mongo-pipebuilder 0.4.0

Type-safe, fluent MongoDB aggregation pipeline builder

mongo-pipebuilder

Type-safe, fluent MongoDB aggregation pipeline builder for Python.

Overview

mongo-pipebuilder provides a clean, type-safe way to build MongoDB aggregation pipelines using the Builder Pattern with a fluent interface for maximum readability and safety.

Features

• Type-safe: Full type hints support with IDE autocomplete
• Fluent interface: Chain methods for readable, maintainable code
• Zero dependencies: Pure Python, lightweight package
• Extensible: Easy to add custom stages via add_stage()
• Well tested: Comprehensive test suite with 96%+ coverage

Installation

pip install mongo-pipebuilder

Quick Start

from mongo_pipebuilder import PipelineBuilder

# Build a pipeline
pipeline = (
    PipelineBuilder()
    .match({"status": "active"})
    .lookup(
        from_collection="users",
        local_field="userId",
        foreign_field="_id",
        as_field="user"
    )
    .project({"name": 1, "user.email": 1})
    .sort({"name": 1})
    .limit(10)
    .build()
)

# Use with pymongo
from pymongo import MongoClient
client = MongoClient()
collection = client.db.my_collection
results = collection.aggregate(pipeline)

API Reference

PipelineBuilder

Main class for building aggregation pipelines.

Methods

match(conditions: Dict[str, Any]) -> Self

Adds a $match stage to filter documents.

.match({"status": "active", "age": {"$gte": 18}})

match_expr(expr: Dict[str, Any]) -> Self

Adds a $match stage with an $expr condition (expression-based filter; useful for comparing fields or using variables from let in subpipelines).

.match_expr({"$eq": ["$id", "$$teamId"]})
.match_expr({"$and": [{"$gte": ["$field", "$other"]}, {"$lte": ["$score", 100]}]})

lookup(from_collection: str, local_field: str, foreign_field: str, as_field: str, pipeline: Optional[List[Dict[str, Any]]] = None) -> Self

Adds a $lookup stage to join with another collection.

.lookup(
    from_collection="users",
    local_field="userId",
    foreign_field="_id",
    as_field="user",
    pipeline=[{"$match": {"active": True}}]  # Optional nested pipeline
)

lookup_let(from_collection: str, let: Dict[str, Any], pipeline: Union[List[Dict[str, Any]], PipelineBuilder], as_field: str) -> Self

Adds a $lookup stage with let and pipeline (join by expression; variables from the current document are available in the subpipeline as $$var). Use this when the join condition is an expression (e.g. $expr) rather than equality of two fields.

# With list of stages
.lookup_let(
    from_collection="teams",
    let={"teamId": "$idTeam"},
    pipeline=[
        {"$match": {"$expr": {"$eq": ["$_id", "$$teamId"]}}},
        {"$project": {"name": 1, "_id": 0}}
    ],
    as_field="team"
)

# With PipelineBuilder for the subpipeline (optionally using match_expr)
sub = PipelineBuilder().match_expr({"$eq": ["$_id", "$$teamId"]}).project({"name": 1, "_id": 0})
.lookup_let("teams", {"teamId": "$idTeam"}, sub, as_field="team")

union_with(coll: str, pipeline: Optional[Union[List[Dict[str, Any]], PipelineBuilder]] = None) -> Self

Adds a $unionWith stage to combine documents from the current pipeline with documents from another collection. Optionally runs a subpipeline on the other collection before merging.

# Union with another collection (no subpipeline)
.union_with("other_coll")

# With subpipeline as list of stages
.union_with("logs", [{"$match": {"level": "error"}}, {"$limit": 100}])

# With PipelineBuilder for the subpipeline
sub = PipelineBuilder().match({"source": "individual"}).project({"name": 1})
.union_with("sso_individual_statistics", sub)

add_fields(fields: Dict[str, Any]) -> Self

Adds a $addFields stage to add or modify fields.

.add_fields({"fullName": {"$concat": ["$firstName", " ", "$lastName"]}})

project(fields: Dict[str, Any]) -> Self

Adds a $project stage to reshape documents.

.project({"name": 1, "email": 1, "_id": 0})

group(group_by: Dict[str, Any], accumulators: Dict[str, Any]) -> Self

Adds a $group stage to group documents.

.group(
    group_by={"category": "$category"},
    accumulators={"total": {"$sum": "$amount"}}
)

unwind(path: str, preserve_null_and_empty_arrays: bool = False, include_array_index: Optional[str] = None) -> Self

Adds a $unwind stage to deconstruct arrays.

.unwind("tags", preserve_null_and_empty_arrays=True)
.unwind("items", include_array_index="itemIndex")

sort(fields: Dict[str, int]) -> Self

Adds a $sort stage.

.sort({"createdAt": -1, "name": 1})

limit(limit: int) -> Self

Adds a $limit stage.

.limit(10)

skip(skip: int) -> Self

Adds a $skip stage.

.skip(20)

unset(fields: Union[str, List[str]]) -> Self

Adds a $unset stage to remove fields from documents.

.unset("temp_field")
.unset(["field1", "field2", "field3"])

replace_root(new_root: Dict[str, Any]) -> Self

Adds a $replaceRoot stage to replace the root document.

.replace_root({"newRoot": "$embedded"})
.replace_root({"newRoot": {"$mergeObjects": ["$doc1", "$doc2"]}})

replace_with(replacement: Any) -> Self

Adds a $replaceWith stage (alias for $replaceRoot in MongoDB 4.2+).

.replace_with("$embedded")
.replace_with({"$mergeObjects": ["$doc1", "$doc2"]})

facet(facets: Dict[str, List[Dict[str, Any]]]) -> Self

Adds a $facet stage for parallel execution of multiple sub-pipelines.

.facet({
    "items": [{"$skip": 10}, {"$limit": 20}],
    "meta": [{"$count": "total"}]
})

count(field_name: str = "count") -> Self

Adds a $count stage to count documents.

.match({"status": "active"}).count("active_count")

set_field(fields: Dict[str, Any]) -> Self

Adds a $set stage (alias for $addFields in MongoDB 3.4+).

.set_field({"status": "active", "updatedAt": "$$NOW"})

add_stage(stage: Dict[str, Any]) -> Self

Adds a custom stage for advanced use cases.

.add_stage({"$facet": {
    "categories": [{"$group": {"_id": "$category"}}],
    "total": [{"$count": "count"}]
}})

prepend(stage: Dict[str, Any]) -> Self

Adds a stage at the beginning of the pipeline.

builder.match({"status": "active"})
builder.prepend({"$match": {"deleted": False}})
# Pipeline: [{"$match": {"deleted": False}}, {"$match": {"status": "active"}}]

insert_at(position: int, stage: Dict[str, Any]) -> Self

Inserts a stage at a specific position (0-based index) in the pipeline.

builder.match({"status": "active"}).group("$category", {"count": {"$sum": 1}})
builder.insert_at(1, {"$sort": {"name": 1}})
# Pipeline: [{"$match": {...}}, {"$sort": {...}}, {"$group": {...}}]

Note: For inserting before a specific stage type, combine with get_stage_types():

stage_types = builder.get_stage_types()
group_index = stage_types.index("$group")
builder.insert_at(group_index, {"$addFields": {"x": 1}})

copy() -> PipelineBuilder

Creates an independent copy of the builder with current stages. Useful for creating immutable variants and composing pipelines.

builder1 = PipelineBuilder().match({"status": "active"})
builder2 = builder1.copy()
builder2.limit(10)

# Original unchanged
assert len(builder1) == 1
assert len(builder2) == 2

See Composing and Reusing Pipelines for practical examples.

validate() -> bool

Validates the pipeline before execution. Checks that:

• Pipeline is not empty
• $out and $merge stages are the last stages (critical MongoDB rule)
• $out and $merge are not used together

builder = PipelineBuilder()
builder.match({"status": "active"}).validate()  # Returns True

# Invalid: $out not last
builder.add_stage({"$out": "output"}).match({"status": "active"})
builder.validate()  # Raises ValueError: $out stage must be the last stage

get_stage_at(index: int) -> Dict[str, Any]

Gets a specific stage from the pipeline by index. Returns a copy of the stage.

builder = PipelineBuilder()
builder.match({"status": "active"}).limit(10)
stage = builder.get_stage_at(0)  # Returns {"$match": {"status": "active"}}

pretty_print(indent: int = 2, ensure_ascii: bool = False) -> str

Returns a formatted JSON string representation of the pipeline. Useful for debugging.

builder = PipelineBuilder()
builder.match({"status": "active"}).limit(10)
print(builder.pretty_print())
# [
#   {
#     "$match": {
#       "status": "active"
#     }
#   },
#   {
#     "$limit": 10
#   }
# ]

pretty_print_stage(stage: Union[int, Dict[str, Any]], indent: int = 2, ensure_ascii: bool = False) -> str

Returns a formatted JSON string representation of a single stage (by index or by dict).

builder = PipelineBuilder().match({"status": "active"}).limit(10)
print(builder.pretty_print_stage(0))  # Prints the $match stage

to_json_file(filepath: Union[str, Path], indent: int = 2, ensure_ascii: bool = False, metadata: Optional[Dict[str, Any]] = None) -> None

Saves the pipeline to a JSON file. Useful for debugging, comparison, or versioning.

builder = PipelineBuilder()
builder.match({"status": "active"}).limit(10)

# Basic usage
builder.to_json_file("debug_pipeline.json")

# With metadata
builder.to_json_file(
    "pipeline.json",
    metadata={"version": "1.0", "author": "developer"}
)

compare_with(other: PipelineBuilder, context_lines: int = 3) -> str

Returns a unified diff between two pipelines (useful for comparing “new” builder pipelines vs legacy/template pipelines).

legacy = PipelineBuilder().match({"status": "active"}).limit(10)
new = PipelineBuilder().match({"status": "inactive"}).limit(10)

print(new.compare_with(legacy))

build() -> List[Dict[str, Any]]

Returns the complete pipeline as a list of stage dictionaries.

Examples

Complex Pipeline with Nested Lookup

pipeline = (
    PipelineBuilder()
    .match({"status": "published"})
    .lookup(
        from_collection="authors",
        local_field="authorId",
        foreign_field="_id",
        as_field="author"
    )
    .unwind("author", preserve_null_and_empty_arrays=True)
    .lookup(
        from_collection="categories",
        local_field="categoryId",
        foreign_field="_id",
        as_field="category",
        pipeline=[
            {"$match": {"active": True}},
            {"$project": {"name": 1, "slug": 1}}
        ]
    )
    .unwind("category")
    .add_fields({
        "authorName": "$author.name",
        "categoryName": "$category.name"
    })
    .project({
        "title": 1,
        "authorName": 1,
        "categoryName": 1,
        "publishedAt": 1
    })
    .sort({"publishedAt": -1})
    .limit(20)
    .build()
)

Lookup by expression (lookup_let)

When the join condition is an expression (e.g. $expr) rather than matching two fields, use lookup_let. The subpipeline can be built with match_expr():

sub = (
    PipelineBuilder()
    .match_expr({"$eq": ["$_id", "$$teamId"]})
    .project({"name": 1, "slug": 1, "_id": 0})
)
pipeline = (
    PipelineBuilder()
    .match({"status": "active"})
    .lookup_let(
        from_collection="teams",
        let={"teamId": "$idTeam"},
        pipeline=sub,
        as_field="team"
    )
    .unwind("team", preserve_null_and_empty_arrays=True)
    .project({"title": 1, "teamName": "$team.name"})
    .build()
)

Aggregation with Grouping

pipeline = (
    PipelineBuilder()
    .match({"date": {"$gte": "2024-01-01"}})
    .group(
        group_by={"month": {"$dateToString": {"format": "%Y-%m", "date": "$date"}}},
        accumulators={
            "totalSales": {"$sum": "$amount"},
            "avgAmount": {"$avg": "$amount"},
            "count": {"$sum": 1}
        }
    )
    .sort({"month": 1})
    .build()
)

Composing and Reusing Pipelines

The copy() method allows you to create immutable variants of pipelines, enabling safe composition and reuse. This is useful when you need to:

• Create multiple variants from a base pipeline
• Compose pipelines functionally
• Cache base pipelines safely
• Pass pipelines to functions without side effects

Example: Building Multiple Variants from a Base Pipeline

from mongo_pipebuilder import PipelineBuilder

# Base pipeline with common filtering and joining
base_pipeline = (
    PipelineBuilder()
    .match({"status": "published", "deleted": False})
    .lookup(
        from_collection="authors",
        local_field="authorId",
        foreign_field="_id",
        as_field="author"
    )
    .unwind("author", preserve_null_and_empty_arrays=True)
    .project({
        "title": 1,
        "authorName": "$author.name",
        "publishedAt": 1
    })
)

# Create variants with different sorting and limits
recent_posts = base_pipeline.copy().sort({"publishedAt": -1}).limit(10).build()
popular_posts = base_pipeline.copy().sort({"views": -1}).limit(5).build()
author_posts = base_pipeline.copy().match({"authorName": "John Doe"}).build()

# Base pipeline remains unchanged
assert len(base_pipeline) == 4  # Still has 4 stages

Example: Functional Composition Pattern

def add_pagination(builder, page: int, page_size: int = 10):
    """Add pagination to a pipeline."""
    return builder.copy().skip(page * page_size).limit(page_size)

def add_sorting(builder, sort_field: str, ascending: bool = True):
    """Add sorting to a pipeline."""
    return builder.copy().sort({sort_field: 1 if ascending else -1})

# Compose pipelines functionally
base = PipelineBuilder().match({"status": "active"})

# Create different variants
page1 = add_pagination(add_sorting(base, "createdAt"), page=0)
page2 = add_pagination(add_sorting(base, "createdAt"), page=1)
sorted_by_name = add_sorting(base, "name", ascending=True)

# All variants are independent
assert len(base) == 1  # Base unchanged
assert len(page1) == 3  # match + sort + skip + limit

Example: Caching Base Pipelines

from functools import lru_cache

@lru_cache(maxsize=100)
def get_base_pipeline(user_id: str):
    """Cache base pipeline for a user."""
    return (
        PipelineBuilder()
        .match({"userId": user_id, "status": "active"})
        .lookup(
            from_collection="profiles",
            local_field="userId",
            foreign_field="_id",
            as_field="profile"
        )
    )

# Reuse cached base pipeline with different modifications
user_id = "12345"
base = get_base_pipeline(user_id)

# Create multiple queries from cached base
recent = base.copy().sort({"createdAt": -1}).limit(10).build()
by_category = base.copy().match({"category": "tech"}).build()
with_stats = base.copy().group("$category", {"count": {"$sum": 1}}).build()

# Base pipeline is safely cached and reused

Best Practices

Array _id after $group: prefer $arrayElemAt and materialize fields

If you use $group with an array _id (e.g. ["_idSeason", "_idTournament"]), avoid relying on $_id later in the pipeline. Instead, extract elements with $arrayElemAt and store them into explicit fields, then use those fields in subsequent stages.

pipeline = (
    PipelineBuilder()
    .group(
        group_by=["$idSeason", "$idTournament"],
        accumulators={"idTeams": {"$addToSet": "$idTeam"}},
    )
    .project({
        "idSeason": {"$arrayElemAt": ["$_id", 0]},
        "idTournament": {"$arrayElemAt": ["$_id", 1]},
        "idTeams": 1,
        # Optional: preserve array _id explicitly if you really need it later
        # "_id": "$_id",
    })
    .build()
)

This pattern reduces surprises and helps avoid errors like: $first's argument must be an array, but is object.

Example: Pipeline Factories

class PipelineFactory:
    """Factory for creating common pipeline patterns."""
    
    @staticmethod
    def base_article_pipeline():
        """Base pipeline for articles."""
        return (
            PipelineBuilder()
            .match({"status": "published"})
            .lookup(
                from_collection="authors",
                local_field="authorId",
                foreign_field="_id",
                as_field="author"
            )
        )
    
    @staticmethod
    def with_author_filter(builder, author_name: str):
        """Add author filter to pipeline."""
        return builder.copy().match({"author.name": author_name})
    
    @staticmethod
    def with_date_range(builder, start_date: str, end_date: str):
        """Add date range filter to pipeline."""
        return builder.copy().match({
            "publishedAt": {"$gte": start_date, "$lte": end_date}
        })

# Usage
base = PipelineFactory.base_article_pipeline()
johns_articles = PipelineFactory.with_author_filter(base, "John Doe")
recent_johns = PipelineFactory.with_date_range(
    johns_articles, 
    start_date="2024-01-01",
    end_date="2024-12-31"
).sort({"publishedAt": -1}).limit(10).build()

Key Benefits:

• Safe reuse: Base pipelines remain unchanged
• Functional composition: Build pipelines from smaller parts
• Caching friendly: Base pipelines can be safely cached
• No side effects: Functions can safely modify copies
• Thread-safe: Multiple threads can use copies independently

Development

Project Structure

mongo-pipebuilder/
├── src/
│   └── mongo_pipebuilder/
│       ├── __init__.py
│       └── builder.py
├── tests/
│   └── test_builder.py
├── examples/
│   └── examples.py
├── pyproject.toml
├── README.md
└── LICENSE

Running Tests

pytest tests/

Contributing

See DEVELOPMENT.md for development guidelines.

License

MIT License - see LICENSE file for details.