Fixes in CDH 5.5 make writing Parquet data for Apache Impala (incubating) much easier.
Over the last few months, several Cloudera customers have provided the feedback that Parquet is too hard to configure, with the main problem being finding the right layout for great performance in Impala. For that reasons, CDH 5.5 contains new features that make those configuration problems go away.
Auto-Detection of HDFS Block Size
Recent improvements to Apache Hadoop’s native backup utility, which are now shipping in CDH, make that process much faster.
DistCp is a popular tool in Apache Hadoop for periodically backing up data across and within clusters. (Each run of DistCp in the backup process is referred to as a backup cycle.) Its popularity has grown in popularity despite relatively slow performance.
In this post, we’ll provide a quick introduction to DistCp.
Via a combination of beta functionality in CDH 5.5 and new Cloudera Labs packages, you now have access to Apache HTrace for doing performance tracing of your HDFS-based applications.
HTrace is a new Apache incubator project that provides a bird’s-eye view of the performance of a distributed system. While log files can provide a peek into important events on a specific node, and metrics can answer questions about aggregate performance,
This new open source complement to HDFS and Apache HBase is designed to fill gaps in Hadoop’s storage layer that have given rise to stitched-together, hybrid architectures.
The set of data storage and processing technologies that define the Apache Hadoop ecosystem are expansive and ever-improving, covering a very diverse set of customer use cases used in mission-critical enterprise applications. At Cloudera, we’re constantly pushing the boundaries of what’s possible with Hadoop—making it faster,
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.