Load Data from mongodb
to motherduck
using Python dlt
Library
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This page provides technical documentation on how to utilize the dlt
Python library to load data from MongoDB
to MotherDuck
. MongoDB
is a developer data platform built on a modern database that simplifies working with data and accelerates the process of bringing ideas to market. On the other hand, MotherDuck
is an in-process analytical database known for its speed and rich SQL dialect along with deep integrations into client APIs. The dlt
library serves as a bridge between these two platforms, facilitating the transfer of data. For more information on MongoDB
, visit https://www.mongodb.com/.
dlt
Key Features
Pipeline Metadata:
dlt
pipelines utilize metadata to offer robust governance capabilities. This includes load IDs, which track data loads and facilitate data lineage and traceability. Read more about lineage.Schema Enforcement and Curation:
dlt
allows users to enforce and curate schemas, ensuring data consistency and quality. By adhering to predefined schemas, pipelines maintain data integrity and facilitate standardized data handling practices. Read more: Adjust a schema docs.Schema Evolution:
dlt
alerts users to schema changes, allowing them to take necessary actions, such as reviewing and validating the changes, updating downstream processes, or performing impact analysis.Scaling and Finetuning:
dlt
offers several mechanisms and configuration options to scale up and finetune pipelines, including parallel extraction, normalization, and load, as well as the ability to finetune memory buffers, intermediary file sizes, and compression options. Read more about performance.Community Support:
dlt
is a constantly growing library supported by a community of users and developers. It offers many features and use cases needed by the community. Join the Slack community to find recent releases or discuss what you can build withdlt
.
Getting started with your pipeline locally
0. Prerequisites
dlt
requires Python 3.8 or higher. Additionally, you need to have the pip
package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.
1. Install dlt
First you need to install the dlt
library with the correct extras for MotherDuck
:
pip install "dlt[motherduck]"
The dlt
cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from MongoDB
to MotherDuck
. You can run the following commands to create a starting point for loading data from MongoDB
to MotherDuck
:
# create a new directory
mkdir mongodb_pipeline
cd mongodb_pipeline
# initialize a new pipeline with your source and destination
dlt init mongodb motherduck
# install the required dependencies
pip install -r requirements.txt
The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt
:
pymongo>=4.3.3
dlt[motherduck]>=0.3.5
You now have the following folder structure in your project:
mongodb_pipeline/
├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── mongodb/ # folder with source specific files
│ └── ...
├── mongodb_pipeline.py # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)
2. Configuring your source and destination credentials
The dlt
cli will have created a .dlt
directory in your project folder. This directory contains a config.toml
file and a secrets.toml
file that you can use to configure your pipeline. The automatically created version of these files look like this:
generated config.toml
# put your configuration values here
[runtime]
log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true
generated secrets.toml
# put your secret values and credentials here. do not share this file and do not push it to github
[sources.mongodb]
connection_url = "connection_url" # please set me up!
[destination.motherduck.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
2.1. Adjust the generated code to your usecase
3. Running your pipeline for the first time
The dlt
cli has also created a main pipeline script for you at mongodb_pipeline.py
, as well as a folder mongodb
that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.
The main pipeline script will look something like this:
from typing import List
import dlt
from dlt.common import pendulum
from dlt.common.pipeline import LoadInfo
from dlt.common.typing import TDataItems
from dlt.pipeline.pipeline import Pipeline
# As this pipeline can be run as standalone script or as part of the tests, we need to handle the import differently.
try:
from .mongodb import mongodb, mongodb_collection # type: ignore
except ImportError:
from mongodb import mongodb, mongodb_collection
def load_select_collection_db(pipeline: Pipeline = None) -> LoadInfo:
"""Use the mongodb source to reflect an entire database schema and load select tables from it.
This example sources data from a sample mongo database data from [mongodb-sample-dataset](https://github.com/neelabalan/mongodb-sample-dataset).
"""
if pipeline is None:
# Create a pipeline
pipeline = dlt.pipeline(
pipeline_name="local_mongo",
destination='motherduck',
dataset_name="mongo_select",
)
# Configure the source to load a few select collections incrementally
mflix = mongodb(incremental=dlt.sources.incremental("date")).with_resources(
"comments"
)
# Run the pipeline. The merge write disposition merges existing rows in the destination by primary key
info = pipeline.run(mflix, write_disposition="merge")
return info
def load_select_collection_db_items(parallel: bool = False) -> TDataItems:
"""Get the items from a mongo collection in parallel or not and return a list of records"""
comments = mongodb(
incremental=dlt.sources.incremental("date"), parallel=parallel
).with_resources("comments")
return list(comments)
def load_select_collection_db_filtered(pipeline: Pipeline = None) -> LoadInfo:
"""Use the mongodb source to reflect an entire database schema and load select tables from it.
This example sources data from a sample mongo database data from [mongodb-sample-dataset](https://github.com/neelabalan/mongodb-sample-dataset).
"""
if pipeline is None:
# Create a pipeline
pipeline = dlt.pipeline(
pipeline_name="local_mongo",
destination='motherduck',
dataset_name="mongo_select_incremental",
)
# Configure the source to load a few select collections incrementally
movies = mongodb_collection(
collection="movies",
incremental=dlt.sources.incremental(
"lastupdated", initial_value=pendulum.DateTime(2016, 1, 1, 0, 0, 0)
),
)
# Run the pipeline. The merge write disposition merges existing rows in the destination by primary key
info = pipeline.run(movies, write_disposition="merge")
return info
def load_select_collection_hint_db(pipeline: Pipeline = None) -> LoadInfo:
"""Use the mongodb source to reflect an entire database schema and load select tables from it.
This example sources data from a sample mongo database data from [mongodb-sample-dataset](https://github.com/neelabalan/mongodb-sample-dataset).
"""
if pipeline is None:
# Create a pipeline
pipeline = dlt.pipeline(
pipeline_name="local_mongo",
destination='motherduck',
dataset_name="mongo_select_hint",
)
# Load a table incrementally with append write disposition
# this is good when a table only has new rows inserted, but not updated
airbnb = mongodb().with_resources("listingsAndReviews")
airbnb.listingsAndReviews.apply_hints(
incremental=dlt.sources.incremental("last_scraped")
)
info = pipeline.run(airbnb, write_disposition="append")
return info
def load_entire_database(pipeline: Pipeline = None) -> LoadInfo:
"""Use the mongo source to completely load all collection in a database"""
if pipeline is None:
# Create a pipeline
pipeline = dlt.pipeline(
pipeline_name="local_mongo",
destination='motherduck',
dataset_name="mongo_database",
)
# By default the mongo source reflects all collections in the database
source = mongodb()
# Run the pipeline. For a large db this may take a while
info = pipeline.run(source, write_disposition="replace")
return info
if __name__ == "__main__":
# Credentials for the sample database.
# Load selected tables with different settings
print(load_select_collection_db())
# print(load_select_collection_db_filtered())
# Load all tables from the database.
# Warning: The sample database is large
# print(load_entire_database())
Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:
python mongodb_pipeline.py
4. Inspecting your load result
You can now inspect the state of your pipeline with the dlt
cli:
dlt pipeline local_mongo info
You can also use streamlit to inspect the contents of your MotherDuck
destination for this:
# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline local_mongo show
5. Next steps to get your pipeline running in production
One of the beauties of dlt
is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:
The Deploy section will show you how to deploy your pipeline to
- Deploy with Github Actions:
dlt
can be easily deployed using Github Actions. This includes scheduling the actions and setting up the necessary flags for the deployment. - Deploy with Airflow: With
dlt
, you can deploy your pipelines using Airflow. This includes creating an Airflow DAG and setting up the necessary environment variables. - Deploy with Google Cloud Functions:
dlt
also supports deployment using Google Cloud Functions. This includes setting up the Google Cloud environment and deploying the pipeline. - Other Deployment Methods: Besides the above,
dlt
supports various other deployment methods. You can find more information about them here.
The running in production section will teach you about:
- Monitor Your Pipeline: With
dlt
, you can easily monitor the performance of your pipeline and keep track of its progress. Check out the guide on how to monitor your pipeline for more details. - Set up Alerts:
dlt
provides an easy way to set up alerts to keep you informed about any changes or issues with your pipeline. Learn more about how to set up alerts. - Set up Tracing: Tracing is a crucial feature of
dlt
that allows you to keep track of your pipeline's activity and performance. You can find more about how to set up tracing in our guide.
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