Cloudera Engineering Blog · HDFS Posts
This new feature gives Hadoop admins the commonplace ability to replace failed DataNode drives without unscheduled downtime.
Hot swapping—the process of replacing system components without shutting down the system—is a common and important operation in modern, production-ready systems. Because disk failures are common in data centers, the ability to hot-swap hard drives is a supported feature in hardware and server operating systems such as Linux and Windows Server, and sysadmins routinely upgrade servers or replace a faulty components without interrupting business-critical services.
Having a good grasp of HDFS recovery processes is important when running or moving toward production-ready Apache Hadoop. In the conclusion to this two-part post, pipeline recovery is explained.
An important design requirement of HDFS is to ensure continuous and correct operations that support production deployments. For that reason, it’s important for operators to understand how HDFS recovery processes work. In Part 1 of this post, we looked at lease recovery and block recovery. Now, in Part 2, we explore pipeline recovery.
Having a good grasp of HDFS recovery processes is important when running or moving toward production-ready Apache Hadoop.
An important design requirement of HDFS is to ensure continuous and correct operations to support production deployments. One particularly complex area is ensuring correctness of writes to HDFS in the presence of network and node failures, where the lease recovery, block recovery, and pipeline recovery processes come into play. Understanding when and why these recovery processes are called, along with what they do, can help users as well as developers understand the machinations of their HDFS cluster.
Support for transparent, end-to-end encryption in HDFS is now available and production-ready (and shipping inside CDH 5.3 and later). Here’s how it works.
Apache Hadoop 2.6 adds support for transparent encryption to HDFS. Once configured, data read from and written to specified HDFS directories will be transparently encrypted and decrypted, without requiring any changes to user application code. This encryption is also end-to-end, meaning that data can only be encrypted and decrypted by the client. HDFS itself never handles unencrypted data or data encryption keys. All these characteristics improve security, and HDFS encryption can be an important part of an organization-wide data protection story.
Applications using HDFS, such as Impala, will be able to read data up to 59x faster thanks to this new feature.
Server memory capacity and bandwidth have increased dramatically over the last few years. Beefier servers make in-memory computation quite attractive, since a lot of interesting data sets can fit into cluster memory, and memory is orders of magnitude faster than disk.
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. A filesystem that implements extended attributes also provides system calls and shell commands to get, list, set, and remove attributes (and values) to/from a file or directory.
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).
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; all you get is a filesystem and whatever else you can dream up — well, code up).
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.
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):