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.
Using Apache Impala (incubating) on top of Apache Kudu (incubating) has significant performance benefits
Apache Kudu (incubating) is the newest addition to the set of storage engines that integrate with the Apache Hadoop ecosystem. The promise of Kudu is to deliver high-scan performance, targeting analytical workloads, while allowing users to concurrently insert, update, and delete records. With these properties, Kudu becomes a viable alternative to existing combinations of HDFS and/or Apache HBase to achieve similar results with less complicated ETL pipelines,
Cluster admins will love the new cluster utilization reporting available in Cloudera Manager 5.7.
Enterprise data hub clusters often are shared by several teams. In such multi-tenant environments, cluster administrators are required to ensure that resources are shared fairly so that one tenant cannot run jobs that starve others. To give better visibility into resource consumption in multi-tenant environments, Cloudera Manager 5.7 (in Cloudera Enterprise Flex and Data Hub Editions) has a new feature for reporting cluster utilization that provides information about overall cluster usage,
Engineers from across the Apache Hadoop community are collaborating to establish Arrow as a de-facto standard for columnar in-memory processing and interchange. Here’s how it works.
Apache Arrow is an in-memory data structure specification for use by engineers building data systems. It has several key benefits:
- A columnar memory-layout permitting O(1) random access. The layout is highly cache-efficient in analytics workloads and permits SIMD optimizations with modern processors.
New testing results show a significant difference between the analytic database performance of Impala compared to batch and procedural development engines, as well as Impala running all 99 TPC-DS-derived queries in the benchmark workload.
2015 was an exciting year for Apache Impala (incubating). Cloudera’s Impala team significantly improved Impala’s scale and stability, which enabled many customers to deploy Impala clusters with hundreds of nodes, run millions of queries,