Extended attributes in HDFS will facilitate at-rest encryption for Project Rhino, but they have many other uses, too.
Many mainstream Linux filesystems implement extended attributes, which let you associate metadata with a file or directory beyond common “fixed” attributes like filesize, permissions, modification dates, and so on. Extended attributes are key/value pairs in which the values are optional; generally, the key and value sizes are limited to some implementation-specific limit.
An update on community efforts to bring at-rest encryption to HDFS — a major theme of Project Rhino.
Encryption is a key requirement for many privacy and security-sensitive industries, including healthcare (HIPAA regulations), card payments (PCI DSS regulations), and the US government (FISMA regulations).
Although network encryption has been provided in the Apache Hadoop platform for some time (since Hadoop 2.02-alpha/CDH 4.1), at-rest encryption,
Organizing your data inside Hadoop doesn’t have to be hard — Kite SDK helps you try out new data configurations quickly in either HDFS or HBase.
Kite SDK is a Cloudera-sponsored open source project that makes it easier for you to build applications on top of Apache Hadoop. Its premise is that you shouldn’t need to know how Hadoop works to build your application on it, even though that’s an unfortunately common requirement today (because the Hadoop APIs are low-level;
Understanding how checkpointing works in HDFS can make the difference between a healthy cluster or a failing one.
Checkpointing is an essential part of maintaining and persisting filesystem metadata in HDFS. It’s crucial for efficient NameNode recovery and restart, and is an important indicator of overall cluster health. However, checkpointing can also be a source of confusion for operators of Apache Hadoop clusters.
In this post, I’ll explain the purpose of checkpointing in HDFS,
Hadoop 2.3.0 includes hundreds of new fixes and features, but none more important than HDFS caching.
The Apache Hadoop community has voted to release Hadoop 2.3.0, which includes (among many other things):
- In-memory caching for HDFS, including centralized administration and management
- Groundwork for future support of heterogeneous storage in HDFS
- Simplified distribution of MapReduce binaries via the YARN Distributed Cache
You can read the release notes here.