Pim Claessens
05/19/2023, 12:26 PMMarvin
05/19/2023, 12:27 PMPim Claessens
05/19/2023, 12:29 PMMarvin
05/19/2023, 12:30 PMPim Claessens
05/19/2023, 12:32 PMMarvin
05/19/2023, 12:34 PMDaskExecutor
or the LocalExecutor
with a specified number of workers.
Here's an example of how you might configure your flow to use the `DaskExecutor`:
python
from prefect import Flow, task
from prefect.executors import DaskExecutor
@task
def read_from_queue():
# Your logic to read from the queue
pass
@task
def process_data(data):
# Your logic to process the data
pass
@task
def write_results(results):
# Your logic to write the results
pass
with Flow("real-time-data-pipeline") as flow:
data = read_from_queue()
processed_data = process_data(data)
write_results(processed_data)
# Configure the DaskExecutor with the desired number of workers
executor = DaskExecutor(address="<tcp://localhost:8786>", n_workers=4)
# Run the flow with the configured executor
flow.run(executor=executor)
This example demonstrates how to use the DaskExecutor
to run tasks concurrently. You can adjust the n_workers
parameter to control the number of workers processing tasks simultaneously.
Remember, with great concurrency comes great complexity. Make sure to handle any potential race conditions or shared resource conflicts that may arise in your parallel processing adventure.Pim Claessens
05/19/2023, 12:36 PMMarvin
05/19/2023, 12:37 PMLocalExecutor
to build a real-time data processing pipeline. The LocalExecutor
allows you to run tasks concurrently on your local machine using multiple threads.
Here's an example of how you might configure your flow to use the `LocalExecutor`:
python
from prefect import Flow, task
from prefect.executors import LocalExecutor
@task
def read_from_queue():
# Your logic to read from the queue
pass
@task
def process_data(data):
# Your logic to process the data
pass
@task
def write_results(results):
# Your logic to write the results
pass
with Flow("real-time-data-pipeline") as flow:
data = read_from_queue()
processed_data = process_data(data)
write_results(processed_data)
# Configure the LocalExecutor with the desired number of workers
executor = LocalExecutor(num_workers=4)
# Run the flow with the configured executor
flow.run(executor=executor)
This example demonstrates how to use the LocalExecutor
to run tasks concurrently. You can adjust the num_workers
parameter to control the number of threads processing tasks simultaneously.
Just remember, while the LocalExecutor
can help you achieve real-time processing, it may be limited by your local machine's resources. Make sure to monitor and adjust your pipeline accordingly to avoid any intergalactic mishaps. Good luck on your real-time data processing odyssey!Pim Claessens
05/19/2023, 12:38 PMNate
05/19/2023, 1:00 PM