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# Storage and Persistence
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Storage levels and caching mechanisms for optimizing RDD persistence across memory and disk with various replication strategies.
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## Capabilities
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### StorageLevel
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Defines how RDD partitions are stored, including memory usage, disk usage, and replication settings.
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```scala { .api }
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/**
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 * Storage level for RDD persistence, defining memory/disk usage 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) extends Externalizable {
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  def useDisk: Boolean = _useDisk
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  def useMemory: Boolean = _useMemory
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  def useOffHeap: Boolean = _useOffHeap
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  def deserialized: Boolean = _deserialized
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  def replication: Int = _replication
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  override def toString: String = {
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    "StorageLevel(%s, %s, %s, %s, %d)".format(
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      _useDisk, _useMemory, _useOffHeap, _deserialized, _replication)
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  }
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}
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object StorageLevel {
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  // Predefined storage levels
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  val NONE = new StorageLevel(false, false, false, false, 1)
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  val DISK_ONLY = new StorageLevel(true, false, false, false, 1)
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  val DISK_ONLY_2 = new StorageLevel(true, false, false, false, 2)
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  val MEMORY_ONLY = new StorageLevel(false, true, false, true, 1)
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  val MEMORY_ONLY_2 = new StorageLevel(false, true, false, true, 2)
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  val MEMORY_ONLY_SER = new StorageLevel(false, true, false, false, 1)
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  val MEMORY_ONLY_SER_2 = new StorageLevel(false, true, false, false, 2)
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  val MEMORY_AND_DISK = new StorageLevel(true, true, false, true, 1)
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  val MEMORY_AND_DISK_2 = new StorageLevel(true, true, false, true, 2)
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  val MEMORY_AND_DISK_SER = new StorageLevel(true, true, false, false, 1)
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  val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, false, 2)
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  val OFF_HEAP = new StorageLevel(false, false, true, false, 1)
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  /** Create a new StorageLevel with custom settings */
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  def apply(
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    useDisk: Boolean,
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    useMemory: Boolean,
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    useOffHeap: Boolean,
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    deserialized: Boolean,
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    replication: Int): StorageLevel = {
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    new StorageLevel(useDisk, useMemory, useOffHeap, deserialized, replication)
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  }
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}
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```
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### RDD Persistence Methods
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Methods available on RDD for controlling persistence and caching behavior.
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```scala { .api }
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// RDD persistence methods
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def persist(): RDD[T] = persist(StorageLevel.MEMORY_ONLY)
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def persist(storageLevel: StorageLevel): RDD[T]
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def cache(): RDD[T] = persist(StorageLevel.MEMORY_ONLY)
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def unpersist(blocking: Boolean = true): RDD[T]
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// Query persistence status
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def getStorageLevel: StorageLevel
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def isCheckpointed: Boolean
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def getCheckpointFile: Option[String]
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// Checkpointing methods
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def checkpoint(): Unit
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def localCheckpoint(): RDD[T]
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```
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**Usage Examples:**
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```scala
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import org.apache.spark.{SparkContext, SparkConf}
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import org.apache.spark.storage.StorageLevel
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val sc = new SparkContext(new SparkConf().setAppName("Storage Example").setMaster("local[*]"))
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val data = sc.textFile("hdfs://large-dataset.txt")
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// Basic caching (MEMORY_ONLY)
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val cachedData = data.cache()
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// Explicit persistence with different storage levels
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val memoryOnlyData = data.persist(StorageLevel.MEMORY_ONLY)
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val memoryAndDiskData = data.persist(StorageLevel.MEMORY_AND_DISK)
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val diskOnlyData = data.persist(StorageLevel.DISK_ONLY)
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val serializedData = data.persist(StorageLevel.MEMORY_ONLY_SER)
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// Replication for fault tolerance
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val replicatedData = data.persist(StorageLevel.MEMORY_ONLY_2) // 2 replicas
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// Off-heap storage (requires off-heap memory configuration)
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val offHeapData = data.persist(StorageLevel.OFF_HEAP)
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// Custom storage level
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val customStorage = 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, // serialized for memory efficiency
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  replication = 2
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)
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val customPersistedData = data.persist(customStorage)
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// Use persisted data multiple times
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val wordCounts = cachedData
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  .flatMap(_.split(" "))
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  .map((_, 1))
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  .reduceByKey(_ + _)
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val lineCount = cachedData.count()
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val charCount = cachedData.map(_.length).sum()
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// Clean up when done
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cachedData.unpersist()
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memoryAndDiskData.unpersist(blocking = false) // Non-blocking cleanup
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```
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### Checkpointing
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Fault-tolerance mechanism that saves RDD data to reliable storage for recovery.
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```scala { .api }
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// SparkContext checkpointing methods
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def setCheckpointDir(directory: String): Unit
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def getCheckpointDir: Option[String]
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// RDD checkpointing methods  
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def checkpoint(): Unit
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def localCheckpoint(): RDD[T]
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def isCheckpointed: Boolean
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def getCheckpointFile: Option[String]
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```
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**Usage Examples:**
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```scala
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import org.apache.spark.{SparkContext, SparkConf}
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val sc = new SparkContext(new SparkConf().setAppName("Checkpoint Example"))
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// Set checkpoint directory (should be on reliable storage like HDFS)
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sc.setCheckpointDir("hdfs://namenode:port/spark-checkpoints")
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val data = sc.textFile("hdfs://input-data.txt")
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// Long chain of transformations
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val processedData = data
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  .filter(_.nonEmpty)
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  .map(_.toLowerCase)
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  .flatMap(_.split(" "))
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  .map((_, 1))
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  .reduceByKey(_ + _)
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  .filter(_._2 > 5)
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  .map(_.swap) // (count, word)
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  .sortByKey(ascending = false)
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  .map(_.swap) // back to (word, count)
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// Checkpoint to break lineage and prevent recomputation
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processedData.checkpoint()
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// Trigger checkpointing (requires an action)
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processedData.count()
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// Verify checkpointing
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println(s"Is checkpointed: ${processedData.isCheckpointed}")
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println(s"Checkpoint file: ${processedData.getCheckpointFile}")
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// Use checkpointed RDD
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val topWords = processedData.take(100)
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val totalUniqueWords = processedData.count()
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// Local checkpointing (for shorter lineages)
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val shortChain = data.map(_.toUpperCase)
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val localCheckpointed = shortChain.localCheckpoint()
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localCheckpointed.count() // Trigger local checkpointing
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```
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## Storage Optimization Strategies
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### Memory Management
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```scala
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// Monitor storage usage
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val largeDataset = sc.textFile("hdfs://very-large-file.txt")
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// Use serialized storage for memory efficiency
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val efficientStorage = largeDataset.persist(StorageLevel.MEMORY_ONLY_SER)
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// Check storage usage programmatically
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def printStorageInfo(sc: SparkContext): Unit = {
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  val storageStatusListener = sc.statusTracker
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  val executorInfos = storageStatusListener.getExecutorInfos
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  executorInfos.foreach { executor =>
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    println(s"Executor ${executor.executorId}: " +
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      s"${executor.memoryUsed / (1024 * 1024)}MB used, " +
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      s"${executor.memoryTotal / (1024 * 1024)}MB total")
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  }
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}
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// Use the storage info
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efficientStorage.count()
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printStorageInfo(sc)
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```
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### Adaptive Storage Strategies
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```scala
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import org.apache.spark.storage.StorageLevel
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def adaptiveStorage[T](rdd: RDD[T], estimatedSize: Long): RDD[T] = {
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  val memoryFraction = 0.6 // Assume 60% of executor memory available for storage
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  val executorMemory = sc.getConf.getSizeAsBytes("spark.executor.memory", "1g")
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  val availableMemory = (executorMemory * memoryFraction).toLong
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  val storageLevel = if (estimatedSize < availableMemory) {
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    StorageLevel.MEMORY_ONLY
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  } else if (estimatedSize < availableMemory * 2) {
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    StorageLevel.MEMORY_ONLY_SER
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  } else if (estimatedSize < availableMemory * 5) {
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    StorageLevel.MEMORY_AND_DISK_SER
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  } else {
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    StorageLevel.DISK_ONLY
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  }
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  println(s"Using storage level: $storageLevel for estimated size: ${estimatedSize / (1024 * 1024)}MB")
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  rdd.persist(storageLevel)
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}
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// Usage
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val dataSize = estimateRDDSize(largeDataset) // Custom function to estimate size
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val optimizedRDD = adaptiveStorage(largeDataset, dataSize)
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```
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### Multi-Stage Processing with Checkpointing
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```scala
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// Complex ETL pipeline with strategic checkpointing
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def processLargeDataset(inputPath: String, outputPath: String): Unit = {
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  sc.setCheckpointDir("hdfs://checkpoints/etl-pipeline")
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  // Stage 1: Initial data loading and cleaning
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  val rawData = sc.textFile(inputPath)
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  val cleanedData = rawData
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    .filter(_.nonEmpty)
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    .filter(!_.startsWith("#")) // Remove comments
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    .map(_.trim)
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    .cache() // Cache cleaned data for reuse
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  val recordCount = cleanedData.count()
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  println(s"Loaded and cleaned $recordCount records")
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  // Stage 2: Complex transformations
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  val transformedData = cleanedData
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    .map(parseRecord) // Complex parsing
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    .filter(_.isValid) // Remove invalid records
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    .map(enrichRecord) // Add external data
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    .groupBy(_.category)
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    .mapValues(aggregateRecords) // Complex aggregation
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  // Checkpoint after expensive transformations
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  transformedData.checkpoint()
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  val checkpointCount = transformedData.count() // Trigger checkpointing
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  println(s"Checkpointed $checkpointCount transformed records")
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  // Stage 3: Final processing using checkpointed data
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  val finalResults = transformedData
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    .filter(_._2.score > threshold)
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    .sortBy(_._2.score, ascending = false)
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    .take(1000)
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  // Clean up cached data
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  cleanedData.unpersist()
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  // Save results
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  sc.parallelize(finalResults).saveAsTextFile(outputPath)
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}
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```
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### Performance Monitoring
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```scala
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import org.apache.spark.storage.RDDInfo
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def monitorRDDStorage(sc: SparkContext): Unit = {
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  val rddInfos = sc.getRDDStorageInfo
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  println("RDD Storage Information:")
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  println("=" * 50)
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  rddInfos.foreach { rddInfo =>
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    println(s"RDD ID: ${rddInfo.id}")
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    println(s"Name: ${rddInfo.name}")
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    println(s"Storage Level: ${rddInfo.storageLevel}")
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    println(s"Cached Partitions: ${rddInfo.numCachedPartitions}/${rddInfo.numPartitions}")
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    println(s"Memory Size: ${rddInfo.memSize / (1024 * 1024)} MB")
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    println(s"Disk Size: ${rddInfo.diskSize / (1024 * 1024)} MB")
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    println("-" * 30)
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  }
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}
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// Custom storage metrics
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case class StorageMetrics(
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  rddId: Int,
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  memoryUsed: Long,
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  diskUsed: Long,
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  partitionsCached: Int,
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  totalPartitions: Int
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)
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def collectStorageMetrics(sc: SparkContext): List[StorageMetrics] = {
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  sc.getRDDStorageInfo.map { rddInfo =>
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    StorageMetrics(
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      rddId = rddInfo.id,
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      memoryUsed = rddInfo.memSize,
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      diskUsed = rddInfo.diskSize,
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      partitionsCached = rddInfo.numCachedPartitions,
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      totalPartitions = rddInfo.numPartitions
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    )
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  }.toList
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}
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// Usage in application
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val beforeMetrics = collectStorageMetrics(sc)
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// ... perform operations ...
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val afterMetrics = collectStorageMetrics(sc)
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// Compare metrics
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println("Storage usage changes:")
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afterMetrics.foreach { after =>
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  beforeMetrics.find(_.rddId == after.rddId) match {
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    case Some(before) =>
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      val memoryChange = after.memoryUsed - before.memoryUsed
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      val diskChange = after.diskUsed - before.diskUsed
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      println(s"RDD ${after.rddId}: Memory: ${memoryChange / (1024 * 1024)}MB, Disk: ${diskChange / (1024 * 1024)}MB")
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    case None =>
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      println(s"New RDD ${after.rddId}: Memory: ${after.memoryUsed / (1024 * 1024)}MB, Disk: ${after.diskUsed / (1024 * 1024)}MB")
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  }
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}
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```
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## Best Practices
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### When to Use Different Storage Levels
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```scala
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// 1. MEMORY_ONLY: Small datasets, frequently accessed
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val smallFrequentData = sc.parallelize(1 to 1000).persist(StorageLevel.MEMORY_ONLY)
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// 2. MEMORY_ONLY_SER: Larger datasets, CPU available for deserialization
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val mediumData = sc.textFile("medium-file.txt").persist(StorageLevel.MEMORY_ONLY_SER)
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// 3. MEMORY_AND_DISK: Important datasets, some memory pressure
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val importantData = sc.textFile("important-file.txt").persist(StorageLevel.MEMORY_AND_DISK)
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// 4. DISK_ONLY: Very large datasets, limited memory
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val hugeData = sc.textFile("huge-file.txt").persist(StorageLevel.DISK_ONLY)
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// 5. Replication levels: Critical data in fault-prone environments
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val criticalData = sc.textFile("critical-file.txt").persist(StorageLevel.MEMORY_AND_DISK_2)
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```
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### Efficient Cache Management
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```scala
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// Cache management pattern
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class CacheManager(sc: SparkContext) {
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  private var cachedRDDs = List.empty[RDD[_]]
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  def cacheRDD[T](rdd: RDD[T], storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK): RDD[T] = {
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    val cachedRDD = rdd.persist(storageLevel)
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    cachedRDDs = cachedRDD :: cachedRDDs
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    cachedRDD
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  }
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  def unpersistAll(): Unit = {
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    cachedRDDs.foreach(_.unpersist())
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    cachedRDDs = List.empty
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  }
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  def getStorageInfo: List[RDDInfo] = {
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    cachedRDDs.flatMap { rdd =>
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      sc.getRDDStorageInfo.find(_.id == rdd.id)
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    }
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  }
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}
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// Usage
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val cacheManager = new CacheManager(sc)
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val data1 = cacheManager.cacheRDD(sc.textFile("file1.txt"))
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val data2 = cacheManager.cacheRDD(sc.textFile("file2.txt"), StorageLevel.MEMORY_ONLY_SER)
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// Process data...
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// Clean up all cached RDDs
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cacheManager.unpersistAll()
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```
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### Checkpointing Strategy
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```scala
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// Intelligent checkpointing based on lineage depth
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def smartCheckpoint[T](rdd: RDD[T], maxLineageDepth: Int = 10): RDD[T] = {
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  def countLineageDepth(rdd: RDD[_]): Int = {
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    rdd.dependencies.map {
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      case dep => countLineageDepth(dep.rdd) + 1
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    }.foldLeft(0)(math.max)
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  }
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  val depth = countLineageDepth(rdd)
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  if (depth > maxLineageDepth) {
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    rdd.checkpoint()
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    println(s"Checkpointing RDD with lineage depth: $depth")
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  }
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  rdd
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}
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// Usage in complex pipeline
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val result = inputData
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  .map(transform1)
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  .filter(filter1)
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  .map(transform2)
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  .groupByKey()
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  .map(transform3) // This might have deep lineage
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val checkpointedResult = smartCheckpoint(result)
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checkpointedResult.count() // Trigger checkpointing if needed
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// Continue processing with potentially checkpointed RDD
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val finalResult = checkpointedResult
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  .filter(finalFilter)
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  .sortBy(_._2)
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```