Category Archives: Performance

How-to: Improve Apache HBase Performance via Data Serialization with Apache Avro

Categories: Avro HBase Performance

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.

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Apache Impala (incubating) in CDH 5.7: 4x Faster for BI Workloads on Apache Hadoop

Categories: CDH Impala Performance

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.

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Benchmarking Apache Parquet: The Allstate Experience

Categories: Avro Parquet Performance

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,

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How-to: Use Impala and Kudu Together for Analytic Workloads

Categories: Data Science Hadoop How-to Impala Kudu Performance

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,

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New in Cloudera Manager 5.7: Cluster Utilization Reporting

Categories: Cloudera Manager Impala Ops and DevOps Performance YARN

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,

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