Big data & Spark
PySpark jobs, partitioning, batch processing, and large-dataset transform patterns.
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.
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…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.
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…Modeling a Hive Metastore Table Schema in Python
A dataclass that mimics a Hive metastore table schema—columns, partition keys, storage format, and location—with helper methods for description and mutation.
from dataclasses import dataclass, field
from typing import Dict, List, Optional
@dataclass
class HiveTable:
"""Simple mock of a Hive metastore table schema."""
name: str
database: str = "default"
columns: List[Dict[str, str]] = field(default_factory=list)
partition_keys: List[Dict[str, str]] = f…Browse by section
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Big data & Spark — Python code examples
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