• Static Memory Manager
• Unified Memory Manager

1. Execution Memory

• 解决Shuffles, Joins, Sorts和Aggregations过程中为了避免频繁IO需要的buffer
• 通过参数spark.shuffle.memoryFraction(default=0.2)配置

2. Storage Memory

• 用来存储Block Cache(调用Rdd.cache, Rdd.persist等方法)
• 以及Broadcasts和Task Results
• 通过参数spark.storage.memoryFraction(default=0.6)设置

3. Other Memory

• 用户代码申请的内存，以及Spark内部使用的内存(defulat=0.2)

为了避免OOM，Spark为每个存储区设置了Safety Fraction

Execution Memory真正的可用内存是

• spark.shuffle.memoryFraction(default=0.2) * spark.shuffle.safetyFraction(default=0.8)
• 即0.2 * 0.8 = 16%

Storage Memory真正的可用内存是

• spark.storage.memoryFraction(default=0.6) * spark.storagge.safetyFraction(default=0.9)
• 即0.6 * 0.9 = 54%

• 用来保存Block Manage收到iterator类型的数据后展开的数据
• 通过参数spark.storage.unrollFraction(default=0.2)配置

• spark.shuffle.memoryFraction(default=0.2)
  • spark.shuffle.safetyFraction(default=0.8)
• spark.storage.memoryFraction(default=0.6)
  • spark.storage.safetyFraction(default=0.9)
  • spark.storage.unrollFraction(default=0.2)

1. 谁都不能超过自己的上限，规定了是多少就是多少，虽然另外一片内存空闲
2. 增加使用者配置的难度，需要根据应用的特性配置相关的参数

Executor向Storage借Memory

• Execution内存不足时，会触发向Storage申请内存
• 能借到的最大内存 = Max(Storage向Executor借的内存, Storage空闲内存)

Storage向Executor借Memory

• Storage内存不足时，会触发向Executor申请内存
• 当且仅当Execution有空闲内存时，才能借走该内存

• 配置参数spark.testing.reservedMemory(default=300M)
• usableMemory = 实际内存 - reservedMemory
• ExecutionMemory 和 StorageMemory 会共享usableMemory * spark.memory.fraction(defulat=0.75)的内存

• spark.memory.useLegacyMode(default=false)
• spark.testing.reservedMemory(default=300M)
• spark.memory.fraction(default=0.75)
• spark.memory.storageFraction(default=0.5)

• UnifiedMemoryManager.acquireStorageMemory

• UnifiedMemoryManager.acquireExecutionMemory

• SparkEnv.create

• BloackManager.初始化
• MemoryStore
  • tryToPut
  • evictBlocksToFreeSpace
• Executor.run
• TaskMemoryManager.acquireExecutionMemory

• spill()

子类

• BytesToBytesMap
• ShuffleExternalSorter
• UnsafeExternalSorter

• Memory Overhead
• Non-heap内存管理 (Tungsten)

• SPARK-10000 Consolidate storage and execution memory management
• Unified Memory Management in Spark 1.6
• Spark 1.6 内存管理模型( Unified Memory Management)分析