Spark2.0 shuffle service
Spark 的shuffle 服务是spark的核心,本文介绍了非ExternalShuffleClient的方式,看BlockService的整个架构。ShuffleClient是整个框架的基础,有init方法和fetchBlock两个方法。
/** Provides an interface for reading shuffle files, either from an Executor or external service. */
public abstract class ShuffleClient implements Closeable { /**
* Initializes the ShuffleClient, specifying this Executor's appId.
* Must be called before any other method on the ShuffleClient.
* 初始化ShuffleClient, 传入本执行器的程序ID,本方法必须在访问ShuffleClient的其它方法前调用。
*/
public void init(String appId) { } /**
* Fetch a sequence of blocks from a remote node asynchronously,
*
* Note that this API takes a sequence so the implementation can batch requests, and does not
* return a future so the underlying implementation can invoke onBlockFetchSuccess as soon as
* the data of a block is fetched, rather than waiting for all blocks to be fetched.
* 异步的从远程结点取一系列的数据块,并且不返回future对象,所以当取到一个数据块的数据时,底层的实现可以调用onBlockFetchSuccess方法,
* 并不用等所有的数据块都取完。
*/
public abstract void fetchBlocks(
String host,
int port,
String execId,
String[] blockIds,
BlockFetchingListener listener);
}
BlockFetchingListener接口,onBlockFetchSuccess方法:每次成功取得一个数据块时调用。当本方法返回时,数据必须被自动释放。 如果数据被传递给另一个线程,接收者必须自己调用retain()和release(),或者拷贝数据到一个新的缓冲区。onBlockFetchFailure方法,数据块获取失败时,至少被调用一次。
public interface BlockFetchingListener extends EventListener {
/**
* Called once per successfully fetched block. After this call returns, data will be released
* automatically. If the data will be passed to another thread, the receiver should retain()
* and release() the buffer on their own, or copy the data to a new buffer.
*/
void onBlockFetchSuccess(String blockId, ManagedBuffer data);
/**
* Called at least once per block upon failures.
*/
void onBlockFetchFailure(String blockId, Throwable exception);
}
BlockTransferService扩展了ShuffleClient,有一些方法的公共的实现。
private[spark]
abstract class BlockTransferService extends ShuffleClient with Closeable with Logging { /**
* Initialize the transfer service by giving it the BlockDataManager that can be used to fetch
* local blocks or put local blocks.
* 通过传递给他BlockDataManager对象来初始化传输服务,BlockDataManager可以用来存取本地数据块。
*/
def init(blockDataManager: BlockDataManager): Unit /**
* Tear down the transfer service.
* 关闭传输服务。
*/
def close(): Unit /**
* Port number the service is listening on, available only after [[init]] is invoked.
* 传输服务所在的端口号,在调用init方法后可用。
*/
def port: Int /**
* Host name the service is listening on, available only after [[init]] is invoked.
* 传输服务所在的主机名,在调用init方法后可用。
*/
def hostName: String /**
* Fetch a sequence of blocks from a remote node asynchronously,
* available only after [[init]] is invoked.
*
* Note that this API takes a sequence so the implementation can batch requests, and does not
* return a future so the underlying implementation can invoke onBlockFetchSuccess as soon as
* the data of a block is fetched, rather than waiting for all blocks to be fetched.
*
* 异步的从远程结点取一系列的数据块,,仅在调用init方法后可用。
* 注意本API用一个序列,所以实现可以使用批量请求,并且不返回future对象,所以当取到一个数据块的数据时,底层的实现可以调用onBlockFetchSuccess方法,
* 并不用等所有的数据块都取完。
*/ override def fetchBlocks( host: String, port: Int, execId: String, blockIds: Array[String], listener: BlockFetchingListener): Unit /**
* Upload a single block to a remote node, available only after [[init]] is invoked.
* 上传一个数据块到远程结点,仅在调用init方法后可用。
*/
def uploadBlock(
hostname: String,
port: Int,
execId: String,
blockId: BlockId,
blockData: ManagedBuffer,
level: StorageLevel,
classTag: ClassTag[_]): Future[Unit] /**
* A special case of [[fetchBlocks]], as it fetches only one block and is blocking.
*
* It is also only available after [[init]] is invoked.
* fetchBlocks的一个特别方法,他只取一个数据块并且阻塞,仅在调用init方法后可用。
。
*/
def fetchBlockSync(host: String, port: Int, execId: String, blockId: String): ManagedBuffer = {
// A monitor for the thread to wait on.
val result = Promise[ManagedBuffer]()
fetchBlocks(host, port, execId, Array(blockId),
new BlockFetchingListener {
override def onBlockFetchFailure(blockId: String, exception: Throwable): Unit = {
result.failure(exception)
}
override def onBlockFetchSuccess(blockId: String, data: ManagedBuffer): Unit = {
val ret = ByteBuffer.allocate(data.size.toInt)
ret.put(data.nioByteBuffer())
ret.flip()
result.success(new NioManagedBuffer(ret))
}
})
ThreadUtils.awaitResult(result.future, Duration.Inf)
} /**
* Upload a single block to a remote node, available only after [[init]] is invoked.
*
* This method is similar to [[uploadBlock]], except this one blocks the thread
* until the upload finishes.
* 上传一个数据块到远程结点,仅在调用init方法后可用。
* 这个方法和uploadBlock方法类似,除了直到上传结点,本方法会一直阻塞。
*/
def uploadBlockSync(
hostname: String,
port: Int,
execId: String,
blockId: BlockId,
blockData: ManagedBuffer,
level: StorageLevel,
classTag: ClassTag[_]): Unit = {
val future = uploadBlock(hostname, port, execId, blockId, blockData, level, classTag)
ThreadUtils.awaitResult(future, Duration.Inf)
}
}
NettyBlockTransferService扩展了BlockTransferServie
Spark2.0 shuffle service的更多相关文章
- hadoop-2.7.3.tar.gz + spark-2.0.2-bin-hadoop2.7.tgz + zeppelin-0.6.2-incubating-bin-all.tgz(master、slave1和slave2)(博主推荐)(图文详解)
不多说,直接上干货! 我这里,采取的是ubuntu 16.04系统,当然大家也可以在CentOS6.5里,这些都是小事 CentOS 6.5的安装详解 hadoop-2.6.0.tar.gz + sp ...
- Ubuntu14.04或16.04下安装JDK1.8+Scala+Hadoop2.7.3+Spark2.0.2
为了将Hadoop和Spark的安装简单化,今日写下此帖. 首先,要看手头有多少机器,要安装伪分布式的Hadoop+Spark还是完全分布式的,这里分别记录. 1. 伪分布式安装 伪分布式的Hadoo ...
- 图文解析Spark2.0核心技术(转载)
导语 Spark2.0于2016-07-27正式发布,伴随着更简单.更快速.更智慧的新特性,spark 已经逐步替代 hadoop 在大数据中的地位,成为大数据处理的主流标准.本文主要以代码和绘图的方 ...
- Spark2.0机器学习系列之1: 聚类算法(LDA)
在Spark2.0版本中(不是基于RDD API的MLlib),共有四种聚类方法: (1)K-means (2)Latent Dirichlet allocation (LDA) ...
- 在centos7上安装部署hadoop2.7.3和spark2.0.0
一.安装装备 下载安装包: vmware workstations pro 12 三台centos7.1 mini 虚拟机 网络配置NAT网络如下: 二.创建hadoop用户和hadoop用户组 1. ...
- hive on spark (spark2.0.0 hive2.3.3)
hive on spark真的很折腾人啊!!!!!!! 一.软件准备阶段 maven3.3.9 spark2.0.0 hive2.3.3 hadoop2.7.6 二.下载源码spark2.0.0,编译 ...
- Spark2.0集成Hive操作的相关配置与注意事项
前言 已完成安装Apache Hive,具体安装步骤请参照,Linux基于Hadoop2.8.0集群安装配置Hive2.1.1及基础操作 补充说明 Hive中metastore(元数据存储)的三种方式 ...
- 降本增效利器!趣头条Spark Remote Shuffle Service最佳实践
王振华,趣头条大数据总监,趣头条大数据负责人 曹佳清,趣头条大数据离线团队高级研发工程师,曾就职于饿了么大数据INF团队负责存储层和计算层组件研发,目前负责趣头条大数据计算层组件Spark的建设 范振 ...
- Magnet: Push-based Shuffle Service for Large-scale Data Processing
本文是阅读 LinkedIn 公司2020年发表的论文 Magnet: Push-based Shuffle Service for Large-scale Data Processing 一点笔记. ...
随机推荐
- Emulator Error: Could not load OpenGLES emulation library: Could not load DLL!
Copy the file below from SDK\tools\lib to SDK\tools. libEGL_translator.dlllibGLES_CM_translator.dlll ...
- windows下配置VisualSVN Server服务器
下载安装文件: 服务端安装文件:VisualSVN-Server-1.6.2 客户端安装文件:TortoiseSVN-1.5.5.14361-win32-svn-1.5.4 上面是我使用的版本. 在V ...
- bootstrap里面的popover组件如何使鼠标移入可以对弹出框进行一系列的操作
在bootstrap里面,有一个组件很可爱,它就是popover,它是对标签title属性的优化,奉上连接一枚:http://docs.demo.mschool.cn/components/popov ...
- js中解析json时候的eval和$.parseJSON()的区别以及JSON.stringify()
1.第一个区别是:安全性 json格式非常受欢迎,而解析json的方式通常用JSON.parse()但是eval()方法也可以解析,这两者之间有什么区别呢? JSON.parse()之可以解 ...
- poj_2406 kmp
题目大意 给出一个字符串S,S可能是多个较短的字符串的重复连接.比如"ababab"为字符串"ab"重复三次的结果:"abc" 为" ...
- 网络子系统45_ip协议tos处理
//ip报头tos字段,一个字节 // 二进制位:[0 1 2] [3] [4] [5] [6] [7] // 1.[0 1 2] 表示优先级: // 000 路由 // 001 优先级 // 010 ...
- hihocoder [Offer收割]编程练习赛14 小Hi和小Ho的礼物
题目1 : 小Hi和小Ho的礼物 时间限制:10000ms 单点时限:1000ms 内存限制:256MB 描述 某人有N袋金币,其中第i袋内金币的数量是Ai.现在他决定选出2袋金币送给小Hi,再选2袋 ...
- poj1568 Find the Winning Move[极大极小搜索+alpha-beta剪枝]
Find the Winning Move Time Limit: 3000MS Memory Limit: 32768K Total Submissions: 1286 Accepted: ...
- 【黑金ZYNQ7000系列原创视频教程】03.体验FPGA里的ARM——裸机helloworld实验
黑金论坛地址: http://www.heijin.org/forum.php?mod=viewthread&tid=36637&extra=page%3D1 爱奇艺地址: http: ...
- .net asp [转载]ASP:循环滚动图片的代码+解释
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/ ...