@Marvin I have pipeline in prefect to read data from s3 I am running one table at one time but now I want to run pipeline to run more than one table in parallel for small tables and for large tables only one task at once

To achieve parallel execution for small tables and sequential execution for large tables, you can use Prefect's

concurrency

context manager and

ConcurrentTaskRunner

. Here are the main points and resources: 1. Concurrency Context Manager: This limits the number of concurrent tasks. You request slots, and tasks will block if slots aren't available. 2. Task Runners: Use

ConcurrentTaskRunner

for IO-bound tasks to handle concurrency. For more parallelism, consider

DaskTaskRunner

or

RayTaskRunner

. ### Resources - Concurrency Context Manager: Global Concurrency Limits - Task Runners: Prefect Task Runners ### Example Snippet

python
from prefect import task, flow
from prefect.task_runners import ConcurrentTaskRunner
from prefect.runtime import concurrency

@task
def read_small_table(table_name):
    # Code to read from S3

@task
def read_large_table(table_name):
    # Code to read from S3

@flow(task_runner=ConcurrentTaskRunner())
def read_tables(tables):
    for table in tables:
        if table["size"] == "small":
            read_small_table.submit(table["name"])
        else:
            with concurrency("large_tables", occupy=1):
                read_large_table.submit(table["name"])

if __name__ == "__main__":
    tables = [{"name": "table1", "size": "small"}, {"name": "table2", "size": "large"}]
    read_tables(tables)

Refer to the Moving Data Guide for more specifics on S3 interactions. Feel free to ask if you need further details!