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 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 Stream Join Windowed Mock Topics in Python
Simulates two message topics and joins their events when timestamps fall within a sliding time window using Python generators and deques.
import itertools
import random
import time
from collections import deque
from dataclasses import dataclass, field
@dataclass
class Event:
key: str
value: int
timestamp: float = field(default_factory=time.time)
def generate_topic(prefix, keys, start_time):
while True:
yield Event(
…
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…
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…
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Streaming & messaging — Python code examples
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