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Big data & Spark

PySpark jobs, partitioning, batch processing, and large-dataset transform patterns.

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Big data & Spark easy

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.

broadcast lookup-table dictionary
Python
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…
14 0 Open
Big data & Spark easy

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.

hash-join dictionaries data-join
Python
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 …
13 0 Open
Big data & Spark easy

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.

pyspark broadcast spark
Python
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…
13 0 Open

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