Category Archives: Impala

Assessment of Apache Impala Performance using Cloudera Manager Metrics – Part 1 of 3

Categories: CDH Cloudera Manager Impala Performance

For a user-facing system like Apache Impala, bad performance and downtime can have serious negative impacts on your business. Given the complexity of the system and all the moving parts, troubleshooting can be time-consuming and overwhelming.

In this blog post series, we are going to show how the charts and metrics on Cloudera Manager (CM) can help troubleshoot Impala performance issues. They can also help to monitor the system to predict and prevent future outages.

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New in Cloudera 5.15: Simplifying the end user Data Catalog for the Self Service Analytic Database

Categories: Analytic Database CDH Cloud Cloudera Navigator Hue Impala

Self-service BI and exploratory analytics are some of the most common use cases we see our customers running on Cloudera’s analytic database solution. Over the past year, we made significant advancements to provide a simpler user experience for SQL developers and make them more productive for their everyday self-service BI tasks and workflows by leveraging Hue as the SQL development workbench.

With the recent release of Cloudera 5.15,

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New in Cloudera 5.14: Query Assistance improvements and ADLS integration for the Self Service Analytic Database

Categories: Analytic Database Cloud Hue Impala Search

Self-service BI and exploratory analytics are some of the most common use cases we see our customers running on Cloudera’s analytic database solution. Over the past year, we made significant advancements to provide a more powerful user experience for SQL developers and make them more productive for their everyday self-service BI tasks and workflows. Leveraging Hue as the SQL development workbench, we continue to see usage of the platform increase and the number of analytic use cases grow –

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Faster Performance for Selective Queries

Categories: CDH Impala

One of the principal features used in analytic databases is table partitioning. This feature is so frequently used because of its ability to significantly reduce query latency by allowing the execution engine to skip reading data that is not necessary for the query. For example, consider a table of events partitioned on the event time using calendar day granularity. If the table contained 2 years of events and a user wanted to find the events for a given 7-day window,

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