原文地址：http://blog.csdn.net/aiuyjerry/article/details/8595991
Storage模块主要负责数据存取，包括MapReduce Shuffle中间结果、MapReduce task中间stage结果、cache结果。下面从架构和源码细节上来分析Storage模块的实现。Storage模块主要由两大部分组成：
- BlockManager部分主要负责Master和Slave之间的block通信，主要包括BlockManager状态上报、心跳，add, remove, update block.
- BlockStore部分主要负责数据存取，Spark根据不同选择可以在Memory或(和)Disk中存储序列化数据.
Storage模块类图如下所示：
- SparkEnv创建时会实例化BlockManagerMaster对象和BlockManager对象。
- BlockManagerMaster对象会根据自己是master还是slave来创建BlockManagerMasterActor或是连接到BlockManagerMasterActor。
- BlockManager承担两种角色：
- 负责向BlockManagerMaster上报block信息，保持心跳和接收block信息
- 负责通过BlockStore从Memory或Disk读取、写入block数据
- BlockManagerMessages封装与master传输的meta信息的具体格式。
- Slave通过BlockManager向BlockManagerMaster注册自己，在注册自己时会创建BlockManagerSlaveActor，用来Master向Slave通信，目前唯一request是请求Slave删除block。
- BlockManagerWorker则负责Slave之间的通信，包括get, put非本地的block
- BlockMessage类封装了与Master通信的block message的具体格式，而BlockMessageArray则是批处理接口。
- BlockStore提供持久化数据的接口，DiskStore和MemoryStore实例化了BlockStore接口，实现serialize, deserialize数据到Disk或Memory。
Spark Storage模块master和slave之间通信的信息包括：
- Slave --------> Master
- RegisterBlockManager
- HeartBeat
- UpdateBlockInfo
- GetLocations
- GetLocationsMutipleBlockIds
- GetPeers
- RemoveExecutor
- StopBlockManagerMaster
- GetMemoryStatus
- ExpireDeadHosts
- GetStorageStatus
- Master ---------> Slave
- RemoveBlock
Storage模块存取数据分析
MemoryStore：
Memory内部使用LinkedHashMap来作为block的存储结构，其中key是block id，value是Entry类，代码如所示:
case class Entry(value: Any, size: Long, deserialized: Boolean, var dropPending: Boolean = false)
private val entries = new LinkedHashMap[String, Entry](32, 0.75f, true)
而内部存储会调用如下代码：
private def tryToPut(blockId: String, value: Any, size: Long, deserialized: Boolean): Boolean = {
putLock.synchronized {
if (ensureFreeSpace(blockId, size)) {
val entry = new Entry(value, size, deserialized)
entries.synchronized { entries.put(blockId, entry) }
currentMemory += size
if (deserialized) {
logInfo("Block %s stored as values to memory (estimated size %s, free %s)".format(
blockId, Utils.memoryBytesToString(size), Utils.memoryBytesToString(freeMemory)))
} else {
logInfo("Block %s stored as bytes to memory (size %s, free %s)".format(
blockId, Utils.memoryBytesToString(size), Utils.memoryBytesToString(freeMemory)))
}
true
} else {
// Tell the block manager that we couldn't put it in memory so that it can drop it to
// disk if the block allows disk storage.
val data = if (deserialized) {
Left(value.asInstanceOf[ArrayBuffer[Any]])
} else {
Right(value.asInstanceOf[ByteBuffer].duplicate())
}
blockManager.dropFromMemory(blockId, data)
false
}
}
}
private def ensureFreeSpace(blockIdToAdd: String, space: Long): Boolean = {
logInfo("ensureFreeSpace(%d) called with curMem=%d, maxMem=%d".format(
space, currentMemory, maxMemory))
if (space > maxMemory) {
logInfo("Will not store " + blockIdToAdd + " as it is larger than our memory limit")
return false
}
if (maxMemory - currentMemory < space) {
val rddToAdd = getRddId(blockIdToAdd)
val selectedBlocks = new ArrayBuffer[String]()
var selectedMemory = 0L
entries.synchronized {
val iterator = entries.entrySet().iterator()
while (maxMemory - (currentMemory - selectedMemory) < space && iterator.hasNext) {
val pair = iterator.next()
val blockId = pair.getKey
if (rddToAdd != null && rddToAdd == getRddId(blockId)) {
logInfo("Will not store " + blockIdToAdd + " as it would require dropping another " +
"block from the same RDD")
return false
}
selectedBlocks += blockId
selectedMemory += pair.getValue.size
}
}
if (maxMemory - (currentMemory - selectedMemory) >= space) {
logInfo(selectedBlocks.size + " blocks selected for dropping")
for (blockId <- selectedBlocks) {
val entry = entries.synchronized { entries.get(blockId) }
// This should never be null as only one thread should be dropping
// blocks and removing entries. However the check is still here for
// future safety.
if (entry != null) {
val data = if (entry.deserialized) {
Left(entry.value.asInstanceOf[ArrayBuffer[Any]])
} else {
Right(entry.value.asInstanceOf[ByteBuffer].duplicate())
}
blockManager.dropFromMemory(blockId, data)
}
}
return true
} else {
return false
}
}
return true
} tryToPut会调用ensureFreeSpace来淘汰掉一些block，为此block的存储释放新的空间，而tryToPut会将其添加到LinkedHashMap中。如果ensureFreeSpace无法获得足够的空间去存储此block，tryToPut会调用dropFreeMemory来drop此block。
DiskStore：
Spark会根据配置项spark.local.dir在本地建立目录，所有的block都会依照不同路径存储到此目录下，当spark.local.dir中配置了多个path时，Spark会根据hash将block存储到不同的path下
- 首先，Spark会根据spark.local.dir的配置在所有配置目录下建立localDir，localDir命名为spark-local-%s-%04x,其中%s是格式化后的当前时间(yyyyMMddHHmmss)，%d是一个小于65535的随机16进制数字。
- 其次，每当要存储block时，Spark会根据blockId在localDir下建立子目录和相应的文件，block存储目录的选择规律是：
- 根据blockId的hash值计算出dirId和subDirId
- 取出或创建subDir
- 在subDir下面以blockId为名字创建文件
val subDirsPerLocalDir = System.getProperty("spark.diskStore.subDirectories", "64").toInt
val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir))
// Figure out which local directory it hashes to, and which subdirectory in that
val hash = math.abs(blockId.hashCode)
val dirId = hash % localDirs.length
val subDirId = (hash / localDirs.length) % subDirsPerLocalDir
// Create the subdirectory if it doesn't already exist
var subDir = subDirs(dirId)(subDirId)
if (subDir == null) {
subDir = subDirs(dirId).synchronized {
val old = subDirs(dirId)(subDirId)
if (old != null) {
old
} else {
val newDir = new File(localDirs(dirId), "%02x".format(subDirId))
newDir.mkdir()
subDirs(dirId)(subDirId) = newDir
newDir
}
}
}
new File(subDir, blockId)- 最后，根据压缩和序列化方式选择将block存储到文件中