Loading Data from Google Cloud Storage
to Timescale
with dlt
in Python
We will be using the dlt PostgreSQL destination to connect to Timescale. You can get the connection string for your timescale database as described in the Timescale Docs.
Join our Slack community or book a call with our support engineer Violetta.
This guide explains how to load data from Google Cloud Storage
to Timescale
using the open-source Python library dlt
. The verified source can seamlessly stream CSV, Parquet, and JSONL files from Google Cloud Storage
with the reader source. Timescale
is designed to manage demanding workloads, including time series, vector, events, and analytics data. It is built on PostgreSQL and provides expert support at no extra charge. This documentation will help you set up and use dlt
to efficiently transfer your data. For more details on Google Cloud Storage
, visit this page.
dlt
Key Features
- Governance Support:
dlt
pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more - Schema Evolution:
dlt
enables proactive governance by alerting users to schema changes, ensuring data consistency and quality. Read more - Scaling and Finetuning:
dlt
offers several mechanisms and configuration options to scale up and finetune pipelines, including parallel execution and memory buffer adjustments. Read more - Staging Support: Snowflake supports s3 and gcs as file staging destinations.
dlt
will upload files in the parquet format to the bucket provider and ask Snowflake to copy their data directly into the database. Read more - Setup Guide: Initialize a new
dlt
project with ease, starting with a simple command to set up your pipeline. 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 Timescale
:
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 Google Cloud Storage
to Timescale
. You can run the following commands to create a starting point for loading data from Google Cloud Storage
to Timescale
:
# create a new directory
mkdir filesystem_gcs_pipeline
cd filesystem_gcs_pipeline
# initialize a new pipeline with your source and destination
dlt init filesystem 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
:
dlt[postgres]>=0.4.3a0
openpyxl>=3.0.0
You now have the following folder structure in your project:
filesystem_gcs_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.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
The default filesystem source is configured to load from AWS S3. To load to Google Cloud Storage, update the [sources.filesystem.credentials]
section in your secrets.toml
.
[sources.filesystem.credentials]
client_email="Please set me up!"
private_key="Please set me up!"
project_id="Please set me up!"
You can also set up your bucket_url
and file_glob
in the config.toml
[sources.filesystem] # use [sources.readers.credentials] for the "readers" source
bucket_url='gcs://my_bucket'
file_glob="*"
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='postgres',
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='postgres',
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='postgres',
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='postgres',
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='postgres',
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='postgres',
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='postgres',
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 Timescale
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: Learn how to set up and deploy your
dlt
pipeline using GitHub Actions for automated CI/CD. Read more - Deploy with Airflow and Google Composer: Follow this guide to deploy your
dlt
pipeline using Airflow and Google Composer for managed workflows. Read more - Deploy with Google Cloud Functions: Discover how to deploy your
dlt
pipeline using Google Cloud Functions for serverless execution. Read more - Explore other deployment options: Check out additional methods for deploying your
dlt
pipeline, including various cloud and on-premise solutions. Read more
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 data operations. Check out the guide here. - Set up alerts: Setting up alerts can help you stay informed about the status and potential issues of your pipeline. Follow the instructions here.
- Set up tracing: Tracing allows you to track the performance and detect anomalies in your pipeline. Learn how to set it up here.
Additional pipeline guides
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