Big data & Spark
PySpark jobs, partitioning, batch processing, and large-dataset transform patterns.
Approximate Distinct Count in Python with HyperLogLog
Mock a large data stream and estimate the number of distinct items with a HyperLogLog-style probabilistic counter to save memory.
import random
import string
from collections import Counter
import math
class ApproxCountDistinct:
def __init__(self, num_buckets=16):
self.num_buckets = num_buckets
self.max_zeros = [0] * num_buckets
def _hash(self, item):
# Simple string hash to a 32-bit integer
h = …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.
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…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.
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 …Browse by section
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Big data & Spark — Python code examples
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