Big data & Spark
PySpark jobs, partitioning, batch processing, and large-dataset transform patterns.
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 …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.
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"]
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Big data & Spark — Python code examples
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