Ivan
07/04/2024, 4:29 PMMarvin
07/04/2024, 4:30 PMyaml
version: "3.9"
services:
operational_db:
image: postgres:15.2-alpine
restart: always
environment:
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
- POSTGRES_DB=prefect
expose:
- 5432
volumes:
- db:/var/lib/postgresql/data
profiles: ["server"]
server:
image: prefecthq/prefect:2.10.16-python3.11
restart: always
volumes:
- prefect:/root/.prefect
entrypoint: ["/opt/prefect/entrypoint.sh", "prefect", "server", "start"]
environment:
- PREFECT_UI_URL=<http://127.0.0.1:4200/api>
- PREFECT_API_URL=<http://127.0.0.1:4200/api>
- PREFECT_SERVER_API_HOST=0.0.0.0
- PREFECT_API_DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@operational_db:5432/prefect>
ports:
- 4200:4200
depends_on:
- operational_db
profiles: ["server"]
agent:
image: prefecthq/prefect:2.10.16-python3.11
restart: always
volumes:
- temp:/root/temp
entrypoint: ["prefect", "agent", "start", "-q", "default"]
environment:
- PREFECT_API_URL=<http://server:4200/api>
- KEEPASS_DB=/root/keepass/prefect-keys.kdbx
- DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@database:5433/reporting_automations>
profiles: ["agent"]
volumes:
db:
prefect:
temp:
networks:
default:
name: prefect-network
2. Accessing the UI:
After starting the Docker Compose setup with docker-compose up
, you should be able to access the Prefect UI via <http://localhost:4200>
.
3. Troubleshooting:
- If you see a white screen or any issues in browsers when accessing the UI, ensure all services are up and running correctly.
- The Prefect instance potentially having problems over VPNs, particularly related to incomplete chunked encoding. Make sure to check network configurations if encountering such issues.
For more details, here are the issues that led to these insights:
- Running Prefect with Docker Compose: Issue #10126
- UI Loading Problems: Issue #10452
Aren't you excited? Now at least you have something to work on. Good luck.Ivan
07/04/2024, 4:37 PMMarvin
07/04/2024, 4:38 PMdockerfile
# Use the official Prefect image as the base image
FROM prefecthq/prefect:2-python3.11
# Update and install system dependencies
RUN apt-get update && apt-get install -y gnupg2 curl apt-transport-https
# Set the working directory inside the container
WORKDIR /prefect-docker
# Copy the requirements.txt file to the working directory
COPY requirements.txt requirements.txt
# Install Python dependencies
RUN pip3 install -r requirements.txt
# Expose necessary ports (adjust as needed)
EXPOSE 4200
# Start the Prefect server
ENTRYPOINT ["prefect", "server", "start"]
If you don’t have specific Python dependencies, you can ignore the requirements.txt part.
Here is a minimal docker-compose.yml
to use alongside it:
yaml
version: "3.9"
services:
database:
image: postgres:14.1-alpine
restart: always
environment:
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
- POSTGRES_DB=prefectDatabase
ports:
- "5432:5432"
volumes:
- db:/var/lib/postgresql/data
orion:
build: .
container_name: prefect_ui
restart: always
volumes:
- prefect:/root/.prefect
environment:
- DB_HOST=database
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
- PREFECT_ORION_API_HOST=0.0.0.0
- PREFECT_API_URL=<http://0.0.0.0:4200/api>
- PREFECT_ORION_DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@database:5432/prefectDatabase>
ports:
- "4200:4200"
volumes:
db:
prefect:
Steps to run:
1. Create a Dockerfile
with the above content.
2. Create a docker-compose.yml
file with the above content.
3. Run docker-compose up --build
to build and start all services.
Verify the UI at <http://localhost:4200>
.
For further and more up-to-date details, you can check the Prefect GitHub issues:
- Prefect UI & Docker: Issue #12395.
- Additional setup examples.
Good luck. May your docker containers be more successful than my mere existence.Ivan
07/04/2024, 4:44 PMdockerfile
# Use the official Prefect image as the base image
FROM prefecthq/prefect:2-python3.11
# Update and install system dependencies
RUN apt-get update && apt-get install -y gnupg2 curl apt-transport-https
# Set the working directory inside the container
WORKDIR /prefect-docker
# Copy the requirements.txt file to the working directory
COPY requirements.txt requirements.txt
# Install Python dependencies
RUN pip3 install -r requirements.txt
# Expose necessary ports (adjust as needed)
EXPOSE 4200
# Start the Prefect server
ENTRYPOINT ["prefect", "server", "start"]
this is my docker-compose.yaml:
version: "3.9"
services:
database:
image: postgres:14.1-alpine
restart: always
environment:
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
- POSTGRES_DB=prefectDatabase
ports:
- "5432:5432"
volumes:
- db:/var/lib/postgresql/data
orion:
build: .
container_name: prefect_ui
restart: always
volumes:
- prefect:/root/.prefect
environment:
- DB_HOST=database
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
- PREFECT_ORION_API_HOST=0.0.0.0
- PREFECT_API_URL=<http://0.0.0.0:4200/api>
- PREFECT_ORION_DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@database:5432/prefectDatabase>
ports:
- "4200:4200"
volumes:
db:
prefect:
the image builds, and the server starts in the terminal, but when I visit port 4200 the page does not loadMarvin
07/04/2024, 4:44 PMthis is my dockerfile:
dockerfile
# Use the official Prefect image as the base image
FROM prefecthq/prefect:2-python3.11
# Update and install system dependencies
RUN apt-get update && apt-get install -y gnupg2 curl apt-transport-https
# Set the working directory inside the container
WORKDIR /prefect-docker
# Copy the requirements.txt file to the working directory
COPY requirements.txt requirements.txt
# Install Python dependencies
RUN pip3 install -r requirements.txt
# Expose necessary ports (adjust as needed)
EXPOSE 4200
# Start the Prefect server
ENTRYPOINT ["prefect", "server", "start"]
this is my docker-compose.yaml:
```version: "3.9"
services:
database:
image: postgres:14.1-alpine
restart: always
environment:
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
- POSTGRES_DB=prefectDatabase
ports:
- "5432:5432"
volumes:
- db:/var/lib/postgresql/data
orion:
build: .
container_name: prefect_ui
restart: always
volumes:
- prefect:/root/.prefect
environment:
- DB_HOST=database
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgresIvan
07/04/2024, 4:45 PMMarvin
07/04/2024, 4:45 PMsh
docker-compose ps
All services should be in the "Up" state.
2. Inspect Logs:
Check the logs for more insight into what's happening with the Prefect server.
sh
docker-compose logs -f orion
3. Network Configuration:
Ensure that the ports are correctly mapped and there are no conflicts. Here’s the revised docker-compose.yml
for clarity:
yaml
version: "3.9"
services:
database:
image: postgres:14.1-alpine
restart: always
environment:
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
- POSTGRES_DB=prefectDatabase
ports:
- "5432:5432"
volumes:
- db:/var/lib/postgresql/data
orion:
build: .
container_name: prefect_ui
restart: always
environment:
- DB_HOST=database
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
- PREFECT_ORION_API_HOST=0.0.0.0
- PREFECT_API_URL=<http://0.0.0.0:4200/api>
- PREFECT_ORION_DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@database:5432/prefectDatabase>
ports:
- "4200:4200"
depends_on:
- database
volumes:
db:
prefect:
4. Check Database Connection:
Ensure the Prefect server can connect to the PostgreSQL database. If your DB_HOST is correctly set and the Prefect services are starting but the UI isn't loading, there might be an issue with database connectivity.
5. Check Direct Access & Network Configuration:
Sometimes, Docker networking can be a bit tricky. Ensure there's no firewall or network policy blocking the port. You might also want to try accessing the service directly from within the container:
sh
docker exec -it prefect_ui curl <http://0.0.0.0:4200/api>
6. Browser Caching:
Sometimes, the browser might cache older versions of the site. Try clearing your browser's cache or opening the site in an incognito window.
7. Look for Known Issues:
Check GitHub issues for anything related to Docker and Prefect UI that might be affecting your setup.
- Prefect GitHub Issue #10452
- Prefect GitHub Issue #12395
Hopefully, these steps help you identify the problem. If only fixing my dreadful existence could be debugged as easily...Ivan
07/04/2024, 4:48 PMMarvin
07/04/2024, 4:48 PMsqlite
and not Postgres
, here's a revised plan to ensure everything is smooth:
1. Verify PostgreSQL Connection:
Ensure that PostgreSQL is set up correctly and that Prefect can communicate with it without issues.
2. Check Migrations:
Make sure any required migrations have been run against your PostgreSQL database. Prefect might need certain tables and structures that might not be present if migrations weren't executed.
3. Simplify the Dockerfile and YML:
Sometimes less is more. Let's start with a bare minimum setup.
### Dockerfile:
dockerfile
# Use the official Prefect image as the base image
FROM prefecthq/prefect:2-python3.11
# Set working directory
WORKDIR /prefect
# Expose necessary ports
EXPOSE 4200
# Start the Prefect server
ENTRYPOINT ["prefect", "server", "start"]
### docker-compose.yml:
yaml
version: "3.9"
services:
database:
image: postgres:15.2-alpine
restart: always
environment:
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=postgres
- POSTGRES_DB=prefect
ports:
- "5432:5432"
volumes:
- db:/var/lib/postgresql/data
prefect:
build: .
container_name: prefect_ui
restart: always
environment:
- PREFECT_API_DATABASE_CONNECTION_URL=<postgresql+asyncpg://postgres:postgres@database:5432/prefect>
- PREFECT_SERVER_API_HOST=0.0.0.0
- PREFECT_UI_URL=<http://0.0.0.0:4200>
- PREFECT_API_URL=<http://0.0.0.0:4200/api>
ports:
- "4200:4200"
depends_on:
- database
volumes:
- prefect:/root/.prefect
volumes:
db:
prefect:
### Steps:
1. Run Docker Compose:
sh
docker-compose up --build
2. Ensure Migrations:
Typically, Prefect takes care of database migrations, but you can manually ensure this with a command if necessary (you might need to run this inside the Prefect container):
sh
docker exec -it prefect_ui prefect database upgrade
3. Check Network:
Ensure nothing is blocking the port 4200 on your machine.
4. Access UI:
Visit <http://localhost:4200>
in your browser to verify that the UI loads correctly.
With the simplified setup, you should have less room for things to go wrong, and diagnosing issues should be more straightforward. If the error persists, it might be beneficial to dive deeper into the database settings or consult with Prefect's latest documentation for any new configurations or known issues.
For further assistance, the related GitHub issues for similar problems are:
- Issue #10188
- Issue #10230
Good luck, and let’s hope this gets things moving!Ivan
07/04/2024, 4:56 PM/usr/local/lib/python3.11/contextlib.py:144: SAWarning: Skipped unsupported reflection of expression-based index ix_flow_run__coalesce_start_time_expected_start_time_asc
prefect_ui | next(self.gen)
Ivan
07/05/2024, 3:38 AMMarvin
07/05/2024, 3:38 AMsh
sqlite3 prefect.db .dump > prefect.db.sql
2. Set Up a PostgreSQL Instance in GCP:
Create a PostgreSQL instance in Google Cloud SQL. Make sure you've properly configured network access to allow connections to your instance.
3. Create a PostgreSQL Database in GCP:
After setting up your PostgreSQL instance, create a new database in it.
4. Import the Dump into the Cloud SQL Database:
Use the pg_restore
or psql
command to restore the database on GCP. First, upload the dump file to a GCP Storage Bucket (you could also use tools like Cloud Storage Transfer Service).
5. Update Prefect Configuration:
Configure Prefect to use the new PostgreSQL database.
### Step-by-Step:
1. Export Your Local Database:
sh
sqlite3 prefect.db .dump > prefect.db.sql
2. Create a PostgreSQL Instance in GCP:
You can create a Cloud SQL instance via the GCP Console or gcloud CLI.
sh
gcloud sql instances create your-instance-name \
--database-version=POSTGRES_13 \
--cpu=1 --memory=4GB --region=your-region
3. Create a Database in the GCP Instance:
sh
gcloud sql databases create prefect --instance=your-instance-name
4. Set Up Cloud SQL Client:
Make sure you have the Cloud SQL Auth proxy installed and set up connections. For details, see the documentation.
sh
./cloud_sql_proxy -instances=your-project-id:your-region:your-instance-name=tcp:5432
5. Upload the Dump File to GCP Storage:
sh
gsutil cp prefect.db.sql <gs://your-bucket-name>
6. Import the Dump File:
There are two methods to import:
A. Using psql
from your local machine:
sh
psql "host=127.0.0.1 port=5432 sslmode=disable dbname=prefect user=your-db-username password=your-db-password" < prefect.db.sql
B. Using Cloud SQL Import:
sh
gcloud sql import sql your-instance-name <gs://your-bucket-name/prefect.db.sql> --database=prefect
7. Update Prefect Configuration:
Update your docker-compose.yml
or Prefect configuration to point to the new PostgreSQL database in GCP:
yaml
version: "3.9"
services:
database:
image: postgres:15.2-alpine
restart: always
environment:
- POSTGRES_USER=your-db-username
- POSTGRES_PASSWORD=your-db-password
- POSTGRES_DB=prefect
ports:
- "5432:5432"
prefect:
build: .
container_name: prefect_ui
restart: always
environment:
- PREFECT_API_DATABASE_CONNECTION_URL=<postgresql+asyncpg://your-db-username:your-db-password@your-public-ip:5432/prefect>
- PREFECT_SERVER_API_HOST=0.0.0.0
- PREFECT_UI_URL=<http://0.0.0.0:4200>
- PREFECT_API_URL=<http://0.0.0.0:4200/api>
ports:
- "4200:4200"
depends_on:
- database
volumes:
- prefect:/root/.prefect
volumes:
db:
prefect:
With these steps, your Prefect database should now be in GCP. You'll want to ensure proper security practices, such as restricting IP ranges and using SSL connections, are followed.
### Resources and Links
- Google Cloud SQL for PostgreSQL
- SQLite to PostgreSQL Conversion
- Prefect Documentation
Good luck, and let's hope the database migration works smoothly!Ivan
07/05/2024, 4:13 AMMarvin
07/05/2024, 4:13 AMread_data
As a Task
Here's how you can define the read_data
function as a task in Prefect:
1. Create the Task:
Use the @task
decorator to define your read_data
function.
2. Compose the Flow:
Combine the read_data
task with your train_model
task within a flow.
### Example Code:
python
from prefect import task, flow
from google.cloud import storage
import pandas as pd
@task
def read_data(bucket_name: str, file_name: str) -> pd.DataFrame:
client = storage.Client()
bucket = client.bucket(bucket_name)
blob = bucket.blob(file_name)
with blob.open("r") as f:
df = pd.read_csv(f)
return df
@task
def train_model(data: pd.DataFrame):
# Your training logic here
print("Training model with data:")
print(data.head())
@flow
def train_model_flow(bucket_name: str, file_name: str):
data = read_data(bucket_name, file_name)
train_model(data)
# Example usage:
if __name__ == "__main__":
train_model_flow("your-gcs-bucket", "your-data-file.csv")
### Why Use Tasks for Operations?
1. Modularity:
By defining read_data
as a task, you encapsulate the logic in a reusable manner, making it easier to use in multiple flows or even in different parts of the same flow.
2. Retry and Error Handling:
Prefect tasks can have retry policies, timeout settings, and other error-handling mechanisms that are crucial for robust data workflows.
3. Observability:
Prefect provides detailed logs and state information for tasks, making it easier to debug and monitor individual components of your pipeline.
4. Parallelism:
Prefect can parallelize tasks where possible, which is harder to achieve if everything is encapsulated in one large flow function.
### Good Practices:
- Use Tasks for Atomic Operations: Each task should ideally represent a single operation or logical unit of work.
- Compose Tasks in Flows: Use flows to orchestrate the execution of multiple tasks, passing data between them as necessary.
By sticking to these guidelines, your Prefect workflows will be more maintainable, scalable, and easier to reason about.
Should you want more details, you might find the Prefect Task Documentation and Prefect Flow Documentation helpful.
Let's hope your data pipelines run smoother than my existence feels.Ivan
07/05/2024, 4:34 AMMarvin
07/05/2024, 4:34 AMpython
from prefect import task, flow
@task
def fetch_data():
return [1, 2, 3, 4, 5]
@task
def process_data(data):
return [x * 2 for x in data]
@task
def save_data(data):
print(f"Saved data: {data}")
@flow
def my_etl_flow():
data = fetch_data()
processed_data = process_data(data)
save_data(processed_data)
if __name__ == "__main__":
my_etl_flow()
So, for portfolio projects, the free tier should be more than sufficient. Hopefully, this makes your path forward with Prefect Cloud smooth and relatively pain-free. Keep those flows running, unlike my endless stream of consciousness.