Streaming & messaging
Kafka-style pub/sub, event consumers, async pipelines, and message-driven workflows.
How to Aggregate Periodic Snapshot Data in Python
Generates mock snapshot data and groups values into periods to compute average aggregates with Python's standard library.
import random
from collections import defaultdict
def snapshot_aggregate(n=10, period=3):
data = defaultdict(list)
for i in range(n):
key = f"item_{i % period}"
data[key].append(random.randint(1, 100))
return dict(data)
def aggregate_periodic(snapshots, period=3):
result = {}
for …How to Encode and Decode Avro Data in Python (Roundtrip)
Serialize a Python dict to Avro binary bytes and decode it back using the fastavro-compatible avro library.
import io
import json
from avro.schema import parse
from avro.io import DatumWriter, DatumReader, BinaryEncoder, BinaryDecoder
def avro_roundtrip(schema_json, data):
schema = parse(json.dumps(schema_json))
bytes_writer = io.BytesIO()
encoder = BinaryEncoder(bytes_writer)
writer = DatumWriter(schema)
…How to Mock Offset Commit Auto vs Manual in Python
Demonstrates a Kafka-style offset commit function with auto/manual modes and tests it using unittest.mock.patch.
from unittest.mock import Mock, patch
def commit_offsets(topic_partition_offsets, auto_commit=False):
"""Manually commit offsets or simulate auto-commit."""
if auto_commit:
print(f"Auto-committing offsets: {topic_partition_offsets}")
return {"status": "auto_committed"}
print(f"Manuall…How to Mock a Kafka Producer Batch Send in Python
Simulate a Kafka producer in Python that sends batched JSON events with mock partitions and latency for testing streaming pipelines without a real broker.
import json
import random
import time
from datetime import datetime
class MockKafkaProducer:
def __init__(self, topic):
self.topic = topic
self.sent_messages = []
def send(self, value, key=None):
message = {
"topic": self.topic,
"key": key,
"value"…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…How to Simulate RabbitMQ Exchange Routing in Python
Simulate RabbitMQ exchange routing using a nested dict, matching routing keys against patterns like error.* and info.# to return bound queues.
from collections import defaultdict
def route_message(exchanges, exchange_name, routing_key):
"""
Simulate RabbitMQ exchange routing using a nested dict structure.
Returns list of queue names that match the routing key.
"""
queues = exchanges.get(exchange_name, {})
matched = []
for pa…How to Track Session Windows with Gap Timeout in Python
A Python class that groups events into sessions, closing a session when the gap between events exceeds a timeout threshold.
import time
class SessionWindow:
"""Track sessions with a gap timeout (mock)."""
def __init__(self, timeout_seconds=5):
self.timeout = timeout_seconds
self.session_start = None
self.last_event_time = None
self.event_count = 0
self.events = []
def add_event…Implement a retry queue with visibility timeout in Python
This code simulates a message queue with a visibility timeout, allowing messages to be retried if not deleted before the timeout expires.
import time
from collections import deque
class SimpleQueue:
def __init__(self, visibility_timeout=2):
self.queue = deque()
self.in_flight = {}
self.visibility_timeout = visibility_timeout
def send(self, message):
self.queue.append(message)
def receive(self):
if …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…Mock Redis Streams XADD and XREAD in Python
A pure-Python mock of Redis streams that implements basic XADD, XREAD, and XLEN behavior for local testing without a real Redis server.
import redis
import time
import threading
class MockRedisStreams:
def __init__(self):
self.streams = {}
def xadd(self, stream_name, fields):
if stream_name not in self.streams:
self.streams[stream_name] = []
entry_id = f"{time.time_ns()}-{len(self.streams[stream_name])}"
…Mock Watermark Late Event Side Output in Python
Simulates watermarking in a streaming pipeline by classifying events as on-time or late using timestamps and delays.
from datetime import datetime, timedelta
from typing import List, Tuple
def watermark_mock(
events: List[Tuple[datetime, str]], watermark_delay: timedelta, max_delay: timedelta
) -> Tuple[List[Tuple[datetime, str]], List[Tuple[datetime, str]]]:
"""Simulate watermarking: events arriving on time vs. late by ch…Browse by section
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Streaming & messaging — Python code examples
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