Cloudera Engineering Blog · HDFS Posts

Understanding HDFS Recovery Processes (Part 2)

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.

Understanding HDFS Recovery Processes (Part 1)

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.

New in CDH 5.3: Transparent Encryption in HDFS

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.

New in CDH 5.1: HDFS Read Caching

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.

Why Extended Attributes are Coming to HDFS

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.

Project Rhino Goal: At-Rest Encryption for Apache Hadoop

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).

How-to: Use Kite SDK to Easily Store and Configure Data in Apache Hadoop

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).

A Guide to Checkpointing in Hadoop

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.

Apache Hadoop 2.3.0 is Released (HDFS Caching FTW!)

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):

Apache Hadoop 2 is Here and Will Transform the Ecosystem

The release of Apache Hadoop 2, as announced today by the Apache Software Foundation, is an exciting one for the entire Hadoop ecosystem.

Cloudera engineers have been working hard for many months with the rest of the vast Hadoop community to ensure that Hadoop 2 is the best it can possibly be, for the users of Cloudera’s platform as well as all Hadoop users generally. Hadoop 2 contains many major advances, including (but not limited to):

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