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https://issues.apache.org/jira/browse/HIVE-4160?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Eric Hanson updated HIVE-4160:
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Attachment: Hive-Vectorized-Query-Execution-Design-rev4.docx
> Vectorized Query Execution in Hive
> ----------------------------------
>
> Key: HIVE-4160
> URL: https://issues.apache.org/jira/browse/HIVE-4160
> Project: Hive
> Issue Type: New Feature
> Reporter: Jitendra Nath Pandey
> Assignee: Jitendra Nath Pandey
> Attachments: Hive-Vectorized-Query-Execution-Design.docx,
> Hive-Vectorized-Query-Execution-Design-rev2.docx,
> Hive-Vectorized-Query-Execution-Design-rev3.docx,
> Hive-Vectorized-Query-Execution-Design-rev3.docx,
> Hive-Vectorized-Query-Execution-Design-rev3.pdf,
> Hive-Vectorized-Query-Execution-Design-rev4.docx,
> Hive-Vectorized-Query-Execution-Design-rev4.pdf
>
>
> The Hive query execution engine currently processes one row at a time. A
> single row of data goes through all the operators before the next row can be
> processed. This mode of processing is very inefficient in terms of CPU usage.
> Research has demonstrated that this yields very low instructions per cycle
> [MonetDB X100]. Also currently Hive heavily relies on lazy deserialization
> and data columns go through a layer of object inspectors that identify column
> type, deserialize data and determine appropriate expression routines in the
> inner loop. These layers of virtual method calls further slow down the
> processing.
> This work will add support for vectorized query execution to Hive, where,
> instead of individual rows, batches of about a thousand rows at a time are
> processed. Each column in the batch is represented as a vector of a primitive
> data type. The inner loop of execution scans these vectors very fast,
> avoiding method calls, deserialization, unnecessary if-then-else, etc. This
> substantially reduces CPU time used, and gives excellent instructions per
> cycle (i.e. improved processor pipeline utilization). See the attached design
> specification for more details.
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