@Marvin how can I improve the performance of a flow when a task returns a large dataset (about 80,000 dictionaries)? Passing this result to a subflow takes too long to start running.

To improve the performance of a Prefect flow when dealing with large datasets, consider the following strategies: 1. Task Caching: Use Prefect's caching capabilities to avoid recomputing results that have already been computed. You can configure tasks to cache their outputs, which can be retrieved in subsequent runs. This can be particularly useful if the data doesn't change frequently. Learn more about task caching. 2. Serialization Optimization: When passing large datasets between tasks or subflows, optimize the serialization process. You can use Pydantic models for consistent serialization or define custom cache key functions to handle only the necessary properties of the input data. This reduces the overhead associated with data serialization and deserialization. 3. Concurrency Limits: If your flow involves API calls or other operations that could be rate-limited, set a global concurrency limit to prevent bottlenecks. This ensures tasks run efficiently without hitting external service limits. 4. Data Partitioning: Consider partitioning your data into smaller chunks and processing them in parallel tasks. This approach can help distribute the workload and reduce the time it takes for a subflow to start processing its inputs. 5. Avoid Synchronous Operations: Ensure that operations within the flow are not inadvertently blocking. Prefect's

.map()

and

.submit()

methods are synchronous, so ensure that these are used appropriately to prevent unnecessary blocking. By applying these techniques, you should be able to enhance the performance of your flow when handling large datasets. For more detailed guidance, you can refer to Prefect's documentation on optimizing pipelines.

@Marvin can you give me an example how can I partition my data efficiently and also about serialization optimization?

Here's how you can efficiently partition your data and optimize serialization using Prefect: ### Data Partitioning To partition a large dataset, you can break it into smaller chunks and process each chunk in parallel. Here's a simple example:

python
from prefect import flow, task
from prefect.futures import PrefectFuture

@task
def process_chunk(chunk):
    # Process each chunk of data
    return sum(chunk)

@flow
def partition_and_process(data):
    # Partition your data into chunks
    chunk_size = 1000  # Adjust based on your needs
    chunks = [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]
    
    # Process each chunk in parallel
    results = [process_chunk.submit(chunk) for chunk in chunks]
    
    # Aggregate results
    total = sum([result.result() for result in results])
    return total

# Example usage
data = [i for i in range(80000)]  # Your large dataset
partition_and_process(data)

### Serialization Optimization For serialization optimization, you can use Pydantic models to define how your data is serialized. Here's an example using Pydantic:

python
from pydantic import BaseModel
from prefect import task, flow

class DataModel(BaseModel):
    key: str
    value: int

@task
def serialize_data(data):
    # Serialize data using Pydantic
    return [DataModel(**item).json() for item in data]

@task
def process_serialized_data(serialized_data):
    # Deserialize and process data
    data = [DataModel.parse_raw(item) for item in serialized_data]
    return sum(item.value for item in data)

@flow
def data_serialization_flow(data):
    serialized_data = serialize_data(data)
    result = process_serialized_data(serialized_data)
    return result

# Example usage
data = [{'key': str(i), 'value': i} for i in range(80000)]
data_serialization_flow(data)

These techniques should help you handle large datasets more efficiently in Prefect. For further details, you might want to explore the Prefect serializers module and Prefect's caching documentation.

@Marvin please send me the related documentation for prefect 2, I'm using it.

For optimizing flow performance and handling large datasets in Prefect 2.x, you can refer to the following resources: 1. Global Concurrency Limits: This guide explains how to control the number of concurrent operations, which can help manage resources effectively when dealing with large datasets. - Global Concurrency Limits Guide 2. Results and Serialization: Learn about using different serializers, like Pickle, to handle complex data objects and optimize serialization. - Results and Serialization Concepts 3. Big Data Handling: This guide provides insights into handling large data sets, including writing results to cloud storage to save memory. - Big Data Guide These resources should help you optimize the performance of your Prefect 2.x flows when dealing with large datasets.

@Marvin is there a way to cache the task results in Prefect 2?

Yes, in Prefect 2.x, you can cache task results to efficiently reuse them in subsequent runs. Here's how you can enable caching for a task: 1. Define a Cache Key Function: Use the

cache_key_fn

parameter to specify a function that returns a cache key. This key determines whether a cached result should be used. 2. Set Cache Expiration: Optionally, specify a

cache_expiration

using a

timedelta

to define when the cache should expire. Here's an example:

python
from prefect import task, flow
from datetime import timedelta

@task(cache_key_fn=lambda *args: hash(args), cache_expiration=timedelta(days=1))
def expensive_task(data):
    # Simulate an expensive operation
    return sum(data)

@flow
def my_flow(data):
    result = expensive_task(data)
    return result

# Example usage
data = [1, 2, 3, 4, 5]
my_flow(data)

In this example, the

expensive_task

will reuse cached results if the inputs haven't changed and the cache hasn't expired. You can read more about task caching in the Prefect 2.x Task Concepts documentation.

@Marvin in my case, the subflow is taking too long time to start running. I'll give you an example of my case:

hur_reports = extract_hur_reports.submit(
        name="hur-reports",
        host=host,
        api_key=api_key,
        month=month,
        year=year,
        agent_reports=agent_reports,
    )

try:
        bigquery_load(
            name="usage_metering_agents",
            payload_list=usage_metering_agents,
            table_name="usage_metering_agents",
            field_schema=class_map["usage_metering_agents"].avro_schema_to_python(),
            table_config=TABLE_CONFIG["usage_metering_agents"].model_dump(),
            gcp_bucket=gcp_bucket,
            project_id=project_id,
            is_merge=True,
        )
    except Exception as e:
        print(e)

extract_hur_reports is a task and bigquery_load is a flow (in that case, is being used as a subflow). The extract_hur_reports result is a large data, and this is causing all this boring situation.

In Prefect 2.x, when dealing with subflows and large datasets, the performance can be optimized by considering the following strategies: 1. Use of Storage Blocks: For large datasets, consider writing the data to external storage (e.g., cloud storage) and passing references (such as file paths or object keys) to the subflow instead of the entire dataset. This avoids the overhead of passing large data in memory. 2. Task Mapping: If the data can be processed in parallel, use Prefect's

.map()

functionality to process data chunks in parallel tasks, which can then feed into the subflow. This can distribute the workload and reduce the time it takes for the subflow to start. 3. Optimize Serialization: Ensure that the data serialization and deserialization processes are efficient. If possible, use lightweight serialization formats or customize the serialization process to only include necessary data. 4. Subflow Design: Re-evaluate whether the operation being performed in the subflow can be broken down into smaller tasks. Prefect recommends using tasks for discrete, specific pieces of work. For more details, you can refer to the Prefect 2.x Flows Concepts documentation.