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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Check if a Timestamp Falls in a Daily Maintenance Window in Python
A small Python function that returns True when a datetime falls inside a daily maintenance window, and a demo printing yes/no for sample timestamps.
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
def in_maintenance_window(now: datetime, start_hour: int = 2, duration_hours: int = 4) -> bool:
"""Return True if 'now' falls inside the daily maintenance window."""
day_start = now.replace(hour=start_hour, minute=0, second=0, microsecond…Generate Synthetic CPU Utilization Metrics in Python
Creates realistic time-series CPU utilization samples with timestamps, noise, and output as structured JSON for observability demos and testing.
from datetime import datetime, timedelta
import random
import json
def generate_metric_samples(base_value, noise, count=60, interval_minutes=1):
"""Generate realistic CPU utilization samples for a given time window."""
timestamps = []
values = []
now = datetime.utcnow()
start_time = now - timede…How to Build a Metrics Counter with Increment and Snapshot in Python
A simple dict-backed MetricsCounter class that increments named counters and returns a snapshot of the current values.
class MetricsCounter:
def __init__(self):
self._metrics = {}
def increment(self, key, delta=1):
self._metrics[key] = self._metrics.get(key, 0) + delta
def snapshot(self):
return dict(self._metrics)
if __name__ == "__main__":
counter = MetricsCounter()
counter.increment("…How to Create a Mock OpenTelemetry Trace in Python
Create a mock OpenTelemetry trace in memory to test span creation, attributes, and parent-child relationships without exporting to a backend.
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
def create_mock_trace():
tracer_provider = TracerProvider()
span_exporter =…How to Parse Log Lines with Regex in Python
Extracts timestamp, log level, service name, and message from a log line using compiled regex named groups.
import re
LOG_PATTERN = re.compile(
r'^(?P<timestamp>\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}) '
r'\[(?P<level>\w+)\] '
r'\((?P<service>[^)]+)\) '
r'(?P<message>.*)$'
)
def parse_log_line(line: str) -> dict:
match = LOG_PATTERN.match(line)
if not match:
return {"error": "invalid log format…Mocking a Metrics Gauge's set_value Method in Python
Demonstrates using unittest.mock.Mock with wraps to intercept a gauge's set_value call while verifying arguments and preserving real behavior.
from unittest.mock import Mock
class MetricsGauge:
def __init__(self, name):
self.name = name
self.value = 0.0
def set_value(self, new_value):
self.value = float(new_value)
return self.value
# Usage demonstration with a mock
gauge = MetricsGauge("cpu_usage")
gauge_mock = Mock…Rotate Log Files by Size in Python
A mock log rotation script that renames log files exceeding a size threshold, appending numbered backups.
import os
from pathlib import Path
def rotate_logs(directory: str, max_size: int = 100) -> None:
"""Rotate log files that exceed max_size bytes."""
log_dir = Path(directory)
for log_file in sorted(log_dir.glob("*.log"), key=lambda p: str(p)):
if log_file.stat().st_size > max_size:
for …Fallback cached response mock in Python
Wraps a mock function with a fallback to a real service and caches results to mask transient failures.
import time
from functools import wraps
class CachedMock:
def __init__(self, cache_ttl=5):
self.cache = {}
self.cache_ttl = cache_ttl
def get(self, key):
cached = self.cache.get(key)
if cached and time.time() - cached["timestamp"] < self.cache_ttl:
return cached["v…How to Implement an Exactly-Once Deduplication Store in Python
Implement a Python class that deduplicates keys exactly once, tracking first-seen timestamps and duplicate counts.
from datetime import datetime
from typing import Any, Hashable
class ExactlyOnceStore:
def __init__(self) -> None:
self._seen: set[Hashable] = set()
self._first_seen: dict[Hashable, datetime] = {}
self._counts: dict[Hashable, int] = {}
def add(self, key: Hashable, value: Any = None) …Mock a Sidecar Logger with Python Metrics
Simulate a sidecar logger that tracks request counts, error rates, and endpoint hits, producing a metrics snapshot.
import random
import time
from collections import defaultdict
class SidecarLogger:
def __init__(self):
self.metrics = defaultdict(int)
self.total_requests = 0
self.error_count = 0
def log_request(self, endpoint, status_code):
"""Simulate logging a request and updating metrics…Python Saga Compensating Steps Mock
Mock a distributed transaction saga with forward steps and compensating actions that reverse partial progress on failure.
from datetime import datetime
def make_payment(user_id, amount):
print(f"[{datetime.now():%H:%M:%S}] Payment of ${amount} processed for user {user_id}")
return {"step": "payment", "status": "ok", "details": f"${amount} charged"}
def deduct_inventory(order_id, items):
print(f"[{datetime.now():%H:%M:%S}]…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 Create a Mock Iceberg Snapshot Manifest in Python
Build a mock Iceberg snapshot manifest structure with metadata and data entries using Python dictionaries and JSON.
import json
from datetime import datetime, timezone
def create_mock_manifest(snapshot_id: int, file_paths: list[str]) -> dict:
"""Create a mock Iceberg snapshot manifest structure."""
manifest_file = {
"manifest_path": f"/warehouse/table/metadata/snap-{snapshot_id}-m0.avro",
"manifest_length"…How to Shuffle Items by Group in Python
Randomly shuffle items within each group while keeping groups contiguous, using a seed for reproducible results.
import random
def shuffle_sort_groups(items, group_key, seed=None):
"""Randomize order within groups, keeping groups contiguous."""
rng = random.Random(seed)
groups = {}
for item in items:
key = group_key(item)
groups.setdefault(key, []).append(item)
result = []
for k…How to implement a tumbling window aggregation in Python
Build a mock tumbling window aggregator in Python that groups streaming events into fixed time intervals and computes count, sum, and average per window.
import time
from collections import deque
class TumblingWindow:
def __init__(self, duration_seconds):
self.duration = duration_seconds
self.buffer = deque()
self.window_start = None
def add(self, item):
current_time = time.time()
if self.window_start is None:
…Hudi Upsert Mock Copy on Write in Python
Simulates Apache Hudi's Copy-on-Write upsert behavior by merging update records into a deep copy of base records, replacing matches or appending new ones.
import copy
from typing import Dict, List, Any
def upsert_copy_on_write(base_records: List[Dict[str, Any]], updates: List[Dict[str, Any]], key_field: str = "id") -> List[Dict[str, Any]]:
"""Simulate Hudi Copy-on-Write upsert: merge updates into a copy of base records."""
result = copy.deepcopy(base_records)
…Mock RDD in Python: Simulate Spark RDD Lazy Transformations
Simulate Apache Spark RDD behavior in Python with lazy maps, filters, partitions, and a collect action.
import random
def mock_rdd(data, num_slices=2):
"""
A simple simulation of Spark RDD behavior with lazy evaluation,
transformations, and an action.
"""
class SimpleRDD:
def __init__(self, data, num_slices=2):
self.data = data
self.num_slices = num_slices
…Partition Data by Hash Key Mod N in Python
Returns a partition index for a string key by hashing it with MD5 and taking modulo N, then groups sample keys into partitions.
import hashlib
def partition_key(key: str, num_partitions: int) -> int:
"""Return partition index for key using MD5 hash mod N."""
digest = hashlib.md5(key.encode()).hexdigest()
return int(digest, 16) % num_partitions
if __name__ == "__main__":
keys = ["alice", "bob", "carol", "dave", "eve"]
nu…Session window gap mock in Python
Group sorted timestamps into sessions where any gap between consecutive events exceeds a threshold starts a new session.
from datetime import datetime, timedelta
def session_windows(timestamps, gap_seconds=300):
"""Group timestamps into sessions where gaps > gap_seconds start new sessions."""
if not timestamps:
return []
# Sort timestamps chronologically to ensure correct windowing
timestamps = sorted(timestam…How to Build a Simple ML Pipeline with ZenML in Python
Build a mock machine learning pipeline with ZenML steps for data loading, training, and evaluation, and run it to print the final accuracy.
from zenml import pipeline, step
@step
def load_data() -> dict:
"""Simulate loading data from a source."""
return {"accuracy": 0.0, "loss": 1.0}
@step
def train_model(data: dict) -> dict:
"""Simulate training a model."""
data["accuracy"] = 0.95
data["loss"] = 0.1
return data
@step
def eva…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 Create a Mock ONNX Model in Python
Build and export a minimal mock ONNX model with a Reshape and Gemm layer using the onnx helper API.
import onnx
import numpy as np
from onnx import helper, TensorProto
def create_mock_model():
# Define input and output tensors
input_tensor = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1, 3, 224, 224])
output_tensor = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10])
…How to Detect Data Drift with PSI in Python
Calculate the Population Stability Index (PSI) in Python to compare expected vs actual distributions and detect data drift in machine learning pipelines.
import numpy as np
def calculate_psi(expected, actual, buckets=10):
"""Calculate Population Stability Index (PSI) between two distributions."""
# Create bucket edges based on expected distribution percentiles
edges = np.percentile(expected, np.linspace(0, 100, buckets + 1))
edges[-1] = np.inf # Ensur…How to Generate Experiment Tracking Run IDs in Python
Generate unique experiment run IDs with timestamps and random suffixes for tracking ML pipeline executions.
import random
import string
import time
def generate_run_id(prefix="exp"):
timestamp = time.strftime("%Y%m%d_%H%M%S")
suffix = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
return f"{prefix}_{timestamp}_{suffix}"
if __name__ == "__main__":
# Simulate tracking three experiment r…Browse by section
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