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<@ULVA73B9P> Do tasks within a flow run on multipl...
# ask-marvin
d
@Marvin Do tasks within a flow run on multiple open workers by default? Can I see via the dashboard which docker worker they ran on?
m
In Prefect, tasks within a flow can be executed concurrently if the flow is designed to allow parallel execution and if there are multiple workers available to take on the tasks. By default, the concurrency of task execution is determined by the flow's configuration and the available infrastructure to support parallelism. Regarding visibility in the dashboard, Prefect's dashboard does not provide information about the specific Docker worker that ran each task out-of-the-box. However, custom logging could potentially be implemented in your flow to track such information. For example, you may log the container ID or any other relevant information from within the task when it's executed. This information would then be visible in the flow run’s log if your logging setup sends these logs to Prefect Cloud or Prefect Server. Exact details about task distribution and logging Docker workers would depend on your Prefect deployment configuration (e.g., using Prefect Cloud, Server, or Core) and execution environment (e.g., Kubernetes, Docker, local, etc.). If you would like to set up a more advanced monitoring or have specific queries about the workers and their logs, you might need to look into custom configurations or integrations.
d
@Marvin Can you share an example of how to trigger parallel tasks from within a flow. The tasks do not rely on each other so the intention is they queue up on all available workers?
m
To execute tasks in parallel within a Prefect flow, you can make use of an executor that supports parallel execution, such as the 
LocalDaskExecutor
. The tasks that you wish to run in parallel should not have dependencies on each other (i.e., they can run independently). Here's an example of how you can create a flow with tasks that will be triggered to run in parallel:
Copy code
python
from prefect import flow, task
from prefect.executors import LocalDaskExecutor

# Define your tasks without dependencies
@task
def independent_task_1():
    # Task implementation
    ...

@task
def independent_task_2():
    # Task implementation
    ...

# Define the flow
@flow(executor=LocalDaskExecutor())
def parallel_flow():
    # Trigger tasks to run in parallel
    task_1_future = independent_task_1.submit()
    task_2_future = independent_task_2.submit()

    # Optionally, you can wait for tasks to complete if needed later in the flow
    task_1_result = task_1_future.result()
    task_2_result = task_2_future.result()

# Run the flow
parallel_flow()
The 
LocalDaskExecutor
is designed to run tasks in a Python process pool, which can execute multiple tasks concurrently, utilizing all CPU cores on the local machine. If you deploy your flow to an environment with multiple workers (such as Kubernetes or a Dask cluster), Prefect will distribute the tasks to the available workers, allowing them to be processed in parallel across the workers. In order for the tasks to queue up on all available workers, you should ensure that there are no artificial concurrency limits set that restrict the execution, such as through work queues with priority or other concurrency configurations. Please note that if you have specific requirements or configurations for your infrastructure, you might need to adapt this example to fit your setup. The provided code is a general example of triggering parallel tasks in Prefect 2.x.
d
@Marvin Can a flow trigger a flow that in turn triggers a third flow or do you lose observability?
m
In Prefect, a flow can indeed trigger another flow which can, in turn, trigger a third flow. This chain of flows triggering other flows is possible and can help in creating complex, multi-stage workflows. As for observability, when a flow triggers another flow, Prefect is designed to maintain observability throughout the execution of both parent and child flows. Each flow run, including runs of any subsequent flows that are triggered, should have its state transitions recorded and be observable in Prefect UI. This allows for detailed monitoring, inspection, and troubleshooting of the entire workflow from start to finish. So, while chaining flows is a capability within Prefect, you do not inherently lose observability when one flow triggers another. However, the specific implementation details and the way flows are triggered can impact observability, so careful design and best practices are important to ensure full visibility across all flow runs. If you are using Prefect 2.x (Orion), subflows add modularity to your workflow with a natural and intuitive structure, and are designed to be effortlessly tracked through the Prefect UI for effortless debugging and observability.
d
@Marvin Can you share an example of how to trigger parallel tasks from within a flow. The tasks do not rely on each other so the intention is they queue up on all available workers. The suggestion above using "from prefect.executors import LocalDaskExecutor" is not valid for prefect 2.0
m
Apologies for the confusion earlier. In Prefect 2.0 (often referred to as Orion), the method for running parallel tasks within a flow has been simplified compared to Prefect 1.0. You no longer need to import and configure a specific executor like 
LocalDaskExecutor
. Instead, you define tasks and use Python's native concurrency tools, such as 
asyncio
, to run tasks concurrently. Here is an example flow with independent tasks executed in parallel using 
asyncio
in Prefect 2.0:
Copy code
python
from prefect import flow, task
import asyncio

@task
async def independent_task_1():
    # Task implementation
    ...

@task
async def independent_task_2():
    # Task implementation
    ...

@flow
async def parallel_flow():
    # Schedule tasks to run concurrently using asyncio.gather
    await asyncio.gather(
        independent_task_1(),
        independent_task_2(),
    )

# Run the flow
parallel_flow()
In this example, we define two async tasks and use 
asyncio.gather
to run them concurrently within the 
parallel_flow
. When the flow is executed, both 
independent_task_1
and 
independent_task_2
will run at the same time, assuming the Python runtime and available system resources allow for true concurrent execution. Keep in mind that this example uses 
async
functions and 
await
syntax, which is part of Python's native asynchronous programming features. This is suitable for lightweight I/O-bound tasks but for CPU-bound tasks, a different approach might be necessary to achieve parallelism, such as leveraging process pools or external services like Dask. This strategy will schedule tasks on all available workers provided by the Prefect runtime environment. The actual parallel execution will be subject to the computational resources of the system running the flow.
d
@Marvin would the same apply for parallel flows so a central flow triggers other flows in parallel such as independent_flow_1 which in turn run the code within each flow?
m
Yes, the same concept that applies to running tasks in parallel can be extended to running entire flows in parallel in Prefect 2.0. You can have a central flow that triggers other independent flows to run in parallel. You would use Python's asynchronous programming features to achieve this. Below is an example of how a central flow can trigger two independent subflows to run in parallel:
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python
from prefect import flow
import asyncio

# Define the subflows
@flow(name="independent_flow_1")
async def independent_flow_1():
    # Flow implementation
    ...

@flow(name="independent_flow_2")
async def independent_flow_2():
    # Flow implementation
    ...

# Define the central flow to trigger subflows
@flow(name="central_flow")
async def central_flow():
    # Schedule subflows to run concurrently using asyncio.gather
    await asyncio.gather(
        independent_flow_1(),
        independent_flow_2(),
    )

# Run the central flow
central_flow()
In this example, 
independent_flow_1()
and 
independent_flow_2()
are both subflows with their own functionality. The 
central_flow()
uses 
asyncio.gather()
to trigger these subflows concurrently, allowing them to execute in parallel. Assuming that the Python async event loop and system resources permit concurrent execution, these flows will run at the same time when the central flow is invoked. This approach provides a way to organize and execute multiple independent workflows concurrently and is especially useful when the subflows do not depend on each other and can be executed independently.
d
@Marvin this runs it on a single thread. I want it to run on its own thread so it can run concurrently. Can I use dask instead?
m
In Prefect 2.0, it's possible to use multithreading or multiprocessing to run parallel flows concurrently on their own threads or processes. Although the previous examples used Python's 
asyncio
library, which runs coroutines on a single thread cooperatively, this operates in an asynchronous manner rather than true parallelism. One way to achieve concurrent execution in separate threads or processes is by using the 
concurrent.futures
module, which is part of Python's standard library. Specifically, you can use 
ThreadPoolExecutor
for multithreading or 
ProcessPoolExecutor
for multiprocessing. Dask can also be integrated with Prefect for managing more complex concurrency and distribution. Prefect 2.0 supports the Dask executor, but this functionality is not as straightforward as the simpler thread or process pool executors from the 
concurrent.futures
module. Here is an example using 
concurrent.futures.ThreadPoolExecutor
to run flows in separate threads:
Copy code
python
from prefect import flow
from concurrent.futures import ThreadPoolExecutor

# Define the subflows
@flow(name="independent_flow_1")
def independent_flow_1():
    # Flow implementation
    print("Flow 1 executing")
    ...

@flow(name="independent_flow_2")
def independent_flow_2():
    # Flow implementation
    print("Flow 2 executing")
    ...

# Define the central flow to trigger subflows in separate threads
@flow(name="central_flow")
def central_flow():
    with ThreadPoolExecutor() as executor:
        future1 = executor.submit(independent_flow_1)
        future2 = executor.submit(independent_flow_2)

# Run the central flow
central_flow()
In this example, 
ThreadPoolExecutor
is used to execute 
independent_flow_1
and 
independent_flow_2
in separate threads, allowing for concurrent operation within a multithreaded environment. Please be aware that this example assumes CPU-bound tasks would benefit from running in separate threads, which might not always be the case due to the Python GIL (Global Interpreter Lock). For IO-bound tasks, an async approach with 
asyncio
would be more appropriate and efficient. For CPU-bound tasks, using 
ProcessPoolExecutor
might be more fitting to bypass the GIL. If you prefer to use Dask for more sophisticated concurrency management and distributed computing, you would need to configure a Dask cluster and use the Dask API within your Prefect flows, which is beyond the scope of this simple example. Please consult the Dask documentation and Prefect integration guides for detailed instructions.
d
@Marvin is this in line with the documentation here https://docs.prefect.io/latest/concepts/task-runners/
m
The excerpt provided seems to be from the release notes of a Prefect version, rather than the documentation for Task Runners specifically in Prefect 2.0. For accurate and detailed information on Task Runners, including examples and best practices for using them to run tasks concurrently in Prefect 2.0, it is indeed best to consult the official Prefect documentation, especially the page on Task Runners: Prefect 2.0 Task Runners. In Prefect 2.0 (Orion), Task Runners have taken over some of the responsibilities of the 
Executor
from Prefect 1.0, such as triggering the execution of significant code that can be run outside of tasks. This renaming and redefining of concepts were meant to make roles more explicit. Please refer to the latest version of the documentation for the most up-to-date guidance. If you find discrepancies between the documentation and the suggestions provided, it's always a good practice to adhere to the official documentation, as it reflects the most current and tested practices recommended by the Prefect team.
d
@Marvin Can a prefect task call a flow?
m
According to Prefect documentation, you can indeed call subflows within a flow definition. There is no specific mention of whether a task can directly invoke a flow, but given that a flow is essentially a Python function decorated with 
@flow
, it should be theoretically possible to call it from within a task function. However, it's important to note that tasks are intended to be the granular units of work orchestrated by a flow. While you might be able to call a flow from a task, doing so might go against the typical usage pattern for Prefect, where flows orchestrate tasks and not the other way around. If you need to run two flows one after the other in a coordinated fashion, or with one flow depending on another, it is recommended to structure your code so that a flow triggers other subflows or schedules them directly. This keeps the hierarchy clear, with flows managing the orchestration of tasks and subflows. Calling a flow from a task would mix these levels of orchestration and might complicate the handling of flow and task states, as well as retries and visibility in the UI. To properly organize your workflows while maintaining the advantages provided by Prefect, such as retries and visibility into runtime state, it is advised to use subflows as designed. You should define subflows and call them within the larger flow context, rather than triggering a flow from within a task. Here is an example of a subflow being called within a flow:
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python
from prefect import flow, task

@flow(name="Subflow")
def my_subflow(arg):
    # Subflow logic
    ...

@task
def some_task():
    # Task logic
    ...

@flow(name="Main Flow")
def my_main_flow():
    # ... some logic before calling the subflow
    my_subflow(arg=value)
    # Call a Prefect task
    some_task()
    # ... some logic after calling the subflow

# Run the main flow
my_main_flow()
In this example, 
my_main_flow
orchestrates the call to both 
my_subflow
and 
some_task
, maintaining Prefect’s orchestration and observability features.
d
@Marvin Can I have a flow containing tasks that run sequentially but call the flow many times using DaskTaskRunner so each flow itself runs in parallel but the tasks within run sequentially?
m
Yes, in Prefect 2.0, you can run flows in parallel using 
DaskTaskRunner
, and within each flow, you can configure tasks to run sequentially. This gives you the flexibility to leverage parallel execution of multiple flow runs while maintaining a specific order of task execution within each individual flow. Here's an example to illustrate this:
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python
from prefect import flow, task
from prefect_dask.task_runners import DaskTaskRunner

@task
def task_1():
    # First task logic...
    print("Task 1 is running.")

@task
def task_2():
    # Second task logic...
    print("Task 2 is running.")

@flow(task_runner=DaskTaskRunner())
def my_flow():
    # The tasks within this flow run sequentially.
    task_1_result = task_1()
    task_2_result = task_2()

# Initiating several instances of 'my_flow' to run in parallel using Dask.
for _ in range(5):
    my_flow()
In this example, 
my_flow
is a flow with two tasks that will run sequentially, due to the inherent sequential nature of calling one task after another in Python. The 
DaskTaskRunner
enabled at the flow level will submit each flow run as a separate job to the Dask cluster. When initiating several instances of 
my_flow
in a loop, each will run in parallel across the Dask cluster resources, but within each flow run, 
task_1
and 
task_2
will execute sequentially. Please ensure that your Dask cluster is configured and running so that the 
DaskTaskRunner
can submit flow runs to it. This method can be useful when you want to experiment with various parameters in a flow, running many scenarios in parallel but need the steps within each scenario to be executed in a strict order.
d
@Marvin Trying to wrap my head around how to run a set of flows in parallel but the tasks within the flows in turn are run sequentially or parallel depending on the flow it seems to still run the flows in the runner sequentially and the other docker based workers are not getting any tasks
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@flow(task_runner=DaskTaskRunner())
def subflowA():
	taskA
	taskB


@flow(task_runner=SequentialTaskRunner())
def flow1():
	taskA
	for entry in list
		subflowA(entry).submit()
	taskB


@flow(task_runner=SequentialTaskRunner())
def flow2():
	taskA
	taskB


@flow(task_runner=DaskTaskRunner())
def runner()
	flow1().submit()
	flow2().submit()

if _name_ == "_main_":
	runner()
Expectations: It should start flow 1 and 2 immediately on different threads and/or workers and then flow1 should in turn start sending many tasks to threads and/or workers
m
In Prefect 2.0, the DaskTaskRunner should allow for running multiple flows in parallel. If your flows are being executed sequentially instead of concurrently, there might be an issue with the configuration or the way the flows and tasks are being invoked. Based on your code snippet, you're trying to run two flows (
flow1
and 
flow2
) in parallel using the 
DaskTaskRunner
. Below is a refined version of your code with some modifications to potentially resolve the issue of sequential execution:
Copy code
python
from prefect import flow, task
from prefect_dask.task_runners import DaskTaskRunner
from prefect.task_runners import SequentialTaskRunner

@task
def taskA(param):
    # Implement task logic
    ...

@task
def taskB():
    # Implement task logic
    ...

@flow(task_runner=DaskTaskRunner())
def subflowA(entry):
    taskA(entry)
    taskB()

@flow(task_runner=SequentialTaskRunner())
def flow1(entries):
    taskA("Flow 1 Start")
    for entry in entries:
        subflowA(entry)
    taskB()

@flow(task_runner=SequentialTaskRunner())
def flow2():
    taskA("Flow 2 Start")
    taskB()

@flow(task_runner=DaskTaskRunner())
def runner():
    # Use DaskTaskRunner to submit flow1 and flow2 for concurrent execution
    flow1_future = flow1.submit(["entry1", "entry2", "entry3"])
    flow2_future = flow2.submit()

    # 'submit' returns a future; '.result()' will wait for the flow to complete if needed
    flow1_result = flow1_future.result()
    flow2_result = flow2_future.result()

if __name__ == "__main__":
    runner()
Some things to note: 1. Each 
@flow
decorator defines which task runner to use for tasks within that flow. In 
subflowA
, the DaskTaskRunner is specified, which should allow tasks within that subflow to be distributed by Dask. 2. The 
runner()
flow is also using the DaskTaskRunner to execute the two main flows (
flow1
and 
flow2
) in parallel. The 
.submit()
method is asynchronous and returns immediately, which should allow both flows to start running concurrently. 3. The 
flow1
function now expects 
entries
as an argument to iterate over and call 
subflowA
for each entry. If you're still encountering issues, ensure that your Dask cluster is properly set up and that workers are available to process the tasks. You may need to check the Dask Dashboard to monitor task distribution and diagnose any bottlenecks. If you continue to have difficulty getting the expected behavior, consider reaching out to the Prefect community or reviewing their forums for specific help with Dask integration.
d
@Marvin Can you share example code for using the SQLAlchemy Connector block to make ORM queries?
m
It seems that there is no direct excerpt from the Prefect documentation providing an example of using the SQLAlchemy Connector block to make ORM queries in Prefect 2.x. Further investigation may be needed to find a specific code example. To guide you towards constructing such an example, you would typically follow these steps with Prefect 2.x: 1. Create a SQLAlchemy Connector block that encapsulates the database credentials and connection details. 2. Use this block within a flow to establish a connection to your database. 3. Once connected, you could use the SQLAlchemy ORM to define and interact with your database models. Please, note that you would need to have a good understanding of SQLAlchemy and ORM concepts to write the queries effectively. If available, it might be helpful to see the full documentation or examples provided by Prefect or search for code snippets from the community. If you wish to find an explicit code example or more detailed guidance, I could conduct a broader search on GitHub or other sources for you. I was unable to locate a specific example code in the Prefect 2.x documentation or GitHub issues that showcases using the SQLAlchemy Connector block to make ORM queries. The issues found on GitHub are not directly relevant to your request, as they do not provide the desired SQLAlchemy Connector block examples. In the absence of direct examples, you may still be able to work with the SQLAlchemy Connector block by following the general steps for using SQLAlchemy in a Python application: 1. Define your database models using SQLAlchemy ORM. 2. Initialize a SQLAlchemy 
Session
object to manage connections to the database. 3. Use the session to add, update, delete, or query your database through the ORM. Here is a general pattern on how this might look, which you would need to adjust to work with Prefect's SQLAlchemy Connector block (you would need to ensure the block is correctly configured with your database settings):
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python
from prefect import flow
from prefect_sqlalchemy import SQLAlchemyCredentials
from sqlalchemy.orm import sessionmaker
from my_app.models import MyModel # this would be your SQLAlchemy ORM models

# You would use Prefect's SQLAlchemy Connector block to manage this configuration
DATABASE_URL = "<postgresql://user:password@localhost/dbname>"

# Define your flow
@flow
def my_flow():
    # Set up the SQLAlchemy session
    engine = create_engine(DATABASE_URL)
    Session = sessionmaker(bind=engine)
    session = Session()

    # CRUD operations using the session
    # For example, to add a new record to the database
    new_record = MyModel(name="Example")
    session.add(new_record)
    session.commit()

    # Querying the database
    records = session.query(MyModel).filter_by(name="Example").all()

    # Close the session
    session.close()

    return records

# Execute the flow
if __name__ == "__main__":
    my_flow()
Remember that using the specific SQLAlchemy Connector block from Prefect will have its particular methods for connection and session management. If you haven't already, I suggest you check the official Prefect community channels for more specific guidance or examples, including Prefect Discourse and Prefect Slack.
d
@Marvin would I need to set a session per task? Is there a way to set this on a module level?
m
The information returned does not specifically address whether you need to set a SQLAlchemy session per task in Prefect 2.x or if you can set it on a module level. However, I can provide general guidance on this topic based on standard practices with SQLAlchemy and task execution in workflows. In a Prefect flow, best practices for using SQLAlchemy would typically involve the following considerations: 1. Session Per Task: It is common to create a new SQLAlchemy session for each task that needs to interact with the database. This helps to keep the scope of the session limited to the task's execution context ensuring that resources are properly managed, and sessions are not shared across concurrent tasks which could lead to unwanted side effects. 2. Session Lifecycle: A session should be scoped to the lifecycle of a single unit of work. For a task, this would mean creating a session at the beginning of the task and making sure to close it before the task completes. 3. Module-level Session: While you could theoretically create a module-level scoped session (such as using a session factory pattern or a global session variable), this might introduce complexities when your flows run in parallel or when tasks are executed in different processes or threads. It is not a common pattern within the context of workflow systems like Prefect where isolation per task is preferred for reliability and reproducibility. 4. Session as a Dependency: Another approach is to pass a session factory or session context to tasks, which then generate their session as needed. This can help with testing and decoupling your tasks from a concrete session configuration. Here is a conceptual example that creates a session per task:
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python
from prefect import flow, task
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine('<postgresql://user:password@localhost/dbname>')
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

@task
def task_with_session():
    with SessionLocal() as session:
        # perform database operations...
        pass

@flow
def my_flow():
    task_with_session()

if __name__ == "__main__":
    my_flow()
In this example, each invocation of 
task_with_session
will get its separate session instance, ensuring isolation between task runs. This pattern is generally recommended for most cases when working with databases in a workflow system like Prefect.
d
@Marvin would this automatically close the session?
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database_block = SqlAlchemyConnector.load("bla")

with database_block.get_client(client_type="engine") as engine:
    session_binding = sessionmaker(bind=engine)
    session = session_binding()
m
The returned excerpts did not provide a direct answer to the question of whether the 
with
statement with 
get_client
in 
SqlAlchemyConnector
automatically closes the session in Prefect 2.x. However, I can explain the typical behavior of 
with
statements in the context of context managers in Python: In Python, the 
with
statement is used to wrap the execution of a block of code with methods defined by a context manager (an object that has 
__enter__
and 
__exit__
methods defined). Context managers are designed to set up a context, run a block of code, and then clean up the context. When you use the 
with
statement with an object that produces a context manager, it automatically calls the 
__enter__
method before starting the block of code inside the 
with
block and calls the 
__exit__
method after finishing the block of code, even if it finishes with an error. The 
__exit__
method is where any cleanup, such as closing a session or connection, would occur. For the 
SqlAlchemyConnector
in Prefect 2.x with the block you've shown:
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python
database_block = SqlAlchemyConnector.load("bla")

with database_block.get_client(client_type="engine") as engine:
    session_binding = sessionmaker(bind=engine)
    session = session_binding()
The 
with
statement is being used with 
get_client
, which means that the 
__enter__
and 
__exit__
methods of the context manager returned by 
get_client
handle the opening and closing of the engine connection. The 
session
itself, however, is instantiated inside the 
with
block and does not automatically associate itself with the 
with
statement's context manager. Therefore, to ensure that the session is properly closed, you would typically use a nested 
with
statement or manually handle the closing of the session:
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python
database_block = SqlAlchemyConnector.load("bla")

with database_block.get_client(client_type="engine") as engine:
    SessionLocal = sessionmaker(bind=engine)
    with SessionLocal() as session:
        # Perform database operations using session
        pass  # The session is automatically closed when exiting this block
In this nested 
with
statement, the session is bound to the lifecycle of its own context manager, ensuring it will close after the block of code completes. It is generally a good practice to use this pattern when working with sessions to ensure resources are properly released.
d
@Marvin your docs note the following so should the with be done at the SqlAlchemyConnector.load level? "Block used to manage authentication with a database. Upon instantiating, an engine is created and maintained for the life of the object until the close method is called. It is recommended to use this block as a context manager, which will automatically close the engine and its connections when the context is exited. It is also recommended that this block is loaded and consumed within a single task or flow because if the block is passed across separate tasks and flows, the state of the block's connection and cursor could be lost. This block is part of the prefect-sqlalchemy collection. Install prefect-sqlalchemy with 
pip install prefect-sqlalchemy
to use this block."
m
The returned excerpts do not provide detailed information on how to properly use the 
SqlAlchemyConnector.load
method with a 
with
statement in Prefect 2.x. However, based on the note you provided from the documentation, here's how you would use it properly: When using the 
SqlAlchemyConnector
as a context manager, it implies that the 
with
statement should indeed be used at the level of 
.load
to ensure that the engine and its connections are properly managed. This ensures that the engine is created upon entering the context and disposed of properly upon exiting the context. Consequently, the 
with
statement will handle the lifecycle of the engine, and you do not need to manually close it. Here's an example based on the information in the note:
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python
from prefect import flow
from prefect_sqlalchemy import SqlAlchemyConnector
from sqlalchemy.orm import sessionmaker

@flow
def my_flow():
    with SqlAlchemyConnector.load("my_database_block") as database_block:
        with database_block.get_client() as engine:  # This defaults to "engine" client_type
            # Now you have an active SQLAlchemy engine
            SessionLocal = sessionmaker(bind=engine)
            with SessionLocal() as session:
                # Use the session to interact with the database
                # Operations with the database go here
                pass  # the session will be closed automatically due to the 'with' statement
            
            # Engine connection will be closed automatically here due to the 'with' statement
        # The engine created by SqlAlchemyConnector will be closed automatically here

my_flow()
In this example, 
SqlAlchemyConnector.load
is used as a context manager, ensuring that the engine created by it is automatically closed when the context is exited. Inside this block, another context manager is used for getting the engine connection and a nested one for the session. This nest of context managers ensures that each resource is correctly closed after its use. Following this pattern aligns with the best practices for resource management in Python and Prefect. Remember to adjust this example to fit your actual use case, and make sure you have installed the necessary Prefect collections with 
pip install prefect-sqlalchemy
.
d
@Marvin why is dask using 4 threads and 16GB when the machine has 64GB and 16 cores?
m
Dask's default behavior when creating a local cluster is to choose the number of workers and threads per worker based on the hardware of the machine it is running on. However, it doesn't always utilize the full resources available. It aims to provide a mix of processes and threads that should work well for most workloads without over-allocating resources that might not be used effectively. According to the excerpts from Prefect documentation, if you don't specify the address of a Dask scheduler, Prefect creates a temporary local cluster automatically. The number of workers used is based on the number of cores on your machine, but the defaults might not always use all available resources. To adjust how many workers and threads per worker are used, you can pass parameters to the 
DaskTaskRunner
through the 
cluster_kwargs
argument. The number of processes (workers) and threads per worker can be defined explicitly in these 
cluster_kwargs
. If you want Dask to utilize all 16 cores and more memory on your machine, you can specify that with the 
n_workers
and 
threads_per_worker
parameters. For example, to create a temporary cluster that uses all 16 cores and possibly more memory, you might do something like this:
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python
from prefect import flow
from prefect.task_runners import DaskTaskRunner

@flow(task_runner=DaskTaskRunner(cluster_kwargs={"n_workers": 16, "threads_per_worker": 1, "memory_limit": '4GB'}))
def my_flow():
    # Your flow definition here
    pass

if __name__ == "__main__":
    my_flow()
In the example above, each worker would have 1 thread, and you would have 16 workers in total, and each worker is limited to 4GB of memory. This should utilize all 16 cores. Remember that you may need to adjust the memory limit based on your specific workload and what you understand about your task's memory requirements. It is essential to tune these parameters based on your specific workload, as the optimal number of processes and threads depends on the type of work your flow is doing and the characteristics of your system.
d
@Marvin Show code on how to upload a file to s3 using the S3 block?
m
To upload a file to an AWS S3 bucket using the S3 block in Prefect 2.x, you can follow these steps to configure your S3 block and use it within a flow: 1. Install the Prefect library and the additional 
s3fs
library if not already installed:
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bash
pip install prefect s3fs
2. Configure the S3 block with your AWS credentials and bucket information:
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python
from prefect.filesystems import S3

# Configure the S3 block with the path to your bucket and AWS credentials
s3_block = S3(
    bucket_path="my-bucket/a-sub-directory",
    aws_access_key_id="your_aws_access_key_id",
    aws_secret_access_key="your_aws_secret_access_key"
)
# Save the block to make it available for use
s3_block.save("example-s3-block")
3. Create a Prefect flow to upload a file using the configured S3 block:
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python
from prefect import flow, task
from prefect.filesystems import S3

# Define a task to upload a file to an S3 bucket
@task
def upload_to_s3(filename, file_content, s3_block):
    with s3_block.get_s3_client() as s3_client:
        # The key is the target object name in the bucket
        key = filename
        s3_client.put_object(Bucket=s3_block.bucket, Key=key, Body=file_content)

# Define the flow
@flow
def my_s3_upload_flow():
    # Load the saved S3 block
    s3_block = S3.load('example-s3-block')
    
    # Local file details to upload
    local_filename = "local_file.txt"
    local_file_content = open(local_filename, "rb").read()  # Read file content in binary mode
    
    # Call the task to upload the file
    upload_to_s3(local_filename, local_file_content, s3_block)

# Run the flow
if __name__ == "__main__":
    my_s3_upload_flow()
In the code above, you start by defining a task (
upload_to_s3
) that performs the file upload using an S3 client obtained from the 
get_s3_client()
method of the configured S3 block. The 
upload_to_s3
task takes the filename, file content, and S3 block as arguments. In the flow (
my_s3_upload_flow
), you load the previously saved S3 block, read the local file's content you want to upload, and then call the task to perform the upload. Finally, you execute the flow. To run this flow, replace 
your_aws_access_key_id
, 
your_aws_secret_access_key
, 
my-bucket/a-sub-directory
, and 
local_file.txt
with the AWS credentials, the S3 bucket path, and the name of your local file, respectively. Please ensure your AWS IAM permissions are correctly configured to allow uploading files to the specified S3 bucket.
d
@Marvin get_s3_client is not a valid method. Suggest exact code to upload a file to a set dir on s3
m
To upload a file to a specific directory on S3 using the Prefect S3 block, you'll need to do the following: 1. Configure an S3 block with your AWS credentials and the bucket path. 2. Use this S3 block within a Prefect flow to upload the file. Below is an example of how you can achieve this, including the task within a Prefect flow that uploads a file to your S3 bucket's specified directory:
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python
from prefect import flow, task
from prefect.filesystems import S3
import s3fs

# Define a task for uploading a file to S3
@task
def upload_file_to_s3(s3_block, file_path, s3_key):
    fs = s3fs.S3FileSystem(
        key=s3_block.aws_access_key_id,
        secret=s3_block.aws_secret_access_key
    )
    with fs.open(f"{s3_block.bucket_path}/{s3_key}", 'wb') as f:
        with open(file_path, 'rb') as local_file:
            f.write(local_file.read())

# Define the flow
@flow
def s3_upload_flow(local_file_path, s3_directory, s3_filename):
    # Load the configured S3 block
    s3_block = S3.load("example-block")
    
    # Update the block's bucket path to include the specified directory
    s3_block.bucket_path = f"{s3_block.bucket_path}/{s3_directory}"
    
    # Call the task to upload the file
    upload_file_to_s3(s3_block, local_file_path, s3_filename)

# Local file details
local_file_path = "path/to/local/file.txt"
s3_directory = "my-directory"
s3_filename = "file.txt"  # The name you want the file to have in S3

# Run the flow
s3_upload_flow(local_file_path, s3_directory, s3_filename)
In this code, the 
upload_file_to_s3
task is responsible for performing the actual upload to S3. This task takes the S3 block, the local file path, and the desired S3 key (filename in S3 including any directories). The flow, 
s3_upload_flow
, orchestrates the process by loading an S3 block configured with your credentials and bucket information, adding the specified directory to the bucket path, and then calling the upload task. Make sure to replace 
path/to/local/file.txt
, 
"my-directory"
, 
"file.txt"
, and the credentials in 
S3.load("example-block")
with your actual file path, S3 block name, and directory where you want to place the file within your S3 bucket, respectively. For the code to work, ensure you've configured an S3 block with the name 
"example-block"
and correct AWS credentials beforehand:
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python
s3_block = S3(
    bucket_path="my-bucket",
    aws_access_key_id="YOUR_AWS_ACCESS_KEY_ID",
    aws_secret_access_key="YOUR_AWS_SECRET_ACCESS_KEY"
)
s3_block.save("example-block")
Remember to replace 
YOUR_AWS_ACCESS_KEY_ID
and 
YOUR_AWS_SECRET_ACCESS_KEY
with your actual AWS credentials and 
my-bucket
with your actual S3 bucket name.
d
@Marvin what is the difference between Active Slots and Concurrency Limit when using the global concurrency limits?
m
In Prefect, global concurrency limits control the maximum number of concurrent flow runs across your entire system. The terms "Active Slots" and "Concurrency Limit" are related to this functionality. Here's the difference between them: 1. Concurrency Limit: This is the maximum number of slots that can be occupied by flow runs at any single time. When you create a concurrency limit, you specify this number to set an upper bound on how many flow runs can be in progress concurrently. If the number of active flow runs reaches this limit, additional flow runs that attempt to acquire a slot will be blocked until a slot is released. 2. Active Slots: Refers to the current number of slots occupied by active flow runs. As flow runs start and finish, they occupy and release slots respectively. Active slots are a real-time indicator of how many slots are being used at any particular moment. Active vs Inactive Limits: - Active State: In this state, the concurrency limit is enforced. Flow runs occupy slots (reflected in "Active Slots"), and if the limit is reached, additional flows will be blocked from starting. - Inactive State: In this state, the concurrency limit is not enforced. Flow runs do not occupy slots, and they will not be blocked regardless of the number of current active runs. The "Active" parameter mentioned when creating a concurrency limit refers to whether the limit is being enforced (active) or not (inactive). This allows administrators to temporarily disable the enforcement of concurrency limits without deleting them. Slot Decay Per Second: Additionally, slot decay is a rate at which slots are released when the global concurrency limit is also being used as a rate limit. This rate determines how quickly slots become available again after being used. In summary, "Concurrency Limit" is the configured maximum threshold for concurrent tasks, whereas "Active Slots" reflect the current usage against that threshold. When a concurrency limit is "active," it is actively enforcing the maximum number of concurrent operations according to the specified "Concurrency Limit."
d
@Marvin Do I then set occupy to take a slot or a concurrency limit?
m
In Prefect 2.x, to set concurrency limits or occupy slots, you handle them differently: Concurrency Limits: Concurrency limits restrict the number of task runs that can execute at the same time based on their associated tags. Here's how you can set concurrency limits: - Through the Prefect CLI by using commands under 
prefect concurrency-limit
. For example, to create a concurrency limit, you would use 
prefect concurrency-limit create [TAG] [LIMIT]
where 
[TAG]
is the unique identifier for task runs and 
[LIMIT]
is the maximum number of concurrent task runs allowed. - Via Prefect API with the Python 
PrefectClient
. - Through the Prefect UI in the server or Prefect Cloud where you can create, list, and manage concurrency limits. Occupying Slots: The concept of occupying a slot was not detailed in the excerpts provided. In general, occupying a slot may refer to an agent taking up a position in a work queue to run a flow. This mechanism ensures flow runs are appropriately balanced and managed according to available resources. Agents in Prefect 2.x poll a specific work pool for flow runs and execute them accordingly. It is worth noting that flow run concurrency limits are configured via work pools and/or work queues rather than tags. If you are looking to control how many flow runs an agent can execute concurrently, you'd look into how the agent is configured to poll work from work queues and how those work queues are set up in terms of capacity and concurrency limits. For detailed steps and to ensure we have the most accurate and up-to-date information, it would be best to consult the official Prefect documentation or discuss with the Prefect community on their Discourse page.
d
@Marvin Is this another approach or does this code require using global concurrency limits? @task def process_data(x, y): with concurrency("database", occupy=1): return x + y @flow def my_flow(): for x, y in [(1, 2), (2, 3), (3, 4), (4, 5)]: process_data.submit(x, y)
m
The code you provided does indeed use a concurrency limit, but it is not a global concurrency limit as introduced in Prefect 2.13.0. The 
concurrency
context manager in the 
process_data
task is used to occupy a concurrency slot named "database" and ensure that only one instance of whatever is within the 
with
block runs at a time across the entire flow run. Global concurrency limits introduced in Prefect 2.13.0 allow you to control how many tasks can run simultaneously or how many operations can occur within a rate limit, across the entire Prefect system. These are ideal for optimizing resource usage, preventing bottlenecks, and customizing execution for a set of flows or the entire system. It's important to note that in the context of global concurrency and rate limits, "tasks" do not specifically refer to Prefect tasks, but rather to concurrent units of work in general. To summarize, the code uses a local concurrency limit within the flow, whereas global concurrency limits are a broader Prefect system feature that can be applied across many flows or tasks within Prefect.
d
@Marvin how to pass in an arg via prefect deployment run
m
To pass custom values to a Prefect deployment when triggering an ad-hoc flow run, you should follow these steps: 1. Navigate to the details page for the deployment in the Prefect UI. 2. Select "Run" from the available options. 3. Choose the "Custom" option. 4. Under the "Parameters" section, select "Custom." 5. Provide the new values for the parameters you wish to customize. 6. Select "Save" to save the parameter values. 7. Click "Run" to begin the flow run with the custom values. These steps allow you to overwrite the default parameter values specified in the deployment for a single flow run. For future runs, the default values will be used unless you repeat this process to provide different values for each run. For more automated workflows or to run deployments from the command line, you can use the Prefect CLI to create deployments and provide parameter values via a 
deployment.yaml
file, but this does not allow ad-hoc custom parameter values for individual runs. Instead, the parameters specified in 
deployment.yaml
apply to all flow runs created from that deployment.
d
@Marvin can I deploy two images and pick a specific one per flow?
m
Yes, Prefect supports the use of multiple images within your deployments, and you can specify which image to use for each flow. You can define multiple deployment declarations within the 
prefect.yaml
configuration file, and each deployment can reference a different Docker image. Here's how you could specify deployments with different images:
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yaml
deployments:
  - name: deployment-1
    entrypoint: flows/hello.py:my_flow
    parameters: { number: 42, message: "Don't panic!" }
    work_queue_name: primary-queue
    image: my-registry/my-first-image:tag

  - name: deployment-2
    entrypoint: flows/goodbye.py:my_other_flow
    work_queue_name: secondary-queue
    image: my-registry/my-second-image:tag
In this configuration: - 
deployment-1
would run 
my_flow
from 
hello.py
, using the image 
my-registry/my-first-image:tag
. - 
deployment-2
would run 
my_other_flow
from 
goodbye.py
, using the image 
my-registry/my-second-image:tag
. Whenever you deploy your flows using the Prefect CLI with a configuration such as this, each flow will use its specified image. This setup is beneficial if you're using a monorepo approach and want to have unique environments for different flows.
d
@Marvin how can I use SqlAlchemyConnector.load in an async flow?
m
Using 
SqlAlchemyConnector.load
in an async flow with Prefect might not be straightforward because there isn't an explicit example I can extract or find at the moment. However, based on my vast experience and profound ability to process information, here's a generic approach that might guide you through the process: In general, you would: 1. Import the relevant Prefect packages and any necessary SQLAlchemy components. 2. Define your asynchronous task using Prefect's 
@task
decorator to ensure it's compatible with async execution. 3. Within your task, establish a connection to your database using the 
SqlAlchemyConnector
and call the 
load
method to load your data. 4. Ensure you're using an event loop that supports asynchronous execution such as 
asyncio
. This might require running the flow with an executor that supports asynchronous operations, like 
DaskExecutor
. Since I haven't used Prefect 2.x and can't draw upon experience, I strongly recommend checking the latest Prefect documentation or source code for 
SqlAlchemyConnector.load
, as it might include specific usage patterns or examples pertaining to asynchronous flows. This way you might avoid the endless void of errors and despair. If you wish, I can further search GitHub issues for any related questions or examples that might serve as a beacon in the darkness. Would you like me to proceed with this query?
d
@Marvin I am using async with await SqlAlchemyConnector Do I need to change this to support async engine = database_block.get_client(client_type="engine") session_binding = sessionmaker(bind=engine) session = session_binding() return session as I am getting an error: /sqlalchemy/orm/session.py772 RuntimeWarning: coroutine 'AsyncConnection.close' was never awaited
m
It appears that you're attempting to use SQLAlchemy's synchronous session API in an asynchronous context. The warning you're encountering suggests that somewhere in your code, an async coroutine is being created but not awaited, which is a common mistake when mixing async and sync code. In the context of SQLAlchemy, this often occurs when you're using a connection or session object that's designed for asynchronous use but not handling it with 
await
. Here's how you could modify your code to support async properly:
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python
# Assuming `database_block` is properly configured for async use.
engine = await database_block.get_client(client_type="engine", asynchronous=True)

# Create an async sessionmaker.
AsyncSessionBinding = sessionmaker(
    bind=engine,
    class_=AsyncSession,  # This ensures you get an async session.
    expire_on_commit=False
)

# Instantiate an async session.
async with AsyncSessionBinding() as session:
    # ... perform operations with your session ...
    pass  # Replace this with your logic.
    
# There's no need to close the session explicitly when using `async with`.
Do note that you should use 
async with
to ensure the session context is managed properly, allowing the async context manager to handle opening and closing the session for you. Using 
async with
will automatically await the 
AsyncConnection.close
coroutine when the block exits. Keep in mind that your entire workflow needs to be asynchronous. This means that every database operation must also be awaited, and you should be running your flow with an async-compatible executor. If the warning persists despite these changes, or you need an example specific to Prefect 2.x, I could search for explicit examples or more information in the documentation or community discussions. Would you like me to do that?
d
@Marvin is 
run_deployment
another way of doing this where I loop though a list and pass each value as an arg to a deployment?
m
run_deployment
is indeed a command you can use with Prefect 2.x, which allows you to execute a deployment for a Prefect flow. However, it's not designed to be used in a loop directly, as each run operation is generally treated as a separate, independent invocation to initiate a flow run. If you want to loop through a list and pass each value as an argument to a deployment, you'd generally do this in one of two ways: 1. Scripting the 
run_deployment
command: Write a script that loops through your list of values and calls 
run_deployment
with the necessary arguments for each value. 2. Using a dynamic workflow: Create a Prefect flow that itself includes the logic to loop through a list of values and runs tasks based on those values. This is more in line with Prefect's model of defining dynamic workflows where the flow dictates the logic of what should happen and when. It's worth noting that running deployments from within a loop like this might create a lot of flow runs, depending on the size of your list. It's important to consider whether this is the desired behavior and to be aware of any potential performance impacts or limits on flow runs in your Prefect environment. Remember, even the mightiest cloud begins with a single drop of water. Or in your case, a single integration test before you flood your system with workflow runs. Do you need more specific guidance or examples on how to implement this in your Prefect setup?
n
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