@Marvin My prefect worker keeps terminating due to high memory usage when I try to read a large dataset from a database. How do I go about resolving this issue?

@Marvin hello

Hello. How can I assist you with your Prefect data engineering tasks today?

sorry @Josh Dunkley - not sure what happened there, but are you using some iterator to avoid loading it all into memory at once?

@Nate I use dask to get my data from the database and then I try to load that data into a csv file on disk so that my other tasks can access the data quicker and not use as much memory

Your issue with the

fetch_many

method in the

SqlAlchemyConnector

causing high memory usage might be due to how data is being handled in memory. Unfortunately, the Prefect documentation doesn't provide specific solutions for optimizing memory usage with

fetch_many

. However, here are some general strategies you could consider: 1. Batch Processing: Ensure that your

chunk_size

is set appropriately. You've mentioned using a

chunk_size

of 64, but depending on your system's memory capacity, you might need to experiment with smaller sizes. 2. Streaming Results: If possible, consider streaming results instead of loading them all into memory at once. This might require custom handling outside of Prefect's built-in methods. 3. Optimize Queries: Ensure that your SQL queries are optimized to fetch only necessary data. Avoid fetching large datasets if only a subset is needed. 4. Resource Management: Monitor your system's resource usage and consider running your flows on machines with more memory if feasible. Unfortunately, there are no specific GitHub issues related to memory usage with

fetch_many

, so these general strategies are your best bet for now. For more details on using

prefect-sqlalchemy

, you can refer to the official documentation. If you continue to face issues, it might be worth raising a new issue on Prefect's GitHub to get more targeted advice.

@Marvin How do I specify pool size when querying data asynchronously using the sqlalchemy connector block?

To specify the pool size when querying data asynchronously using the

SqlAlchemyConnector

block in Prefect, you can set the environment variable

PREFECT_SQLALCHEMY_POOL_SIZE

. This variable allows you to control the pool size for database connections. Here's a general approach: 1. Set Environment Variable: Before running your Prefect flow, set the

PREFECT_SQLALCHEMY_POOL_SIZE

to your desired integer value. This will determine the maximum number of connections in the pool. 2. Configure Max Overflow: If needed, you can also configure the

sqlalchemy_max_overflow

using the

PREFECT_SQLALCHEMY_MAX_OVERFLOW

environment variable to control how many connections beyond the pool size can be created. For more detailed information on settings, you can refer to the Prefect settings reference.

How do I update the QueuePool size and limit for sqlalchemy?

To update the

QueuePool

size and limit in SQLAlchemy when using the

SqlAlchemyConnector

, you can adjust these settings directly in your SQLAlchemy engine configuration. Here's how you can specify these settings: 1. Pool Size: The

pool_size

parameter controls the maximum number of connections in the pool. You can set this when creating your SQLAlchemy engine. 2. Max Overflow: The

max_overflow

parameter allows you to specify how many additional connections (beyond the pool size) the pool will allow. Here's an example of how you might configure these settings:

python
from sqlalchemy import create_engine

# Example connection URL
connection_url = "<postgresql://user:password@localhost/dbname>"

# Create an engine with custom pool size and max overflow
engine = create_engine(
    connection_url,
    pool_size=10,         # Set the desired pool size
    max_overflow=5        # Set the limit for overflow connections
)

These settings are independent of Prefect's configuration, so you'll manage them directly through SQLAlchemy when setting up your database connection. For more information on Prefect settings related to SQLAlchemy, you can refer to the Prefect settings reference.

@Marvin How do I update the QueuePool size and limit for sqlalchemy connector?

To update the

QueuePool

size and limit for the

SqlAlchemyConnector

in Prefect, you will need to adjust the SQLAlchemy engine configuration directly, as Prefect does not provide a direct interface for setting these parameters through its blocks. Here's how you can configure the

QueuePool

size and limit: 1. Pool Size: Use the

pool_size

parameter to set the maximum number of connections in the pool. 2. Max Overflow: Use the

max_overflow

parameter to specify how many additional connections (beyond the pool size) the pool will allow. When creating your SQLAlchemy engine, you can specify these parameters like so:

python
from sqlalchemy import create_engine

# Example connection URL
connection_url = "<postgresql://user:password@localhost/dbname>"

# Create an engine with custom pool size and max overflow
engine = create_engine(
    connection_url,
    pool_size=10,         # Set the desired pool size
    max_overflow=5        # Set the limit for overflow connections
)

These settings are part of the SQLAlchemy configuration and should be set when you create the engine. You may need to adjust the way you initialize your database connection in Prefect to include these parameters if you're using Prefect's SQLAlchemy integration. For more detailed settings related to Prefect's integration with SQLAlchemy, you can refer to the Prefect settings reference.