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Load Data from MongoDB to CockroachDB Using dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to CockroachDB. You can get the connection string for your CockroachDB database as described in the CockroachDB Docs.

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Loading data from MongoDB to CockroachDB is simplified with the open source Python library dlt. MongoDB offers a developer-friendly data platform built on a leading modern database, making data handling straightforward. On the other hand, CockroachDB provides a simple and reliable SQL API, optimized for distributed, cloud-native environments, and is Kubernetes compatible. With dlt, you can seamlessly transfer data between these two powerful systems, leveraging MongoDB's ease of use and CockroachDB's robust SQL capabilities. For more details about MongoDB, visit their website.

dlt Key Features​

  • Scalability via iterators, chunking, and parallelization: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Learn more
  • Implicit extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. Learn more
  • Incremental loading: Incrementally load new data and deduplicate existing data to ensure your dataset is always up-to-date. Learn more
  • Data fetching from APIs: Easily fetch data from APIs and load it into your destination of choice. Learn more
  • Configurable sources: Make reusable data sources by handling secrets securely and reducing code redundancy. 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 CockroachDB:

pip install "dlt[postgres]"

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

# create a new directory
mkdir mongodb_pipeline
cd mongodb_pipeline
# initialize a new pipeline with your source and destination
dlt init mongodb postgres
# 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[postgres]>=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.postgres]
dataset_name = "dataset_name" # please set me up!

[destination.postgres.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 5432
connect_timeout = 15

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the MongoDB source in our docs.
  • Read more about setting up the CockroachDB destination in our docs.

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='postgres',
            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='postgres',
            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='postgres',
            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='postgres',
            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 CockroachDB 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: Learn how to deploy your dlt pipeline using GitHub Actions with a simple cron schedule. Follow the detailed guide here.
  • Deploy with Airflow and Google Composer: Set up your dlt pipeline in a managed Airflow environment provided by Google. Detailed instructions can be found here.
  • Deploy with Google Cloud Functions: Utilize Google Cloud Functions to deploy your dlt pipeline serverlessly. Learn how to set it up here.
  • Explore More Deployment Options: Discover various other methods to deploy your dlt pipeline, including serverless functions and more, by visiting the comprehensive guide here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline to ensure smooth operations and quickly identify any issues. How to Monitor your pipeline
  • Set up alerts: Set up alerts to notify you of any critical issues or changes in your dlt pipeline, ensuring you can respond promptly. Set up alerts
  • Set up tracing: Implement tracing in your dlt pipeline to capture detailed information about the execution flow and performance, which aids in debugging and optimization. And set up tracing

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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