This article is syndicated with permission from the Apache HBase blog and highlights a collaboration between our partners at Intel and Alibaba engineering in time for “Singles Day“, the biggest shopping day on the net. For more on HBase, mark your calendars! On June 12th, 2017 the Apache HBase community will be hosting their annual HBaseCon.
HBase is the core storage system in Alibaba’s Search Infrastructure.
Before CDH 5.10, every CDH cluster had to have its own Apache Hive Metastore (HMS) backend database. This model is ideal for clusters where each cluster contains the data locally along with the metadata. In the cloud, however, many CDH clusters run directly on a shared object store (like Amazon S3), making it possible for the data to live across multiple clusters and beyond any cluster’s lifespan. In this scenario clusters need to regenerate and coordinate metadata for the underlying shared data individually.
The Apache Hadoop project announced the release of 3.0.0-alpha2 on January 25th, 2017. This is the second alpha release in the 3.0.0 release series leading up to 3.0.0 GA, and incorporates 857 new fixes, improvements, and features since 3.0.0-alpha1 last September. It’s worth reading our previous blog post about 3.0.0-alpha1; in this post, we’ll discuss the new improvements that landed in alpha2.
Classpath Isolation for Hadoop Client Jars
The pain of classpath isolation has been experienced by many Java developers.
Previously in Part 4, we described the most commonly used FairScheduler properties in Apache Hadoop. In Part 5, we’ll provide some examples to show how properties can be used, individually and in combination, to achieve commonly desired behavior such as application prioritization and organizing queues.
Example: Best Effort Queue
Summary: Create a “best effort” queue that runs applications when the cluster is underutilized.
Implementation: In FairScheduler,
Zbigniew Baranowski is a database systems specialist and a member of a group which provides and supports central database and Hadoop-based services at CERN. This blog was originally released on CERN’s “Databases at CERN” blog, and is syndicated here with CERN’s permission.
This post presents a performance comparison of few popular data formats and storage engines available in the Apache Hadoop ecosystem: Apache Avro,