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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to Mock RabbitMQ Ack Nack Requeue in Python
A mock RabbitMQ channel and consumer that simulates ack, nack, and requeue handling for testing message processing logic without a broker.
import json
from collections import deque
class MockChannel:
def __init__(self):
self.acked = []
self.nacked = []
self.requeued = []
def basic_ack(self, delivery_tag):
self.acked.append(delivery_tag)
def basic_nack(self, delivery_tag, requeue=False):
self.nacked.…How to Mock a Kafka Rebalance Listener in Python
Simulate Kafka consumer rebalance callbacks (on_partitions_revoked and on_partitions_assigned) with a mock consumer to test listener logic.
import time
from collections import defaultdict
class MockKafkaConsumer:
def __init__(self):
self.assignments = defaultdict(list)
self.rebalances = 0
def assign(self, partitions):
self.rebalances += 1
self.assignments.clear()
for partition in partitions:
s…How to Read Redis Streams with XREADGROUP in Python
Read new messages from a Redis stream using a consumer group with XREADGROUP, handling JSON payloads and group creation.
import redis
import json
def read_group_messages(stream_key, group_name, consumer_name, count=10):
r = redis.Redis(host="localhost", port=6379, decode_responses=True)
try:
r.xgroup_create(stream_key, group_name, id="0", mkstream=True)
except redis.exceptions.ResponseError:
pass
messag…Implement the Transactional Outbox Pattern with SQLite in Python
A Python implementation of the transactional outbox pattern using SQLite, ensuring atomic writes of order data and outbox events in a single transaction while supporting reliable message publishing and consumption.
import sqlite3
from dataclasses import dataclass
from datetime import datetime, timezone
import json
@dataclass
class Order:
order_id: str
amount: float
status: str
class TransactionalOutbox:
def __init__(self, db_path=":memory:"):
self.conn = sqlite3.connect(db_path)
self._create_tab…Kafka Consumer Poll Loop Mock in Python
Simulate a Kafka consumer poll loop with a mock class, process messages in batches, and commit offsets to understand streaming consumption patterns.
import time
class MockKafkaConsumer:
def __init__(self, topic, messages):
self.topic = topic
self.messages = list(messages)
self.position = 0
def poll(self, timeout_ms=100):
if self.position >= len(self.messages):
time.sleep(timeout_ms / 1000)
return []…Mock Kafka Consumer Group Partition Assignment in Python
Simulates a Kafka consumer group's round-robin partition assignment with a Python class and prints assignments per consumer.
from collections import defaultdict
class ConsumerGroupAssignment:
def __init__(self, group_name, topics_partitions):
self.group_name = group_name
self.consumers = {}
self.assignments = defaultdict(set)
topics_partitions = sorted(
[(topic, partition) for topic, partiti…How to Mock Redis Streams Consumer Groups in Python
Simulate Redis Streams producer and consumer group behavior in Python using a standalone mock class for testing and development.
import time
import json
from collections import defaultdict
class RedisStreamMock:
def __init__(self):
self.streams = defaultdict(list)
self.consumer_groups = defaultdict(dict)
self.pending_entries = defaultdict(list)
def xadd(self, stream, fields):
entry_id = f"{time.time_ns(…At Least Once with Idempotent Consumer in Python
Implements a thread-safe idempotent consumer that processes each unique message exactly once, even when a producer sends duplicates under an at-least-once delivery model.
import threading
import time
import uuid
from collections import Counter
class IdempotentConsumer:
def __init__(self):
self.processed = set()
self._lock = threading.Lock()
def consume(self, message_id, payload):
with self._lock:
if message_id in self.processed:
…How to Deduplicate Messages in Python by ID
This code consumes a mock inbox of JSON messages and deduplicates them by message ID, keeping either the first or last occurrence.
import json
from collections import OrderedDict
mock_inbox = [
{"id": 1, "message": "hello", "timestamp": "2024-01-01T10:00:00Z"},
{"id": 2, "message": "world", "timestamp": "2024-01-01T10:01:00Z"},
{"id": 1, "message": "hello", "timestamp": "2024-01-01T10:00:00Z"},
{"id": 3, "message": "test", "times…Generate Synthetic SRE Metrics and Calculate Availability in Python
Create realistic service metrics with random latency, error rate, and request counts, then compute availability and summarize the stream for SLO checks.
from datetime import datetime, timedelta
import random
def generate_service_metrics(service_name: str, minutes: int = 30) -> list[dict]:
"""Generate synthetic SRE metrics for a service across recent minutes."""
metrics = []
now = datetime.now()
for i in range(minutes):
timestamp = now - t…How to Build a Burn Rate Alert with Multiple Time Windows in Python
Track token consumption and trigger alerts when the burn rate exceeds a threshold across multiple time windows using deque and time-based sliding windows.
import time
from collections import deque
class BurnRateAlert:
def __init__(self, windows_seconds=(60, 300, 900), threshold_rate=0.8):
self.windows = {w: deque() for w in windows_seconds}
self.threshold_rate = threshold_rate
self.previous_tokens = None
def record_sample(self, current_…How to Build a Consumer Lag Gauge in Python
Simulate Kafka consumer lag with a Python class that tracks lag over time and reports health and averages.
import time
import random
from collections import deque
class ConsumerLagGauge:
"""Mock consumer lag gauge measuring how far behind a consumer is."""
def __init__(self, producer_rate=10, consumer_rate=7, initial_lag=0):
self.producer_rate = producer_rate
self.consumer_rate = consumer_rate
…How to Group Alerts by Time Window in Python
Group alert occurrences that fall within a sliding time window per alert key, reducing noise and summarizing bursts into single events.
from collections import defaultdict
from datetime import datetime, timedelta
def group_alerts(alerts, window_minutes=10):
"""Group alerts that occur within the same time window."""
alerts_by_key = defaultdict(list)
for alert in alerts:
key = alert["key"]
timestamp = alert["timestamp"]…Python Observability Data Helper for Beginners
A beginner-friendly Python helper to log events, record metrics, summarize observability data, and export it as JSON.
import json
from datetime import datetime
from collections import defaultdict
class ObservabilityDataHelper:
"""Helper for exploring basic observability data patterns."""
def __init__(self):
self.events = []
self.metrics = defaultdict(list)
def log_event(self, service, level, message):
…Summary Quantile Mock Sketch in Python
Build a memory-efficient sketch that stores sorted bins of data points to answer approximate quantile queries like median without keeping all values in memory.
import random
import statistics
from collections import Counter
class SummaryQuantileSketch:
"""
A simple sketch that stores a fixed-size summary of data (min, max, deciles)
using sorted bins, then answers approximate quantile queries.
"""
def __init__(self, bins=10):
self.bins = bins
…Consumer Driven Contract Pact Mock in Python
Define and verify consumer-driven contracts using Pact's Consumer and Provider classes, mocking the provider to assert expected interactions.
from pact import Consumer, Provider
pact = Consumer('OrderService').has_pact_with(Provider('InventoryService'))
@Pact.verify()
class TestInventoryContract:
def test_get_inventory(self):
expected = {"item": "widget", "quantity": 100}
(pact
.given('inventory exists for widget')
.u…Idempotent Consumer Event Processing in Python
Track processed event IDs to skip duplicates and count event types for a reliable, idempotent consumer.
import json
from collections import defaultdict
class EventProcessor:
def __init__(self):
self.processed_ids = set()
self.counts = defaultdict(int)
def process_event(self, event):
event_id = event["id"]
if event_id in self.processed_ids:
return {"status": "skipped"…How to Pivot and Group Aggregate in Python
Group records by a key, collect values, and apply an aggregate function (like sum) to build a pivot-style summary dictionary.
from collections import defaultdict
def pivot_group_aggregate(records, group_key, value_key, agg_func):
groups = defaultdict(list)
for record in records:
groups[record[group_key]].append(record[value_key])
return {key: agg_func(values) for key, values in groups.items()}
if __name__ == "__main__":…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:
…Build a Data Helper Class in Python for ML Pipelines
A beginner-friendly Python class that summarizes, filters, and exports ML dataset rows as JSON.
from typing import List, Dict, Any
import json
class DataHelper:
"""Beginner-friendly helpers for ML data pipelines."""
def __init__(self, data: List[Dict[str, Any]]):
self.data = data
self.keys = list(data[0].keys()) if data else []
def summary(self) -> Dict[str, Any]:
"…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 …How to Build a Guardrail Metrics Monitor in Python
This code implements a mock monitor that records metric values, checks them against thresholds, and summarizes pass/alert statistics.
import random
import time
from collections import defaultdict
class GuardrailMetricsMonitor:
def __init__(self):
self.metrics = defaultdict(list)
self.thresholds = {
"prompt_toxicity": 0.8,
"response_length": 500,
"latency_ms": 1000,
}
def record(s…How to Run a Permutation Test in Python
Run a Monte Carlo permutation test to compute a p-value for comparing two group means without parametric assumptions.
import random
import statistics
def permutation_test(group_a, group_b, n_permutations=10000, seed=42):
random.seed(seed)
combined = group_a + group_b
observed_diff = abs(statistics.mean(group_a) - statistics.mean(group_b))
count = 0
n = len(group_a)
for _ in range(n_permutations):
…How to Simulate Fixed-Horizon Testing in Python
Simulate a fixed-horizon experiment by labeling data before the horizon as warmup and after as active/inactive, then summarize via CSV.
import csv
import io
def fixed_horizon_mock(data: list[tuple[float, float, float]], horizon: int) -> str:
"""Simulate fixed-horizon testing, then summarize with CSV output."""
output = io.StringIO()
writer = csv.writer(output)
writer.writerow(["day", "value", "signal", "status"])
for day, value,…Browse by section
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