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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark
/**
* This package implements Spark's memory management system. This system consists of two main
* components, a JVM-wide memory manager and a per-task manager:
*
* - [[org.apache.spark.memory.MemoryManager]] manages Spark's overall memory usage within a JVM.
* This component implements the policies for dividing the available memory across tasks and for
* allocating memory between storage (memory used caching and data transfer) and execution
* (memory used by computations, such as shuffles, joins, sorts, and aggregations).
* - [[org.apache.spark.memory.TaskMemoryManager]] manages the memory allocated by individual
* tasks. Tasks interact with TaskMemoryManager and never directly interact with the JVM-wide
* MemoryManager.
*
* Internally, each of these components have additional abstractions for memory bookkeeping:
*
* - [[org.apache.spark.memory.MemoryConsumer]]s are clients of the TaskMemoryManager and
* correspond to individual operators and data structures within a task. The TaskMemoryManager
* receives memory allocation requests from MemoryConsumers and issues callbacks to consumers
* in order to trigger spilling when running low on memory.
* - [[org.apache.spark.memory.MemoryPool]]s are a bookkeeping abstraction used by the
* MemoryManager to track the division of memory between storage and execution.
*
* Diagrammatically:
*
* {{{
* +-------------+
* | MemConsumer |----+ +------------------------+
* +-------------+ | +-------------------+ | MemoryManager |
* +--->| TaskMemoryManager |----+ | |
* +-------------+ | +-------------------+ | | +------------------+ |
* | MemConsumer |----+ | | | StorageMemPool | |
* +-------------+ +-------------------+ | | +------------------+ |
* | TaskMemoryManager |----+ | |
* +-------------------+ | | +------------------+ |
* +---->| |OnHeapExecMemPool | |
* * | | +------------------+ |
* * | | |
* +-------------+ * | | +------------------+ |
* | MemConsumer |----+ | | |OffHeapExecMemPool| |
* +-------------+ | +-------------------+ | | +------------------+ |
* +--->| TaskMemoryManager |----+ | |
* +-------------------+ +------------------------+
* }}}
*
*
* There are two implementations of [[org.apache.spark.memory.MemoryManager]] which vary in how
* they handle the sizing of their memory pools:
*
* - [[org.apache.spark.memory.UnifiedMemoryManager]], the default in Spark 1.6+, enforces soft
* boundaries between storage and execution memory, allowing requests for memory in one region
* to be fulfilled by borrowing memory from the other.
* - [[org.apache.spark.memory.StaticMemoryManager]] enforces hard boundaries between storage
* and execution memory by statically partitioning Spark's memory and preventing storage and
* execution from borrowing memory from each other. This mode is retained only for legacy
* compatibility purposes.
*/
package object memory
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