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.
Released with CDH 5.8, Impala 2.6 brings solid performance improvements, particularly for clusters secured by Kerberos running BI workloads on Apache Hadoop.
Just a few months back, we showed you how Impala 2.5 delivered a 4x performance boost compared to Impala 2.3 for BI workloads on Hadoop via the introduction of several features like runtime filters. Here’s an update: Compared to two releases ago, Impala 2.6 delivers 12x better performance on secure workloads and continues this drumbeat of consistent performance improvement.
This new (alpha) C++ client library for Apache Impala (incubating) and Apache Hive provides high-performance data access from Python.
Earlier this year, members of the Python data tools and Impala teams at Cloudera began collaborating to create a new C++ library to eventually become a faster, more memory-efficient replacement for impyla, PyHive, and other (largely pure Python) client libraries for talking to Hive and Impala.
We are excited to release this effort,
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.
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. Developers can create very fast algorithms which process Arrow data structures.