This case study is an instructive example of how performance analysis is a multi-faceted process that often leads one in surprising directions.
Apache Solr Near Real Time (NRT) Search allows Solr users to search documents indexed just seconds ago. It’s a critical feature in many real-time analytics applications. As Solr indexes more and more documents in near real time, end-user expectations for performance get higher and higher.
Thanks to new optimizations for running Impala on Amazon S3, doubling cluster size on AWS doubles multi-user performance while keeping total workload cost roughly the same.
With public-cloud deployments becoming increasingly popular, Cloudera is continuing to build out the capabilities of its platform to best take advantage of the cost-effective and flexible nature of the cloud. The current release of Cloudera’s platform (5.8) includes a major step forward in that area with Impala 2.6 able to store and query data directly from the Amazon S3 object store.
For the first time, this new study by Intel software engineers analyzes the performance impact of using Apache HBase on various modern storage technologies.
As more “fast” storage technologies (such as SSD and NVMe SSD) emerge, organizations with big data use cases want to make better use of them to achieve better throughput and latency. But to this point, there have been no detailed analyses published about the true significance of that performance boost,
Taking a thoughtful approach to data serialization can achieve significant performance improvements for HBase deployments.
The question of using tall versus wide tables in Apache HBase is a commonly discussed design pattern (see reference here and here). However, there are more considerations here than making that simple choice. Because HBase stores each column of a table as an independent row in the underlying HFiles, significant storage overhead can occur when storing small pieces of information.
Impala 2.5, now shipping in CDH 5.7, brings significant performance improvements and some highly requested features.
Impala has proven to be a high-performance analytics query engine since the beginning. Even as an initial production release in 2013, it demonstrated performance 2x faster than a traditional DBMS, and each subsequent release has continued to demonstrate the wide performance gap between Impala’s analytic-database architecture and SQL-on-Apache Hadoop alternatives.