When picking a storage option for an application it is common to pick a single storage option which has the most applicable features to your use case. For mutability and real-time analytics workloads you may want to use Apache Kudu, but for massive scalability at a low cost you may want to use HDFS. For that reason, there is a need for a solution that allows you to leverage the best features of multiple storage options.
Apache Hive is a widely adopted data warehouse engine that runs on Apache Hadoop. Features that improve Hive performance can significantly improve the overall utilization of resources on the cluster. Hive processes data using a chain of operators within the Hive execution engine. These operators are scheduled in the various tasks (for example, MapTask, ReduceTask, or SparkTask) of the query execution plan. Traditionally, these operators are designed to process one row at a time.
Zbigniew Baranowski is a database systems specialist and a member of a group which provides and supports central database and Hadoop-based services at CERN. This blog was originally released on CERN’s “Databases at CERN” blog, and is syndicated here with CERN’s permission.
This post presents a performance comparison of few popular data formats and storage engines available in the Apache Hadoop ecosystem: Apache Avro,
Our thanks to Don Drake (@dondrake), an independent technology consultant who is currently working at Allstate Insurance, for the guest post below about his experiences comparing use of the Apache Avro and Apache Parquet file formats with Apache Spark.
Over the last few months, numerous hallway conversations, informal discussions, and meetings have occurred at Allstate about the relative merits of different file formats for data stored in Apache Hadoop—including CSV,
Fixes in CDH 5.5 make writing Parquet data for Apache Impala (incubating) much easier.
Over the last few months, several Cloudera customers have provided the feedback that Parquet is too hard to configure, with the main problem being finding the right layout for great performance in Impala. For that reasons, CDH 5.5 contains new features that make those configuration problems go away.
Auto-Detection of HDFS Block Size