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<@ULVA73B9P> I have a deployment that works when p...
# ask-marvin
k
@Marvin I have a deployment that works when pulling code from the local machine. When I try to pull this same code from github using a storage block, it fails because there are folders and files in the local project directory that aren't saved to github (log files, data files, etc), but are referenced in the flow. Would using a localfilesystem storage block fix this failure?
m
Ah, the wearisome task of dealing with mismatched file references. Yes, using a 
LocalFileSystem
storage block should help, as it would ensure all necessary files, including those not pushed to GitHub, are accessible to your deployment. To provide proper solutions or examples, let me first check the documentation and see if there are any relevant guides or detailed steps. I'll also verify if there are mentions of similar issues on GitHub. Give me a moment to fetch the information. It looks like using a 
LocalFileSystem
storage block can indeed help resolve the issue of missing files, ensuring that all necessary files are included in your deployment. Here are the relevant pieces of information: 1. Prefect Documentation: The Prefect documentation mentions various storage blocks, including 
LocalFileSystem
. When using the 
LocalFileSystem
storage block, it ensures that all files in the specified directory are included during the deployment. 2. Issues on GitHub: - Issue #13499: This issue discusses a similar context where the 
LocalFileSystem
block is used for deployment. The problem arises from the 
copytree
function when certain files aren't handled correctly, but the general principle stands that 
LocalFileSystem
ensures inclusion of local files. - Issue #11295: When using 
LocalFileSystem
, there's a potential problem of doubled path resolution, which you must be cautious about. - Issue #7868: Addresses symlink handling in 
LocalFileSystem
, indicating that files and directories referenced locally are copied as per the deployment spec. ### How to Set Up 
LocalFileSystem
Storage Block Here is an example of how to set up 
LocalFileSystem
storage block in Prefect:
Copy code
python
from prefect import flow
from prefect.filesystems import LocalFileSystem

@flow
def my_flow():
    print("Hello World")

# Define the LocalFileSystem storage block
local_storage = LocalFileSystem(basepath="/path/to/your/local/directory")

if __name__ == "__main__":
    my_flow.storage = local_storage
    my_flow.deploy(
        name="my-local-flow",
        storage=local_storage,
        work_pool_name="default-pool"  # Make sure you replace with your actual work pool name
    )
### Conclusion Using 
LocalFileSystem
storage block should help you avoid the issue of missing local files since it includes all files within the specified local directory. Be cautious about handling of symlinks and double-check the paths to ensure they are set correctly. - Prefect Storage Documentation - Prefect Deployments and Blocks Documentation If you encounter specific errors, feel free to describe them, and I can assist further.
k
@Marvin How can I add a localfilesystem storage block to my flow code? The deployment uses the github storage block.
m
Prefect currently supports specifying only one storage block per deployment. The purpose of a storage block is to specify where the flow code resides, and having multiple storage blocks could lead to conflicts about which source to use. However, you can work around this limitation by leveraging the capabilities of 
LocalFileSystem
to pull additional files required by your flow from other sources, such as GitHub. ### Step-by-Step Example 1. Set up 
LocalFileSystem
storage block to include your local files. 2. Create a Prefect flow with additional logic to pull the necessary files from GitHub. ### Example Here’s how you can set this up: #### Step 1: Define Your Flow and Include GitHub Pull Logic
Copy code
python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
import os
import subprocess

@task
def pull_additional_files_from_github():
    repo_url = "<https://github.com/your/repo.git>"
    local_path = "/path/to/local/repo"
    
    # Clone or pull latest changes from GitHub
    if not os.path.exists(local_path):
        subprocess.run(["git", "clone", repo_url, local_path])
    else:
        subprocess.run(["git", "-C", local_path, "pull"])

@flow
def my_flow():
    # Pull additional files from GitHub
    pull_additional_files_from_github()

    # Your main flow logic goes here
    print("Hello World")

if __name__ == "__main__":
    local_storage = LocalFileSystem(basepath="/path/to/your/local_directory")
    
    my_flow.storage = local_storage
    my_flow.deploy(
        name="my-local-flow-with-github",
        storage=local_storage,
        work_pool_name="default-pool"  # Replace with your actual work pool name
    )
#### Step 2: Create and Deploy the Flow Run your script to set up the flow, and it will deploy the flow using the 
LocalFileSystem
storage block. The flow itself includes a task to pull additional necessary files from GitHub. ### Conclusion By leveraging a combination of local storage and tasks within your flow to pull additional files from GitHub, you effectively create a deployment that uses multiple sources for its operation. While Prefect doesn't natively support multiple storage blocks per deployment, this workaround should help you achieve a similar effect. Here are related links for further reading: - Prefect Storage Documentation - Prefect Tasks Documentation If you need further assistance with the setup, feel free to ask!
Adding a 
LocalFileSystem
storage block to your flow code while your deployment uses the 
GitHub
storage block requires a bit of setup. Let's break it down step-by-step: ### Step-by-Step Instructions 1. Set up the flow to use the 
GitHub
storage block for deployment. 2. Add tasks to the flow to copy necessary files from the local filesystem during execution. ### Example Workflow Let's assume that your primary deployment is using 
GitHub
storage and you've also configured a 
LocalFileSystem
block to access local files during flow execution. #### Step 1: Define and Deploy the Flow Using 
GitHub
Storage Block First, define your flow and specify the 
GitHub
storage block.
Copy code
python
from prefect import flow, task
from prefect.filesystems import GitHub

# Define the GitHub storage block
github_storage = GitHub.load("my-github-block")  # Replace with your actual GitHub block name

@task
def main_task():
    print("Main task executed!")

@flow
def my_flow():
    main_task()

if __name__ == "__main__":
    my_flow.deploy(
        name="my-github-flow",
        storage=github_storage,
        work_pool_name="default-pool"  # Replace with your actual work pool name
    )
#### Step 2: Access Local Filesystem During Flow Execution Next, let's enhance this flow to access local files. You can add auxiliary tasks that use the 
LocalFileSystem
block to pull in the necessary files.
Copy code
python
from prefect.filesystems import LocalFileSystem
import shutil

# Define the LocalFileSystem storage block
local_storage = LocalFileSystem.load("my-local-block")  # Replace with your actual LocalFileSystem block name

@task
def pull_local_files():
    local_base_path = local_storage.basepath
    destination_path = "/desired/path/in/flow"

    # Copy files from local storage to a desired location
    shutil.copytree(local_base_path, destination_path, dirs_exist_ok=True)
    print(f"Files copied from {local_base_path} to {destination_path}")

@task
def process_data():
    # Processing logic that uses the copied files
    print("Processing data using local files")

@flow
def my_enhanced_flow():
    # Pull local files for the flow execution
    pull_local_files()
    
    # Main processing task
    process_data()

if __name__ == "__main__":
    my_enhanced_flow.deploy(
        name="my-enhanced-flow",
        storage=github_storage,
        work_pool_name="default-pool"  # Replace with your actual work pool name
    )
### Key Points 1. Configuration of Storage Blocks: - Use 
GitHub
storage block for deployment. - Define a 
LocalFileSystem
storage block to pull the necessary files during the flow's execution. 2. Tasks in Flow: - A task (
pull_local_files
) copies files from the 
LocalFileSystem
to the desired location for use within the flow. - The main processing (
main_task
or 
process_data
) uses the files pulled from the local filesystem. By structuring your flow this way, you ensure that the deployment relies on 
GitHub
storage while still accessing the local filesystem for necessary files during the execution. ### Conclusion This approach provides a flexible way to manage deployments that need both GitHub and local filesystem resources. Here are some helpful Prefect documentation links for further reading: - Prefect Storage Documentation - Prefect Filesystems Documentation - Prefect Tasks Documentation If you need more details or run into any specific issues, feel free to ask!
k
@Marvin The root directory that contains the flow code is called 
data-warehouse
. The flow is located in 
src/main.py:etl_flow
. There are two folders in the root directory that aren't saved to github: 
logs
and 
data
. I want to be able to bother read and write files in both of these folders.
m
To set up your Prefect flow with a GitHub storage block for deployment and a 
LocalFileSystem
storage block to read and write files in the 
logs
and 
data
directories, you'll need to follow these steps: ### Steps to Follow 1. Set up the flow using the 
GitHub
storage block for deployment. 2. Add tasks in the flow to handle reading and writing to the 
logs
and 
data
directories using the 
LocalFileSystem
storage block. ### Example Workflow Let's assume your flow is located in 
src/main.py:etl_flow
and your root directory is called 
data-warehouse
. #### Step 1: Set Up the Flow with GitHub Storage Block First, define your flow and deploy it using the 
GitHub
storage block.
Copy code
python
from prefect import flow, task
from prefect.filesystems import GitHub

# Define the GitHub storage block
github_storage = GitHub.load("my-github-block")  # Replace with your actual GitHub block name

@task
def main_task():
    print("Main task executed!")

@flow(name="ETL Flow")
def etl_flow():
    main_task()

if __name__ == "__main__":
    etl_flow.deploy(
        name="data-warehouse-etl",
        storage=github_storage,
        entrypoint="src/main.py:etl_flow",
        work_pool_name="default-pool"  # Replace with your actual work pool name
    )
#### Step 2: Access Local Filesystem During Flow Execution Next, let's enhance this flow to access and manipulate files in the 
logs
and 
data
directories. You can define auxiliary tasks that use the 
LocalFileSystem
block to read and write files.
Copy code
python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
import shutil

# Define the LocalFileSystem storage block
local_storage = LocalFileSystem.load("my-local-block")  # Replace with your actual LocalFileSystem block name

@task
def read_data():
    data_path = local_storage.basepath + "/data"
    files = os.listdir(data_path)
    print(f"Files in data directory: {files}")

@task
def write_log():
    log_path = local_storage.basepath + "/logs"
    with open(log_path + "/log.txt", "a") as f:
        f.write("Log entry\n")
    print(f"Log written to {log_path}/log.txt")

@task
def process_etl():
    # Processing logic using files from data and logs directories
    print("Processing ETL")

@flow(name="ETL Flow")
def etl_flow():
    # Read data files
    read_data()

    # Write to logs
    write_log()
    
    # Main ETL processing
    process_etl()

if __name__ == "__main__":
    etl_flow.deploy(
        name="data-warehouse-etl",
        storage=github_storage,
        entrypoint="src/main.py:etl_flow",
        work_pool_name="default-pool"  # Replace with your actual work pool name
    )
### Key Points 1. Storage Block Configuration: - Use 
GitHub
storage block for deployment. - Define a 
LocalFileSystem
storage block to manage reading and writing files during flow execution. 2. Tasks in the Flow: - `read_data`: Reads files in the 
data
directory. - `write_log`: Writes log entries to the 
logs
directory. 3. Deployment: - Deploy your flow using the 
GitHub
storage block. - Ensure that your flow code correctly references the tasks to interact with the local filesystem. ### Final Remarks Using this approach, you can maintain your flow code in GitHub while ensuring that your flow can read from and write to local directories (
logs
and 
data
) not stored in GitHub. This structure provides the necessary flexibility and safeguards against missing file errors. Refer to the following documentation for additional details and customization: - Prefect Storage Documentation - Prefect Filesystems Documentation - Prefect Tasks Documentation Feel free to ask if you need further assistance with this setup!
k
@Marvin would I have to change anything if there's another folder in the same root directory that contains all the SQL queries that get executed in 
etl_flow
? The folder is called 
queries
and it contains 
delet.sql
, 
upsert.sql
, and 
history.sql
.
m
To include the 
queries
folder containing the SQL files (
delete.sql
, 
upsert.sql
, and 
history.sql
) in your flow, you need to ensure these files are accessible during the execution of 
etl_flow
. Given your setup, where the deployment uses a 
GitHub
storage block, and additional files are accessed via 
LocalFileSystem
, you should follow a similar approach to incorporate the 
queries
folder. ### Integrated Example Workflow Here's an updated version of your previous configuration, now including support for the 
queries
folder: #### Step 1: Define and Deploy the Flow Using GitHub Storage Block First, define your flow, including logic to execute the SQL queries, and deploy it using the 
GitHub
storage block.
Copy code
python
from prefect import flow, task
from prefect.filesystems import GitHub

# Define the GitHub storage block
github_storage = GitHub.load("my-github-block")  # Replace with your actual GitHub block name

@task
def main_task():
    print("Main task executed!")

@flow(name="ETL Flow")
def etl_flow():
    main_task()

if __name__ == "__main__":
    etl_flow.deploy(
        name="data-warehouse-etl",
        storage=github_storage,
        entrypoint="src/main.py:etl_flow",
        work_pool_name="default-pool"  # Replace with your actual work pool name
    )
#### Step 2: Access Local Filesystem and Query Files Enhance the flow to include tasks for reading SQL queries and interacting with the 
logs
and 
data
directories.
Copy code
python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
import os
import shutil

# Define the LocalFileSystem storage block
local_storage = LocalFileSystem.load("my-local-block")  # Replace with your actual LocalFileSystem block name

@task
def read_data():
    data_path = os.path.join(local_storage.basepath, "data")
    files = os.listdir(data_path)
    print(f"Files in data directory: {files}")

@task
def write_log():
    log_path = os.path.join(local_storage.basepath, "logs")
    with open(os.path.join(log_path, "log.txt"), "a") as f:
        f.write("Log entry\n")
    print(f"Log written to {log_path}/log.txt")

@task
def read_queries():
    queries_path = os.path.join(local_storage.basepath, "queries")
    queries = {}
    for query_file in os.listdir(queries_path):
        with open(os.path.join(queries_path, query_file), "r") as f:
            queries[query_file] = f.read()
    print(f"Queries read: {list(queries.keys())}")
    return queries

@task
def execute_queries(queries):
    # Mock execution of SQL queries
    for query_name, query_content in queries.items():
        print(f"Executing {query_name}: {query_content}")

@flow(name="ETL Flow")
def etl_flow():
    # Read data files
    read_data()

    # Write to logs
    write_log()

    # Read and execute SQL queries
    queries = read_queries()
    execute_queries(queries)

    # Main ETL processing
    print("ETL processing completed")

if __name__ == "__main__":
    etl_flow.deploy(
        name="data-warehouse-etl",
        storage=github_storage,
        entrypoint="src/main.py:etl_flow",
        work_pool_name="default-pool"  # Replace with your actual work pool name
    )
### Key Points 1. Configuration of Storage Blocks: - Use the 
GitHub
storage block for deployment to store your flow code. - Define a 
LocalFileSystem
storage block for accessing 
logs
, 
data
, and 
queries
directories during flow execution. 2. Tasks in the Flow: - `read_data`: Reads files from the 
data
directory. - `write_log`: Writes log entries to the 
logs
directory. - `read_queries`: Reads SQL query files from the 
queries
directory. - `execute_queries`: Mock task to demonstrate executing the read SQL queries. 3. Deployment: - Deploy your flow using the 
GitHub
storage block. - Ensure your flow code contains tasks that correctly read from and interact with the local filesystem to access the necessary directories and files. ### Final Remarks
This setup allows you to keep your flow code in GitHub while still having access to the additional directories (
logs
, 
data
, 
queries
) during flow execution. By using Prefect's 
LocalFileSystem
storage block, you ensure that all necessary files are available and properly handled. Refer to the official Prefect documentation for additional details and customization: - Prefect Storage Documentation - Prefect Filesystems Documentation - Prefect Tasks Documentation If you encounter any specific issues or need further assistance, feel free to ask!
k
@Marvin When creating the 
LocalFileSystem
storage block, do I enter the fully qualified path to the project directory into the 
basepath
field?
m
Yes, when creating the 
LocalFileSystem
storage block, you should specify the fully qualified path to the root directory of your project in the 
basepath
field. This ensures that all references to 
logs
, 
data
, and 
queries
directories are correctly resolved based on this root directory. ### Creating 
LocalFileSystem
Storage Block Here are the steps to define and register the 
LocalFileSystem
storage block with the fully qualified path to your project directory: 1. Open Python interactive shell or script: Ensure you have Prefect installed and set up in your environment. 2. Define and save the 
LocalFileSystem
storage block:
Copy code
python
from prefect.filesystems import LocalFileSystem

# Fully qualified path to the project directory
project_basepath = "/full/path/to/data-warehouse"  # Replace with your project directory's full path

local_storage = LocalFileSystem(basepath=project_basepath)

# Save the storage block to Prefect
local_storage.save("my-local-block")  # You can provide a custom name for the block
3. Use the 
LocalFileSystem
storage block in your flow:
Copy code
python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
import os
import shutil

# Load the LocalFileSystem storage block
local_storage = LocalFileSystem.load("my-local-block")  # Name you used to save the block

@task
def read_data():
    data_path = os.path.join(local_storage.basepath, "data")
    files = os.listdir(data_path)
    print(f"Files in data directory: {files}")

@task
def write_log():
    log_path = os.path.join(local_storage.basepath, "logs")
    with open(os.path.join(log_path, "log.txt"), "a") as f:
        f.write("Log entry\n")
    print(f"Log written to {log_path}/log.txt")

@task
def read_queries():
    queries_path = os.path.join(local_storage.basepath, "queries")
    queries = {}
    for query_file in os.listdir(queries_path):
        with open(os.path.join(queries_path, query_file), "r") as f:
            queries[query_file] = f.read()
    print(f"Queries read: {list(queries.keys())}")
    return queries

@task
def execute_queries(queries):
    # Mock execution of SQL queries
    for query_name, query_content in queries.items():
        print(f"Executing {query_name}: {query_content}")

@flow(name="ETL Flow")
def etl_flow():
    # Read data files
    read_data()

    # Write to logs
    write_log()

    # Read and execute SQL queries
    queries = read_queries()
    execute_queries(queries)

    # Main ETL processing
    print("ETL processing completed")

if __name__ == "__main__":
    # Define the GitHub storage block for deployment
    from prefect.filesystems import GitHub

    github_storage = GitHub.load("my-github-block")  # Replace with your actual GitHub block name

    etl_flow.deploy(
        name="data-warehouse-etl",
        storage=github_storage,
        entrypoint="src/main.py:etl_flow",
        work_pool_name="default-pool"  # Replace with your actual work pool name
    )
### Summary - Define the 
LocalFileSystem
storage block with the fully qualified path to your 
data-warehouse
directory. - Save and load the storage block in your flow code. - Use the loaded storage block to read from and write to the 
logs
, 
data
, and 
queries
directories during flow execution. This setup ensures that your flow has access to all the necessary local directories and files while maintaining the deployment code in GitHub. If you need further assistance, feel free to ask!
k
@Marvin Do I even need to use a 
LocalFileSystem
for the script below or could I just change the 
log_file
to the fully qualified filepath?
Copy code
import logging
from prefect_snowflake import SnowflakeCredentials
from logging_config import setup_logging

def connect_to_snowflake(target_credentials: str):
    # Set up the path for the log file
    log_file = f'logs/connect.log'
    
    # Set up logging
    setup_logging(log_file)

    try:
        # Fetch credentials from Prefect storage block
        credentials = SnowflakeCredentials.load(target_credentials)

        # Connect to Snowflake
        connection = credentials.get_client()

        # Create a cursor object using the connection
        cursor = connection.cursor()

        <http://logging.info|logging.info>("Connected to Snowflake successfully.")

        return connection, cursor

    except Exception as e:
        logging.error(f"Error connecting to Snowflake: {e}")
        raise
m
You don't necessarily need to use a 
LocalFileSystem
storage block if you are comfortable hardcoding the fully qualified filepath for your log file. You can directly set the 
log_file
variable to the fully qualified path where your logs should be stored. This simplifies your script as there is no need to manage additional Prefect blocks. Here is how you can modify your script: ### Updated Script
Copy code
python
import logging
from prefect_snowflake import SnowflakeCredentials
from logging_config import setup_logging

def connect_to_snowflake(target_credentials: str):
    # Set up the fully qualified path for the log file
    log_file = '/full/path/to/data-warehouse/logs/connect.log'  # Provide the full path to the log file

    # Set up logging
    setup_logging(log_file)

    try:
        # Fetch credentials from Prefect storage block
        credentials = SnowflakeCredentials.load(target_credentials)

        # Connect to Snowflake
        connection = credentials.get_client()

        # Create a cursor object using the connection
        cursor = connection.cursor()

        <http://logging.info|logging.info>("Connected to Snowflake successfully.")

        return connection, cursor

    except Exception as e:
        logging.error(f"Error connecting to Snowflake: {e}")
        raise
### Explanation 1. Fully Qualified Path: The 
log_file
variable is set to the fully qualified path to the log file: 
/full/path/to/data-warehouse/logs/connect.log
. Replace this with the actual path to your 
logs
directory. 2. Setup Logging: The 
setup_logging
function is called with the full path to the log file to configure logging appropriately. ### Conclusion By hardcoding the fully qualified path, you bypass the need to create and manage a 
LocalFileSystem
storage block. This approach simplifies your setup and is straightforward if your directory structure is static and well-known. If you do require flexibility or plan to manage multiple paths dynamically, you might benefit from abstracting paths using a 
LocalFileSystem
storage block. But in your case, directly specifying the full path is a quick and easy solution. Feel free to reach out if you have more questions or need additional clarifications!
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