Erasure coding, a new feature in HDFS, can reduce storage overhead by approximately 50% compared to replication while maintaining the same durability guarantees. This post explains how it works.
HDFS by default replicates each block three times. Replication provides a simple and robust form of redundancy to shield against most failure scenarios. It also eases scheduling compute tasks on locally stored data blocks by providing multiple replicas of each block to choose from.
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,
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.
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,
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.