💾 PySpark Caching and Persistence

DfCaching

Caching stores the results of an RDD or DataFrame in memory (or disk) to avoid recomputing them from lineage when reused. cache() is shorthand for persist(StorageLevel.MEMORY_AND_DISK).

DfStorage Level

A storage level defines where and how data is persisted: MEMORY_ONLY (deserialized in heap), MEMORY_AND_DISK (spill to disk), MEMORY_ONLY_SER (serialized), DISK_ONLY, or OFF_HEAP (outside JVM).

R_{cache} = \\frac{N_{cache\\_hits}}{N_{total\\_reads}}

Storage Memory Requirement

M_{cache} = N_{partitions} \\times S_{partition} \\times F_{compression}

Here,

$M_{cache}$ =Total memory needed to cache the dataset
$N_{partitions}$ =Number of partitions
$S_{partition}$ =Size per partition (serialized or deserialized)
$F_{compression}$ =Compression ratio (1.0 if uncompressed, 0.3–0.7 typical)

Use MEMORY_AND_DISK for datasets that may not fully fit in memory — Spark will spill to disk rather than recompute. Use MEMORY_ONLY only when you are certain the data fits and recomputation cost is low.

Always call unpersist() on DataFrames that are no longer reused to free cache memory for other datasets. Cached data is pinned in memory and cannot be evicted by the unified memory manager until explicitly unpersisted.

ThCache vs Recompute Trade-off

Theorem: Caching is beneficial when N_{reuses} × Cost_{recompute} > Cost_{cache} where N_{reuses} is the number of times the data is accessed after caching, Cost_{recompute} is the time to recompute from lineage, and Cost_{cache} is the memory footprint cost.

cache() = persist(MEMORY_AND_DISK); use persist() for custom storage levels
Cache only datasets reused 2+ times; always unpersist() when done
Target cache hit ratio > 80%; monitor via Spark UI Storage tab
Checkpointing truncates lineage for very deep DAGs

🏗️ Architecture Diagram

Architecture Diagram

┌─────────────────────────────────────────────────────────────────┐
│                 CACHING ARCHITECTURE OVERVIEW                    │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                  DATAFRAME / RDD                          │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │  Logical Plan → Physical Plan → RDD lineage     │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                          │                                      │
│                          ▼                                      │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                    CACHE / PERSIST                        │   │
│  │                                                          │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │  ┌──────────────┐  ┌──────────────┐            │   │   │
│  │  │  │   MEMORY     │  │    DISK      │            │   │   │
│  │  │  │   (Heap)     │  │   (Local)    │            │   │   │
│  │  │  │              │  │              │            │   │   │
│  │  │  │ ┌──────────┐│  │ ┌──────────┐│            │   │   │
│  │  │  │ │Partition ││  │ │Partition ││            │   │   │
│  │  │  │ │    0     ││  │ │    2     ││            │   │   │
│  │  │  │ └──────────┘│  │ └──────────┘│            │   │   │
│  │  │  │ ┌──────────┐│  │ ┌──────────┐│            │   │   │
│  │  │  │ │Partition ││  │ │Partition ││            │   │   │
│  │  │  │ │    1     ││  │ │    3     ││            │   │   │
│  │  │  │ └──────────┘│  │ └──────────┘│            │   │   │
│  │  │  └──────────────┘  └──────────────┘            │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                          │                                      │
│                          ▼                                      │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │              STORAGE LEVEL DECISION TREE                  │   │
│  │                                                          │   │
│  │  Is data smaller than memory?                            │   │
│  │       │                                                  │   │
│  │       ├─ YES → MEMORY_ONLY (deserialized)              │   │
│  │       │        MEMORY_ONLY_SER (serialized)             │   │
│  │       │                                                  │   │
│  │       └─ NO  → MEMORY_AND_DISK (spill to disk)         │   │
│  │                DISK_ONLY (only on disk)                 │   │
│  │                                                          │   │
│  │  Need off-heap?                                          │   │
│  │       │                                                  │   │
│  │       └─ YES → OFF_HEAP (Tachyon/Alluxio)              │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │              UNIFIED MEMORY MANAGEMENT                    │   │
│  │                                                          │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │         Executor Memory (JVM Heap)               │   │   │
│  │  │                                                  │   │   │
│  │  │  ┌─────────────────┬─────────────────┐         │   │   │
│  │  │  │    Reserved     │     Usable      │         │   │   │
│  │  │  │   (300MB)       │                 │         │   │   │
│  │  │  └─────────────────┴─────────────────┘         │   │   │
│  │  │                                                  │   │   │
│  │  │  ┌─────────────────────────────────────────┐   │   │   │
│  │  │  │              Unified Memory             │   │   │   │
│  │  │  │  ┌──────────────┐  ┌──────────────┐   │   │   │   │
│  │  │  │  │   Storage    │  │  Execution   │   │   │   │   │
│  │  │  │  │   Memory     │  │   Memory    │   │   │   │   │
│  │  │  │  │   (50%)      │  │   (50%)     │   │   │   │   │
│  │  │  │  │              │  │             │   │   │   │   │
│  │  │  │  │ Cached RDDs  │  │  Shuffles   │   │   │   │   │
│  │  │  │  │ Broadcasts   │  │  Joins      │   │   │   │   │
│  │  │  │  │              │  │  Sorts      │   │   │   │   │
│  │  │  │  └──────────────┘  └──────────────┘   │   │   │   │
│  │  │  │                                          │   │   │   │
│  │  │  │  Storage and Execution can borrow from  │   │   │   │
│  │  │  │  each other (soft boundary)              │   │   │   │
│  │  │  └─────────────────────────────────────────┘   │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────┘

Architecture Diagram

┌─────────────────────────────────────────────────────────────────┐
│              STORAGE LEVELS COMPARISON                           │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  MEMORY_ONLY                                             │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │  Data Format: Deserialized Java/Python objects  │   │   │
│  │  │  Storage: JVM Heap Memory                        │   │   │
│  │  │  Speed: ★★★★★ (Fastest access)                 │   │   │
│  │  │  Space: ★★☆☆☆ (3-5x overhead)                 │   │   │
│  │  │  CPU: ★★★★★ (No ser/deser)                    │   │   │
│  │  │  GC: ★★☆☆☆ (High GC pressure)                 │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  MEMORY_ONLY_SER                                        │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │  Data Format: Serialized byte arrays            │   │   │
│  │  │  Storage: JVM Heap Memory                        │   │   │
│  │  │  Speed: ★★★★☆ (Serialization overhead)        │   │   │
│  │  │  Space: ★★★★☆ (1.5-2x overhead)               │   │   │
│  │  │  CPU: ★★★☆☆ (Ser/deser per access)            │   │   │
│  │  │  GC: ★★★★☆ (Less GC pressure)                 │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  MEMORY_AND_DISK                                        │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │  Data Format: Deserialized (memory) / Serialized│   │   │
│  │  │  Storage: JVM Heap + Local Disk                  │   │   │
│  │  │  Speed: ★★★★☆ (Disk spill for overflow)       │   │   │
│  │  │  Space: ★★★☆☆ (Memory + Disk)                  │   │   │
│  │  │  CPU: ★★★★☆ (No ser for memory partitions)    │   │   │
│  │  │  GC: ★★★☆☆ (Medium GC pressure)               │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  DISK_ONLY                                              │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │  Data Format: Serialized byte arrays            │   │   │
│  │  │  Storage: Local Disk only                        │   │   │
│  │  │  Speed: ★★☆☆☆ (Disk I/O required)             │   │   │
│  │  │  Space: ★★★★★ (Minimal memory usage)          │   │   │
│  │  │  CPU: ★★★☆☆ (Ser/deser per access)            │   │   │
│  │  │  GC: ★★★★★ (No GC pressure)                   │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  OFF_HEAP                                               │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │  Data Format: Serialized (Tachyon/Alluxio)       │   │   │
│  │  │  Storage: Off-heap memory (Alluxio)              │   │   │
│  │  │  Speed: ★★★★☆ (Network/disk, but no GC)       │   │   │
│  │  │  Space: ★★★★★ (Outside JVM)                   │   │   │
│  │  │  CPU: ★★☆☆☆ (Ser/deser + network)             │   │   │
│  │  │  GC: ★★★★★ (Zero GC pressure)                 │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────┘

Architecture Diagram

┌─────────────────────────────────────────────────────────────────┐
│              CACHE EVICTION AND SPILL FLOW                       │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  MEMORY PRESSURE DETECTION                               │   │
│  │                                                          │   │
│  │  Storage Memory Usage: ████████████████░░░░ (80%)       │   │
│  │  Execution Memory Need: ░░░░░░░░████████████ (60%)     │   │
│  │                                                          │   │
│  │  Threshold: Storage can't exceed 50% without eviction   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                          │                                      │
│                          ▼                                      │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  EVICTION DECISION                                       │   │
│  │                                                          │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │  LRU (Least Recently Used) Policy               │   │   │
│  │  │                                                  │   │   │
│  │  │  Partitions accessed:                           │   │   │
│  │  │  P0: 100 times (recent)                         │   │   │
│  │  │  P1: 50 times (recent)                          │   │   │
│  │  │  P2: 10 times (old) ← EVICT                    │   │   │
│  │  │  P3: 5 times (old) ← EVICT                     │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                          │                                      │
│                          ▼                                      │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  SPILL TO DISK (MEMORY_AND_DISK)                        │   │
│  │                                                          │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │  Step 1: Serialize partition to byte array       │   │   │
│  │  │  Step 2: Write to local disk                     │   │   │
│  │  │  Step 3: Update partition metadata               │   │   │
│  │  │  Step 4: Free memory                            │   │   │
│  │  │                                                  │   │   │
│  │  │  Disk Path: /tmp/blockmgr-xxx/                   │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                          │                                      │
│                          ▼                                      │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │  RECOMPUTATION (MEMORY_ONLY)                             │   │
│  │                                                          │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │  Step 1: Detect partition not in cache           │   │   │
│  │  │  Step 2: Recompute using lineage                 │   │   │
│  │  │  Step 3: Store in cache                          │   │   │
│  │  │  Step 4: Return to caller                        │   │   │
│  │  │                                                  │   │   │
│  │  │  Cost: Depends on lineage complexity             │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────┘

📚 Detailed Explanation

1. Why Cache?

Caching is essential when a DataFrame or RDD is reused multiple times. Without caching, Spark recomputes the entire lineage from the source for each action, which is extremely inefficient.

Benefits:

Avoids recomputation of expensive transformations
Reduces I/O (read from cache instead of source)
Improves iterative algorithm performance
Enables interactive data exploration

2. Cache vs Persist

cache() is shorthand for persist(StorageLevel.MEMORY_ONLY):

Stores data in JVM heap as deserialized objects
Fastest access but highest memory overhead
Good for small-to-medium datasets that fit in memory

persist() allows choosing a storage level:

MEMORY_ONLY: Same as cache()
MEMORY_ONLY_SER: Serialized, saves space
MEMORY_AND_DISK: Spill to disk if memory full
DISK_ONLY: Only on disk
OFF_HEAP: Outside JVM

3. Storage Levels Deep Dive

MEMORY_ONLY:

Data stored as Java/Python objects
Fastest access (no deserialization)
Highest memory overhead (3-5x)
Best for small datasets, repeated access

MEMORY_ONLY_SER:

Data stored as serialized byte arrays
Lower memory overhead (1.5-2x)
Serialization cost per access
Good for memory-constrained environments

MEMORY_AND_DISK:

Memory-first, spill to disk if needed
Ensures all data is persisted
Disk spill adds I/O overhead
Good for datasets larger than memory

DISK_ONLY:

All data on local disk
Minimal memory usage
Disk I/O required for access
Good for very large datasets

OFF_HEAP:

Data stored outside JVM (Alluxio/Tachyon)
Zero GC pressure
Network/disk access required
Good for shared caching across applications

4. Unified Memory Management

Spark 1.6+ introduced unified memory management where storage and execution memory share a common pool with a soft boundary:

Memory Layout:

Architecture Diagram

Executor Memory
├── Reserved (300MB)
└── Unified Memory
    ├── Storage Memory (50%)
    │   ├── Cached RDDs
    │   ├── Broadcast variables
    │   └── Unroll memory
    └── Execution Memory (50%)
        ├── Shuffle buffers
        ├── Join hash tables
        ├── Sort buffers
        └── Aggregation buffers

Key Behaviors:

Storage can borrow from execution (when execution is idle)
Execution can borrow from storage (forces eviction)
Execution has higher priority for memory
Soft boundary allows flexible memory sharing

5. Cache Eviction Policies

When memory pressure occurs, Spark evicts cached data using LRU (Least Recently Used) policy:

Eviction Process:

Monitor memory usage
When storage exceeds 50% of unified memory
Identify least recently used partitions
Evict or spill to disk based on storage level
Update block manager metadata

Factors Affecting Eviction:

Access frequency
Partition size
Storage level (memory vs disk)
Memory pressure from execution

6. Cache Metrics and Monitoring

Key Metrics to Monitor:

storageLevel: Current storage level
cachedPartitions: Number of cached partitions
cacheSize: Total cached data size
cacheMemory: Memory used for caching
cacheDisk: Disk used for spilling
cacheHitRatio: Percentage of cache hits

Spark UI Locations:

Storage tab: Cached RDDs and DataFrames
Executors tab: Memory usage per executor
SQL tab: Cache usage in query plans

7. Common Cache Pitfalls

Pitfall 1: Caching too much

Fills up memory, causes eviction
Slows down execution (memory pressure)

Pitfall 2: Not caching when needed

Recomputes expensive transformations
Wastes CPU and I/O

Pitfall 3: Wrong storage level

MEMORY_ONLY for too-large datasets → constant recomputation
DISK_ONLY for frequently accessed data → disk I/O overhead

Pitfall 4: Forgetting to unpersist

Memory leaks over time
Slows down other applications

8. Cache Optimization Strategies

Strategy 1: Cache selectively

# Cache only what's reused
df.cache()  # Cache this one
result = df.filter(...).groupBy(...).agg(...)  # Don't cache intermediate

Strategy 2: Choose right storage level

# Small dataset, repeated access
df.cache()  # MEMORY_ONLY

# Large dataset, fits in memory
df.persist(StorageLevel.MEMORY_ONLY_SER)

# Dataset larger than memory
df.persist(StorageLevel.MEMORY_AND_DISK)

Strategy 3: Monitor and adjust

# Check cache status
print(f"Cached: {df.is_cached}")
print(f"Storage level: {df.storageLevel}")

# Unpersist when done
df.unpersist()

🔑 Key Concepts Table

Storage Level	Location	Format	Speed	Space	GC	Use Case
MEMORY_ONLY	Heap	Deserialized	★★★★★	★★☆☆☆	★★☆☆☆	Small, repeated access
MEMORY_ONLY_SER	Heap	Serialized	★★★★☆	★★★★☆	★★★★☆	Memory-constrained
MEMORY_AND_DISK	Heap + Disk	Mixed	★★★★☆	★★★☆☆	★★★☆☆	Large datasets
DISK_ONLY	Disk	Serialized	★★☆☆☆	★★★★★	★★★★★	Very large datasets
OFF_HEAP	Alluxio	Serialized	★★★★☆	★★★★★	★★★★★	Shared caching
MEMORY_ONLY_2	Heap	Deserialized	★★★★★	★★☆☆☆	★★☆☆☆	2 replicas
MEMORY_AND_DISK_2	Heap + Disk	Mixed	★★★★☆	★★★☆☆	★★★☆☆	Fault tolerance

💻 Code Examples

Example 1: Basic Caching

from pyspark.sql import SparkSession
from pyspark import StorageLevel

spark = SparkSession.builder.appName("CachingPersistence").getOrCreate()

# Create expensive DataFrame
df = spark.range(1000000).withColumn(
    "key", col("id") % 1000
).withColumn(
    "value", col("id") * 1.0
)

# Cache the DataFrame (MEMORY_ONLY)
df.cache()

# First action triggers computation and caching
count = df.count()
print(f"Count: {count}")

# Subsequent actions use cached version
sum_val = df.groupBy("key").sum("value").count()
print(f"GroupBy count: {sum_val}")

# Check cache status
print(f"Is cached: {df.is_cached}")
print(f"Storage level: {df.storageLevel}")

# Unpersist when done
df.unpersist()

Example 2: Different Storage Levels

# MEMORY_ONLY_SER - Serialized, saves space
df_ser = df.persist(StorageLevel.MEMORY_ONLY_SER)
print(f"MEMORY_ONLY_SER: {df_ser.storageLevel}")

# MEMORY_AND_DISK - Spill to disk if needed
df_disk = df.persist(StorageLevel.MEMORY_AND_DISK)
print(f"MEMORY_AND_DISK: {df_disk.storageLevel}")

# DISK_ONLY - Only on disk
df_only_disk = df.persist(StorageLevel.DISK_ONLY)
print(f"DISK_ONLY: {df_only_disk.storageLevel}")

# Test performance
import time

start = time.time()
for _ in range(5):
    df.count()
print(f"MEMORY_ONLY time: {time.time() - start:.2f}s")

start = time.time()
for _ in range(5):
    df_ser.count()
print(f"MEMORY_ONLY_SER time: {time.time() - start:.2f}s")

Example 3: Cache Monitoring

# Enable cache metrics
spark.sparkContext.setLogLevel("INFO")

# Create and cache DataFrame
df = spark.range(1000000).cache()

# Trigger caching
df.count()

# Check storage info
storage_info = spark._jsc.sc().getRDDStorageInfo()
for rdd_info in storage_info:
    print(f"RDD ID: {rdd_info.name()}")
    print(f"Storage Level: {rdd_info.storageLevel()}")
    print(f"Number of Partitions: {rdd_info.numCachedPartitions()}")
    print(f"Memory Size: {rdd_info.memSize() / 1024 / 1024:.2f} MB")
    print(f"Disk Size: {rdd_info.diskSize() / 1024 / 1024:.2f} MB")

Example 4: Cache in Iterative Algorithms

# Example: PageRank-like iterative computation
def iterative_computation(df, num_iterations=10):
    # Cache input data
    df.cache()
    df.count()  # Trigger caching
    
    for i in range(num_iterations):
        # Use cached data in each iteration
        df = df.withColumn(
            "value", col("value") * 0.85 + 0.15
        )
        
        # Trigger computation
        df.count()
        
        print(f"Iteration {i + 1} complete")
    
    # Unpersist when done
    df.unpersist()
    return df

# Run iterative computation
result = iterative_computation(df)

📊 Performance Metrics

Scenario	No Cache	MEMORY_ONLY	MEMORY_ONLY_SER	MEMORY_AND_DISK
1GB, 5 actions	25.0s	5.0s	7.5s	6.0s
10GB, 5 actions	250.0s	50.0s	75.0s	60.0s
1GB, 10 actions	50.0s	5.5s	8.0s	6.5s
10GB, 10 actions	500.0s	55.0s	80.0s	65.0s
Memory Usage	0	3.5GB	2.0GB	3.5GB + Disk
GC Time	0	500ms	200ms	500ms
Disk I/O	0	0	0	2GB

✅ Best Practices

1. Cache Selectively

# Cache DataFrame reused multiple times
df = expensive_computation()
df.cache()

# Use cached DataFrame
result1 = df.filter(...).collect()
result2 = df.groupBy(...).agg(...).collect()

# Unpersist when done
df.unpersist()

2. Choose Appropriate Storage Level

# Small dataset, fits in memory
df.cache()  # MEMORY_ONLY

# Large dataset, serialized saves space
df.persist(StorageLevel.MEMORY_ONLY_SER)

# Very large dataset, spill to disk
df.persist(StorageLevel.MEMORY_AND_DISK)

3. Monitor Cache Usage

# Check cache status
print(f"Cached: {df.is_cached}")
print(f"Storage level: {df.storageLevel}")

# Check storage info in Spark UI
# Storage tab shows cached RDDs/DataFrames

4. Unpersist When Done

# Always unpersist when DataFrame no longer needed
df.unpersist()

# Or use context manager pattern
with df:
    result = df.filter(...).collect()
# df is automatically unpersisted

5. Avoid Cache Bloat

# Don't cache everything
df1.cache()  # Cache this
df2 = expensive_computation(df1)  # Don't cache intermediate
df3 = another_computation(df2)  # Don't cache this either

# Cache only what's reused
result = df3.filter(...).collect()

6. Use Checkpointing for Long Lineages

# For very long lineages, checkpoint to truncate
sc.setCheckpointDir("hdfs:///checkpoint")
df.checkpoint()

# Checkpoint truncates lineage, reduces memory

PySpark Caching and Persistence: Storage Levels, Memory Management

💾 PySpark Caching and Persistence

DfCaching

DfStorage Level

Storage Memory Requirement

ThCache vs Recompute Trade-off

🏗️ Architecture Diagram

📚 Detailed Explanation

1. Why Cache?

2. Cache vs Persist

3. Storage Levels Deep Dive

4. Unified Memory Management

5. Cache Eviction Policies

6. Cache Metrics and Monitoring

7. Common Cache Pitfalls

8. Cache Optimization Strategies

🔑 Key Concepts Table

💻 Code Examples

Example 1: Basic Caching

Example 2: Different Storage Levels

Example 3: Cache Monitoring

Example 4: Cache in Iterative Algorithms

📊 Performance Metrics

✅ Best Practices

1. Cache Selectively

2. Choose Appropriate Storage Level

3. Monitor Cache Usage

4. Unpersist When Done

5. Avoid Cache Bloat

6. Use Checkpointing for Long Lineages

See Also

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