Big data & Spark
PySpark jobs, partitioning, batch processing, and large-dataset transform patterns.
How to Mock Spark Streaming Micro-Batches in Python
Simulate Spark's micro-batch streaming with a simple deque-based class that collects events over time and processes them in timed batches.
import time
from collections import deque
from datetime import datetime
class MicroBatchStream:
def __init__(self, batch_interval_sec=2):
self.batch_interval = batch_interval_sec
self.source = deque()
self.processed = []
def add_events(self, events):
self.source.extend(events…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:
…Session window gap mock in Python
Group sorted timestamps into sessions where any gap between consecutive events exceeds a threshold starts a new session.
from datetime import datetime, timedelta
def session_windows(timestamps, gap_seconds=300):
"""Group timestamps into sessions where gaps > gap_seconds start new sessions."""
if not timestamps:
return []
# Sort timestamps chronologically to ensure correct windowing
timestamps = sorted(timestam…Browse by section
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Big data & Spark — Python code examples
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This page collects big data & spark snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
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