[Interactive Q & A sharing] Stage 1 wins the public welfare lecture hall of spark Asia Pacific Research Institute in the cloud computing Big Data age

 

"Winning the cloud computing Big Data era"

Spark Asia Pacific Research Institute Stage 1 Public Welfare lecture hall [Stage 1 interactive Q & A sharing]

 

Q1: Can spark shuffle point spark_local_dirs to a solid state drive to speed up execution.

• You can point spark_local_dirs to a solid state drive, which can greatly improve the spark execution speed;

• At the same time, if you want to increase the spark Running Speed faster, you can specify multiple shuffle output directories to allow shuffle to read and write disks in parallel;

Q2: Solidation = true: only merge on the same machine, right?

• Solidation = true is to merge on the same machine;

• When merging, the bucket belonging to the same CER is put into the same file, which greatly reduces the number of shuffler files and improves performance;

Q3: Will spark and hadoop coexist in the future?

• Spark and hadoop will coexist, spark + hadoop = a winning combination;

• In the coexistence, hadoop mainly uses HDFS for data storage, and spark is responsible for integrated and diversified big data computing;

This article is from the spark Asia Pacific Research Institute blog, please be sure to keep this source http://rockyspark.blog.51cto.com/2229525/1565214

[Interactive Q & A sharing] Stage 1 wins the public welfare lecture hall of spark Asia Pacific Research Institute in the cloud computing Big Data age