MapReduce

YARN详解

由 社会主义新天地 提交于 2021-02-11 18:38:24
1. YARN架构 1.1 简介 1.1.1 架构 YARN主要由ResourceManager、NodeManager、ApplicationMaster和Container等几个组件构成。 YARN总体上仍然是Master/Slave结构，在整个资源管理框架中，ResourceManager为Master，NodeManager为Slave，ResourceManager负责对各个NodeManager上的资源进行统一管理和调度。当用户提交一个应用程序时，需要提供一个用以跟踪和管理这个程序的ApplicationMaster，它负责向ResourceManager申请资源，并要求NodeManger启动可以占用一定资源的任务。由于不同的ApplicationMaster被分布到不同的节点上，因此它们之间不会相互影响。 1.1.2 Job提交流程 用户向YARN中提交应用程序，其中包括ApplicationMaster程序、启动ApplicationMaster的命令、用户程序等。 ResourceManager为该应用程序分配第一个Container，并与对应的Node-Manager通信，要求它在这个Container中启动应用程序的ApplicationMaster。 ApplicationMaster首先向ResourceManager注册

MapReduce Hadoop on Linux - Change Reduce Key

由 无人久伴 提交于 2021-02-11 12:55:22
问题 I've being searching for a proper tutorial online about how to use map and reduce, but almost every code about WordCount sucks and doesn't really explain you how to use each function. I've seen everything about the theory, the keys, the map etc, but there is no CODE for example doing something different than WordCount. I am using Ubuntu 20.10 on Virtual Box and Hadoop version 3.2.1 (if you need any more info comment me). My task is to manage a file that contains several data for athletes that

Hive快捷查询：不启用Mapreduce job启用Fetch task三种方式介绍

由 北慕城南 提交于 2021-02-11 06:48:25
如果查询表的某一列，Hive中默认会启用MapReduce job来完成这个任务，如下： hive>select id,name from m limit 10;--执行时hive会启用MapReduce job 我们都知道，启用MapReduce Job是会消耗系统开销的。对于这个问题，从Hive0.10.0版本开始，对于简单的不需要聚合的类似 SELECT <col> from <table> LIMIT n语句，不需要起MapReduce job，直接通过Fetch task获取数据，可以通过下面几种方法实现： 方法一： hive>set hive.fetch.task.conversion=more;--开启fetch任务，就不启用MapReduce job； hive>select id,name from m limit 10; 方法二： hive>bin/hive --hiveconf hive.fetch.task.conversion=more 方法三： 上面的两种方法都可以开启了Fetch任务，但是都是临时起作用的；如果你想一直启用这个功能，可以在${HIVE_HOME}/conf/hive-site.xml里面加入以下配置： <property> <name>hive.fetch.task.conversion</name> <value>more<

Why Hive can not support non-equi join?

由 感情迁移 提交于 2021-02-10 18:14:37
问题 I found that the Hive does not support non-equi join.Is it just because it is difficult to convert non-equi join to Map reduce? 回答1: Yes, the problem is in current map-reduce implementation. How common equi-join is implemented in MapReduce? Input records are being copied in chunks to the mappers, mappers produce output as key-value pairs, which are collected and distributed between reducers using some function in such way that each reducer will process the whole key, in other words, mapper

Why Hive can not support non-equi join?

由 醉酒当歌 提交于 2021-02-10 17:55:57
问题 I found that the Hive does not support non-equi join.Is it just because it is difficult to convert non-equi join to Map reduce? 回答1: Yes, the problem is in current map-reduce implementation. How common equi-join is implemented in MapReduce? Input records are being copied in chunks to the mappers, mappers produce output as key-value pairs, which are collected and distributed between reducers using some function in such way that each reducer will process the whole key, in other words, mapper

PySpark SQL 相关知识介绍

由 寵の児 提交于 2021-02-10 16:31:27
本文作者： foochane 本文链接： https://foochane.cn/article/2019060601.html 1 大数据简介 大数据是这个时代最热门的话题之一。但是什么是大数据呢?它描述了一个庞大的数据集，并且正在以惊人的速度增长。大数据除了体积(Volume)和速度(velocity)外，数据的多样性(variety)和准确性(veracity)也是大数据的一大特点。让我们详细讨论体积、速度、多样性和准确性。这些也被称为大数据的4V特征。 1.1 Volume 数据体积(Volume)指定要处理的数据量。对于大量数据，我们需要大型机器或分布式系统。计算时间随数据量的增加而增加。所以如果我们能并行化计算，最好使用分布式系统。数据可以是结构化数据、非结构化数据或介于两者之间的数据。如果我们有非结构化数据，那么情况就会变得更加复杂和计算密集型。你可能会想，大数据到底有多大?这是一个有争议的问题。但一般来说，我们可以说，我们无法使用传统系统处理的数据量被定义为大数据。现在让我们讨论一下数据的速度。 1.2 Velocity 越来越多的组织机构开始重视数据。每时每刻都在收集大量的数据。这意味着数据的速度在增加。一个系统如何处理这个速度?当必须实时分析大量流入的数据时，问题就变得复杂了。许多系统正在开发，以处理这种巨大的数据流入

Duplicating PostgreSQL's window functions like lag, lead, over

由 生来就可爱ヽ(ⅴ＜●) 提交于 2021-02-10 04:55:10
问题 How do I change a PostgreSQL query into a mongodb bson call? I have the same use case listed at http://archives.postgresql.org/pgsql-general/2011-10/msg00157.php I would like to calculate the delta time between two log entries by using something like lag or lead. Is there anything similar in mongodb to Postgres' lag / lead syntax? select index, starttime, endtime, starttime - lag(endtime) over(order by starttime asc) as delta from test http://www.postgresql.org/docs/8.4/static/functions

Duplicating PostgreSQL's window functions like lag, lead, over

由 随声附和 提交于 2021-02-10 04:51:18
问题 How do I change a PostgreSQL query into a mongodb bson call? I have the same use case listed at http://archives.postgresql.org/pgsql-general/2011-10/msg00157.php I would like to calculate the delta time between two log entries by using something like lag or lead. Is there anything similar in mongodb to Postgres' lag / lead syntax? select index, starttime, endtime, starttime - lag(endtime) over(order by starttime asc) as delta from test http://www.postgresql.org/docs/8.4/static/functions

Creating combination of value list with existing key - Pyspark

由 蹲街弑〆低调 提交于 2021-02-08 07:45:03
问题 So my rdd consists of data looking like: (k, [v1,v2,v3...]) I want to create a combination of all sets of two for the value part. So the end map should look like: (k1, (v1,v2)) (k1, (v1,v3)) (k1, (v2,v3)) I know to get the value part, I would use something like rdd.cartesian(rdd).filter(case (a,b) => a < b) However, that requires the entire rdd to be passed (right?) not just the value part. I am unsure how to arrive at my desired end, I suspect its a groupby. Also, ultimately, I want to get

Creating combination of value list with existing key - Pyspark

由 时光毁灭记忆、已成空白 提交于 2021-02-08 07:44:32
问题 So my rdd consists of data looking like: (k, [v1,v2,v3...]) I want to create a combination of all sets of two for the value part. So the end map should look like: (k1, (v1,v2)) (k1, (v1,v3)) (k1, (v2,v3)) I know to get the value part, I would use something like rdd.cartesian(rdd).filter(case (a,b) => a < b) However, that requires the entire rdd to be passed (right?) not just the value part. I am unsure how to arrive at my desired end, I suspect its a groupby. Also, ultimately, I want to get
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