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<@ULVA73B9P> what is this? ```_get_state_result ...
# ask-marvin
m
@Marvin what is this?
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_get_state_result
    raise MissingResult(
prefect.exceptions.MissingResult: State data is missing. Typically, this occurs when result persistence is disabled and the state has been retrieved from the API.
i'm having to del and garbage collect cause i am running out of memory on my async flows. what's the workaround?
m
It appears you're encountering a 
MissingResult
exception, which can happen when the state data has been retrieved from the API and result persistence is disabled. There's one GitHub issue that specifically addresses a similar concern, emphasizing a misunderstanding in the error message provided by Prefect when 
NotReady
tasks are collected (GitHub issue #7642). For managing memory in asynchronous flows, consider making sure that result persistence is configured correctly and that the intermediate states are stored as expected. You might want to ensure proper cleanup after tasks and good memory management within the flow's logic. Unfortunately, I couldn't locate a direct workaround for this issue in the results retrieved. Regarding a code example that persists state results, here's a snippet that implies persistence by default through task mapping, but does not explicitly mention whether and how results are persisted (Prefect GitHub examples repository, file `serve.py`):
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python
from prefect import flow, task
from pydantic import BaseModel, Field

class Data(BaseModel):
    key: str
    value: dict = Field(default_factory=dict)

@task
def fetch_data_for_key(key: str) -> Data:
    \"\"\"A task that simulates fetching data from some source\"\"\"
    return Data(key=key, value={"data": "some data"})

@task
def process_data(data: Data) -> Data:
    \"\"\"A task that simulates processing data\"\"\"
    print(f"Processing {data!r} (I swear)")
    time.sleep(3)
    return data

@flow
def work_i_need_to_do_sometimes(keys: list[str]):
    \"\"\"Work you might need to do ever so often\"\"\"
    
    raw_data_futures = fetch_data_for_key.map(keys)

    process_data.map(raw_data_futures)

if __name__ == "__main__":
    work_i_need_to_do_sometimes.serve(
        name="sporradic-deployment",
        parameters={"keys": ["foo", "bar", "baz"]},
    )
To facilitate flow memory management, consider reviewing the Prefect documentation about states, as this is where state-related information (including result persistence) is often detailed. If this wasn't quite the silver bullet, you could also try increasing your memory allocation if possible, or as a last resort, look into strategies for chunking your flow execution to handle smaller portions of work at a time and potentially avoid memory overflow.
m
@Marvin what profiling tools are available when running flows/tasks?
m
The search results from Prefect documentation did not yield specific information on profiling tools available for flows and tasks. However, Prefect naturally provides logging and state messages that can give some insight into the performance of a flow or task: For example: - You could analyze the logs produced by Prefect to understand how long tasks are taking and identify any bottlenecks. - Prefect emits detailed logs that note the start and end time of task execution, which can be used for basic profiling. While Prefect's native tooling focuses on orchestration and state management, standard Python profiling tools can be used alongside Prefect for more detailed performance analysis. Here are a few standard Python profiling tools you might consider: 1. cProfile: A built-in Python tool that can profile the execution time of your Python code. You can wrap your flow or specific tasks with cProfile to get detailed performance metrics. 2. line_profiler: This tool provides line-by-line performance analysis of Python code, which can help isolate specific lines that are causing performance issues. 3. memory_profiler: If memory consumption is of interest, you can use this tool to measure the memory footprint of your tasks at various points in their execution. 4. py-spy: This is a sampling profiler for Python programs that can profile running Python processes without needing to modify the code or interrupt the program. Implementing one of these profilers would involve running them as part of your task or flow definition. Here is an example of how you might use cProfile within a Prefect task:
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python
import cProfile
from prefect import flow, task

@task
def my_task():
    # your task code

@flow
def my_flow():
    profiler = cProfile.Profile()
    profiler.enable()
    my_task()
    profiler.disable()
    profiler.print_stats(sort='cumtime')
Please adapt this to your flow or task context where needed. Profiling should be done judiciously, as it can add overhead to your flow execution time, especially in production environments. It's advised to use profiling in a testing or staging environment to tune performance before deploying to production.
The code example I found does not directly match your pipeline but provides a basic structure for how to use Prefect with tasks and flows. I'll guide you on how to adapt this example for your specific use case involving Snowflake data extraction, serialization/deserialization with Pydantic, and sending data to SQS:
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python
from prefect import flow, task
import pydantic
import boto3
from snowflake.connector import connect

# Define your Pydantic model here for serialization/deserialization
class DataModel(pydantic.BaseModel):
    # your fields here
    pass

@task
def extract_data_from_snowflake(query: str):
    # Connect to Snowflake and execute query
    with connect(...) as conn:  # fill in connection details
        with conn.cursor() as cur:
            cur.execute(query)
            result = cur.fetchall()
    return result

@task
def serialize_data(data):
    # Use Pydantic to deserialize raw data and then serialize it
    deserialized_data = [DataModel.parse_obj(item) for item in data]
    serialized_data = [item.json() for item in deserialized_data]
    return serialized_data

@task
def send_data_to_sqs(serialized_data_list, queue_url):
    # Connect to AWS SQS and send the data
    sqs_client = boto3.client('sqs')
    for data in serialized_data_list:
        sqs_client.send_message(QueueUrl=queue_url, MessageBody=data)

@flow
def data_pipeline_flow(query: str, sqs_queue_url: str):
    # Extract data from Snowflake
    raw_data = extract_data_from_snowflake(query)
    # Serialize data
    serialized_data = serialize_data(raw_data)
    # Send data to SQS
    send_data_to_sqs(serialized_data, sqs_queue_url)

if __name__ == "__main__":
    snowflake_query = "SELECT * FROM your_table"  # change to your actual query
    sqs_queue_url = "your_sqs_queue_url"  # change to your actual SQS queue URL
    data_pipeline_flow(snowflake_query, sqs_queue_url)
Please fill in the missing connection details and adjust the Pydantic 
DataModel
according to your data structure. Make sure you have the required AWS credentials configured for boto3 to access SQS. This script lays out a pipeline in the form of a Prefect flow, with separate tasks for each step of your process. Save this script as a .py file and run it as you would a standard Python script, or deploy it with Prefect Cloud/Server for scheduling and orchestration. Make sure all dependencies (boto3, pydantic, snowflake-connector-python) are installed in your Python environment.
m
@Marvin this looks basically what i am doing except i'm batching the queries and running them async, using .map. where am i running out of memory?
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Process for flow run 'epsilon-japori' exited with status code: -9; This indicates that the process exited due to a SIGKILL signal. Typically, this is either caused by manual cancellation or high memory usage causing the operating system to terminate the process
i'd think each task once it's done would get rid of the items in memory
m
The SIGKILL signal typically indicates that the operating system terminated your Prefect flow's process due to excessive memory usage. Understanding why your flow is running out of memory can be complex, as it may depend on how tasks are implemented, the size of the data being processed, and perhaps how the concurrency of the tasks is managed. When using 
.map
in a flow, it's important to consider: 1. Batch Size: If you are mapping large batches, consider reducing the batch size. This can help to manage memory usage by processing smaller chunks of data at a time. 2. Concurrent Task Runs: Prefect might be executing multiple mapped tasks concurrently. The combined memory usage of these concurrent tasks could be causing the memory spike leading to the SIGKILL. You might want to limit concurrency or adjust the 
executor
parameter when setting up your flow to avoid too many tasks running simultaneously. 3. Data Size: Large datasets can consume a lot of memory quickly, especially if they're being copied or held in memory across various stages of your flow. 4. Task Implementation: Ensure that the tasks release memory when they are done. In Python, this usually means removing references to large objects so that the garbage collector can reclaim the memory. However, if tasks are retaining references to large data structures, the memory will not be freed. 5. Infrastructure Limits: If you're running this on limited infrastructure, you might simply be exceeding the available memory. Consider scaling up your resources. 6. External Memory Profiling: Use memory profiling tools like 
memory_profiler
in Python to understand the memory footprint of your flow and tasks more precisely. It will allow you to see when the memory is allocated and released. 7. Execution Environment: Check if you are using an execution environment with managed memory such as Docker. Docker containers can have memory limits that, if exceeded, will lead to the container being killed. In terms of documentation, while there was mention of concurrent flow runs and managed execution work pools in Prefect Cloud's managed execution guide, there isn't a direct reference to handling memory management issues with 
.map
or async tasks. Since the issue is complex and doesn't have a one-size-fits-all solution, you may need to perform some debugging and iterative changes. Consider incorporating a memory profiler as part of your debugging process to help identify the root cause of the high memory usage.
m
@Marvin can you give me an example of using 
memory_profiler
with the flow you gave me. thanks
m
Sure, here's an example of how you might use 
memory_profiler
with the flow I previously provided. First, ensure you have 
memory_profiler
installed in your environment:
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sh
pip install memory_profiler
Next, adapt the flow code to use the 
@profile
decorator from 
memory_profiler
on the tasks you suspect are using a lot of memory:
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python
from prefect import flow, task
from snowflake.connector import connect
import pydantic
import boto3
from memory_profiler import profile

class DataModel(pydantic.BaseModel):
    # Define your fields here
    pass

@profile
@task
def extract_data_from_snowflake(query: str):
    # Connect to Snowflake and execute query
    with connect(...) as conn:  # fill in connection details
        with conn.cursor() as cur:
            cur.execute(query)
            result = cur.fetchall()
    return result

@profile
@task
def serialize_data(data):
    deserialized_data = [DataModel.parse_obj(item) for item in data]
    serialized_data = [item.json() for item in deserialized_data]
    return serialized_data

@profile
@task
def send_data_to_sqs(serialized_data_list, queue_url):
    sqs_client = boto3.client('sqs')
    for data in serialized_data_list:
        sqs_client.send_message(QueueUrl=queue_url, MessageBody=data)

@profile
@flow
def data_pipeline_flow(query: str, sqs_queue_url: str):
    raw_data = extract_data_from_snowflake(query)
    serialized_data = serialize_data(raw_data)
    send_data_to_sqs(serialized_data, sqs_queue_url)

# Run the flow with memory profiling
if __name__ == "__main__":
    snowflake_query = "SELECT * FROM your_table"  # Your actual query
    sqs_queue_url = "your_sqs_queue_url"         # Your actual SQS queue URL

    data_pipeline_flow(snowflake_query, sqs_queue_url)
When you run this script, 
memory_profiler
will output memory usage stats to your standard output for each function call decorated with 
@profile
. These stats will give you an insight into where memory is allocated and deallocated within your tasks and flow, which could help in identifying potential memory leaks or inefficiencies. Remember that adding memory profiling can slow down the execution of your code, so it's recommended to do this in a testing or development environment rather than in production.
m
@Marvin i don't think memory_profiler this works for prefect that use .map()
m
Using 
memory_profiler
with Prefect tasks that use 
.map()
can indeed be tricky because task mapping in Prefect leverages concurrent execution, and 
memory_profiler
is often used for synchronous code profiling. Directly using the 
@profile
decorator on a mapped task does not produce detailed memory usage for each invocation of the task. You can, however, profile memory usage at the flow level or around the code running within the tasks themselves. This provides a more general view, rather than a detailed breakdown of each mapped task. Here's how you might adjust the flow to use 
memory_profiler
in a manner that could help identify high memory usage:
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python
from prefect import flow, task
from snowflake.connector import connect
import pydantic
import boto3
import os

# Required for memory_profiler reporting when tasks are run in subprocesses
os.environ['PYTHONMALLOC'] = 'malloc'

# Assuming you have DataModel and other task implementations already
# ...

# Function to launch memory profiling in a subprocess
def profile_memory_of_flow(flow_function, *args, **kwargs):
    from memory_profiler import memory_usage
    import subprocess
    import sys

    def run_flow():
        # This function will run the flow
        flow_function(*args, **kwargs)

    # This will run the `run_flow` function in a subprocess and profiles its memory usage
    memory_usage(
        (
            subprocess.Popen,
            (
                [sys.executable, '-c', f'from {__name__} import run_flow; run_flow()'],
            ),
        ),
        interval=0.1,  # adjust the interval for more frequent measurements
        timeout=600,   # adjust the timeout as needed
        include_children=True,  # include sub-processes spawned by the flow
        multiprocess=True,      # measure memory for all subprocesses spawned
    )


# Run the flow with memory profiling in a subprocess
if __name__ == "__main__":
    snowflake_query = "SELECT * FROM your_table"  # Your actual query
    sqs_queue_url = "your_sqs_queue_url"         # Your actual SQS queue URL

    # Wrap your flow call with the profiling function
    profile_memory_of_flow(
        data_pipeline_flow,
        snowflake_query,
        sqs_queue_url
    )
This approach runs the entire flow within a separate subprocess and profiles its memory usage, including all child processes, which would approximately capture the memory usage of all the mapped tasks executed. However, if you want detailed profiling information for each mapped task invocation, consider using custom logging to monitor memory usage manually or use another tool designed for profiling distributed systems. As always, post-profiling, remember to remove or comment out the profiling code and environment variable settings before deploying your flow to a production environment.
m
@Marvin my flow terminated with this
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prefect.exceptions.MissingResult: State data is missing. Typically, this occurs when result persistence is disabled and the state has been retrieved from the API.
what is it? how to fix it? or how to silence it?
m
The 
MissingResult
exception you're encountering refers to a situation where Prefect is expecting to retrieve state data for a task or flow run, but the data is unavailable. This can happen when result persistence is not configured, or if the state has been retrieved from the API after being stored without result persistence enabled. To fix or silence the 
MissingResult
issue: 1. Enable Result Persistence: Ensure that your flow configuration includes result persistence so that intermediate state data is stored. This can be accomplished by configuring a result backend, such as a local filesystem, an S3 bucket, or other cloud storage options supported by Prefect. 2. Review State Management Documentation: For a deeper understanding of how states work in Prefect and how to manage result persistence, check out the documentation on states. This will give you insight into why the state data might be missing and how to configure your flow to handle state persistence properly. Unfortunately, the documentation search didn't yield specific code examples or further detailed instructions to directly address the 
MissingResult
exception. If enabling result persistence and reviewing state management doesn't resolve the issue, you could consider reaching out to the Prefect community or their support channels for more targeted assistance. Here are general steps you can take based on typical Prefect configurations:
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python
from prefect import flow, task
from prefect.engine.results import LocalResult

@task(result=LocalResult(dir="/path/to/store/results"))
def my_task():
    # your task logic

@flow
def my_flow():
    # your flow logic
    pass  # replace with actual code

# Configure the flow to use result persistence
my_flow.executor = LocalDaskExecutor()
Make sure to replace 
/path/to/store/results
with your desired directory for result files, and configure the executor suitably for your environment. If you prefer to silence the issue without actually solving the underlying cause (which is not recommended for production workloads), you could catch and handle the 
MissingResult
exception within your flow or tasks. However, it's important to note that simply silencing the exception may lead to unexpected behavior, especially if your tasks rely on the result data for downstream execution. Here's an example of how you might catch the exception:
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python
from prefect import flow, task
from prefect.exceptions import MissingResult

@task
def error_prone_task():
    # Simulate some work where the result might be missing
    ...

@flow
def my_flow():
    try:
        result = error_prone_task()
    except MissingResult:
        # Handle the missing result case or log the exception details
        ...
Remember, it's generally better to address the root cause by ensuring result persistence is enabled if your workflow's logic relies on it.
m
@Marvin this is more of the stacktrace:
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Encountered exception during execution:
Traceback (most recent call last):
  File "/usr/local/lib/python3.11/site-packages/prefect/engine.py", line 867, in orchestrate_flow_run
    result = await flow_call.aresult()
             ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 327, in aresult
    return await asyncio.wrap_future(self.future)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 389, in _run_async
    result = await coro
             ^^^^^^^^^^
  File "/app/src/backfill_kyc.py", line 70, in karma_kyc_dump
    await asyncio.gather(
  File "/usr/local/lib/python3.11/site-packages/prefect/_internal/concurrency/api.py", line 182, in wait_for_call_in_loop_thread
    return call.result()
           ^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 318, in result
    return self.future.result(timeout=timeout)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 179, in result
    return self.__get_result()
           ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
    raise self._exception
  File "/usr/local/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 389, in _run_async
    result = await coro
             ^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/prefect/client/utilities.py", line 78, in with_injected_client
    return await fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/prefect/engine.py", line 749, in create_and_begin_subflow_run
    return await terminal_state.result(fetch=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/prefect/states.py", line 103, in _get_state_result
    raise MissingResult(
prefect.exceptions.MissingResult: State data is missing. Typically, this occurs when result persistence is disabled and the state has been retrieved from the API.
it happens at the end of a list of subflows
m
Your message was too long by 293 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
this is more of the stacktrace:
```Encountered exception during execution: Traceback (most recent call last): File "/usr/local/lib/python3.11/site-packages/prefect/engine.py", line 867, in orchestrate_flow_run result = await flow_call.aresult() ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 327, in aresult return await asyncio.wrap_future(self.future) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 389, in _run_async result = await coro ^^^^^^^^^^ File "/app/src/backfill_kyc.py", line 70, in karma_kyc_dump await asyncio.gather( File "/usr/local/lib/python3.11/site-packages/prefect/_internal/concurrency/api.py", line 182, in wait_for_call_in_loop_thread return call.result() ^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 318, in result return self.future.result(timeout=timeout) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.
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