eae04cce43c5469e0f1")},
            "disabled_at": None,
            "banned_at": None,
            "deleted_at": None,
            "network_id": ObjectId("65486eae04cce43c5469e0f2"),
        }
    },
    {
        "$lookup": {
            "from": "edge.follow.account",
            "let": {"id": "$_id"},
            "pipeline": [
                {
                    "$match": {
                        "$expr": {
                            "$and": [
                                {
                                    "$eq": [
                                        "$start",
                                        ObjectId("65486eae04cce43c5469e0f3"),
                                    ]
                                },
                                {"$eq": ["$end", "$$id"]},
                            ]
                        }
                    }
                }
            ],
            "as": "followed",
        }
    },
    {
        "$lookup": {
            "from": "edge.block",
            "let": {"id": "$_id"},
            "pipeline": [
                {
                    "$match": {
                        "$expr": {
                            "$or": [
                                {
                                    "$and": [
                                        {
                                            "$eq": [
                                                "$start",
                                                ObjectId("65486eae04cce43c5469e0f4"),
                                            ]
                                        },
                                        {"$eq": ["$end", "$$id"]},
                                    ]
                                },
                                {
                                    "$and": [
                                        {
                                            "$eq": [
                                                "$end",
                                                ObjectId("65486eae04cce43c5469e0f5"),
                                            ]
                                        },
                                        {"$eq": ["$start", "$$id"]},
                                    ]
                                },
                            ]
                        }
                    }
                }
            ],
            "as": "blocked",
        }
    },
    {"$match": {"followed": [], "blocked": []}},
    {"$group": {"_id": "$username", "phone": {"$first": {"$add": ["$phone", 10]}}}},
    {
        "$redact": {
            "$cond": {
                "if": {"$eq": ["phone", "132"]},
                "then": "$$KEEP",
                "else": "$$PRUNE",
            }
        }
    },
    {"$project": {"username": 0}},
    {"$skip": 5},
    {"$limit": 5},
    {"$out": "account"},
]

In the sample usage above, you can see how Aggify simplifies the construction of MongoDB aggregation pipelines by allowing you to chain filters, lookups, and other operations to build complex queries. For more details and examples, please refer to the documentation and codebase.