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Python Data Loading from mongodb to aws s3 using dlt Library

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

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This page provides technical documentation on how to use the open-source Python library, dlt, to facilitate data loading from MongoDB to AWS S3. MongoDB is a developer data platform built on a modern database that simplifies data handling, accelerating the time it takes to bring your ideas to market. AWS S3 is a remote file system and bucket storage that uses fsspec to abstract file operations. It's primarily used as a staging area for other destinations, but you can also use it to quickly construct a data lake. For more information about MongoDB, please visit https://www.mongodb.com/.

dlt Key Features​

  • MongoDB Support: dlt provides a verified source for MongoDB, allowing users to easily load data from MongoDB databases or collections to the destination of their choice. Learn more
  • Governance Support: dlt offers robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. This promotes data consistency, traceability, and control throughout the data processing lifecycle. Learn more
  • Filesystem & Buckets: dlt can store data in remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage. This feature can be used as a staging for other destinations or to quickly build a data lake. Learn more
  • Normalization and Loading: dlt automatically turns JSON data into a live dataset stored in the destination of your choice. It does this by first extracting the JSON data, then normalizing it to a schema, and finally loading it to the location where you will store it. Learn more
  • Data Types: dlt supports a wide range of data types, including text, double, bool, timestamp, date, time, bigint, binary, complex, decimal, and wei. This ensures that your data is accurately represented in your chosen destination. Learn more

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 AWS S3:

pip install "dlt[filesystem]"

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 AWS S3. You can run the following commands to create a starting point for loading data from MongoDB to AWS S3:

# create a new directory
mkdir my-mongodb-pipeline
cd my-mongodb-pipeline
# initialize a new pipeline with your source and destination
dlt init mongodb filesystem
# 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[filesystem]>=0.3.5

You now have the following folder structure in your project:

my-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:

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

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.filesystem]
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # please set me up!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 destination in the dlt destinations documentation.

Likewise you can find the setup instructions for MongoDB source in the dlt verifed sources documentation.

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='filesystem',
            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='filesystem',
            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='filesystem',
            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='filesystem',
            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 AWS S3 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 provides a command-line interface to deploy your pipeline with Github Actions, a CI/CD runner that you can use for free. You can specify when the GitHub Action should run using a cron schedule expression. Learn more about this deployment method here.
  • Deploy with Airflow: dlt allows you to deploy your pipeline with Airflow, a platform used to programmatically author, schedule and monitor workflows. It will create an Airflow DAG for your pipeline script that you should customize. You can find out more about this deployment method here.
  • Deploy with Google Cloud Functions: With dlt, you can deploy your pipeline with Google Cloud Functions, a serverless execution environment for building and connecting cloud services. This method allows you to run your pipeline in response to events without having to manage a server. Learn more about this deployment method here.
  • Other Deployment Methods: dlt supports various other deployment methods for your pipeline. You can explore more about these methods here.

The running in production section will teach you about:​

  • Monitoring your pipeline: dlt provides various ways to monitor your pipeline's performance and data quality. From the pipeline's runtime trace, you can get timing information on extract, normalize and load steps. You can find more details in the How to Monitor your pipeline guide.
  • Setting up alerts: dlt allows you to set up alerts to notify you of any issues with your pipeline. This helps to ensure that you can quickly address any problems that arise, ensuring the smooth operation of your pipeline. Learn more about this feature in the Set up alerts guide.
  • Setting up tracing: Tracing is a powerful feature in dlt that allows you to track the execution of your pipeline. This can be particularly useful for debugging purposes or for gaining insights into how your pipeline is performing. For more information, check out the Set up tracing guide.

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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