Reference library

Big data & Spark

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

4 matches
Big data & Spark medium

Bloom Filter Join Mock in Python

A mock hash join that uses a Bloom filter to pre-filter one table before performing an exact match, reducing the number of comparisons in large dataset joins.

bloom filter join hashing
Python
import hashlib
import random
import string


class BloomFilter:
    def __init__(self, size: int = 200, num_hashes: int = 3):
        self.bits = [False] * size
        self.size = size
        self.num_hashes = num_hashes

    def _hashes(self, item: str):
        result = []
        for seed in range(self.num_hashes…
12 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 medium

HyperLogLog Cardinality Estimation in Python

A small HyperLogLog implementation using MD5 hashing and 256 registers to estimate the number of unique items in a large stream with fixed memory.

hyperloglog cardinality estimation
Python
import hashlib
import math

class HyperLogLog:
    def __init__(self, b=8):
        self.b = b
        self.m = 1 << b
        self.registers = [0] * self.m
        self.alpha = 0.7213 / (1 + 1.079 / self.m)

    def add(self, item):
        h = int(hashlib.md5(str(item).encode()).hexdigest(), 16)
        idx = h & (s…
14 0 Open
Big data & Spark easy

Partition Data by Hash Key Mod N in Python

Returns a partition index for a string key by hashing it with MD5 and taking modulo N, then groups sample keys into partitions.

hashing partitioning hashlib
Python
import hashlib


def partition_key(key: str, num_partitions: int) -> int:
    """Return partition index for key using MD5 hash mod N."""
    digest = hashlib.md5(key.encode()).hexdigest()
    return int(digest, 16) % num_partitions


if __name__ == "__main__":
    keys = ["alice", "bob", "carol", "dave", "eve"]
    nu…
12 0 Open

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Big data & Spark — Python code examples

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