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How to implement saga orchestration with compensating steps in Python
Orchestrate a distributed transaction across services, rolling back completed steps with compensations when a later step fails.
class InventoryService:
def reserve(self, order_id):
print(f"[Inventory] Reserving stock for order {order_id}")
return True
def compensate(self, order_id):
print(f"[Inventory] Releasing stock for order {order_id}")
class PaymentService:
def charge(self, order_id):
print(f…Saga pattern orchestration with rollback in Python
Orchestrate a distributed transaction with Saga steps and automated compensation rollback on failure.
import time
import random
class SagaStep:
def __init__(self, name):
self.name = name
self.executed = False
def execute(self):
print(f"Executing {self.name}...")
time.sleep(0.2)
if random.random() < 0.3:
raise RuntimeError(f"{self.name} failed")
sel…How to Build a Mock ML Pipeline with Prefect in Python
Create a lightweight Prefect flow with mock preprocessing, training, and evaluation tasks to prototype an ML pipeline end-to-end.
from prefect import task, flow
from datetime import datetime
@task
def preprocess_data(raw_value: float) -> float:
"""Mock preprocessing: normalize the input value."""
return raw_value / 100.0
@task
def train_model(features: float) -> dict:
"""Mock training: return a fake model artifact."""
return …How to Build a Mock TFX Pipeline in Python
Simulate a TFX-style ML pipeline with simple Python functions to understand component orchestration, data flow, and artifact passing.
# Mock TFX pipeline to illustrate component orchestration
def CsvExampleGen(data_path):
"""Mock component: Simulates reading CSV data."""
print(f"ExampleGen: Reading from {data_path}")
return {"records": 100, "name": "examples"}
def StatisticsGen(example_artifact):
"""Mock component: Simulates genera…How to Create a Mock Metaflow Flow in Python
Build a minimal Metaflow flow with two sequential steps that pass data between them using instance attributes.
from metaflow import FlowSpec, step, current
class MockFlow(FlowSpec):
"""A minimal Metaflow flow to demonstrate basic steps and branching."""
@step
def start(self):
self.category = "mock"
print(f"Start step for {self.category} flow")
self.next(self.process)
@step
def pr…How to Define Dagster ML Assets in Python
Define a chain of Dagster software-defined assets that compute raw features, normalized features, and predictions for an ML pipeline.
from dagster import asset
@asset
def raw_features():
return {"sepal_length": [5.1, 4.9, 6.2], "sepal_width": [3.5, 3.0, 3.4]}
@asset
def normalized_features(raw_features):
values = raw_features["sepal_length"]
mean = sum(values) / len(values)
std = (sum((x - mean) ** 2 for x in values) / len(values…How to Simulate an Airflow ML Pipeline in Python
Mock an Airflow ML pipeline in plain Python by defining steps, simulating their execution with delays, and returning a success summary.
from datetime import datetime, timedelta
import time
class MLPipeline:
def __init__(self, pipeline_name):
self.pipeline_name = pipeline_name
self.steps = []
def add_step(self, step_name, duration_seconds):
self.steps.append({"name": step_name, "duration": duration_seconds})
def …Training Pipeline Orchestration Mock DAG in Python
Build a mock DAG orchestrator that runs ML pipeline stages in dependency order using topological sorting (Kahn's algorithm).
from collections import deque
from dataclasses import dataclass, field
@dataclass
class DAGNode:
name: str
task: callable
dependencies: list[str] = field(default_factory=list)
class MockDAG:
def __init__(self, nodes: list[DAGNode]):
self.nodes = {n.name: n for n in nodes}
self.execu…Browse by section
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