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Python: Load Data from aws s3 to clickhouse using dlt Library

Connecting other file sources

This document describes how to set up loading from aws 3, but our filesystem source can not only stream from s3, but also from 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 load data from AWS S3 to ClickHouse. dlt supports streaming of CSV, Parquet, and JSONL files from various sources including AWS S3, Google Cloud Storage, Google Drive, Azure, or local filesystem using the reader source. ClickHouse is a powerful open-source column-oriented database management system that enables real-time generation of analytical data reports using SQL queries. For more details on the source, please visit here.

dlt Key Features​

  • Governance Support in dlt Pipelines: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more about it here.
  • Filesystem & Buckets: Filesystem destination stores data in remote file systems and bucket storages like S3, google storage or azure blob storage. Learn more about it here.
  • Supported File Formats: dlt supports various file formats like SQL Insert, jsonl and parquet. Find out more about the supported file formats here.
  • Authentication Types: Snowflake destination accepts three authentication types - password authentication, key pair authentication and external authentication. Read more about it here.
  • Data Types: dlt supports various data types like text, double, bool, timestamp, date, time, bigint, binary, complex, decimal, wei. Learn more about it here.

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 ClickHouse:

pip install "dlt[clickhouse]"

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

# create a new directory
mkdir my_filesystem_pipeline
cd my_filesystem_pipeline
# initialize a new pipeline with your source and destination
dlt init filesystem clickhouse
# 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:

dlt[clickhouse]>=0.4.3a0
openpyxl>=3.0.0

You now have the following folder structure in your project:

my_filesystem_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── filesystem/                   # folder with source specific files
│   └── ...
├── filesystem_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

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

generated secrets.toml​

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.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!

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

[destination.clickhouse.credentials]
database = "default"
password = "password" # please set me up!
username = "default"
host = "host" # please set me up!
port = 9440
http_port = 8443

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the AWS S3 source in our docs.
  • Read more about setting up the ClickHouse 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 filesystem_pipeline.py, as well as a folder filesystem 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 os
import posixpath
from typing import Iterator

import dlt
from dlt.sources import TDataItems

try:
    from .filesystem import FileItemDict, filesystem, readers, read_csv  # type: ignore
except ImportError:
    from filesystem import (
        FileItemDict,
        filesystem,
        readers,
        read_csv,
    )


TESTS_BUCKET_URL = posixpath.abspath("../tests/filesystem/samples/")


def stream_and_merge_csv() -> None:
    """Demonstrates how to scan folder with csv files, load them in chunk and merge on date column with the previous load"""
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem_csv",
        destination='clickhouse',
        dataset_name="met_data",
    )
    # met_data contains 3 columns, where "date" column contain a date on which we want to merge
    # load all csvs in A801
    met_files = readers(
        bucket_url=TESTS_BUCKET_URL, file_glob="met_csv/A801/*.csv"
    ).read_csv()
    # tell dlt to merge on date
    met_files.apply_hints(write_disposition="merge", merge_key="date")
    # NOTE: we load to met_csv table
    load_info = pipeline.run(met_files.with_name("met_csv"))
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)

    # now let's simulate loading on next day. not only current data appears but also updated record for the previous day are present
    # all the records for previous day will be replaced with new records
    met_files = readers(
        bucket_url=TESTS_BUCKET_URL, file_glob="met_csv/A801/*.csv"
    ).read_csv()
    met_files.apply_hints(write_disposition="merge", merge_key="date")
    load_info = pipeline.run(met_files.with_name("met_csv"))

    # you can also do dlt pipeline standard_filesystem_csv show to confirm that all A801 were replaced with A803 records for overlapping day
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def read_csv_with_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem",
        destination='clickhouse',
        dataset_name="met_data_duckdb",
    )

    # load all the CSV data, excluding headers
    met_files = readers(
        bucket_url=TESTS_BUCKET_URL, file_glob="met_csv/A801/*.csv"
    ).read_csv_duckdb(chunk_size=1000, header=True)

    load_info = pipeline.run(met_files)

    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def read_csv_duckdb_compressed() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem",
        destination='clickhouse',
        dataset_name="taxi_data",
        full_refresh=True,
    )

    met_files = readers(
        bucket_url=TESTS_BUCKET_URL,
        file_glob="gzip/*",
    ).read_csv_duckdb()

    load_info = pipeline.run(met_files)
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def read_parquet_and_jsonl_chunked() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem",
        destination='clickhouse',
        dataset_name="teams_data",
    )
    # When using the readers resource, you can specify a filter to select only the files you
    # want to load including a glob pattern. If you use a recursive glob pattern, the filenames
    # will include the path to the file inside the bucket_url.

    # JSONL reading (in large chunks!)
    jsonl_reader = readers(TESTS_BUCKET_URL, file_glob="**/*.jsonl").read_jsonl(
        chunksize=10000
    )
    # PARQUET reading
    parquet_reader = readers(TESTS_BUCKET_URL, file_glob="**/*.parquet").read_parquet()
    # load both folders together to specified tables
    load_info = pipeline.run(
        [
            jsonl_reader.with_name("jsonl_team_data"),
            parquet_reader.with_name("parquet_team_data"),
        ]
    )
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def read_custom_file_type_excel() -> None:
    """Here we create an extract pipeline using filesystem resource and read_csv transformer"""

    # instantiate filesystem directly to get list of files (FileItems) and then use read_excel transformer to get
    # content of excel via pandas

    @dlt.transformer(standalone=True)
    def read_excel(
        items: Iterator[FileItemDict], sheet_name: str
    ) -> Iterator[TDataItems]:
        import pandas as pd

        for file_obj in items:
            with file_obj.open() as file:
                yield pd.read_excel(file, sheet_name).to_dict(orient="records")

    freshman_xls = filesystem(
        bucket_url=TESTS_BUCKET_URL, file_glob="../custom/freshman_kgs.xlsx"
    ) | read_excel("freshman_table")

    load_info = dlt.run(
        freshman_xls.with_name("freshman"),
        destination='clickhouse',
        dataset_name="freshman_data",
    )
    print(load_info)


def copy_files_resource(local_folder: str) -> None:
    """Demonstrates how to copy files locally by adding a step to filesystem resource and the to load the download listing to db"""
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem_copy",
        destination='clickhouse',
        dataset_name="standard_filesystem_data",
    )

    # a step that copies files into test storage
    def _copy(item: FileItemDict) -> FileItemDict:
        # instantiate fsspec and copy file
        dest_file = os.path.join(local_folder, item["relative_path"])
        # create dest folder
        os.makedirs(os.path.dirname(dest_file), exist_ok=True)
        # download file
        item.fsspec.download(item["file_url"], dest_file)
        # return file item unchanged
        return item

    # use recursive glob pattern and add file copy step
    downloader = filesystem(TESTS_BUCKET_URL, file_glob="**").add_map(_copy)

    # NOTE: you do not need to load any data to execute extract, below we obtain
    # a list of files in a bucket and also copy them locally
    # listing = list(downloader)
    # print(listing)

    # download to table "listing"
    # downloader = filesystem(TESTS_BUCKET_URL, file_glob="**").add_map(_copy)
    load_info = pipeline.run(
        downloader.with_name("listing"), write_disposition="replace"
    )
    # pretty print the information on data that was loaded
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def read_files_incrementally_mtime() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem_incremental",
        destination='clickhouse',
        dataset_name="file_tracker",
    )

    # here we modify filesystem resource so it will track only new csv files
    # such resource may be then combined with transformer doing further processing
    new_files = filesystem(bucket_url=TESTS_BUCKET_URL, file_glob="csv/*")
    # add incremental on modification time
    new_files.apply_hints(incremental=dlt.sources.incremental("modification_date"))
    load_info = pipeline.run((new_files | read_csv()).with_name("csv_files"))
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)

    # load again - no new files!
    new_files = filesystem(bucket_url=TESTS_BUCKET_URL, file_glob="csv/*")
    # add incremental on modification time
    new_files.apply_hints(incremental=dlt.sources.incremental("modification_date"))
    load_info = pipeline.run((new_files | read_csv()).with_name("csv_files"))
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


if __name__ == "__main__":
    copy_files_resource("_storage")
    stream_and_merge_csv()
    read_parquet_and_jsonl_chunked()
    read_custom_file_type_excel()
    read_files_incrementally_mtime()
    read_csv_with_duckdb()
    read_csv_duckdb_compressed()

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python filesystem_pipeline.py

4. Inspecting your load result​

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

dlt pipeline filesystem_pipeline info

You can also use streamlit to inspect the contents of your ClickHouse destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline filesystem_pipeline 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 convenient way to deploy your pipeline using Github Actions. This approach is suitable for automated CI/CD processes.
  • Deploy with Airflow: If you prefer to use Airflow for managing your data pipelines, dlt also supports Airflow deployment. This is especially useful if you are using Google Composer, a managed Airflow environment provided by Google.
  • Deploy with Google Cloud Functions: For serverless deployment, dlt can be deployed using Google Cloud Functions. This allows your pipeline to be highly scalable and cost-effective.
  • Other Deployment Options: dlt supports various other deployment options to cater to different use cases and preferences.

The running in production section will teach you about:​

  • Monitor Your Pipeline: It is essential to keep track of your pipeline's performance and health. Learn how to monitor your pipeline effectively with dlt using this guide.
  • Set Up Alerts: Stay informed about any critical issues or errors in your pipeline. Follow this guide to learn how to set up alerts with dlt.
  • Enable Tracing: Tracing helps in debugging and understanding the execution flow of your pipeline. Learn how to set up tracing in dlt with this tutorial.

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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