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From 明浩 冯 <>
Subject Re: hive on spark job not start enough executors
Date Fri, 09 Sep 2016 09:08:28 GMT
All the parameters except spark.executor.instances are specified in spark-default.conf located
in hive's conf folder.  So I think it's a yes.

I also checked on spark's web page when a hive on spark job is running, the parameters shown
on the web page are exactly what I specified in the config file including spark.shuffle.service.enabled
and spark.dynamicAllocation.enabled.

Should I specify a fixed executor.instances in the file? But it's not good for me.

By the way, the data source of my query is parquet files. In hive side I just created a external
table from the parquet.


Minghao Feng

From: Mich Talebzadeh <>
Sent: Friday, September 9, 2016 4:49:55 PM
To: user
Subject: Re: hive on spark job not start enough executors

when you start hive on spark do you set any parameters for the submitted job (or read them
from init file)?

set spark.master=yarn;
set spark.deploy.mode=client;
set spark.executor.memory=3g;
set spark.driver.memory=3g;
set spark.executor.instances=2;
set spark.ui.port=7777;

Dr Mich Talebzadeh


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On 9 September 2016 at 09:30, ?? ? <<>>

Hi there,

I encountered a problem that makes hive on spark with a very low performance.

I'm using spark 1.6.2 and hive 2.1.0, I specified

    spark.shuffle.service.enabled    true
    spark.dynamicAllocation.enabled  true

in my spark-default.conf file (the file is in both spark and hive conf folder) to make spark
job to get executors dynamically.
The configuration works correctly when I run spark jobs, but when I use hive on spark, it
only started a few executors although there are more enough cores and memories to start more
For example, for the same SQL query, if I run on sparkSQL, it can start more than 20 executors,
but with hive on spark, only 3.

How can I improve the performance on hive on spark? Any suggestions please.

Minghao Feng

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