Loading Data from MongoDB
to AlloyDB
with dlt
in Python
We will be using the dlt PostgreSQL destination to connect to AlloyDB. You can get the connection string for AlloyDB from the GCP AlloyDB Console.
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MongoDB
is a leading modern database that simplifies data management, allowing developers to bring their ideas to market faster. AlloyDB
for PostgreSQL is a fully managed, PostgreSQL-compatible database service designed for demanding workloads, including hybrid transactional and analytical processing. It combines a Google-built database engine with a cloud-based, multi-node architecture to offer enterprise-grade performance, reliability, and availability. This documentation provides technical guidance on loading data from MongoDB
to AlloyDB
using the open-source Python library dlt
. By leveraging dlt
, you can efficiently transfer and manage your data, ensuring seamless integration between these two powerful platforms. For more information on MongoDB
, visit here.
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. Read more - Implicit Extraction DAGs:
dlt
incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. Read more - Schema Enforcement and Curation:
dlt
empowers users to enforce and curate schemas, ensuring data consistency and quality. Read more - Authentication Types: Snowflake destination accepts three authentication types: password authentication, key pair authentication, and external authentication. Read more
- Data Lineage:
dlt
pipelines leverage metadata to provide governance capabilities, including load IDs, which consist of a timestamp and pipeline name. Read 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 AlloyDB
:
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 AlloyDB
. You can run the following commands to create a starting point for loading data from MongoDB
to AlloyDB
:
# 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
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 AlloyDB
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 for CI/CD with this guide. - Deploy with Airflow and Google Composer: Follow this tutorial to deploy your
dlt
pipeline using Airflow and Google Composer. - Deploy with Google Cloud Functions: Use this guide to deploy your
dlt
pipeline with Google Cloud Functions. - Explore other deployment options: Check out additional methods for deploying your
dlt
pipeline in this overview.
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to monitor your
dlt
pipeline in production to ensure smooth operation and quick identification of issues. Read more - Set up alerts: Set up alerts to get notified about important events and potential issues in your
dlt
pipeline. Read more - Set up tracing: Implement tracing to get detailed insights into the execution of your
dlt
pipeline, including timing information and configuration details. Read more
Additional pipeline guides
- Load data from Adobe Analytics to Neon Serverless Postgres in python with dlt
- Load data from Shopify to Snowflake in python with dlt
- Load data from Keap to AlloyDB in python with dlt
- Load data from Box Platform API to AWS Athena in python with dlt
- Load data from CircleCI to AWS Athena in python with dlt
- Load data from PostgreSQL to BigQuery in python with dlt
- Load data from Mux to Azure Synapse in python with dlt
- Load data from Rest API to DuckDB in python with dlt
- Load data from Zendesk to Azure Synapse in python with dlt
- Load data from Google Analytics to Redshift in python with dlt