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.
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,
One of the key principles behind Apache Hadoop is the idea that moving computation is cheaper than moving data — we prefer to move the computation to the data whenever possible, rather than the other way around. Because of this, the Hadoop Distributed File System (HDFS) typically handles many “local reads” reads where the reader is on the same node as the data:
Initially, local reads in HDFS were handled the same way as remote reads: the client connected to the DataNode via a TCP socket and transferred the data via DataTransferProtocol.
Managing and viewing data in HDFS is an important part of Big Data analytics. Hue, the open source web-based interface that makes Apache Hadoop easier to use, helps you do that through a GUI in your browser — instead of logging into a Hadoop gateway host with a terminal program and using the command line.
The first episode in a new series of Hue demos, the video below demonstrates how to get up and running quickly with HDFS file operations via Hue’s File Browser application.