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Cache persist MEMORY_ONLY mock in Python
Mock a MEMORY_ONLY persistence cache in Python with an LRU eviction policy and optional persistence flag.
import time
class LRUCache:
def __init__(self, capacity, persistence="MEMORY_ONLY"):
self.capacity = capacity
self.persistence = persistence
self.cache = {}
self.access_order = []
self.hits = 0
self.misses = 0
def get(self, key):
if key in self.cache:
…Compaction Small Files Mock in Python
Simulates a small-files compaction job by creating small mock files and merging them into a single output file using Python's standard library.
from pathlib import Path
import tempfile
import os
def create_small_files(directory: Path, file_count: int = 5, lines_per_file: int = 3):
"""Create several small mock files with sample content."""
directory.mkdir(exist_ok=True)
for i in range(file_count):
file_path = directory / f"part-{i:04d}.tx…How to Filter and Project Spark DataFrames with PySpark SQL
Simulate a SQL SELECT with WHERE using PySpark DataFrame select and filter to project columns and apply conditions.
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
spark = SparkSession.builder.appName("QueryFilterMock").master("local[2]").getOrCreate()
data = [
("Alice", 28, "Engineering"),
("Bob", 35, "Sales"),
("Carol", 32, "Engineering"),
("David", 25, "Marketing"),
("Eve", 29, "E…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.
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…How to Mock DataFrame Schema Columns in Python
Create an empty pandas DataFrame with only the specified column names to mock a schema before any data is loaded.
import pandas as pd
def mock_schema(columns):
return pd.DataFrame(columns=columns)
if __name__ == "__main__":
cols = ["name", "age", "city"]
df = mock_schema(cols)
print(df)
print(f"Columns: {list(df.columns)}, Shape: {df.shape}")How to Mock Partition Pruning in Python
A dataclass-based mock that filters partitions by year and month to emulate Spark's partition pruning logic.
from dataclasses import dataclass
from typing import List
@dataclass(frozen=True)
class Partition:
id: int
year: int
month: int
class PartitionPruner:
"""Mock partition pruning: only keep partitions that match the filter."""
def __init__(self, partitions: List[Partition]):
self._partiti…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 …How to Mock a User-Defined Function (UDF) in Python
Wrap a real UDF implementation with call logging to simulate and track invocations in a data pipeline.
from typing import Any, Callable
# Mock a user-defined function (UDF) that was previously complex or external
def mock_udf(name: str, implementation: Callable[..., Any], *, calls: list[Any]) -> Callable[..., Any]:
"""Wrap a real implementation with call logging to simulate a UDF."""
def wrapper(*args: Any, *…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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