mapreduce优化_第5页-阿里云

MapReduce中map并行度优化及源码分析

mapTask并行度的决定机制　　一个job的map阶段并行度由客户端在提交job时决定，而客户端对map阶段并行度的规划的基本逻辑为：将待处理数据执行逻辑切片（即按照一个特定切片大小，将待处理数据划分成逻辑上的多个split），然后每一个split分配一个mapTask并行实例处理。 FileI...

YARN and MapReduce的【内存】优化配置详解

在Hadoop2.x中, YARN负责管理MapReduce中的资源(内存, CPU等)并且将其打包成Container。使之专注于其擅长的数据处理任务, 将无需考虑资源调度. 如下图所示 ...

Hadoop 分布式计算框架 MapReduce

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MapReduce优化----Shuffle过程剖析及性能优化

1. Map端当Map 开始产生输出时，它并不是简单的把数据写到磁盘，因为频繁的磁盘操作会导致性能严重下降。它的处理过程更复杂，数据首先是写到内存中的一个缓冲区，并做了一些预排序，以提升效率。每个Map 任务都有一个用来写入输出数...

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开源大数据平台 E-MapReduce

开源大数据平台 E-MapReduce（简称“EMR”）是云原生开源大数据平台，向客户提供简单易集成的Hadoop、Hive、Spark、Flink、Presto、ClickHouse、StarRocks、Delta、Hudi等开源大数据计算和存储引擎服务。

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