Weaverbird python package

The python module provide a way to turn pipelines into transformation functions or queries.


This package is meant as a building block to create servers capable of understanding and executing such pipelines, and returning results to clients. It provides several ways to understand and run weaverbird’s pipelines, called backends.

Backends can either provide:

  • a way to execute pipelines directly (let’s call them executor backends).
  • a way to translate pipelines into queries meant to be run against a database (let’s call them translator backends).


pip install weaverbird


:warning: This doc is provisional, implementation pending

The package exposes:

  • a pydantic model Pipeline which mirror the pipeline definition used by the front-end
  • several weaverbird.backends.xxxx sub-modules, each exposing:
    • either a translate_pipeline function (for translator backends),
    • or an execute_pipeline function (for executor backends).

Pipeline model: validation

Using the pydantic model, one can validate that a series of pipeline steps are valid:

from weaverbird.pipeline import Pipeline

pipeline_steps = [{'name': 'domain', 'domain': 'example'}]

pipeline = Pipeline(steps=pipeline_steps)

A ValidationError is raised when the provided steps are not valid:

> Pipeline()

ValidationError: 1 validation error for Pipeline
  field required (type=value_error.missing)

> Pipeline([{'name': 'domain', 'domain': 'example'}, {'name': 'invalid'}])

ValidationError: 130 validation errors for Pipeline
steps -> 1 -> name
  unexpected value; permitted: 'addmissingdates' (type=value_error.const; given=invalid; permitted=['addmissingdates'])

Executor backends: execute a pipeline

import pandas as pd
from weaverbird.backends.pandas_executor import execute_pipeline

def domain_retriever(domain_name: str) -> pd.DataFrame:
    return pd.read_csv(f'./datasets/{domain_name}.csv')

pipeline = [
  {'name': 'domain', 'domain': 'example'},
  {'name': 'filter', 'condition': {
    'column': 'planet',
    'operator': 'eq',
    'value': 'Earth',

execute_pipeline(pipeline, domain_retriever)


  • pipeline is an instance of the Pipeline model
  • domain_retriever is a function that, from an identifier, returns a corresponding panda’s DataFrame

The result of execute_pipeline is a tuple formed by:

  • the transformed DataFrame,
  • a PipelineExecutionReport with details about time and memory usage for each of its steps.

As of today, only one executor backend exists for python, based on pandas.

Translator backends: translate a pipeline into a query

from weaverbird.backends.sqlite_translator import translate_pipeline

def domain_to_table_identifier(domain_name: str) -> str:
    return domain_name

pipeline = [
  {'name': 'domain', 'domain': 'example'},
  {'name': 'filter', 'condition': {
    'column': 'planet',
    'operator': 'eq',
    'value': 'Earth',

translate_pipeline(pipeline, domain_to_table_identifier)
# SELECT * FROM example WHERE planet='Earth'


  • pipeline is an instance of the Pipeline model
  • domain_to_table_identifier is an optional function that, from an identifier, returns the corresponding identifier of the table in the targeted database

The result of translate_pipeline is a query, generally a str (but other types could be possible, like a list or dict for MongoDB queries).

As of today, no translator backend exists for python. We plan to implement one for MongoDB, and one for Snowflake SQL.

How to: add a new translator

The only requirement is to create a dedicated sub-package inside the weaverbird/backends/ directory, exposing a translate_pipeline function, following the signature already explained in the previous section.

Example of what would look like a basic mongo translator implementing weaverbird’s steps domain, select, lowercase and join:

# weaverbird/backends/mongo_translator/__init__.py
from typing import Callable, List
from weaverbird.pipeline import Pipeline, steps

def domain_to_table_identifier(domain_name: str) -> str:
    return domain_name

def translate_pipeline(
    pipeline: Pipeline, domain_to_collection_identifier: Callable
) -> List[dict]:
    """Translate a weaverbird pipeline to a mongo aggregation pipeline"""
    mongo_pipeline = []

    # Iterate on all the steps of the pipeline, and translate them
    # one by one:
    for step in pipeline.steps:

        if isinstance(step, DomainStep):
            mongo_step = {"$match": {"domain": step.domain}}  # specific to toucan toco

        elif isinstance(step, SelectStep):
            mongo_step = {"$project": {col: 1 for col in step.columns}}

        elif isinstance(step, LowercaseStep):
            mongo_step = {"$addFields": {step.column: {"$toLower": f'${step.column}'}}}

        elif isinstance(step, JoinStep):
            mongo_let = {}
            mongo_expr_and = []
            for (left_on, right_on) in step.on:
                mongo_let[slugify(left_on)] = f'${left_on}'
                        '$eq': [f'${right_on}', f'$${slugify(left_on)}'],

            right_domain = step.right_pipeline[0].domain
            right_without_domain = step.right_pipeline[1:]
            right_mongo_pipeline = translate_pipeline(right_without_domain)
            right_mongo_pipeline.append({"$match": {"$expr": {"$and": mongo_expr_and}}})
            mongo_step = {
                "$lookup": {
                    "from": domain_to_collection_identifier(right_domain),
                    "let": mongo_let,
                    "pipeline": right_mongo_pipeline,
                    "as": '_vqbJoinKey',
            mongo_pipeline.append({'$unwind': '$_vqbJoinKey'})
                '$replaceRoot': {'newRoot': {'$mergeObjects': ['$_vqbJoinKey', '$$ROOT']}},
            mongo_pipeline.append({'$project': {'_vqbJoinKey': 0}})

            raise NotImplementedError

    return mongo_pipeline

Of course in a real case you would split the work in several functions and files.

Test your translator

(work in progress)

For each weaverbird’s step, we provide one or several JSON fixtures containing:

  • some input data
  • the configuration of the step
  • the expected output

It is up to you to write a test executor which will read the input, execute the step, and check the output is the one expected.

If your translator does not implement all weaverbird steps, you must declare which one are supported.

For example, testing a mongo translator would require:

  • spawning a mongodb server
  • store the input data in a collection
  • translate the step with translate_pipeline
  • run the resulting query against the mongodb collection
  • compare the output with the expected one

TODO: show some utils for spawning containers, reading input, comparing ouput

Playground server

See playground.py. It provides a simple server that showcase how to use the module and test it.