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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
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…How to Implement a Token Bucket Rate Limiter per Client IP in Python
Implements a simple sliding-window rate limiter using a dictionary of timestamp lists per client IP to limit requests per window.
from time import time
from collections import defaultdict
class RateLimiter:
def __init__(self, max_requests: int, window_seconds: int):
self.max_requests = max_requests
self.window_seconds = window_seconds
self.clients = defaultdict(list)
def allow(self, ip: str) -> bool:
now…How to implement rate limiting per API key in Python
A simple sliding-window rate limiter that tracks request timestamps per API key and rejects requests exceeding the configured limit.
import time
API_RATE_LIMITS = {"api_key_1": 5, "api_key_2": 3} # max requests per window
WINDOW_SECONDS = 10
class RateLimiter:
def __init__(self, limits, window):
self.limits = limits
self.window = window
self.requests = {key: [] for key in limits}
def allow(self, api_key):
…Mock Distributed Rate Limiter with Dict in Python
Simulates a distributed token-bucket rate limiter with a thread-safe dict, useful for testing before moving to Redis.
import time
import threading
from collections import defaultdict
class DistributedRateLimiter:
"""
A mock distributed rate limiter using a dict with thread-safe access.
Implements a token bucket algorithm per user.
"""
def __init__(self, rate_per_second=5, burst_capacity=10):
self.rate_p…Rate Limit per User ID in Python with a Dict Mock
Implements a simple sliding window rate limiter using a defaultdict of timestamps per user ID, blocking requests that exceed a max count within a time window.
import time
from collections import defaultdict
class RateLimiter:
def __init__(self, max_requests, window_seconds):
self.max_requests = max_requests
self.window_seconds = window_seconds
self.user_timestamps = defaultdict(list)
def allow_request(self, user_id):
now = time.tim…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 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"]…How to Track Cache Hit Ratio in Python
Simulate an LRU cache with hit/miss tracking and compute a real-time hit ratio from random access patterns.
import random
import time
from collections import OrderedDict
class LRUCache:
def __init__(self, capacity: int):
self.cache = OrderedDict()
self.capacity = capacity
self.hits = 0
self.misses = 0
def get(self, key):
if key in self.cache:
self.hits += 1
…How to Build an In-Memory Service Registry Mock in Python
A simple in-memory ServiceRegistry class to register, retrieve, list, and unregister microservice endpoints or configs using a dict, with KeyError guards.
class ServiceRegistry:
def __init__(self):
self._services = {}
def register(self, name, service):
self._services[name] = service
def unregister(self, name):
if name not in self._services:
raise KeyError(f"Service '{name}' not found")
del self._services[name]
…How to Mock a GraphQL Backend in Python
Create an in-memory GraphQL mock backend using dataclasses and resolver methods returning plain dictionaries.
from dataclasses import dataclass, asdict
from typing import Any, Dict, List
@dataclass
class Product:
id: int
name: str
price: float
@dataclass
class User:
id: int
username: str
class MockGraphQLBackend:
def __init__(self) -> None:
self.products = [
Product(id=1, name…How to Mock a Schema Registry Avro Record in Python
Encode a Python dict into Avro binary using an inline schema, mimicking a schema registry record for tests or mocks.
import io
from avro.schema import parse
from avro.io import DatumWriter, BinaryEncoder
schema_json = """
{
"type": "record",
"name": "User",
"fields": [
{"name": "name", "type": "string"},
{"name": "age", "type": "int"},
{"name": "email", "type": ["null", "string"], "default": null}
]
}
"""
schem…How to Mock a Service Registry in Python with an In-Memory Dict
A lightweight ServiceRegistry class backed by a dict, exposing register, unregister, lookup, list, and health-check methods.
class ServiceRegistry:
def __init__(self):
self._services = {}
def register(self, name, endpoint, version="1.0"):
self._services[name] = {
"endpoint": endpoint,
"version": version,
"status": "healthy"
}
def unregister(self, name):
return…How to implement the Database per service pattern in Python
Simulate separate databases per microservice in Python using dataclasses and in-memory dictionaries, showing how services own their data independently.
import json
from dataclasses import dataclass, asdict
from typing import Dict, List
@dataclass
class User:
id: int
name: str
email: str
@dataclass
class Order:
id: int
user_id: int
product: str
amount: float
class UserServiceDB:
"""Simulates a separate database for the User servic…How to Broadcast a Small Lookup Table in Python
Simulates broadcasting a small lookup table by iterating key-value pairs and emitting packed rows to subscribers with deterministic output.
import random
# Generate a deterministic mock broadcast of a small lookup table
# with 5 keys and random integer values (seeded for reproducibility)
data = {
"sensor_a": 22,
"sensor_b": 87,
"sensor_c": 43,
"sensor_d": 65,
"sensor_e": 31,
}
# Simulate a broadcast to subscribers by iterating and p…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 Implement MapReduce Word Count in Python Using a Dict
Simulate a MapReduce word count pipeline in Python with a mock dict, splitting text into words, shuffling, and reducing to frequency counts.
def map_reduce_word_count(text: str) -> dict:
"""Simulate a MapReduce pipeline to count word frequencies."""
# MAP phase: split into words and emit (word, 1) pairs
mapped = []
for word in text.lower().split():
# Clean word of punctuation
clean_word = ''.join(char for char in word if cha…How to Implement a Mock MapReduce for Word Count in Python
Simulates a MapReduce word count pipeline with mapper, shuffle, and reducer phases using Python dicts and standard library modules.
from collections import defaultdict
import re
def mapper(text):
"""Split text into words and emit (word, 1) pairs."""
words = re.findall(r'\b\w+\b', text.lower())
return [(word, 1) for word in words]
def reducer(pairs):
"""Group word-count pairs and sum counts."""
counts = defaultdict(int)
fo…How to Implement collect_list in Python
Group rows by a key and collect all corresponding values into a list — a pure-Python mock of Spark's collect_list aggregation.
from collections import defaultdict
def collect_list(rows, key_field, value_field):
grouped = defaultdict(list)
for row in rows:
grouped[row[key_field]].append(row[value_field])
return dict(grouped)
if __name__ == "__main__":
data = [
{"dept": "sales", "emp": "alice"},
{"dept"…How to Mock a Hash Join on Large and Small Tables in Python
This code efficiently joins a large dataset (1000 rows) with a small lookup table (20 rows) by building a dictionary hash lookup, mimicking a hash join strategy used in big data systems.
import random
from pprint import pprint
# Large table: 1000 rows (id, group_id, value)
large = [{"id": i, "group_id": random.randint(1, 20), "value": random.random() * 100} for i in range(1000)]
# Small table: 20 rows (group_id, label)
small = [{"group_id": g, "label": f"Group-{g}"} for g in range(1, 21)]
# Mock a …How to Mock a UDAF Aggregate Function in Python
This code provides a minimal mock of a User-Defined Aggregate Function (UDAF), simulating the initialize-update-merge-finalize lifecycle with a defaultdict counter.
from collections import defaultdict
class MockUDAF:
"""A minimal mock of a User-Defined Aggregate Function.
Simulates aggregate lifecycle: initialize, update per row,
and finalize the result.
"""
def __init__(self):
self._buffer = defaultdict(int)
def initialize(self):
"""Re…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 Use Broadcast Variables as Read-Only in PySpark (Mock Example)
Share a lookup dict across Spark executors with a broadcast variable and verify its read-only behavior in a local mock.
from pyspark import SparkContext, SparkConf
def main():
conf = SparkConf().setAppName("BroadcastMock").setMaster("local[2]")
sc = SparkContext(conf=conf)
lookup = {"a": 1, "b": 2, "c": 3}
broadcast_lookup = sc.broadcast(lookup)
data = ["a", "b", "c", "a", "unknown"]
rdd = sc.parallel…Build a Mock Random Forest Classifier in Python
Create a simple random-forest-like classifier with random majority voting between trees, including fit, predict, and predict_proba methods.
import random
class MockRandomForest:
def __init__(self, n_trees=10, random_state=42):
self.n_trees = n_trees
self.random_state = random_state
self.classes_ = None
self._class_counts = None
random.seed(random_state)
def fit(self, X, y):
self.classes_ = sorted(…How to Build a Data Validation Schema in Python
Create a lightweight validation schema using dataclasses and lambda validators to check fields in a dictionary.
import re
from dataclasses import dataclass, field
from typing import Any, Callable
@dataclass
class Field:
name: str
validator: Callable[[Any], bool]
required: bool = True
def validate(self, value: Any) -> bool:
if not self.required and value is None:
return True
return …Browse by section
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