Category Archives: HDFS

New in CDH 5.1: HDFS Read Caching

Categories: CDH Hadoop HDFS Impala Performance

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.

For the latest release of CDH 5.1,

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Why Extended Attributes are Coming to HDFS

Categories: HDFS Security

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.

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Project Rhino Goal: At-Rest Encryption for Apache Hadoop

Categories: HBase HDFS Security

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,

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

Categories: HBase HDFS How-to Kite SDK

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;

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A Guide to Checkpointing in Hadoop

Categories: Hadoop HDFS Ops and DevOps

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,

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