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<@ULVA73B9P> I have a flow which analyzes a zip fi...
# marvin-in-the-wild
t
@Marvin I have a flow which analyzes a zip file. There are many functions each one performs a different analysis. Some of the analyzers rely on the results produced by other analyzers. What would be the best way to implement such a program efficiently using prefect?
m
To efficiently implement a flow in Prefect where multiple functions analyze a zip file and rely on each other's results, you should define task dependencies clearly within your flow. Here's how you can do it: ### Using 
wait_for
to Specify Task Dependencies You can use the 
wait_for
parameter to specify that a task should wait for one or more other tasks to complete before starting. Here's an example of how to structure your flow:
Copy code
python
from prefect import flow, task

@task
def analyze_step_1(input_data):
    # Perform analysis step 1
    return result_1

@task
def analyze_step_2(result_1):
    # Perform analysis step 2, relies on result_1 from analyze_step_1
    return result_2

@task
def analyze_step_3(result_2):
    # Perform analysis step 3, relies on result_2 from analyze_step_2
    return result_3

@flow(log_prints=True)
def analysis_flow(zip_file):
    # Initial task to extract data from the zip file
    extracted_data = extract_data_from_zip(zip_file)
    
    # Submit and specify dependencies using wait_for
    result_1 = analyze_step_1.submit(extracted_data)
    result_2 = analyze_step_2.submit(result_1, wait_for=[result_1])
    result_3 = analyze_step_3.submit(result_2, wait_for=[result_2])
    
    # You can add more steps as needed
    return result_3

@task
def extract_data_from_zip(zip_file):
    # Placeholder function for extracting data from zip
    extracted_data = {} # Replace with actual extraction logic
    return extracted_data

if __name__ == "__main__":
    analysis_flow("path/to/zipfile.zip")
### Example Highlights: 1. Use 
@task
Decorator: Decorate each function that represents a step in your analysis pipeline. 2. Specify Dependencies with 
wait_for
: For tasks that depend on the results of other tasks, use the 
wait_for
parameter to define these dependencies. 3. Flow Context: Use the 
@flow
decorator to define the main flow which coordinates the execution of tasks. ### Resources: - Specifying Upstream Dependencies - Prefect Code Example: Basic Task Flow By organizing your tasks and defining dependencies clearly, you'll ensure the analysis proceeds in the correct order and efficiently utilizes resources.
t
@Marvin I want each task to contain the information that it requires instead of having to explicitly call the tasks in the right order
m
In Prefect, you can take advantage of automatic upstream dependencies to simplify your flow. Prefect automatically recognizes the upstream tasks based on the inputs they require. This means you don't need to explicitly call tasks in the right order; you just need to pass the output of one task as the input to another. ### Using Automatic Upstream Dependencies Here's an example of how you can structure your flow:
Copy code
python
from prefect import flow, task

@task
def extract_data_from_zip(zip_file):
    # Placeholder function for extracting data from zip
    extracted_data = {} # Replace with actual extraction logic
    return extracted_data

@task
def analyze_step_1(input_data):
    # Perform analysis step 1
    return result_1

@task
def analyze_step_2(result_1):
    # Perform analysis step 2, relies on result_1 from analyze_step_1
    return result_2

@task
def analyze_step_3(result_2):
    # Perform analysis step 3, relies on result_2 from analyze_step_2
    return result_3

@flow(log_prints=True)
def analysis_flow(zip_file):
    # Extract data from zip file
    extracted_data = extract_data_from_zip(zip_file)
    
    # Chain tasks together using automatic upstream dependencies
    result_1 = analyze_step_1(extracted_data)
    result_2 = analyze_step_2(result_1)
    result_3 = analyze_step_3(result_2)
    
    return result_3

if __name__ == "__main__":
    analysis_flow("path/to/zipfile.zip")
### Explanation: 1. Automatic Upstream Dependencies: When a result from a task is used as input for another task, Prefect automatically recognizes the task that produced that result as an upstream dependency. 2. Flow Context: The 
@flow
decorator defines the main workflow. It manages the execution of tasks and ensures they are run in the correct order based on their inputs and outputs. ### Resources: - Specifying Upstream Dependencies This approach allows each task to contain the information it requires, with Prefect handling the dependencies automatically. This ensures that your tasks are executed in the right order without explicitly having to sequence them.
t
@Marvin I still need to need to specify in my flow which task receives the results of which other task. I would like to loop through all the analyzers and have the order of execution automatically inferred somehow.
m
Prefect does not automatically infer the order of execution for tasks beyond the automatic upstream dependency management. However, you can dynamically create a dependency graph by iterating through the analyzers and setting dependencies programmatically. This way, the order of execution is determined by the dependencies you specify algorithmically within the flow. Here's an advanced approach to dynamically creating dependencies among your analyzers in a flow: ### Example with Dynamic Task Scheduling
Copy code
python
from prefect import flow, task

@task
def extract_data_from_zip(zip_file):
    # Placeholder function for extracting data from zip
    extracted_data = {}  # Replace with actual extraction logic
    return extracted_data

@task
def analyze_step_1(input_data):
    # Perform analysis step 1
    return {'result': 'result_1', 'next': analyze_step_2}

@task
def analyze_step_2(input_data):
    # Perform analysis step 2
    return {'result': 'result_2', 'next': analyze_step_3}

@task
def analyze_step_3(input_data):
    # Perform analysis step 3
    return {'result': 'result_3', 'next': None}

analyzers = [analyze_step_1, analyze_step_2, analyze_step_3]

@flow(log_prints=True)
def analysis_flow(zip_file):
    # Extract data from zip file
    extracted_data = extract_data_from_zip(zip_file)
    
    # Start the first analyzer
    current_task_result = analyzers[0].submit(extracted_data)
    
    # Loop through the analyzers to chain them together
    for analyzer in analyzers[1:]:
        current_task_result = analyzer.submit(current_task_result)  # Automatically sets upstream dependency

if __name__ == "__main__":
    analysis_flow("path/to/zipfile.zip")
### Explanation: 1. Task Results with Next Steps: Each task returns a dictionary containing its result and the next analyzer to run. 2. Loop Through Analyzers: The main flow iterates through the list of analyzers and chains them together by submitting tasks consecutively. 3. Automatic Upstream Dependencies: By passing the result of each task to the next, Prefect automatically manages the upstream dependencies. ### Resources: - Specifying Upstream Dependencies - Prefect Concepts - Deployments and Scheduling This dynamic approach allows the flow to determine task order and dependencies at runtime based on the sequence of analyzers, providing a flexible and automated way to manage task execution order.
t
@Marvin this example assumes that the tasks are sequential. In practice I have dozens of analyzers some of which depend on the results of other analyzers.
m
To handle a more complex scenario where tasks have multiple dependencies, you can structure your Prefect flow to automatically create and resolve dependencies based on the relationships between tasks. Here's an advanced approach to dynamically handling dependencies among dozens of analyzers: ### Dynamic Dependency Resolution 1. Define Analyzers and Dependencies: Create a mapping of analyzers to their dependencies. 2. Topological Sorting: Use topological sorting to resolve the order of execution based on dependencies. ### Example Implementation
Copy code
python
from prefect import flow, task
from collections import defaultdict, deque

@task
def extract_data_from_zip(zip_file):
    # Placeholder function for extracting data from zip
    extracted_data = {}  # Replace with actual extraction logic
    return extracted_data

@task
def analyze_step_1(input_data):
    # Perform analysis step 1
    return "result_1"

@task
def analyze_step_2(input_data):
    # Perform analysis step 2
    return "result_2"

@task
def analyze_step_3(input_data):
    # Perform analysis step 3
    return "result_3"

@task
def analyze_step_4(result_1, result_2):
    # Perform analysis step 4, depends on result_1 and result_2
    return "result_4"


# Define all analyzers
analyzers = {
    "step_1": analyze_step_1,
    "step_2": analyze_step_2,
    "step_3": analyze_step_3,
    "step_4": analyze_step_4,
}

# Define dependencies
dependencies = {
    "step_1": [],
    "step_2": ["step_1"],
    "step_3": ["step_1"],
    "step_4": ["step_2", "step_3"],
}

# Topological sort function to resolve dependencies
def topological_sort(dependencies):
    graph = defaultdict(list)
    in_degree = defaultdict(int)
    
    # Build graph
    for node, deps in dependencies.items():
        for dep in deps:
            graph[dep].append(node)
        in_degree[node] = in_degree.get(node, 0)
        for dep in deps:
            in_degree[node] += 1
            
    # Kahn's algorithm
    queue = deque([node for node in dependencies if in_degree[node] == 0])
    sorted_list = []
    
    while queue:
        node = queue.popleft()
        sorted_list.append(node)
        for neighbor in graph[node]:
            in_degree[neighbor] -= 1
            if in_degree[neighbor] == 0:
                queue.append(neighbor)
                
    if len(sorted_list) < len(dependencies):
        raise ValueError("Cycle detected in dependencies")
        
    return sorted_list

@flow(log_prints=True)
def analysis_flow(zip_file):
    extracted_data = extract_data_from_zip(zip_file)
    task_results = {"extracted_data": extracted_data}
    
    sorted_analyzers = topological_sort(dependencies)
    
    for analyzer in sorted_analyzers:
        if analyzer == "step_1":
            task_results["step_1"] = analyzers<task_results["extracted_data"]|analyzer>
        elif analyzer == "step_2":
            task_results["step_2"] = analyzers<task_results["step_1"]|analyzer>
        elif analyzer == "step_3":
            task_results["step_3"] = analyzers<task_results["step_1"]|analyzer>
        elif analyzer == "step_4":
            task_results["step_4"] = analyzers<task_results["step_1"], task_results["step_2"]|analyzer>
    
    return task_results

if __name__ == "__main__":
    analysis_flow("path/to/zipfile.zip")
### Explanation: 1. Dependencies Mapping: The 
dependencies
dictionary defines which analyzers depend on which others. 2. Topological Sort: The 
topological_sort
function resolves the order of execution by sorting the tasks based on dependencies, preventing circular dependencies. 3. Dynamic Execution: The 
analysis_flow
function dynamically submits tasks in the correct order based on the resolved dependencies. ### Resources: - Specifying Upstream Dependencies - Prefect Concepts - Deployments and Scheduling
This approach ensures that your flow can dynamically resolve dependencies and execute tasks in the correct order, regardless of the complexity of the relationships between analyzers.
t
@Marvin the loop in the end doesn't look so dynamic
m
Let's refine the approach to make the loop truly dynamic. We'll generalize the execution loop so it can handle any analyzer that has dependencies dynamically, based on the preceding steps' results. ### Fully Dynamic Execution Loop Here's how we can improve it:
Copy code
python
from prefect import flow, task
from collections import defaultdict, deque

@task
def extract_data_from_zip(zip_file):
    # Placeholder function for extracting data from zip
    extracted_data = {}  # Replace with actual extraction logic
    return extracted_data

@task
def analyze_step_1(input_data):
    # Perform analysis step 1
    return "result_1"

@task
def analyze_step_2(input_data):
    # Perform analysis step 2
    return "result_2"

@task
def analyze_step_3(input_data):
    # Perform analysis step 3
    return "result_3"

@task
def analyze_step_4(result_1, result_2):
    # Perform analysis step 4, depends on result_1 and result_2
    return "result_4"

# Define all analyzers and their dependency keys
analyzers = {
    "step_1": (analyze_step_1, ["extracted_data"]),
    "step_2": (analyze_step_2, ["step_1"]),
    "step_3": (analyze_step_3, ["step_1"]),
    "step_4": (analyze_step_4, ["step_2", "step_3"]),
}

# Define dependencies
dependencies = {
    "step_1": [],
    "step_2": ["step_1"],
    "step_3": ["step_1"],
    "step_4": ["step_2", "step_3"],
}

# Topological sort function to resolve dependencies
def topological_sort(dependencies):
    graph = defaultdict(list)
    in_degree = defaultdict(int)
    
    # Build graph
    for node, deps in dependencies.items():
        for dep in deps:
            graph[dep].append(node)
        in_degree[node] = in_degree.get(node, 0)
        for dep in deps:
            in_degree[node] += 1
            
    # Kahn's algorithm
    queue = deque([node for node in dependencies if in_degree[node] == 0])
    sorted_list = []

    while queue:
        node = queue.popleft()
        sorted_list.append(node)
        for neighbor in graph[node]:
            in_degree[neighbor] -= 1
            if in_degree[neighbor] == 0:
                queue.append(neighbor)
                
    if len(sorted_list) < len(dependencies):
        raise ValueError("Cycle detected in dependencies")

    return sorted_list

@flow(log_prints=True)
def analysis_flow(zip_file):
    extracted_data = extract_data_from_zip(zip_file)
    task_results = {"extracted_data": extracted_data}

    sorted_analyzers = topological_sort(dependencies)

    for analyzer in sorted_analyzers:
        analyzer_task, dependency_keys = analyzers[analyzer]
        dependencies_results = [task_results[key] for key in dependency_keys]
        task_results[analyzer] = analyzer_task.submit(*dependencies_results)
    
    return task_results

if __name__ == "__main__":
    analysis_flow("path/to/zipfile.zip")
### Explanation: 1. Analyzer Dictionary: Each analyzer is stored in a dictionary along with the keys of its dependencies. 2. Topological Sorting: Perform topological sort to ensure analyzers are executed in the order respecting their dependencies. 3. Dynamic Execution: Loop through the sorted analyzers, extracting the required dependency results dynamically and submitting each task in the correct order. ### Resources: - Specifying Upstream Dependencies - Prefect Concepts - Deployments and Scheduling This approach generalizes the execution flow, making it fully dynamic. It resolves dependencies at runtime and automatically submits tasks in the correct order based on their specified dependencies.
t
@Marvin how can I store the dependencies as part of the tasks and not in a separate dict
m
You can leverage Prefect's task metadata to store dependencies directly within the task definitions. This way, each task carries its own dependency information, making the flow more self-contained and modular. ### Example with Dependencies Stored in Task Metadata We can use task decorators with custom attributes to store dependency information directly within tasks. Here's how to do it:
Copy code
python
from prefect import flow, task
from collections import defaultdict, deque

@task
def extract_data_from_zip(zip_file):
    # Placeholder function for extracting data from zip
    extracted_data = {}  # Replace with actual extraction logic
    return extracted_data

@task
def analyze_step_1(input_data):
    # Perform analysis step 1
    return "result_1"
analyze_step_1.dependencies = ["extract_data_from_zip"]

@task
def analyze_step_2(result_1):
    # Perform analysis step 2
    return "result_2"
analyze_step_2.dependencies = ["analyze_step_1"]

@task
def analyze_step_3(result_1):
    # Perform analysis step 3
    return "result_3"
analyze_step_3.dependencies = ["analyze_step_1"]

@task
def analyze_step_4(result_2, result_3):
    # Perform analysis step 4, depends on result_1 and result_2
    return "result_4"
analyze_step_4.dependencies = ["analyze_step_2", "analyze_step_3"]

# Register all tasks
analyzers = [extract_data_from_zip, analyze_step_1, analyze_step_2, analyze_step_3, analyze_step_4]

# Extract dependencies directly from task metadata
def resolve_dependencies(tasks):
    dependencies = {task.name: getattr(task, 'dependencies', []) for task in tasks}
    return dependencies

# Topological sort to resolve dependencies
def topological_sort(dependencies):
    graph = defaultdict(list)
    in_degree = defaultdict(int)
    
    # Build graph
    for node, deps in dependencies.items():
        for dep in deps:
            graph[dep].append(node)
        in_degree[node] = in_degree.get(node, 0)
        for dep in deps:
            in_degree[node] += 1
            
    # Kahn's algorithm
    queue = deque([node for node in dependencies if in_degree[node] == 0])
    sorted_list = []

    while queue:
        node = queue.popleft()
        sorted_list.append(node)
        for neighbor in graph[node]:
            in_degree[neighbor] -= 1
            if in_degree[neighbor] == 0:
                queue.append(neighbor)
                
    if len(sorted_list) < len(dependencies):
        raise ValueError("Cycle detected in dependencies")

    return sorted_list

@flow(log_prints=True)
def analysis_flow(zip_file):
    task_results = {"extract_data_from_zip": extract_data_from_zip.submit(zip_file)}
    
    dependencies = resolve_dependencies(analyzers)
    sorted_analyzers = topological_sort(dependencies)

    for analyzer_name in sorted_analyzers:
        if analyzer_name == "extract_data_from_zip":
            continue
        analyzer_task = next(task for task in analyzers if task.name == analyzer_name)
        dependency_results = [task_results[dep] for dep in analyzer_task.dependencies]
        task_results[analyzer_name] = analyzer_task.submit(*dependency_results)
    
    return task_results

if __name__ == "__main__":
    analysis_flow("path/to/zipfile.zip")
### How It Works: 1. Task Metadata: Each task defines its dependencies using a custom attribute 
dependencies
. 2. Dependency Resolution: The 
resolve_dependencies
function extracts the dependencies from the tasks' metadata. 3. Topological Sorting: The 
topological_sort
function ensures tasks are executed in the correct order. 4. Dynamic Execution: The main 
analysis_flow
dynamically submits tasks based on the sorted order and their dependencies. ### Advantages: - Self-contained Tasks: Dependencies are part of the tasks, making adding or modifying tasks easier. - Modular and Maintainable: Task definitions and their dependencies are centralized within the task itself. ### Resources:
- Specifying Upstream Dependencies in Prefect - Prefect Task and Flow Management This approach encapsulates dependency information within tasks, ensuring that the flow dynamically handles execution order based on task-specific metadata. This makes the flow more modular and maintainable.
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