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<@ULVA73B9P> What is the format of the file that i...
# marvin-in-the-wild
d
@Marvin What is the format of the file that is used to cache a task? Can the serializer be specified, and can I use Arrow?
m
Here’s a summary of what I found: ### Format of Cached Task Files The file format used for caching a task in Prefect appears not to be explicitly detailed in the documents I reviewed. However, Prefect caches tasks by using "cache keys." Once a task run with a cache key finishes, the key is attached to the state, and Prefect utilizes this key for subsequent cache checks. ### Specifying a Serializer While the specific details about choosing a file format or serializer weren't explicitly mentioned, caching is inherently tied to 
cache_key_fn
within the Prefect framework, allowing developers to customize key generation and retrieval logic. ### Using Arrow for Caching I could not find specific references to using Arrow for task caching within the Prefect documentation or GitHub issues. It seems Prefect currently does not natively support specifying Apache Arrow as a caching serializer. For more detailed information, you might want to browse these specific Prefect documentation pages and Github issues about Prefect task caching. Do you need more information or specific implementation examples?
d
@Marvin Give me an example of what a file cached to disk might look like
m
Here is an example illustrating task caching in Prefect:
Copy code
python
from prefect import flow, task
from prefect.tasks import task_input_hash
from datetime import timedelta
import os

@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
def add(x: int, y: int) -> int:
    result = x + y 
    # Simulating disk caching here by writing result to a file
    with open(f"cache_{x}_{y}.txt", "w") as f:
        f.write(str(result))
    return result

@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
def read_cached_add(x: int, y: int) -> int:
    cache_file = f"cache_{x}_{y}.txt"
    if os.path.exists(cache_file):
        with open(cache_file, "r") as f:
            result = int(f.read())
    else:
        result = add(x, y)  # Computing and caching again if not present
    return result

@flow
def my_flow():
    first_result = read_cached_add(1, 2) 
    second_result = read_cached_add(first_result, first_result)
    final_result = read_cached_add(first_result, second_result)
    return final_result

if __name__ == "__main__":
    final_result = my_flow()
    print(f"Final result: {final_result}")
### Explanation: 1. Task Caching: The 
add
and 
read_cached_add
tasks use 
task_input_hash
to generate cache keys based on inputs. 2. Caching Mechanism: The example writes results to a file for illustration purposes, simulating disk caching. 3. Cache Expiration: Cache is set to expire after one day. Run this script to observe caching in action, and view the generated cache files on the disk. For more code examples, check this GitHub repository. Need more specifics?
d
@Marvin Can I use an Arrow serializer for Prefect result persistence?
m
### Using Arrow Serializer for Prefect Result Persistence Based on the documentation and issues reviewed, Prefect supports customizable result persistence with various serializers. By default, it provides JSON and Pickle serializers but also accepts custom implementations. #### Persisting Results Prefect does not inherently store results but allows for persistence in custom storage locations. This persistence can be crucial for features like task cache keys and flow run retries. #### Compressed Serializer Prefect includes a 
CompressedSerializer
to wrap other serializers, providing compression using libraries like 
lzma
, 
bz2
, and 
zlib
. ### Custom Serializer Example To use Apache Arrow for result persistence, you need to implement a custom serializer. Here is an example of how you might do it:
Copy code
python
import pyarrow as pa
import pyarrow.parquet as pq
from prefect import flow, task
from prefect.serializers import Serializer

class ArrowSerializer(Serializer):
    def dumps(self, obj):
        table = pa.Table.from_pandas(obj)
        sink = pa.BufferOutputStream()
        pq.write_table(table, sink)
        return sink.getvalue().to_pybytes()

    def loads(self, s):
        source = pa.BufferReader(s)
        table = pq.read_table(source)
        return table.to_pandas()

@task(result=ArrowSerializer())
def my_task():
    import pandas as pd
    data = {'col1': [1, 2], 'col2': [3, 4]}
    df = pd.DataFrame(data)
    return df

@flow
def my_flow():
    return my_task()

if __name__ == "__main__":
    result = my_flow()
    print(result)
### Explanation: 1. Custom Serializer: The 
ArrowSerializer
class extends 
Serializer
, implementing 
dumps
(to serialize) and 
loads
(to deserialize) methods using Apache Arrow and Parquet. 2. Task Usage: The 
my_task
function uses this custom serializer for its result. For additional insights, refer to the Prefect Results Documentation and the GitHub Issue on consolidating data persistence documentation. Feel free to reach out if you need further customizations or additional help.
d
@Marvin How long are the persisted results stored for?
m
### Result Persistence Duration in Prefect #### Default Behavior - Persisting Results: Prefect automatically manages result persistence based on the needs of the flow or task features in use (e.g., retries, caching). By default, the 
persist_result
option is set to a null value, enabling persistence only when necessary. - In-Memory Caching: Prefect keeps task and flow results in memory during runs to facilitate downstream processing. #### Custom Configuration - Explicit Persistence: You can explicitly configure result persistence using the 
persist_result
parameter. This flexibility allows you to override the default behavior as needed. #### Prefect Cloud Retention Policy - For Prefect Cloud users, flow runs, including their task runs, are retained based on a Flow Run Retention Policy, which is determined by the account tier: - The retention period starts when a flow reaches a terminal state and is retained according to the set duration. - Subflow runs are managed separately and removed independently based on when they reach their terminal states. Additional details can be found in the Prefect Results Documentation and the Flow Run Retention Policy page. Does this answer your question, or do you need specifics on configuring result retention for a particular setup?
d
@Marvin I’m running Prefect entirely in-process. Where do my results persist to and is there a retention policy still
m
### In-Process Prefect Result Persistence When running Prefect entirely in-process (without Prefect Cloud or a remote orchestrator), the behavior concerning result persistence is slightly different: #### Where Results Persist - In-Memory: By default, Prefect retains task and flow results in memory for the duration of the flow run, enabling the results to be passed to downstream tasks. - Local Storage: If you have configured result persistence, the results can be stored locally on disk. For example, you might specify a local file system or use custom result classes to direct where results are saved. #### Retention Policy - In-Process Runs: There is no automatic retention policy for in-memory results in purely in-process setups. Results exist in memory for the runtime duration and are lost when the process ends. - Configured Persistence: If you configure persistence to a storage location, the retention of these persisted results depends on your own storage management policies. Prefect itself does not enforce a deletion or retention timeline on such stored data. #### Example of Local Persistence Setup Here’s an illustrative example showing how to configure a local storage for result persistence:
Copy code
python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem
from prefect.results import LocalResult
from prefect.serializers import JSONSerializer

# Define a local storage location for results
local_storage = LocalFileSystem(basepath="/path/to/results")

# Define a result configuration
local_result = LocalResult(
    filesystem=local_storage,
    serializer=JSONSerializer()
)

@task(result=local_result)
def my_task(x: int, y: int) -> int:
    return x + y

@flow
def my_flow():
    return my_task(1, 2)

if __name__ == "__main__":
    result = my_flow()
    print(result)
### Summary - In-Memory: Results are retained only during the process runtime. - Local Storage: If configured, results persist locally with duration managed by your own policies. - No Retention Policy: Prefect in-process does not enforce automatic deletion or data retention policies. For more specifics on configuring result persistence, refer to the Prefect Results Documentation. Need any further customization or more examples?
d
@Marvin How do I use the custom serializer? Show me an example with tasks and flows
m
Certainly! Here’s a detailed example that demonstrates how to use a custom serializer with tasks and flows in Prefect. ### Custom Serializer Example with Tasks and Flows We'll create a custom serializer using Apache Arrow, and integrate it with task and flow definitions to persist results. #### Step 1: Define the Custom Serializer ArrowSerializer:
Copy code
python
import pyarrow as pa
import pyarrow.parquet as pq
from prefect.serializers import Serializer

class ArrowSerializer(Serializer):
    def dumps(self, obj):
        table = pa.Table.from_pandas(obj)
        sink = pa.BufferOutputStream()
        pq.write_table(table, sink)
        return sink.getvalue().to_pybytes()

    def loads(self, s):
        source = pa.BufferReader(s)
        table = pq.read_table(source)
        return table.to_pandas()
#### Step 2: Configure Local Storage for Results We'll use a local file system to store our serialized task results. Local Storage Configuration:
Copy code
python
from prefect.filesystems import LocalFileSystem

# Define a local storage location for results
local_storage = LocalFileSystem(basepath="path/to/results")
Note: Make sure the path (e.g., "path/to/results") exists on your file system or update it accordingly. #### Step 3: Define Tasks and Flows with the Custom Serializer Task and Flow Definitions:
Copy code
python
import pandas as pd
from prefect import task, flow
from prefect.results import LocalResult

# Use custom ArrowSerializer
arrow_serializer = ArrowSerializer()

# Define a local result configuration using our custom serializer
local_result = LocalResult(
    filesystem=local_storage,
    serializer=arrow_serializer
)

@task(result=local_result)
def create_dataframe() -> pd.DataFrame:
    data = {'col1': [1, 2], 'col2': [3, 4]}
    df = pd.DataFrame(data)
    return df

@task(result=local_result)
def process_dataframe(df: pd.DataFrame) -> pd.DataFrame:
    df['col3'] = df['col1'] + df['col2']
    return df

@flow
def my_flow():
    df = create_dataframe()
    processed_df = process_dataframe(df)
    return processed_df

if __name__ == "__main__":
    result_df = my_flow()
    print(result_df)
### Explanation: 1. Custom Serializer: 
ArrowSerializer
is defined to convert pandas DataFrame to Arrow Table and store it as Parquet. 2. Local Storage: 
LocalFileSystem
instance is created for the path where results will be stored. 3. Task Definition: - 
create_dataframe
creates a pandas DataFrame and uses 
local_result
to persist the result. - 
process_dataframe
takes the DataFrame, processes it, and persists the result. 4. Flow Definition: 
my_flow
orchestrates the tasks. ### Running the Example When you run this script, it will: 1. Create a DataFrame in 
create_dataframe
. 2. Persist it using the custom Arrow serialization. 3. Process and persist the DataFrame again in 
process_dataframe
. 4. Print the final DataFrame. Make sure to adjust the storage path (
path/to/results
) according to your file system setup. ### Additional Information For detailed configurations and options, explore the Prefect Custom Serializer Documentation. Do you need any more customizations or explanations?
d
@Marvin I am planning on persisting Pandas dataframes
m
Great! Persisting Pandas DataFrames with a custom serializer like Apache Arrow is an excellent choice for both efficiency and compatibility. Let's walk through the full implementation to persist Pandas DataFrames using a custom Arrow serializer in a Prefect flow. ### Step-by-Step Implementation #### Step 1: Define the Custom Arrow Serializer
Copy code
python
import pyarrow as pa
import pyarrow.parquet as pq
from prefect.serializers import Serializer

class ArrowSerializer(Serializer):
    def dumps(self, obj):
        table = pa.Table.from_pandas(obj)
        sink = pa.BufferOutputStream()
        pq.write_table(table, sink)
        return sink.getvalue().to_pybytes()

    def loads(self, s):
        source = pa.BufferReader(s)
        table = pq.read_table(source)
        return table.to_pandas()
#### Step 2: Configure Local Storage for Results
Copy code
python
from prefect.filesystems import LocalFileSystem

# Define a local storage location for results
local_storage = LocalFileSystem(basepath="path/to/results")
Note: Replace 
"path/to/results"
with the actual path on your file system where you want to store the results. #### Step 3: Define Tasks and Flows Using the Custom Serializer
Copy code
python
import pandas as pd
from prefect import task, flow
from prefect.results import LocalResult

# Use the custom ArrowSerializer
arrow_serializer = ArrowSerializer()

# Define a local result configuration using our custom serializer
local_result = LocalResult(
    filesystem=local_storage,
    serializer=arrow_serializer
)

@task(result=local_result)
def create_dataframe() -> pd.DataFrame:
    data = {'col1': [1, 2, 3], 'col2': [3, 4, 5]}
    df = pd.DataFrame(data)
    return df

@task(result=local_result)
def process_dataframe(df: pd.DataFrame) -> pd.DataFrame:
    df['col3'] = df['col1'] + df['col2']
    return df

@flow
def my_flow():
    df = create_dataframe()
    processed_df = process_dataframe(df)
    return processed_df

if __name__ == "__main__":
    result_df = my_flow()
    print(result_df)
### Explanation: 1. Custom Serializer: - 
ArrowSerializer
: This custom serializer converts a Pandas DataFrame to an Arrow Table and writes it as a Parquet file. It also reads Parquet files back into DataFrames. 2. Local Storage: - 
LocalFileSystem
: This is configured to store results in a specified local directory. 3. Tasks: - 
create_dataframe
: Creates a Pandas DataFrame and persists it using the custom Arrow serializer. - 
process_dataframe
: Receives the DataFrame, processes it (adds a new column), and persists the processed DataFrame. 4. Flow: - 
my_flow
 orchestrates the tasks and returns the final DataFrame. ### Running the Example When you run the script: 1. Create a Pandas DataFrame in 
create_dataframe
. 2. Persist it as a Parquet file using Arrow. 3. Process the DataFrame in 
process_dataframe
and persist the result. 4. Print the final DataFrame. ### Conclusion Using the above approach, you can efficiently serialize and persist Pandas DataFrames in your Prefect flows using Apache Arrow. This setup ensures that your data is stored in a highly optimized and compatible format. For further customization or assistance, refer to the Prefect Documentation or feel free to ask more questions!
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