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From "Xuefu Zhang (JIRA)" <>
Subject [jira] [Updated] (HIVE-7768) Integrate with Spark executor scaling [Spark Branch]
Date Wed, 12 Nov 2014 17:02:34 GMT


Xuefu Zhang updated HIVE-7768:
    Summary: Integrate with Spark executor scaling [Spark Branch]  (was: Research growing/shrinking
our Spark Application [Spark Branch])

> Integrate with Spark executor scaling [Spark Branch]
> ----------------------------------------------------
>                 Key: HIVE-7768
>                 URL:
>             Project: Hive
>          Issue Type: Sub-task
>          Components: Spark
>            Reporter: Brock Noland
>            Assignee: Venki Korukanti
>            Priority: Critical
> Scenario:
> A user connects to Hive and runs a query on a small time. Our SC is sized for that small
table. They then run a query on a much larger table. We'll need to "re-size" the SC which
I don't think Spark supports today, so we need to research what is available today in Spark
and how Tez works.
> More details:
> Similar to Tez, it's likely our "SparkContext" is going to be long lived and process
many queries. Some queries will be large and some small. Additionally the SC might be idle
for long periods of time.
> In this JIRA we will research the following:
> * How Spark decides the number of slaves for a given RDD today
> * Given a SC when you create a new RDD based on a much larger input dataset, does the
SC adjust?
> * How Tez increases/decreases the size of the running YARN application (set of slaves)
> * How Tez handles scenarios when it has a running set of slaves in YARN and requests
more resources for a query and fails to get additional resources
> * How Tez decides to timeout idle slaves
> This will guide requirements we'll need from Spark.

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