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.
Over the past year (and through several releases), Apache Impala (incubating) has added numerous new features and performance enhancements better enabling high-performance SQL analytics over big data. Thus, it is time again for an update to the Impala cookbook, which contains best practices for these new features, updated guidelines, and more detailed examples.
Note: This cookbook does not yet capture best practices for the major new advancements available with the recent GA of Kudu.
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,