Introduction HDFS is designed to be a highly scalable storage system and sites at Facebook and Yahoo have 20PB size file systems in production deployments. The HDFS NameNode is the master of the Hadoop Distributed File System (HDFS). It maintains the critical data structures of the entire file system. Most of HDFS design has focussed on scalability of the system, i.e. the ability to support a larg
HADOOP-1700 and related issues have put a lot of efforts to provide the first implementation of append. However, append is such a complex feature. It turns out that there are issues that were initially seemed trivial but needs a careful design. This jira revisits append, aiming for a design and implementation supporting a semantics that are acceptable to its users.
Not your computer? Use a private browsing window to sign in. Learn more about using Guest mode
Not your computer? Use a private browsing window to sign in. Learn more about using Guest mode
Thanks to Tsz-Wo Nicholas Sze and Mahadev Konar for this article. The Problem of Many Small Files The Hadoop Distributed File System (HDFS) is designed to store and process large (terabytes) data sets. At Yahoo!, for example, a large production cluster may have 14 PB disk spaces and store 60 millions of files. However, storing a large number of small files in HDFS is inefficient. We call a file sm
The bottom line is that we achieved the target in Petabytes and got close to the target in the number of files. But this is done with a smaller number of nodes and the need to support a workload close to 100,000 clients has not yet materialized. The question now is whether the goals are feasible with the current system architecture. Namespace Limitations HDFS is based on an architecture where the
It is not a secret anymore! The Datawarehouse Hadoop cluster at Facebook has become the largest known Hadoop storage cluster in the world. Here are some of the details about this single HDFS cluster:21 PB of storage in a single HDFS cluster2000 machines12 TB per machine (a few machines have 24 TB each)1200 machines with 8 cores each + 800 machines with 16 cores each32 GB of RAM per machine15 map-r
Hadoop では一つのノードあたり複数ディスクを使うことができますが,ディスクを増やすことによってどれくらい性能が向上するか調べました. HDFSで使用するディスクをdfs.data.dirにコンマ区切りで記入することで複数使えます. <property> <name>dfs.data.dir</name> <value>/data/local/${user.name}/hadoop/dfs/data, /data/local2/${user.name}/hadoop/dfs/data</value> </property> しかし,これだけではまだダメで,mapタスク,reduceタスクが中間データを書き込むディスクも複数指定しなしとHadoopのジョブで複数ディスクを効率良く使えません.mapred.local.dir で設定可能です. <property> <name>mapre
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く