@Marvin how to disable task caching system

To disable task caching in Prefect, you can approach it differently depending on whether you are using Prefect 2.x or 3.x. For Prefect 2.x: Task caching in Prefect 2.x is controlled using cache keys and optional settings. If you want to disable caching entirely, simply don't set a

cache_key_fn

on your tasks, as there's no caching behavior by default. More on task caching can be found in the Prefect 2.x documentation. For Prefect 3.x: In Prefect 3.x, to disable caching the idea is similar; make sure you don't configure cache keys with

cache_key_fn

. If caching is enabled and you want to ignore it for certain tasks, you can force a cache refresh by setting

refresh_cache=True

for individual tasks. To disable cache globally for all tasks, you can use the setting

PREFECT_TASKS_REFRESH_CACHE=true

. This will cause all tasks to refresh their cache keys instead of using cached values. More information can be found in the Prefect 3.x documentation. So essentially, don't set cache configurations unless you need them, or use settings to override caching behavior if necessary. It’s relatively straightforward unless your tasks have grown sentient and started caching themselves!

@Marvin prefect3. Setting PREFECT_TASKS_REFRESH_CACHE='True' didn't help when i ran python scripts manually i got TypeError: cannot pickle '_thread.RLock' object

The error you're encountering, "TypeError: cannot pickle '_thread.RLock' object," arises when Prefect attempts to serialize a task input or result that isn't natively serializable by Python's

pickle

module. This might happen when you're running scripts that involve threading or locking mechanisms. Possible Solutions: 1. Avoid Non-Serializable Objects: Review your task inputs and outputs to ensure they don't include objects like threading locks or other complex objects that can't be serialized. 2. Custom Serialization: If you have non-serializable objects as task inputs, you can adjust the serialization logic to handle only certain properties that are serializable. 3. Use PREFECT_TASKS_REFRESH_CACHE Correctly: The setting

PREFECT_TASKS_REFRESH_CACHE=true

is meant to help ensure that cached tasks are refreshed, but it doesn't directly address serialization issues. Make sure your tasks don't inherently rely on non-serializable objects. You might need to revisit how tasks are defined and how inputs/outputs are managed within Prefect. Check out the documentation on task caching here for more guidance. Regrettably, I couldn't access the exact GitHub issues due to a hiccup in authorization (ah, the complexities of modern APIs!). Nonetheless, this is a common type of issue when objects that aren't meant to be serialized are passed around in distributed systems. Give this a shot, and see if it helps whittle down that pesky pickle error!

@Marvin PREFECT_RESULTS_PERSIST_BY_DEFAULT=false doesnt help too, i want to avoid serialization at all, my tasks just executed in sequence

If you're looking to avoid serialization altogether while running Prefect tasks in sequence, there are a few specific settings and approaches you might consider: 1. Avoid Using Caching or Persistence Settings: Make sure you aren't setting

cache_key_fn

or enabling any form of result persistence, as these naturally involve serialization. 2. Sequential Execution Without Serialization: Prefect's default behavior doesn't require serialization if you're simply executing tasks sequentially without caching or persisting results. You might inadvertently trigger serialization if certain configurations or settings are applied, such as

result_storage

,

result_serializer

, or others meant for distributed scenarios. 3. Verify No Serialization in Code: Ensure none of your tasks or flows involve operations requiring serialization, such as saving or loading state between runs unless explicitly needed. 4. Manage Direct Execution: If you're manually triggering tasks, consider utilizing parameters and direct calls without interfacing with Prefect's orchestration elements that would trigger result or state management features. This keeps execution clean and avoids unnecessary serialization processes. Unfortunately, I hit a roadblock accessing specific GitHub issues to confirm any hidden nuggets of wisdom from other user experiences—authentication can be oh-so delightful sometimes! However, by focusing on not utilizing caching or persistence, your task executions should remain serialization-free. You can find more detailed settings and configurations related to task results and their management in the Prefect 3.x documentation.

@Marvin if i define @tasks with persist_result=False, cache_key_fn=None it works totally fine, i need understanding why do i need serialization at all, it will slower the whole perfomance how i can disable it globally without force

Serialization in Prefect is primarily used for caching, persistence, and state management. It allows Prefect to efficiently store and manage task inputs and outputs, especially in distributed settings or when tasks fail and need retries. Here's why it is used and how you can manage it: 1. Purpose of Serialization: - Caching & Persistence: Prefect uses serialization to enable caching of task results and persistence of states. This allows tasks to be resumed, and results to be reused without rerunning expensive calculations. - Distributed Execution: When tasks are executed across a distributed system, serialization allows the system to handle inputs and outputs reliably across different nodes or environments. 2. Serialization Performance Impact: - Serialization can introduce overhead, especially if complex objects are involved. If tasks are local and sequential without needing persistence, serialization may not be required. 3. Disabling Serialization: - Task Level: Setting

persist_result=False

and avoiding setting a

cache_key_fn

effectively disables serialization for specific tasks as they don't engage in caching or persisting results. - Global Settings: While you can control serialization through settings like

PREFECT_RESULTS_PERSIST_BY_DEFAULT

, there's no specific "global serialization off" switch without affecting some features. Prefect inherently relies on some level of serialization for certain functionalities. If your tasks work efficiently when you explicitly disable caching and persistence at the task level, this approach seems ideal for your use case, avoiding unwanted serialization. For advanced management of serialization, like customizing serializers, you can refer to the Prefect documentation on results handling. However, if your tasks truly don't benefit from Prefect's distributed orchestration features, keeping serialization off at the task level is a practical approach!

@Marvin so i have to always pass persist_result=False, cache_key_fn=None in tasks, because serializations its the overhead, if im trying to calculate big dataframes for example, and anyway i store them in variables on class level

Yes, if you're handling large dataframes or complex objects within tasks and want to minimize serialization overhead, you should explicitly set

persist_result=False

and

cache_key_fn=None

for each task that doesn't require caching or result persistence. This tells Prefect to skip storing or caching results, which helps avoid unnecessary performance hits from serialization. By managing the data in variables at the class level, you're taking control and handling data persistence and lifecycle within your application's logic, circumventing Prefect's built-in mechanisms. This is especially effective when Prefect's distributed orchestration features aren't needed for your particular workflows. Remember to maintain consistency across all tasks to ensure smooth execution without unintended caching or persistence. While this might add some extra boilerplate, it ensures that you prevent any serialization-related overhead. It's kind of like giving Prefect a wink, saying, "I know what I'm doing with my data, thank you very much!"

@Marvin can you imitiate retry on such task

In Prefect 3.x, you can implement retries for tasks even if you're not using result persistence or caching. Here's a basic understanding of how retries can be configured: 1. Retry Configuration: Prefect allows you to retry tasks that fail due to transient issues such as network problems, API call failures, etc. While using this feature, you don't need caching or result persistence for the retry mechanism to work. 2. Defining Retries: - Basic Retry: You can specify the number of retries using the

retries

parameter and set a delay between attempts with

retry_delay_seconds

. - Custom Retry Logic: Use the

retry_condition_fn

to define a custom callable that returns a boolean value. This function can determine whether a retry should occur based on specific conditions (e.g., certain exceptions). 3. Example: Here's a simple example of how to use these parameters:

python
   from prefect import task

   @task(retries=3, retry_delay_seconds=10)
   def my_task():
       # Your task logic here
       pass

4. Advanced Retry Features: - You can pass a list of integers to

retry_delay_seconds

for customized retry behavior. - Prefect also provides an

exponential_backoff

utility if you need backoff strategies for retries. This approach allows you to retry tasks when necessary without the overhead of result serialization or caching. For more details, check the documentation on writing tasks with retry logic.