There are a large number of variables in core/hdfs/mapred-default.xml that you can override in core/hdfs/mapred-site.xml.
core/hdfs/mapred-default.xml中有许多变量，可以在 core/hdfs/mapred-site.xml 中覆盖它们。
把这些日志放到HDFS中。
It provides metadata services within HDFS.
它在HDFS中提供元数据服务。
Their focus is now on developing Hadoop's distributed file system, HDFS
现在他们的重点是开发Hadoop的分布式文件系统，HDFS
Breaking up storage into a set of volumes (USES multiple HDFS clusters).
把存储拆分成一组文件卷集(使用多个HDFS集群)。
However, the HDFS architecture does not preclude implementing these features.
然而，HDFS架构不排除实现这些特性。
In order to use Hadoop to analyze your data, you need to get the data into HDFS.
为了使用Hadoop来分析您的数据，您必须将数据放在HDFS上。
Releasing federated storage across HDFS instances, in the next major Hadoop release.
在Hadoop的下一个主要版本将会发布跨hdfs实例的联合存储。
The HDFS client software implements checksum checking on the contents of HDFS files.
HDFS客户端软件实现了HDFS文件内容的校验和。
Recall that at the top of the Hadoop cluster is the namenode, which manages the HDFS.
位于Hadoop集群最上层的是namenode，它管理 HDFS。
HDFS is designed to reliably store very large files across machines in a large cluster.
HDFS是被设计成在一个集群中跨机器可靠地存储大量文件。
Hadoop has an abstract notion of filesystems, of which HDFS is just one implementation.
hadoop对于文件系统有一个抽象的概念，HDFS仅是其中一个实现。
Optimizing replica placement distinguishes HDFS from most other distributed file systems.
优化的副本选址使得HDFS有别于大多数分布式文件系统。
Large HDFS instances run on a cluster of computers that commonly spread across many racks.
运行在集群计算机的大型HDFS实例一般是分布在许多机架上。
The complete introduction to HDFS and how to operate on it is beyond the scope of this article.
对HDFS及其使用方法的完整介绍超出了本文的范围。
The previous two commands will generate two directories under HDFS, one "input" and one "output."
前两个命令会在HDFS 中生成两个目录，“input”和 “output”。
Hadoop itself provides the ability to copy files from the file system into the HDFS and vice-versa.
Hadoop本身也提供将文件从文件系统复制到 HDFS 的功能，反之也可以。
When an application is submitted, input and output directories contained in the HDFS should be provided.
在提交应用程序时，应该提供包含在HDFS中的输入和输出目录。
You do this easily with the get utility (analogous to the put you executed earlier to write files into HDFS).
只需使用get实用程序(它与前面在HDFS中写文件所用的put相似)。
Is there any additional work being done to simplify the management of a cluster, the HDFS, MapReduce processes, etc.?
那么管理这样一个集群、HDFS以及MapReduce的处理还有什么额外的工作需要做吗？
For more complex scenarios, you can take advantage of the likes of Sqoop (see Resources), a SQL-to-HDFS database import tool.
对于更复杂的场景，您可以利用像Sqoop(参见参考资料)这类工具，这是一个SQL - to -HDFS数据库导入工具。
If HDFS cluster spans multiple data centers, then a replica that is resident in the local data center is preferred over any remote replica.
如果HDFS集群横跨多个数据中心，那么在本地数据中心的副本将是优先于其他远程副本。
To minimize global bandwidth consumption and read latency, HDFS tries to satisfy a read request from a replica that is closest to the reader.
副本的选择，为了降低整体的带宽消耗和读延时，HDFS会尽量让reader读最近的副本。
This dedicated server is required because Hadoop USES HDFS to store JAR dependencies for your job, static data, and other required information.
该专属服务器是必需的，这是因为Hadoop使用HDFS来存储你工作所需的JAR依赖项、静态数据以及其他必需的信息。
When input data is provided (into the Hadoop file system [HDFS]), it is first partitioned, and then distributed to map workers (via the job tracker).
在提供输入数据时(进入Hadoop文件系统[hdfs])，首先分段，然后分配给map工作线程(通过作业跟踪器)。
When a client creates an HDFS file, it computes a checksum of each block of the file and stores these checksums in a separate hidden file in the same HDFS namespace.
当某个客户端创建一个新的HDFS文件，会计算这个文件每个block的校验和，并作为一个单独的隐藏文件保存这些校验和在同一个HDFS namespace下。
When a client creates an HDFS file, it computes a checksum of each block of the file and stores these checksums in a separate hidden file in the same HDFS namespace.
当某个客户端创建一个新的HDFS文件，会计算这个文件每个block的校验和，并作为一个单独的隐藏文件保存这些校验和在同一个HDFS namespace下。
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