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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 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.

mapreduce word-count dictionary
Python
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…
16 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 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.

pivot group-by aggregation
Python
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__":…
13 0 Open

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