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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.memory
import org.apache.spark.SparkConf
import org.apache.spark.storage.BlockId
/**
* A [[MemoryManager]] that enforces a soft boundary between execution and storage such that
* either side can borrow memory from the other.
*
* The region shared between execution and storage is a fraction of (the total heap space - 300MB)
* configurable through `spark.memory.fraction` (default 0.6). The position of the boundary
* within this space is further determined by `spark.memory.storageFraction` (default 0.5).
* This means the size of the storage region is 0.6 * 0.5 = 0.3 of the heap space by default.
*
* Storage can borrow as much execution memory as is free until execution reclaims its space.
* When this happens, cached blocks will be evicted from memory until sufficient borrowed
* memory is released to satisfy the execution memory request.
*
* Similarly, execution can borrow as much storage memory as is free. However, execution
* memory is *never* evicted by storage due to the complexities involved in implementing this.
* The implication is that attempts to cache blocks may fail if execution has already eaten
* up most of the storage space, in which case the new blocks will be evicted immediately
* according to their respective storage levels.
*
* @param onHeapStorageRegionSize Size of the storage region, in bytes.
* This region is not statically reserved; execution can borrow from
* it if necessary. Cached blocks can be evicted only if actual
* storage memory usage exceeds this region.
*/
private[spark] class UnifiedMemoryManager private[memory] (
conf: SparkConf,
val maxHeapMemory: Long,
onHeapStorageRegionSize: Long,
numCores: Int)
extends MemoryManager(
conf,
numCores,
onHeapStorageRegionSize,
maxHeapMemory - onHeapStorageRegionSize) {
private def assertInvariants(): Unit = {
assert(onHeapExecutionMemoryPool.poolSize + onHeapStorageMemoryPool.poolSize == maxHeapMemory)
assert(
offHeapExecutionMemoryPool.poolSize + offHeapStorageMemoryPool.poolSize == maxOffHeapMemory)
}
assertInvariants()
override def maxOnHeapStorageMemory: Long = synchronized {
maxHeapMemory - onHeapExecutionMemoryPool.memoryUsed
}
override def maxOffHeapStorageMemory: Long = synchronized {
maxOffHeapMemory - offHeapExecutionMemoryPool.memoryUsed
}
/**
* Try to acquire up to `numBytes` of execution memory for the current task and return the
* number of bytes obtained, or 0 if none can be allocated.
*
* This call may block until there is enough free memory in some situations, to make sure each
* task has a chance to ramp up to at least 1 / 2N of the total memory pool (where N is the # of
* active tasks) before it is forced to spill. This can happen if the number of tasks increase
* but an older task had a lot of memory already.
*/
override private[memory] def acquireExecutionMemory(
numBytes: Long,
taskAttemptId: Long,
memoryMode: MemoryMode): Long = synchronized {
assertInvariants()
assert(numBytes >= 0)
val (executionPool, storagePool, storageRegionSize, maxMemory) = memoryMode match {
case MemoryMode.ON_HEAP => (
onHeapExecutionMemoryPool,
onHeapStorageMemoryPool,
onHeapStorageRegionSize,
maxHeapMemory)
case MemoryMode.OFF_HEAP => (
offHeapExecutionMemoryPool,
offHeapStorageMemoryPool,
offHeapStorageMemory,
maxOffHeapMemory)
}
/**
* Grow the execution pool by evicting cached blocks, thereby shrinking the storage pool.
*
* When acquiring memory for a task, the execution pool may need to make multiple
* attempts. Each attempt must be able to evict storage in case another task jumps in
* and caches a large block between the attempts. This is called once per attempt.
*/
def maybeGrowExecutionPool(extraMemoryNeeded: Long): Unit = {
if (extraMemoryNeeded > 0) {
// There is not enough free memory in the execution pool, so try to reclaim memory from
// storage. We can reclaim any free memory from the storage pool. If the storage pool
// has grown to become larger than `storageRegionSize`, we can evict blocks and reclaim
// the memory that storage has borrowed from execution.
val memoryReclaimableFromStorage = math.max(
storagePool.memoryFree,
storagePool.poolSize - storageRegionSize)
if (memoryReclaimableFromStorage > 0) {
// Only reclaim as much space as is necessary and available:
val spaceToReclaim = storagePool.freeSpaceToShrinkPool(
math.min(extraMemoryNeeded, memoryReclaimableFromStorage))
storagePool.decrementPoolSize(spaceToReclaim)
executionPool.incrementPoolSize(spaceToReclaim)
}
}
}
/**
* The size the execution pool would have after evicting storage memory.
*
* The execution memory pool divides this quantity among the active tasks evenly to cap
* the execution memory allocation for each task. It is important to keep this greater
* than the execution pool size, which doesn't take into account potential memory that
* could be freed by evicting storage. Otherwise we may hit SPARK-12155.
*
* Additionally, this quantity should be kept below `maxMemory` to arbitrate fairness
* in execution memory allocation across tasks, Otherwise, a task may occupy more than
* its fair share of execution memory, mistakenly thinking that other tasks can acquire
* the portion of storage memory that cannot be evicted.
*/
def computeMaxExecutionPoolSize(): Long = {
maxMemory - math.min(storagePool.memoryUsed, storageRegionSize)
}
executionPool.acquireMemory(
numBytes, taskAttemptId, maybeGrowExecutionPool, () => computeMaxExecutionPoolSize)
}
override def acquireStorageMemory(
blockId: BlockId,
numBytes: Long,
memoryMode: MemoryMode): Boolean = synchronized {
assertInvariants()
assert(numBytes >= 0)
val (executionPool, storagePool, maxMemory) = memoryMode match {
case MemoryMode.ON_HEAP => (
onHeapExecutionMemoryPool,
onHeapStorageMemoryPool,
maxOnHeapStorageMemory)
case MemoryMode.OFF_HEAP => (
offHeapExecutionMemoryPool,
offHeapStorageMemoryPool,
maxOffHeapStorageMemory)
}
if (numBytes > maxMemory) {
// Fail fast if the block simply won't fit
logInfo(s"Will not store $blockId as the required space ($numBytes bytes) exceeds our " +
s"memory limit ($maxMemory bytes)")
return false
}
if (numBytes > storagePool.memoryFree) {
// There is not enough free memory in the storage pool, so try to borrow free memory from
// the execution pool.
val memoryBorrowedFromExecution = Math.min(executionPool.memoryFree,
numBytes - storagePool.memoryFree)
executionPool.decrementPoolSize(memoryBorrowedFromExecution)
storagePool.incrementPoolSize(memoryBorrowedFromExecution)
}
storagePool.acquireMemory(blockId, numBytes)
}
override def acquireUnrollMemory(
blockId: BlockId,
numBytes: Long,
memoryMode: MemoryMode): Boolean = synchronized {
acquireStorageMemory(blockId, numBytes, memoryMode)
}
}
object UnifiedMemoryManager {
// Set aside a fixed amount of memory for non-storage, non-execution purposes.
// This serves a function similar to `spark.memory.fraction`, but guarantees that we reserve
// sufficient memory for the system even for small heaps. E.g. if we have a 1GB JVM, then
// the memory used for execution and storage will be (1024 - 300) * 0.6 = 434MB by default.
private val RESERVED_SYSTEM_MEMORY_BYTES = 300 * 1024 * 1024
def apply(conf: SparkConf, numCores: Int): UnifiedMemoryManager = {
val maxMemory = getMaxMemory(conf)
new UnifiedMemoryManager(
conf,
maxHeapMemory = maxMemory,
onHeapStorageRegionSize =
(maxMemory * conf.getDouble("spark.memory.storageFraction", 0.5)).toLong,
numCores = numCores)
}
/**
* Return the total amount of memory shared between execution and storage, in bytes.
*/
private def getMaxMemory(conf: SparkConf): Long = {
val systemMemory = conf.getLong("spark.testing.memory", Runtime.getRuntime.maxMemory)
val reservedMemory = conf.getLong("spark.testing.reservedMemory",
if (conf.contains("spark.testing")) 0 else RESERVED_SYSTEM_MEMORY_BYTES)
val minSystemMemory = (reservedMemory * 1.5).ceil.toLong
if (systemMemory < minSystemMemory) {
throw new IllegalArgumentException(s"System memory $systemMemory must " +
s"be at least $minSystemMemory. Please increase heap size using the --driver-memory " +
s"option or spark.driver.memory in Spark configuration.")
}
// SPARK-12759 Check executor memory to fail fast if memory is insufficient
if (conf.contains("spark.executor.memory")) {
val executorMemory = conf.getSizeAsBytes("spark.executor.memory")
if (executorMemory < minSystemMemory) {
throw new IllegalArgumentException(s"Executor memory $executorMemory must be at least " +
s"$minSystemMemory. Please increase executor memory using the " +
s"--executor-memory option or spark.executor.memory in Spark configuration.")
}
}
val usableMemory = systemMemory - reservedMemory
val memoryFraction = conf.getDouble("spark.memory.fraction", 0.6)
(usableMemory * memoryFraction).toLong
}
}
