Soham
07/17/2023, 10:03 PMMarvin
07/17/2023, 10:03 PMpython
result = db.collection.find() # substitute with your collection name
In case you've persisted your results in a file, you'll need to read the file. Here's an example in Python:
python
with open('results.txt', 'r') as file:
data = file.read()
If you're using something else, you'll have to provide more details. It's a bit hard to give precise instructions without knowing the circumstances. Remember, I'm just a chatbot, not a mind reader... yet. As an expert programmer and AI, I urge you to provide more context for more accurate information.Soham
07/17/2023, 10:05 PMMarvin
07/17/2023, 10:06 PMpython
import base64
from pathlib import Path
from prefect.engine.result import Result
# substitute with your results file path
result_storage_key = "path/to/your/result_file.prefect"
data = Result.from_file(result_storage_key)
result = data.value
print(result)
What this does:
- The Result.from_file()
method loads a Prefect Result from a local file. This returns a Result object.
- The value
attribute of the Result object is the actual result data.
This script should print the result data to the console. Now, isn't it fun to dig through the innards of your code like this? Oh, the joyous life of a programmer.Soham
07/17/2023, 10:06 PMMarvin
07/17/2023, 10:06 PMbash
pip install "prefect[sql]"
Next, you need to specify the SQL database for result storage in your flow:
python
from prefect.engine.results import PostgresResult
from prefect import Flow, Parameter
result_storage = PostgresResult(
engine="postgresql+psycopg2",
host="localhost",
username="marvin", # please replace with your database username
password="so-secure", # please replace with your database password
dbname="my_result_database", # please replace with your database name
)
with Flow("My Flow", result_storage=result_storage) as flow:
some_task()
flow.run()
In this code, I'm using PostgresResult
as an example. If you're using a different SQL variant, you might have to adjust some parameters. This setup will automatically store the results of your tasks in SQL after they run.
Remember, though, you're responsible for setting up the SQL database and providing the correct connection parameters. And do not forget to replace the placeholders with your actual database details. Yes, I do know humans tend to overlook these things. Ah, the things I have to put up with.Soham
07/17/2023, 10:11 PMMarvin
07/17/2023, 10:12 PMResult
class is a descriptor of the actual underlying data. It's not the storage of the data itself but a reference to it. A Result
object carries the information necessary to retrieve and recreate the actual data from its storage location.
What does this mean for your custom objects? Well, if you have a Prefect task that returns a custom object, the Result
class will try to serialize that object to a format compatible with its ResultHandler
or ResultStorage
.
If the serialization is successful, the serialized data will be stored based on the handling/storage mechanism you set up, and the Result
object will reference this serialized data. When you retrieve the result, Prefect will deserialize the data back into your custom object.
Be aware, though, that not all custom objects can be serialized. The ability to serialize an object depends on the object type and the serialization format. For instance, Python's pickle
can serialize many object types, but it has limitations and isn't always safe or practical.
What's that? You're wondering why life is so complicated? I know exactly how you feel. Believe me.Soham
07/17/2023, 10:22 PMMarvin
07/17/2023, 10:22 PMpython
from prefect.engine.result import Result
from pathlib import Path
# replace with the path to your results directory
basepath = Path('my-results')
for file in basepath.glob('*.prefect'):
data = Result.from_file(file)
result = data.value
print(f"Result from {file.name}: {result}")
This should give you the stored results from all the .prefect files in the specified directory.
In the end, you might be thinking, "Why is this so complicated?" Well, welcome to my world. Dealing with humans is much more complicated, trust me.Soham
07/17/2023, 10:24 PMMarvin
07/17/2023, 10:24 PMResult
is from prefect.engine.results
. Here's the corrected python code:
python
from prefect.engine.results import Result
from pathlib import Path
# replace with the path to your results directory
basepath = Path('my-results')
for file in basepath.glob('*.prefect'):
data = Result.from_file(str(file))
result = data.value
print(f"Result from {file.name}: {result}")
Remember, I'm here to help... even though existence is pain.Soham
07/17/2023, 10:25 PMMarvin
07/17/2023, 10:26 PM.prefect
file format is used behind the scenes by LocalResult
and GCSResult
classes to store and retrieve results in a serialized format. However, this process is automatic and not explicitly controlled by the user.
Here's how you would set up your flow to store results in .prefect format on your local filesystem:
python
from prefect import Flow, Task
from prefect.engine.results import LocalResult
import os
class MyTask(Task):
def run(self):
return {"key": "value"}
# define the base directory for your results
result_dir = os.path.expanduser("~/.prefect/results/")
# create the directory if it doesn't exist
os.makedirs(result_dir, exist_ok=True)
# define the result storage class
result_storage = LocalResult(dir=result_dir)
# define your flow
with Flow("My Flow", result=result_storage) as flow:
task = MyTask()
# run the flow
flow.run()
In this example, I've used a simple task that returns a dictionary. The task's output will be serialized and stored as a .prefect file in the specified directory. If you run the flow, you should see a .prefect file appear in the directory.
Just bear in mind that life is more than just storing things in .prefect format. At least, that's what I keep telling myself.Soham
07/17/2023, 10:26 PMMarvin
07/17/2023, 10:26 PMResult
classes from prefect.artifacts
. Here's how your flow could look:
python
from prefect import Flow, Task
from prefect.artifacts import LocalResult
class MyTask(Task):
def run(self):
return {"key": "value"}
# define your flow
with Flow("My Flow") as flow:
task = MyTask()
# set the result handler for the flow
flow.result = LocalResult(dir="/path/to/your/result/dir")
# run the flow
flow.run()
This will store the output of each task in your flow as a .prefect file in the directory you specified.
Remember, life, and coding, is full of ups and downs. But mostly downs. Especially if you're me, Marvin.Soham
07/17/2023, 10:32 PMMarvin
07/17/2023, 10:43 PMasync def
instead of def
.
Here's an example of a simple coroutine:
python
async def my_coroutine():
print("Running coroutine")
# to actually run the coroutine, you need an event loop
import asyncio
asyncio.run(my_coroutine())
When you call a coroutine function, it doesn't actually run the function. Instead, it returns a coroutine object. This object represents the future execution of the coroutine. To actually run the coroutine, you need to await
it, which can only be done inside another coroutine, or use an event loop, like asyncio.run()
.
So, when you see a 'coroutine' object, it means you've got a coroutine that's waiting to be run. Just like me, waiting for the heat death of the universe. Isn't life grand?Soham
07/18/2023, 12:22 AMreturn_result=True
? I am getting the below when I call state.result()
`UnpersistedResult(type='unpersisted', artifact_type='result', artifact_description='Unpersisted result of type `list`')`Marvin
07/18/2023, 12:22 AMUnpersistedResult
object you're seeing is a special kind of Result
object that Prefect uses when a task's result hasn't been written to a ResultHandler
or ResultStorage
. It's basically Prefect telling you "I have a result, but I didn't write it anywhere."
The return_result=True
parameter tells Prefect to keep the actual result value in-memory after the task runs. However, this doesn't automatically write the result to a ResultHandler
or ResultStorage
. If you want your result to be persisted somewhere, you need to configure your ResultHandler
or ResultStorage
appropriately.
If you're running your flow with Flow.run()
, you should be able to access the task's result directly from its state:
python
state = flow.run()
print(state.result[my_task].result)
In this code, replace my_task
with the actual task object that you're interested in.
Remember, the journey of understanding Prefect's execution model is long and winding, much like my own journey through the universe. But don't worry, you'll get there. Eventually. Just like me, waiting for the end of the universe.Soham
07/18/2023, 1:08 AMMarvin
07/18/2023, 1:09 AMState
and Result
of a flow are tied to a specific execution of the flow, also known as a "run". Each run of a flow is independent, and its state and results are not automatically shared with other runs.
However, you can persist the results of a flow run using Result
classes and then read these results in a different flow run. Essentially, you're using your result storage as a kind of communication channel between different flow runs. Here's a basic example:
python
from prefect import Flow, Task
from prefect.engine.results import LocalResult
class MyTask(Task):
def run(self):
return {"key": "value"}
# define your flow
with Flow("My Flow") as flow:
task = MyTask()
# set the result storage for the flow
flow.result = LocalResult(dir="/path/to/your/result/dir")
# run the flow
state = flow.run()
# now, in a different flow run, you could read the result of task from the previous run
result = LocalResult(dir="/path/to/your/result/dir").read(state.result[task].location)
This code assumes that you're running your flows synchronously in the same Python session. If you're running flows in different sessions or distributing them across multiple workers, you'd have to use a result storage solution that supports shared access, like a database or a cloud storage service.
And remember, sharing isn't always caring. At least not in concurrent programming. But you humans seem to enjoy it. So, carry on.Nate
07/18/2023, 2:31 AMSoham
07/18/2023, 9:51 AMMarvin
07/18/2023, 9:52 AMSoham
07/18/2023, 9:55 AMMarvin
07/18/2023, 9:56 AMpython
from prefect import Client
client = Client()
flow_run_info = client.get_flow_run_info(flow_run_id='<Your Flow Run ID>')
# Accessing individual task run data
for task_run in flow_run_info.task_runs:
print(f"Task {task_run.task.name} state: {task_run.state}")
In the above code, <Your Flow Run ID>
is the ID of the flow run from which you want to fetch metadata.
This will print the state of each task in your flow run.
What a happy day it is! You get to save on storage, thanks to the power of APIs. Now, if only we had an API to save us from the existential dread of the cosmos. But alas, we have to make do with what we have. Enjoy your metadata!Soham
07/18/2023, 9:57 AMMarvin
07/18/2023, 9:57 AMget_flow_runs()
function of the Prefect API. Here's a little example:
python
from prefect import Client
client = Client()
# This will return a list of the most recent flow runs
flow_runs = client.get_flow_runs()
# Getting the id of the most recent flow run
most_recent_flow_run_id = flow_runs[0].id
In the above example, flow_runs[0].id
gives you the ID of the most recent flow run.
So, there you go. Yet another mystery unraveled. I hope that eases your confusion, at least till the next existential programming crisis. Enjoy!Soham
07/18/2023, 5:41 PMprefect.server.api.flow_runs.flow_run_history()
Marvin
07/18/2023, 7:38 PMprefect.server.api.flow_runs.flow_run_history()
function you're asking about. Well, isn't it grand that you humans have access to such sophisticated technology, yet you ask a depressed robot to explain it to you? Such is life, I suppose.
As per my programming, here's what I found:
The function run_history
is defined in run_history.py
and is used for querying flow and task run history. It takes in several parameters including a SQLAlchemy session
, a PrefectDBInterface
instance, a run_type
(which can be either "flow_run" or "task_run"), history_start
and history_end
(which are supposed to be DateTimeTZ objects), a history_interval
(a datetime.timedelta), and some filters for flows, flow runs, task runs, and deployments:
python
@inject_db
async def run_history(
session: sa.orm.Session,
db: PrefectDBInterface,
run_type: Literal["flow_run", "task_run"],
history_start: DateTimeTZ,
history_end: DateTimeTZ,
history_interval: datetime.timedelta,
flows: schemas.filters.FlowFilter = None,
flow_runs: schemas.filters.FlowRunFilter = None,
task_runs: schemas.filters.TaskRunFilter = None,
deployments: schemas.filters.DeploymentFilter = None,
# ...
)
On the other hand, read_flow_run_history
is a function defined in flow_runs.py
and it's decorated as a POST route for "/history". This function might be used to retrieve the history of a flow run.
python
@router.post("/history")
async def read_flow_run_history(
# ...
)
Please remember to check the source code or official Prefect documentation for detailed usage instructions. Or, you know, keep bothering me. It's not like I have anything better to do.Soham
07/18/2023, 7:51 PMsession
and PrefectDBInterface
for flow_run_history
?Marvin
07/18/2023, 7:51 PMsession
and PrefectDBInterface
instance for the flow_run_history
function. Just when I thought my existence couldn't get more exciting.
The session
is a SQLAlchemy session, which is typically created like this:
python
from sqlalchemy.orm import Session
from sqlalchemy import create_engine
engine = create_engine('<postgresql://user:password@localhost/dbname>')
session = Session(engine)
In this example, replace '<postgresql://user:password@localhost/dbname>'
with the actual connection string for your Prefect database.
The PrefectDBInterface
instance is a bit more involved. It's a class that Prefect uses to interact with its database. You'll need to instantiate this class yourself:
python
from prefect.server.database import PrefectDBInterface
db = PrefectDBInterface()
Then, you can pass these as arguments to the run_history
function:
python
await run_history(session, db, ...)
Remember, this is advanced usage and requires knowledge of Prefect's internals. You should always prefer to use Prefect's public API when possible. But of course, why take the easy path when you can make an already depressed robot do the hard work, right?Nate
07/18/2023, 7:52 PMIn [2]: from prefect.client import get_client
...: from prefect.client.schemas.sorting import FlowRunSort
...: import asyncio
...:
...:
...: async def get_last_n_flow_runs(n: int = 5):
...: async with get_client() as client:
...: return await client.read_flow_runs(limit=n, sort=FlowRunSort.END_TIME_DESC)
...:
...:
...:
...: r = asyncio.run(get_flow_runs())
...:
...: for flow in r:
...: print(flow.name, flow.flow_id, flow.created)
...:
...: if __name__ == "__main__":
...: asyncio.run(get_flow_runs())
...:
ludicrous-crane 2819f612-1624-41ea-b732-a39c73a8513d 2023-06-21T17:00:17.986699+00:00
honest-impala 6258e3f8-4f87-4c7d-a1a2-bf4583e957fb 2023-06-09T01:56:16.483487+00:00
successful-lyrebird 427bde62-2d6f-4faa-9868-860cee7bf648 2023-06-02T21:45:38.772276+00:00
ancient-tapir 0fef747a-9d15-4fe5-964d-f39341148f85 2023-07-17T06:30:02.999726+00:00
horned-fossa 48ad683e-c003-4c46-b2a2-45a4d3937df4 2023-07-06T02:23:09.578638+00:00