Thrift client authentication and doAs impersonation, introduced in HBase 1.0, provides more flexibility for your HBase installation.
In the two-part blog series “How-to: Use the HBase Thrift Interface” (Part 1 and Part 2), Jesse Anderson explained the Thrift interface in detail, and demonstrated how to use it. He didn’t cover running Thrift in a secure Apache HBase cluster, however, because there was no difference in the client configuration with the HBase releases available at that time.
Thanks to Pengyu Wang, software developer at FINRA, for permission to republish this post.
Salted Apache HBase tables with pre-split is a proven effective HBase solution to provide uniform workload distribution across RegionServers and prevent hot spots during bulk writes. In this design, a row key is made with a logical key plus salt at the beginning. One way of generating salt is by calculating n (number of regions) modulo on the hash code of the logical row key (date,
Learn about the design decisions behind HBase’s new support for MOBs.
Apache HBase is a distributed, scalable, performant, consistent key value database that can store a variety of binary data types. It excels at storing many relatively small values (<10K), and providing low-latency reads and writes.
However, there is a growing demand for storing documents, images, and other moderate objects (MOBs) in HBase while maintaining low latency for reads and writes.
Evaluating which streaming architectural pattern is the best match to your use case is a precondition for a successful production deployment.
The Apache Hadoop ecosystem has become a preferred platform for enterprises seeking to process and understand large-scale data in real time. Technologies like Apache Kafka, Apache Flume, Apache Spark, Apache Storm, and Apache Samza are increasingly pushing the envelope on what is possible. It is often tempting to bucket large-scale streaming use cases together but in reality they tend to break down into a few different architectural patterns,
The following post about the new request throttling feature in HBase 1.1 (now shipping in CDH 5.4) originally published in the ASF blog. We re-publish it here for your convenience.
Running multiple workloads on HBase has always been challenging, especially when trying to execute real-time workloads while concurrently running analytical jobs. One possible way to address this issue is to throttle analytical MR jobs so that real-time workloads are less affected.