HDFS

Read/Write files on hdfs using Python

由 旧城冷巷雨未停 提交于 2021-02-11 09:52:11
问题 I am a newbie to Python, I want to read a file from hdfs (which I have achieved). after reading the file I am doing some string operations and I want to write these modified contents into the output file. Reading the file I achieved using subprocess (which took a lot of time) since open didn't work for me. cat = Popen(["hadoop", "fs", "-cat", "/user/hdfs/test-python/input/test_replace"],stdout=PIPE) Now, how to write to the output file with the modified contents is the question. Your help is

Read/Write files on hdfs using Python

由 风格不统一 提交于 2021-02-11 09:52:10
问题 I am a newbie to Python, I want to read a file from hdfs (which I have achieved). after reading the file I am doing some string operations and I want to write these modified contents into the output file. Reading the file I achieved using subprocess (which took a lot of time) since open didn't work for me. cat = Popen(["hadoop", "fs", "-cat", "/user/hdfs/test-python/input/test_replace"],stdout=PIPE) Now, how to write to the output file with the modified contents is the question. Your help is

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

Fix corrupt HDFS Files without losing data (files in the datanode still exist)

由 无人久伴 提交于 2021-02-10 14:41:04
问题 I am new to the HDFS system and I come across a HDFS question. We have a HDFS file system, with the namenode on a server (with this server named as 0002) and datanode on two other servers (with these two severs named as 0004 and 0005 respectively). The original data comes from a Flume application and with the "Sink" in the Flume as HDFS. The Flume will write the original data (txt files) into the datanode on the servers 0004 and 0005. So, the original data is replicated twice and saved under

Fix corrupt HDFS Files without losing data (files in the datanode still exist)

由 随声附和 提交于 2021-02-10 14:40:47
问题 I am new to the HDFS system and I come across a HDFS question. We have a HDFS file system, with the namenode on a server (with this server named as 0002) and datanode on two other servers (with these two severs named as 0004 and 0005 respectively). The original data comes from a Flume application and with the "Sink" in the Flume as HDFS. The Flume will write the original data (txt files) into the datanode on the servers 0004 and 0005. So, the original data is replicated twice and saved under

Most efficient way of saving a pandas dataframe or 2d numpy array into h5py, with each row a seperate key, using a column

由 末鹿安然 提交于 2021-02-10 05:36:11
问题 This is a follow up to this stackoverflow question Column missing when trying to open hdf created by pandas in h5py Where I am trying to create save a large amount of data onto a disk (too large to fit into memory), and retrieve sepecific rows of the data using indices. One of the solutions given in the linked post is to create a seperate key for every every row. At the moment I can only think of iterating through each row, and setting the keys directly. For example, if this is my data

Java 简单操作hdfs API

由 六月ゝ 毕业季﹏ 提交于 2021-02-10 04:39:37
注：图片如果损坏，点击文章链接： https://www.toutiao.com/i6632047118376780295/ 启动Hadoop出现问题：datanode的clusterID 和 namenode的clusterID 不匹配 从日志中可以看出，原因是因为datanode的clusterID 和 namenode的clusterID 不匹配。 打开hdfs-site.xml里配置的datanode和namenode对应的目录，分别打开current文件夹里的VERSION，可以看到clusterID项正如日志里记录的一样，确实不一致，修改datanode里VERSION文件的clusterID 与namenode里的一致，再重新启动dfs（执行start-dfs.sh）再执行jps命令可以看到datanode已正常启动。 出现该问题的原因：在第一次格式化dfs后，启动并使用了hadoop，后来又重新执行了格式化命令（hdfs namenode -format)，这时namenode的clusterID会重新生成，而datanode的clusterID 保持不变。 验证伪分布环境是否完成 Java操作hdfs 新创建一个maven项目 编写pom文件 编写测试代码 我们运行一下看一看 这种简单的写法是本地模式，所以我们去看下本地文件是不是有了

10亿+文件数压测，阿里云JindoFS轻松应对

由 若如初见. 提交于 2021-02-08 11:54:09
简介： Apache Hadoop FileSystem (HDFS) 是被广为使用的大数据存储方案，其核心元数据服务 NameNode 将全部元数据存放在内存中，因此所能承载的元数据规模受限于内存，单个实例所能支撑的文件个数大约 4亿。JindoFS块模式是阿里云基于 OSS 海量存储自研的一个存储优化系统，提供了高效的数据读写加速能力和元数据优化能力。JindoFS 实际表现如何，我们在 10亿文件数规模下做了压测，验证 JindoFS 在达到这个规模的时候是否还可以保持稳定的性能。 主要介绍 Apache Hadoop FileSystem (HDFS) 是被广为使用的大数据存储方案，其核心元数据服务 NameNode 将全部元数据存放在内存中，因此所能承载的元数据规模受限于内存，单个实例所能支撑的文件个数大约 4亿。JindoFS块模式是阿里云基于 OSS 海量存储自研的一个存储优化系统，提供了高效的数据读写加速能力和元数据优化能力。在设计上避免了 NameNode 上的内存限制，与HDFS不同的一点是，JindoFS元数据服务采用RocksDB作为底层元数据存储，RocksDB可以存储在大容量本地高速磁盘，解决了内存容量瓶颈问题。借助于内存缓存，将10%~40%的热文件元数据存放于内存缓存，从而保持稳定的优秀的读写性能。借助于Raft机制

HDFS Command Line Append

由 人盡茶涼 提交于 2021-02-08 03:41:53
问题 Is there any way to append to a file on HDFS from command line like copying file: hadoop fs -copyFromLocal <localsrc> URI 回答1: This feature is implemented in Hadoop 2.3.0 as appendToFile with a syntax like: hdfs dfs -appendToFile localfile /user/hadoop/hadoopfile (it was first suggested in 2009 when the HDFS Append feature was being contemplated: https://issues.apache.org/jira/browse/HADOOP-6239 ) 回答2: cli doesn't support append, but httpfs and fuse both has support for appending files. w301%
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