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# Storage and Persistence
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Memory management and persistence strategies for optimizing RDD storage across cluster nodes, including various storage levels and caching mechanisms.
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## Capabilities
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### StorageLevel
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Defines how RDDs should be stored in memory and/or disk across the cluster.
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```scala { .api }
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/**
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 * Storage level for an RDD. Determines how the RDD should be stored in memory and/or disk.
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 * Storage levels can specify whether to use disk, memory, off-heap memory, serialization,
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 * and replication.
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 */
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class StorageLevel private(
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    private var _useDisk: Boolean,
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    private var _useMemory: Boolean,
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    private var _useOffHeap: Boolean,  
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    private var _deserialized: Boolean,
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    private var _replication: Int = 1) extends Externalizable {
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  /** Whether to use disk storage */
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  def useDisk: Boolean = _useDisk
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  /** Whether to use memory storage */
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  def useMemory: Boolean = _useMemory
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  /** Whether to use off-heap memory */
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  def useOffHeap: Boolean = _useOffHeap
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  /** Whether to store RDD as deserialized objects */
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  def deserialized: Boolean = _deserialized
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  /** Number of replicas to store */
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  def replication: Int = _replication
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  /** Create a copy with different replication level */
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  def clone(newReplication: Int): StorageLevel
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}
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/**
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 * Storage level constants for common storage strategies
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 */
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object StorageLevel {
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  /** No persistence - RDD partitions are not stored */
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  val NONE = new StorageLevel(false, false, false, false)
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  /** Store RDD partitions only on disk */
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  val DISK_ONLY = new StorageLevel(true, false, false, false)
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  /** Store RDD partitions on disk with 2x replication */
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  val DISK_ONLY_2 = new StorageLevel(true, false, false, false, 2)
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  /** Store RDD partitions only in memory as deserialized Java objects */
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  val MEMORY_ONLY = new StorageLevel(false, true, false, true)
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  /** Store RDD partitions in memory with 2x replication */
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  val MEMORY_ONLY_2 = new StorageLevel(false, true, false, true, 2)
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  /** Store RDD partitions in memory as serialized Java objects (more space-efficient) */
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  val MEMORY_ONLY_SER = new StorageLevel(false, true, false, false)
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  /** Store RDD partitions in memory as serialized objects with 2x replication */
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  val MEMORY_ONLY_SER_2 = new StorageLevel(false, true, false, false, 2)
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  /** Store RDD partitions in memory, spill to disk if not enough memory */
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  val MEMORY_AND_DISK = new StorageLevel(true, true, false, true)
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  /** Store RDD partitions in memory and disk with 2x replication */
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  val MEMORY_AND_DISK_2 = new StorageLevel(true, true, false, true, 2)
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  /** Store RDD partitions in memory as serialized objects, spill to disk if needed */
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  val MEMORY_AND_DISK_SER = new StorageLevel(true, true, false, false)
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  /** Store RDD partitions in memory and disk as serialized objects with 2x replication */
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  val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, false, 2)
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  /** Store RDD partitions in off-heap memory (requires off-heap memory configured) */
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  val OFF_HEAP = new StorageLevel(false, false, true, false)
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}
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```
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**Usage Examples:**
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```scala
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import org.apache.spark.storage.StorageLevel
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val sc = new SparkContext(conf)
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val data = sc.parallelize(1 to 1000000)
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// Cache in memory (equivalent to .cache())
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data.persist(StorageLevel.MEMORY_ONLY)
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// Cache in memory with serialization (more memory efficient)
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data.persist(StorageLevel.MEMORY_ONLY_SER)
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// Memory with disk fallback
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data.persist(StorageLevel.MEMORY_AND_DISK)
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// Disk only storage
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data.persist(StorageLevel.DISK_ONLY)
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// High availability with replication
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data.persist(StorageLevel.MEMORY_AND_DISK_2)
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// Custom storage level
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val customLevel = new StorageLevel(
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  useDisk = true,
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  useMemory = true, 
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  useOffHeap = false,
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  deserialized = false,
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  replication = 3
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)
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data.persist(customLevel)
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```
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### RDD Persistence Methods
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Core methods for controlling RDD persistence and caching.
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```scala { .api }
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/**
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 * Persistence methods available on all RDDs
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 */
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abstract class RDD[T: ClassTag] extends Serializable {
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  /** Persist this RDD with the default storage level (MEMORY_ONLY) */
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  def cache(): RDD.this.type = persist()
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  /** Persist this RDD with the specified storage level */
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  def persist(newLevel: StorageLevel): RDD.this.type
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  /** Mark the RDD as non-persistent, and remove all blocks for it from memory and disk */
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  def unpersist(blocking: Boolean = true): RDD.this.type
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  /** Get the current storage level of this RDD */
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  def getStorageLevel: StorageLevel
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  /** Mark this RDD for checkpointing */
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  def checkpoint(): Unit
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  /** Return whether this RDD has been checkpointed or not */
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  def isCheckpointed: Boolean
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  /** Gets the name of the directory to which this RDD was checkpointed */
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  def getCheckpointFile: Option[String]
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  /** Mark this RDD for local checkpointing using Spark's existing caching layer */  
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  def localCheckpoint(): RDD.this.type
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}
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```
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**Usage Examples:**
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```scala
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val expensiveRDD = sc.textFile("large-dataset.txt")
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  .map(heavyProcessingFunction)
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  .filter(_.nonEmpty)
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// Cache for multiple uses
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expensiveRDD.cache()
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// Use the cached RDD multiple times
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val count1 = expensiveRDD.count()
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val count2 = expensiveRDD.filter(_.contains("error")).count()
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// Check storage level
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println(s"Storage level: ${expensiveRDD.getStorageLevel}")
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// Remove from cache when done
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expensiveRDD.unpersist()
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// Checkpoint for fault tolerance
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sc.setCheckpointDir("hdfs://path/to/checkpoint")
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expensiveRDD.checkpoint()
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expensiveRDD.count() // Trigger checkpoint creation
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```
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### Storage Management in SparkContext
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Methods for managing storage and cache across the application.
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```scala { .api }
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class SparkContext(config: SparkConf) extends Logging {
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  /** Set the directory under which RDDs are going to be checkpointed */
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  def setCheckpointDir(directory: String): Unit
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  /** Return the directory where checkpoints are stored, if one is set */
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  def getCheckpointDir: Option[String]
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}
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```
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### BlockManager Integration
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Understanding how Spark manages storage blocks internally.
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```scala { .api }
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/**
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 * Storage information for RDD blocks
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 */
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case class RDDInfo(
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    id: Int,
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    name: String,
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    numPartitions: Int,
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    storageLevel: StorageLevel,
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    numCachedPartitions: Int,
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    memSize: Long,
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    diskSize: Long) {
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  /** Whether this RDD is currently persisted */
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  def isCached: Boolean = numCachedPartitions > 0
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}
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/**
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 * Information about memory usage for storage
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 */
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case class MemoryStatus(maxMemory: Long, memoryUsed: Long, memoryRemaining: Long) {
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  def memoryFraction: Double = memoryUsed.toDouble / maxMemory
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}
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/**
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 * Information about a storage block
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 */
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case class BlockStatus(storageLevel: StorageLevel, memSize: Long, diskSize: Long) {
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  def isCached: Boolean = memSize > 0 || diskSize > 0
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}
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```
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## Storage Strategy Guidelines
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### Choosing the Right Storage Level
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**MEMORY_ONLY (default cache())**
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- Best for: Small to medium RDDs that fit in memory
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- Fastest access but limited by memory capacity
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- Use when: RDD will be accessed multiple times and fits in cluster memory
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```scala
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// Good for small, frequently accessed RDDs
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val smallLookupTable = sc.parallelize(lookupData).cache()
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```
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**MEMORY_ONLY_SER**
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- Best for: Large RDDs where memory is constrained
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- Slower than MEMORY_ONLY but uses less memory
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- Use when: Memory is limited but want to avoid disk I/O
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```scala
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// Memory-efficient caching for large datasets
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val largeRDD = sc.textFile("huge-file.txt")
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  .map(expensiveTransformation)
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  .persist(StorageLevel.MEMORY_ONLY_SER)
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```
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**MEMORY_AND_DISK**
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- Best for: Large RDDs that may not fit entirely in memory
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- Graceful degradation from memory to disk
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- Use when: Want speed of memory with disk fallback
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```scala
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// Balanced approach for uncertain memory requirements
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val uncertainSizeRDD = sc.textFile("variable-size-data/*")
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  .persist(StorageLevel.MEMORY_AND_DISK)
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```
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**DISK_ONLY**
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- Best for: Very large RDDs where memory is needed for other operations
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- Slowest but most reliable for large datasets
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- Use when: Memory is needed for computations, not storage
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```scala
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// Store intermediate results on disk
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val intermediateResults = complexProcessing(inputRDD)
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  .persist(StorageLevel.DISK_ONLY)
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```
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**Replication Levels (_2 suffix)**
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- Best for: Critical RDDs where fault tolerance is important
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- Doubles storage cost but provides redundancy
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- Use when: Recomputation cost is very high
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```scala
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// High availability for critical data
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val criticalRDD = sc.textFile("important-data.txt")
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  .map(veryExpensiveComputation)
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  .persist(StorageLevel.MEMORY_AND_DISK_2)
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```
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### Performance Optimization Patterns
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**Iterative Algorithms**
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```scala
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var currentRDD = initialData.cache()
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for (i <- 1 to numIterations) {
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  val nextRDD = currentRDD.map(iterationFunction).cache()
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  currentRDD.unpersist() // Remove previous iteration
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  currentRDD = nextRDD
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}
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```
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**Multi-Stage Pipelines**
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```scala
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// Cache intermediate stages that branch
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val cleaned = rawData.filter(isValid).cache()
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val analysis1 = cleaned.map(analysisFunction1).collect()
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val analysis2 = cleaned.map(analysisFunction2).collect()
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cleaned.unpersist() // Clean up when done
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```
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**Memory Management**
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```scala
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// Monitor and manage memory usage
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val rdd1 = data.map(transform1).persist(StorageLevel.MEMORY_ONLY_SER)
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val rdd2 = rdd1.map(transform2).persist(StorageLevel.MEMORY_ONLY_SER)
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// Use the RDDs
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processResults(rdd2)
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// Clean up in reverse order
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rdd2.unpersist()
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rdd1.unpersist()
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```
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### Checkpointing Strategies
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**When to Use Checkpointing**
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- Long lineage chains that would be expensive to recompute
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- Iterative algorithms where intermediate state is valuable
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- After expensive operations that you don't want to repeat
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```scala
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// Set checkpoint directory once per application
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sc.setCheckpointDir("hdfs://cluster/checkpoints/myapp")
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// Checkpoint expensive transformations
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val expensiveResult = rawData
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  .map(veryExpensiveFunction)
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  .filter(complexPredicate)
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expensiveResult.checkpoint() // Mark for checkpointing
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expensiveResult.count() // Trigger the checkpoint
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// Now expensiveResult can be used without recomputing
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val analysis = expensiveResult.map(analysisFunction).collect()
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```
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**Local vs Reliable Checkpointing**
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```scala
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// Local checkpointing (faster but less fault-tolerant)
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rdd.localCheckpoint()
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// Reliable checkpointing (slower but fault-tolerant)
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rdd.checkpoint() // Requires checkpoint directory
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```
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## Monitoring Storage Usage
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### Spark UI Storage Tab
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- Monitor cached RDDs and their memory/disk usage
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- See which RDDs are taking up the most space
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- Identify RDDs that should be unpersisted
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### Programmatic Monitoring
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```scala
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// Get storage status for all RDDs
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val storageStatus = sc.getExecutorStorageStatus
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storageStatus.foreach { status =>
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  println(s"Executor ${status.blockManagerId}: ${status.memoryUsed} / ${status.maxMemory}")
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}
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// Get specific RDD storage info (if available through custom monitoring)
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def checkRDDStorage(rdd: RDD[_]): Unit = {
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  println(s"RDD ${rdd.id} storage level: ${rdd.getStorageLevel}")
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  println(s"RDD ${rdd.id} is cached: ${rdd.getStorageLevel != StorageLevel.NONE}")
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}
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```
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### Memory Tuning Parameters
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Key Spark configuration properties for storage tuning:
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```scala
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val conf = new SparkConf()
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  // Fraction of heap space used for storage (default: 0.6)
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  .set("spark.storage.memoryFraction", "0.7")
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  // Fraction of storage memory immune to eviction (default: 0.5) 
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  .set("spark.storage.safetyFraction", "0.6")
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  // Whether to compress RDD partitions in memory (default: false)
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  .set("spark.rdd.compress", "true")
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  // Whether to compress broadcast variables (default: true)
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  .set("spark.broadcast.compress", "true")
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  // Size of serializer buffer (default: 64k)
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  .set("spark.serializer.objectStreamReset", "100")
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```
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## Common Storage Anti-patterns
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### Over-caching
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```scala
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// Bad: Caching everything
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val rdd1 = data.map(f1).cache() // Only used once
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val rdd2 = rdd1.map(f2).cache() // Only used once  
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val result = rdd2.collect()
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// Good: Cache only what's reused
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val rdd1 = data.map(f1)
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val rdd2 = rdd1.map(f2)
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val result = rdd2.collect()
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```
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### Not Unpersisting
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```scala
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// Bad: Never cleaning up
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def processData(): Unit = {
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  val cached = data.map(expensiveFunc).cache()
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  cached.collect()
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  // cached remains in memory forever
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}
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// Good: Clean up when done
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def processData(): Unit = {
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  val cached = data.map(expensiveFunc).cache()
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  try {
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    cached.collect()
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  } finally {
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    cached.unpersist()
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  }
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}
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```
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### Wrong Storage Level
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```scala
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// Bad: Using MEMORY_ONLY for large datasets
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val huge = sc.textFile("100GB-file.txt").cache() // Will spill and thrash
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// Good: Use appropriate level for data size
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val huge = sc.textFile("100GB-file.txt")
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  .persist(StorageLevel.MEMORY_AND_DISK_SER)
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```
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### Caching Too Early
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```scala
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// Bad: Caching before filtering
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val cached = allData.cache()
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val filtered = cached.filter(rarePredicate) // Most data filtered out
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// Good: Filter first, then cache
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val filtered = allData.filter(rarePredicate).cache()
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```