`

The Design of HDFS

阅读更多
HDFS is a filesystem designed for storing very large files with streaming data access patterns, running on clusters of commodity hardware. Let’s examine this statement in more detail:

Very large files
“Very large” in this context means files that are hundreds of megabytes, gigabytes, or terabytes in size. There are Hadoop clusters running today that store petabytes of data.

Streaming data access
HDFS is built around the idea that the most efficient data processing pattern is a write-once, read-many-times pattern. A dataset is typically generated or copied from source, and then various analyses are performed on that dataset over time. Each analysis will involve a large proportion, if not all, of the dataset, so the time to read the whole dataset is more important than the latency in reading the first record.

Commodity hardware
Hadoop doesn’t require expensive, highly reliable hardware. It’s designed to run on clusters of commodity hardware (commonly available hardware that can be obtained from multiple vendors) for which the chance of node failure across the cluster is high, at least for large clusters. HDFS is designed to carry on working without a noticeable interruption to the user in the face of such failure.

It is also worth examining the applications for which using HDFS does not work so well. Although this may change in the future, these are areas where HDFS is not a good fit today:

Low-latency data access
Applications that require low-latency access to data, in the tens of milliseconds range, will not work well with HDFS. Remember, HDFS is optimized for delivering a high throughput of data, and this may be at the expense of latency. HBase is currently a better choice for low-latency access.

Lots of small files
Because the namenode holds filesystem metadata in memory, the limit to the number of files in a filesystem is governed by the amount of memory on the namenode. As a rule of thumb, each file, directory, and block takes about 150 bytes. So, for example, if you had one million files, each taking one block, you would need at least 300 MB of memory. Although storing millions of files is feasible, billions is beyond the capability of current hardware.

Multiple writers, arbitrary file modifications
Files in HDFS may be written to by a single writer. Writes are always made at the end of the file. There is no support for multiple writers or for modifications at arbitrary offsets in the file. (These might be supported in the future, but they are likely to be relatively inefficient.) 
 
 
 
 
 
 
 
 
2
4
分享到:
评论

相关推荐

    hadoop_the_definitive_guide_3nd_edition

    The Design of HDFS 45 HDFS Concepts 47 Blocks 47 Namenodes and Datanodes 48 HDFS Federation 49 HDFS High-Availability 50 The Command-Line Interface 51 Basic Filesystem Operations 52 Hadoop Filesystems...

    hdfs.pages

    第1章 大数据概论 第2章 从Hadoop框架讨论大数据生态 第3章 Hadoop运行环境搭建(开发重点) 第4章 Hadoop运行模式 第5章 Hadoop编译源码

    Pro Apache Hadoop(Apress,2ed,2014)

    Revised to cover Hadoop 2.0, the book covers the very latest developments such as YARN (aka MapReduce 2.0), new HDFS high-availability features, and increased scalability in the form of HDFS ...

    Hadoop: The Definitive Guide [Paperback]

    Use the Hadoop Distributed File System (HDFS) for storing large datasets, then run distributed computations over those datasets with MapReduce Become familiar with Hadoop’s data and I/O building ...

    Hadoop The Definitive Guide 3rd Edition

    Design, build, and administer a dedicated Hadoop cluster—or run Hadoop in the cloud Load data from relational databases into HDFS, using Sqoop Perform large-scale data processing with the Pig query ...

    HBase.High.Performance.Cookbook.epub

    Apache HBase is a non-relational NoSQL database management system that runs on top of HDFS. It is an open source, disturbed, versioned, column-oriented store and is written in Java to provide random ...

    基于Hadoop 的海量数据的分布式存储关键技术研究

    anyzesthe mapReduce distributed computing, and disscuses the key technology of the distributed storage.Then based on this,design a distributed storage system,promote the mining and processing of ...

    Scala: Guide for Data Science Professionals

    Scala is especially good for analyzing large sets of data as the scale of the task doesn't have any significant impact on performance. Scala's powerful functional libraries can interact with databases...

    Python Data Analysis Cookbook

    Python Data Analysis Cookbook by Ivan Idris-P2P ...By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.

    Apache HBase Primer

    Chapter 2: Apache HBase and HDFS Chapter 3: Application Characteristics Part II: Data Model Chapter 4: Physical Storage Chapter 5: Column Family and Column Qualifi er Chapter 6: Row ...

    Using Flume(O'Reilly,2014)

    With this complete reference guide, you’ll learn Flume’s rich set of features for collecting, aggregating, and writing large amounts of streaming data to the Hadoop Distributed File System (HDFS), ...

    Using.Flume.Flexible.Scalable.and.Reliable.Data.Streaming.pdf

    With this complete reference guide, you’ll learn Flume’s rich set of features for collecting, aggregating, and writing large amounts of streaming data to the Hadoop Distributed File System (HDFS), ...

Global site tag (gtag.js) - Google Analytics