Over-Caching Memory Waste

Performance Impact

Memory exhaustion, slower jobs - Caching DataFrames used only once wastes precious executor memory.

The Problem

Caching every DataFrame "just in case" consumes executor memory that could be used for actual computation. This leads to memory pressure, spilling, and degraded performance.

❌ Problematic Code

# BAD: Cache everything approach
df1 = spark.read.parquet("data1.parquet").cache()  # Used once
df2 = spark.read.parquet("data2.parquet").cache()  # Used once  
df3 = spark.read.parquet("data3.parquet").cache()  # Used once

result = df1.join(df2, "key").join(df3, "key")  # Memory wasted!

Over-Caching Consequences

Memory IssuesPerformance Impact

• Executor memory exhaustion
• Increased GC pressure
• Spilling to disk (defeats cache purpose)

• Reduced performance for actually reused data
• Slower job execution
• Resource contention

Solutions

Strategic CachingSmart Caching DecisionCache ManagementCache Monitoring

✅ Cache Only Reused DataFrames

# GOOD: Cache only reused DataFrames
expensive_df = df.groupBy("category").agg(
    count("*").alias("count"),
    avg("price").alias("avg_price"),
    sum("revenue").alias("total_revenue")
)

# This will be reused multiple times
expensive_df.cache()

# Multiple operations using cached data
high_volume = expensive_df.filter(col("count") > 1000)
low_volume = expensive_df.filter(col("count") < 100)
mid_range = expensive_df.filter(
    (col("count") >= 100) & (col("count") <= 1000)
)

# Clean up when done
expensive_df.unpersist()

✅ Intelligent Caching Logic

def should_cache(df, usage_count, computation_cost="medium"):
    """Decide whether to cache based on usage patterns"""

    cost_weights = {
        "low": 1,      # Simple transformations
        "medium": 3,   # Joins, groupBy
        "high": 10     # Complex aggregations, multiple joins
    }

    weight = cost_weights.get(computation_cost, 3)
    cache_benefit_score = usage_count * weight

    # Memory consideration
    partition_count = df.rdd.getNumPartitions()
    memory_concern = partition_count > 1000

    return {
        "should_cache": cache_benefit_score >= 6 and not memory_concern,
        "score": cache_benefit_score,
        "memory_concern": memory_concern
    }

# Usage example
expensive_computation = df.groupBy("category", "region").agg(
    countDistinct("user_id"),
    avg("amount")
)

cache_decision = should_cache(
    expensive_computation, 
    usage_count=3, 
    computation_cost="high"
)

if cache_decision["should_cache"]:
    expensive_computation.cache()
    print(f"✅ Caching (score: {cache_decision['score']})")
else:
    print(f"❌ Not caching (score: {cache_decision['score']})")

✅ Automatic Cache Cleanup

from contextlib import contextmanager

@contextmanager
def managed_cache(df, storage_level=None):
    """Context manager for automatic cache cleanup"""
    if storage_level:
        df.persist(storage_level)
    else:
        df.cache()

    try:
        df.count()  # Trigger cache
        yield df
    finally:
        df.unpersist()

# Usage - automatic cleanup
expensive_df = df.groupBy("category").agg(count("*"))

with managed_cache(expensive_df) as cached_df:
    result1 = cached_df.filter(col("count") > 100).collect()
    result2 = cached_df.filter(col("count") < 50).collect()
    # Automatic cleanup when exiting context

✅ Monitor Cache Usage

def monitor_cache_usage():
    """Monitor current cache usage across cluster"""
    print("=== Cache Usage Report ===")

    storage_infos = spark.sparkContext._jsc.sc().getRDDStorageInfo()

    total_memory_used = 0
    total_disk_used = 0

    for storage_info in storage_infos:
        rdd_id = storage_info.id()
        memory_size_mb = storage_info.memSize() / (1024 * 1024)
        disk_size_mb = storage_info.diskSize() / (1024 * 1024)

        total_memory_used += memory_size_mb
        total_disk_used += disk_size_mb

        print(f"RDD {rdd_id}: {memory_size_mb:.1f}MB memory, {disk_size_mb:.1f}MB disk")

    print(f"Total: {total_memory_used:.1f}MB memory, {total_disk_used:.1f}MB disk")

    # Get executor memory info
    executors = spark.sparkContext.statusTracker().getExecutorInfos()
    total_executor_memory = sum(exec.maxMemory for exec in executors)
    cache_ratio = (total_memory_used * 1024 * 1024) / total_executor_memory

    print(f"Cache memory ratio: {cache_ratio:.1%}")

    if cache_ratio > 0.8:
        print("⚠️  High cache memory usage!")

# Monitor periodically
monitor_cache_usage()

Key Takeaways

Caching Best Practices

• Cache when: DataFrame used 2+ times, expensive computation, memory available
• Don't cache: One-time use, simple transformations, memory constrained
• Monitor usage regularly to prevent memory issues
• Clean up with .unpersist() when done

Measuring Impact

Cache Optimization Results

# Before: 5 cached DataFrames (3 unused) → 60% memory waste
# After:  2 strategically cached DataFrames → optimal memory usage
# Improvement: 3x better memory efficiency, faster execution

Strategic caching is about quality over quantity. Cache the right DataFrames at the right time, not everything "just in case".