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How to Simulate a Queue Depth Gauge in Python
Simulate a queue depth over time using a random enqueue/dequeue process, returning depth values that can be used for monitoring or testing dashboards.
import collections
import random
import time
def simulate_queue_depth(max_depth=10, steps=20):
queue = collections.deque()
depth_history = []
for _ in range(steps):
# Randomly enqueue or dequeue
if random.random() < 0.6 and len(queue) < max_depth:
queue.append("task")
…How to mock SLI availability success ratio in Python
Simulate request outcomes with deterministic randomness and compute the SLI availability success ratio to check if a target is met.
import random
from collections import defaultdict
def mock_availability(num_requests=1000, target_ratio=0.995):
"""
Simulate request outcomes and compute the SLI availability success ratio.
Args:
num_requests: Total number of requests to simulate
target_ratio: Target availability rati…How to Mock Eventual Consistency UI Notes in Python
Simulates a UI note that shows local state until a pending server update is confirmed, mocking eventual consistency behavior in distributed systems.
class EventualConsistencyNote:
def __init__(self, entity_id, note):
self.entity_id = entity_id
self.note = note
self.confirmed = False
self.pending_updates = []
def add_pending_update(self, update):
self.pending_updates.append(update)
def confirm_update(self):
…How to Mock a Server-Side Load Balancer in Python
A simple Python class that mimics a server-side load balancer with round-robin, random, and least-connections selection strategies.
import itertools
import random
class LoadBalancer:
def __init__(self, servers=None):
self.servers = servers if servers else ["server1", "server2", "server3"]
self.counter = itertools.count(1)
def round_robin(self):
return next(self.counter) % len(self.servers)
def random_selectio…How to Mock a Service Mesh Sidecar Proxy in Python
Simulate a service mesh sidecar proxy with route registration, service discovery, and request proxying using a simple Python class.
class SidecarProxy:
def __init__(self, name):
self.name = name
self.routes = {}
self.services = {}
self.requests_processed = 0
def register_service(self, service_name, address, port):
self.services[service_name] = f"{address}:{port}"
def add_route(self, path, servi…Sliding Window Streaming Mock in Python
A simple Python class that maintains a sliding window of recent streaming values and computes the running average.
import time
import random
class StreamingMock:
"""Produces a stream of numbers using a sliding window."""
def __init__(self, window_size=5):
self.window = []
self.window_size = window_size
def push(self, value):
"""Add a value, sliding the window forward."""
s…Champion Challenger Deployment Mock in Python
Simulates an A/B champion-challenger ML deployment workflow — comparing two mock model accuracies and deciding which to promote to production.
import random
import time
class ModelMocker:
def __init__(self, name="Model", accuracy=0.85):
self.name = name
self.accuracy = accuracy
def predict(self, data):
"""Simulate prediction with some randomness."""
time.sleep(0.005) # simulate compute time
return 1 if rando…How to Mock Shadow Mode Inference in Python
Simulates running multiple candidate models in shadow mode by adding randomized delays and returning their outputs alongside a primary model's output.
import random
import time
def shadow_mode_inference(candidates, mock_delay=0.1):
"""
Simulates running multiple candidate models in 'shadow mode'
by adding tiny randomized delays and returning their outputs
alongside the primary model's output.
"""
primary_output = "primary: answer"
shado…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 …Difference in Differences Mock in Python
Generate mock panel data with a known treatment effect and compute a difference-in-differences estimate using group and period means.
import numpy as np
import pandas as pd
# Generate mock panel data: 2 groups (control=0, treatment=1) × 2 periods (pre=0, post=1)
rng = np.random.default_rng(42)
n_per_cell = 50
data = []
for group in [0, 1]:
for period in [0, 1]:
# True effect: treatment increases outcome by 5 in the post period
…Generate a Mock Multi-Armed Bandit Report in Python
Simulate a multi-armed bandit experiment with random pulls and rewards, then output a JSON report with per-arm statistics.
import random
import json
def generate_mock_bandit_report(num_arms=5, num_rounds=100, seed=42):
random.seed(seed)
arms = ["A", "B", "C", "D", "E"][:num_arms]
true_means = {arm: random.uniform(0.3, 0.7) for arm in arms}
pulls = {arm: 0 for arm in arms}
rewards = {arm: 0 for arm in arms}
for _ …How to Mock a Confidence Interval for a Proportion in Python
Simulate a Bernoulli sample and compute a 95% confidence interval for a proportion using the normal approximation in Python.
import random
import math
def mock_ci(n=100, p_true=0.5, z=1.96, seed=42):
"""Simulate a sample proportion and compute its 95% confidence interval."""
random.seed(seed)
successes = sum(1 for _ in range(n) if random.random() < p_true)
p_hat = successes / n
se = math.sqrt(p_hat * (1 - p_hat) / n)
…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,…Simulate a Ramp Rollout Percentage in Python
Simulates a percentage-based ramp rollout with deterministic seeding, returning success/failure/in-progress counts for a mock user population.
import random
from enum import Enum
class RolloutStatus(Enum):
SUCCESS = "success"
FAILED = "failed"
IN_PROGRESS = "in_progress"
def simulate_ramp_rollout(total_users: int, percentage: int, seed: int = 42) -> dict:
"""
Simulates a mock ramp rollout for a given percentage of users.
Returns sta…How to Mock Replica Lag Monitoring in Python
Simulates database replica lag with a mock monitor class that generates realistic lag metrics and health statuses.
import time
import random
from datetime import datetime, timedelta
class MockReplicaLagMonitor:
def __init__(self, replicas=3, base_lag=0.5, jitter=0.2):
self.replicas = [f"replica-{i}" for i in range(replicas)]
self.base_lag = base_lag
self.jitter = jitter
self.last_write = dateti…How to mock directory-based sharding in Python
Simulates distributing files into logical shards using a deterministic hash of each filename, mocking how a database might shard rows across nodes.
import os
import hashlib
from collections import defaultdict
from pathlib import Path
def get_shard_for_key(key: str, num_shards: int) -> int:
"""Return a deterministic shard index (0..num_shards-1) for a key."""
digest = hashlib.md5(key.encode('utf-8')).hexdigest()
return int(digest, 16) % num_shards
…Monitor Database Index Bloat in Python
Simulates index bloat checks for database tables using random ratio thresholds and reports alerts per index.
import random
import time
class IndexBloatMonitor:
def __init__(self, thresholds=(0.5, 0.8, 0.9)):
self.thresholds = thresholds
self.indices = {
"users_pk": 48.2,
"orders_created_idx": 124.7,
"products_name_idx": 15.3,
"payments_user_idx": 203.9,
…UUID vs sequential primary key in Python
Simulate and compare UUID vs sequential primary key generation in Python to understand trade-offs in ordering and uniqueness.
import uuid
import time
def create_record_with_uuid(name):
record_id = uuid.uuid4()
return {"id": record_id, "name": name}
def create_record_with_sequential_id(name, counter):
counter += 1
return {"id": counter, "name": name}
if __name__ == "__main__":
# Simulate users inserting records
sequ…How to Build a Synthetic Monitor Mock in Python
Simulates a synthetic monitoring system in Python that collects latency samples, averages them, and reports service status as UP or DEGRADED.
import random
import time
from dataclasses import dataclass, field
from statistics import mean
@dataclass
class SyntheticMonitor:
service: str
endpoint: str
latency_ms: list[float] = field(default_factory=list)
def check(self) -> float:
latency = random.uniform(50.0, 250.0)
self.late…How to Implement a Manual Approval Gate Mock in Python
Simulates a manual approval workflow with threshold-based rules, random decisions for medium amounts, and logs each result with timing.
import random
import time
def approve_request(amount: float) -> bool:
if amount <= 1000:
return True
if amount <= 5000:
return random.random() < 0.7
return False
def main():
requests = [500, 1200, 7500, 3000, 50]
for amount in requests:
start = time.perf_counter()
…How to Mock Multi-Stage Docker Builds in Python
Simulate a multi-stage Docker build in pure Python using classes and temp directories to understand how build stages copy artifacts into a final image.
# Simulate multi-stage Docker build with pure Python
from pathlib import Path
import tempfile
import shutil
class BuildContext:
"""Mimics a Docker build context with stages."""
def __init__(self, name):
self.name = name
self.files = {}
def add_file(self, dest, content):
s…How to Mock Terraform Plan and Apply in Python
This code provides a lightweight Python mock of Terraform's plan and apply commands, helping you simulate infrastructure changes without real cloud resources.
class MockTerraform:
def __init__(self):
self.plans = [
{"id": 1, "action": "create", "resource": "aws_instance.web"},
{"id": 2, "action": "update", "resource": "aws_s3_bucket.data"},
{"id": 3, "action": "destroy", "resource": "aws_iam_user.legacy"}
]
sel…How to Mock a CI Pipeline with Build, Test, and Deploy Stages in Python
Simulate a three-stage CI pipeline (build, test, deploy) in Python with random pass/fail logic, early exit on failure, and measured stage durations.
import time
import random
from dataclasses import dataclass
@dataclass
class StageResult:
name: str
status: str
duration: float
def run_stage(name: str, success_chance: float = 0.9) -> StageResult:
"""Simulate a pipeline stage with random success/failure."""
start = time.time()
time.sleep(r…How to Mock a Dockerfile Multi-Stage Build in Python
Simulate a Dockerfile multi-stage build process in Python using dataclasses to validate stage ordering and file availability before you write the real Dockerfile.
from dataclasses import dataclass
from pathlib import Path
@dataclass
class BuildStage:
name: str
base_image: str
files: list[str]
commands: list[str]
def run_build(stage: BuildStage, context_dir: Path):
print(f"=== Stage: {stage.name} (base: {stage.base_image}) ===")
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