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Loading Data from PostgreSQL to AWS S3 Using dlt in Python

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PostgreSQL is a powerful, open-source object-relational database system with over 35 years of active development, earning a strong reputation for reliability, feature robustness, and performance. This documentation covers how to load data from PostgreSQL to AWS S3 using the open-source Python library dlt. The AWS S3 destination stores data on AWS S3, allowing you to create data lakes easily. You can upload data as JSONL, Parquet, or CSV. For more information about PostgreSQL, visit here.

dlt Key Features​

  • Easy to get started: dlt is a Python library that is easy to use and understand. It is designed to be simple to use and easy to understand. Type pip install dlt and you are ready to go.
  • Postgres Destination: dlt supports loading data into PostgreSQL with multithreaded loading and various write dispositions. Learn more.
  • Amazon Redshift Destination: dlt supports loading data into Amazon Redshift, including staging support with S3 and various file formats. Learn more.
  • Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement, and schema change alerts. Learn more.
  • DuckDB Destination: dlt supports loading data into DuckDB with multithreaded loading and various file formats. 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 PostgreSQL to AWS S3. You can run the following commands to create a starting point for loading data from PostgreSQL to AWS S3:

# create a new directory
mkdir sql_database_postgres_pipeline
cd sql_database_postgres_pipeline
# initialize a new pipeline with your source and destination
dlt init sql_database 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:


sqlalchemy>=1.4
dlt[filesystem]>=0.4.7

You now have the following folder structure in your project:

sql_database_postgres_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── sql_database/                   # folder with source specific files
│   └── ...
├── sql_database_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.sql_database.credentials]
drivername = "drivername" # please set me up!
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 = 0 # please set me up!

[destination.filesystem]
dataset_name = "dataset_name" # please set me up!
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!

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the PostgreSQL source in our docs.
  • Read more about setting up the AWS S3 destination in our docs.

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

The default sql_database pipeline is configured to connect to an example postgres database. The sql_database source supports all sql dialects supported by SQLAlchemy. Please refer to the PostgreSQL of the SQLAlchemy docs for additional info about SQLAlchemy and PostgresSQL.

To connect to PostgresSQL with this example pipeline, you'll need to install an PostgresSQL Python DB API driver:

pip install psycopg2

And use an PostgresSQL connection String in your code:

credentials = ConnectionStringCredentials(
    "postgresql+psycopg2://username:password@host:5432/database"
)

Or your secrets.toml:

[sources.sql_database.credentials]
drivername = "postgresql+psycopg2" 
database = "database"
password = "password"
username = "username"
host = "host"
port = 5432

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at sql_database_pipeline.py, as well as a folder sql_database 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:


import sqlalchemy as sa
import humanize

import dlt
from dlt.common import pendulum
from dlt.sources.credentials import ConnectionStringCredentials

from sql_database import sql_database, sql_table, Table


def load_select_tables_from_database() -> None:
    """Use the sql_database source to reflect an entire database schema and load select tables from it.

    This example sources data from the public Rfam MySQL database.
    """
    # Create a pipeline
    pipeline = dlt.pipeline(
        pipeline_name="rfam", destination='filesystem', dataset_name="rfam_data"
    )

    # Credentials for the sample database.
    # Note: It is recommended to configure credentials in `.dlt/secrets.toml` under `sources.sql_database.credentials`
    credentials = ConnectionStringCredentials(
        "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam"
    )
    # To pass the credentials from `secrets.toml`, comment out the above credentials.
    # And the credentials will be automatically read from `secrets.toml`.

    # Configure the source to load a few select tables incrementally
    source_1 = sql_database(credentials).with_resources("family", "clan")
    # Add incremental config to the resources. "updated" is a timestamp column in these tables that gets used as a cursor
    source_1.family.apply_hints(incremental=dlt.sources.incremental("updated"))
    source_1.clan.apply_hints(incremental=dlt.sources.incremental("updated"))

    # Run the pipeline. The merge write disposition merges existing rows in the destination by primary key
    info = pipeline.run(source_1, write_disposition="merge")
    print(info)

    # Load some other tables with replace write disposition. This overwrites the existing tables in destination
    source_2 = sql_database(credentials).with_resources("features", "author")
    info = pipeline.run(source_2, write_disposition="replace")
    print(info)

    # Load a table incrementally with append write disposition
    # this is good when a table only has new rows inserted, but not updated
    source_3 = sql_database(credentials).with_resources("genome")
    source_3.genome.apply_hints(incremental=dlt.sources.incremental("created"))

    info = pipeline.run(source_3, write_disposition="append")
    print(info)


def load_entire_database() -> None:
    """Use the sql_database source to completely load all tables in a database"""
    pipeline = dlt.pipeline(
        pipeline_name="rfam", destination='filesystem', dataset_name="rfam_data"
    )

    # By default the sql_database source reflects all tables in the schema
    # The database credentials are sourced from the `.dlt/secrets.toml` configuration
    source = sql_database()

    # Run the pipeline. For a large db this may take a while
    info = pipeline.run(source, write_disposition="replace")
    print(
        humanize.precisedelta(
            pipeline.last_trace.finished_at - pipeline.last_trace.started_at
        )
    )
    print(info)


def load_standalone_table_resource() -> None:
    """Load a few known tables with the standalone sql_table resource, request full schema and deferred
    table reflection"""
    pipeline = dlt.pipeline(
        pipeline_name="rfam_database",
        destination='filesystem',
        dataset_name="rfam_data",
        full_refresh=True,
    )

    # Load a table incrementally starting at a given date
    # Adding incremental via argument like this makes extraction more efficient
    # as only rows newer than the start date are fetched from the table
    # we also use `detect_precision_hints` to get detailed column schema
    # and defer_table_reflect to reflect schema only during execution
    family = sql_table(
        credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam",
        table="family",
        incremental=dlt.sources.incremental(
            "updated",
        ),
        detect_precision_hints=True,
        defer_table_reflect=True,
    )
    # columns will be empty here due to defer_table_reflect set to True
    print(family.compute_table_schema())

    # Load all data from another table
    genome = sql_table(
        credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam",
        table="genome",
        detect_precision_hints=True,
        defer_table_reflect=True,
    )

    # Run the resources together
    info = pipeline.extract([family, genome], write_disposition="merge")
    print(info)
    # Show inferred columns
    print(pipeline.default_schema.to_pretty_yaml())


def select_columns() -> None:
    """Uses table adapter callback to modify list of columns to be selected"""
    pipeline = dlt.pipeline(
        pipeline_name="rfam_database",
        destination='filesystem',
        dataset_name="rfam_data_cols",
        full_refresh=True,
    )

    def table_adapter(table: Table) -> None:
        print(table.name)
        if table.name == "family":
            # this is SqlAlchemy table. _columns are writable
            # let's drop updated column
            table._columns.remove(table.columns["updated"])

    family = sql_table(
        credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam",
        table="family",
        chunk_size=10,
        detect_precision_hints=True,
        table_adapter_callback=table_adapter,
    )

    # also we do not want the whole table, so we add limit to get just one chunk (10 records)
    pipeline.run(family.add_limit(1))
    # only 10 rows
    print(pipeline.last_trace.last_normalize_info)
    # no "updated" column in "family" table
    print(pipeline.default_schema.to_pretty_yaml())


def select_with_end_value_and_row_order() -> None:
    """Gets data from a table withing a specified range and sorts rows descending"""
    pipeline = dlt.pipeline(
        pipeline_name="rfam_database",
        destination='filesystem',
        dataset_name="rfam_data",
        full_refresh=True,
    )

    # gets data from this range
    start_date = pendulum.now().subtract(years=1)
    end_date = pendulum.now()

    family = sql_table(
        credentials="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam",
        table="family",
        incremental=dlt.sources.incremental(  # declares desc row order
            "updated", initial_value=start_date, end_value=end_date, row_order="desc"
        ),
        chunk_size=10,
    )
    # also we do not want the whole table, so we add limit to get just one chunk (10 records)
    pipeline.run(family.add_limit(1))
    # only 10 rows
    print(pipeline.last_trace.last_normalize_info)


def my_sql_via_pyarrow() -> None:
    """Uses pyarrow backend to load tables from mysql"""

    # uncomment line below to get load_id into your data (slows pyarrow loading down)
    # dlt.config["normalize.parquet_normalizer.add_dlt_load_id"] = True

    # Create a pipeline
    pipeline = dlt.pipeline(
        pipeline_name="rfam_cx",
        destination='filesystem',
        dataset_name="rfam_data_arrow_4",
    )

    def _double_as_decimal_adapter(table: sa.Table) -> None:
        """Return double as double, not decimals"""
        for column in table.columns.values():
            if isinstance(column.type, sa.Double):
                column.type.asdecimal = False

    sql_alchemy_source = sql_database(
        "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam?&binary_prefix=true",
        backend="pyarrow",
        table_adapter_callback=_double_as_decimal_adapter,
    ).with_resources("family", "genome")

    info = pipeline.run(sql_alchemy_source)
    print(info)


def create_unsw_flow() -> None:
    """Uploads UNSW_Flow dataset to postgres via csv stream skipping dlt normalizer.
    You need to download the dataset from https://github.com/rdpahalavan/nids-datasets
    """
    from pyarrow.parquet import ParquetFile

    # from dlt.destinations import postgres

    # use those config to get 3x speedup on parallelism
    # [sources.data_writer]
    # file_max_bytes=3000000
    # buffer_max_items=200000

    # [normalize]
    # workers=3

    data_iter = ParquetFile("UNSW-NB15/Network-Flows/UNSW_Flow.parquet").iter_batches(
        batch_size=128 * 1024
    )

    pipeline = dlt.pipeline(
        pipeline_name="unsw_upload",
        # destination=postgres("postgres://loader:loader@localhost:5432/dlt_data"),
        destination='filesystem',
        progress="log",
    )
    pipeline.run(
        data_iter,
        dataset_name="speed_test",
        table_name="unsw_flow_7",
        loader_file_format="csv",
    )


def test_connectorx_speed() -> None:
    """Uses unsw_flow dataset (~2mln rows, 25+ columns) to test connectorx speed"""
    import os

    # from dlt.destinations import filesystem

    unsw_table = sql_table(
        "postgresql://loader:loader@localhost:5432/dlt_data",
        "unsw_flow_7",
        "speed_test",
        # this is ignored by connectorx
        chunk_size=100000,
        backend="connectorx",
        # keep source data types
        detect_precision_hints=True,
        # just to demonstrate how to setup a separate connection string for connectorx
        backend_kwargs={"conn": "postgresql://loader:loader@localhost:5432/dlt_data"},
    )

    pipeline = dlt.pipeline(
        pipeline_name="unsw_download",
        destination='filesystem',
        # destination=filesystem(os.path.abspath("../_storage/unsw")),
        progress="log",
        full_refresh=True,
    )

    info = pipeline.run(
        unsw_table,
        dataset_name="speed_test",
        table_name="unsw_flow",
        loader_file_format="parquet",
    )
    print(info)


def test_pandas_backend_verbatim_decimals() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rfam_cx",
        destination='filesystem',
        dataset_name="rfam_data_pandas_2",
    )

    def _double_as_decimal_adapter(table: sa.Table) -> None:
        """Emits decimals instead of floats."""
        for column in table.columns.values():
            if isinstance(column.type, sa.Float):
                column.type.asdecimal = True

    sql_alchemy_source = sql_database(
        "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam?&binary_prefix=true",
        backend="pandas",
        table_adapter_callback=_double_as_decimal_adapter,
        chunk_size=100000,
        # set coerce_float to False to represent them as string
        backend_kwargs={"coerce_float": False, "dtype_backend": "numpy_nullable"},
        # preserve full typing info. this will parse
        detect_precision_hints=True,
    ).with_resources("family", "genome")

    info = pipeline.run(sql_alchemy_source)
    print(info)


if __name__ == "__main__":
    # Load selected tables with different settings
    load_select_tables_from_database()

    # load a table and select columns
    # select_columns()

    # load_entire_database()
    # select_with_end_value_and_row_order()

    # Load tables with the standalone table resource
    # load_standalone_table_resource()

    # Load all tables from the database.
    # Warning: The sample database is very large
    # 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 sql_database_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline sql_database 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 sql_database 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.
  • Deploy with Airflow: Follow this guide to deploy your dlt pipeline with Airflow and Google Composer.
  • Deploy with Google Cloud Functions: Discover how to deploy your dlt pipeline using Google Cloud Functions.
  • Other Deployment Options: Explore various other ways to deploy your dlt pipeline here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure smooth and reliable data processing. How to Monitor your pipeline
  • Set up alerts: Set up alerts to get notified of any issues or changes in your dlt pipeline, enabling proactive management and quick resolution of problems. Set up alerts
  • Set up tracing: Implement tracing to gain detailed insights into the execution of your dlt pipeline, helping you to diagnose issues and optimize performance. 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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