Hi all,
I have 2 questions:
1. I found out the meaning of deterministic in D-Stream model is :
given a particular input, will always produce the same output that is
necessary for recomputation in lineage.
Is it right?
2. Why spark stream needs to replicate input data for making deterministic
Hi Key,
Thank you so much for your update!!
Look forward to the shared code from AMPLab. As a member of the Spark
community, I really hope that I could help to run TPC-DS on SparkSQL. At the
moment, I am trying TPC-H 22 queries on SparkSQL 1.1.0 +Hive 0.12, and Hive
0.13.1 respectively
Two thoughts here:
1. The real flaw with the sort benchmark was that Hadoop wasn't run on the
same hardware. Given the advances in networking (availabIlity of
10GB Ethernet) and disks (SSDs) since the Hadoop benchmarks it was compared
to, it's an apples to oranges comparison. Without that, it
Good points raised. Some comments.
Re: #1
It seems like there is a misunderstanding of the purpose of the Daytona
Gray benchmark. The purpose of the benchmark is to see how fast you can
sort 100 TB of data (technically, your sort rate during the operation)
using *any* hardware or software
Kay,
Is this effort related to the existing AMPLab Big Data benchmark that
covers Spark, Redshift, Tez, and Impala?
Nick
2014년 10월 31일 금요일, Kay Ousterhoutk...@eecs.berkeley.edu님이 작성한 메시지:
There's been an effort in the AMPLab at Berkeley to set up a shared
codebase that makes it easy to run
Hi Nick,
No -- we're doing a much more constrained thing of just trying to get
things set up to easily run TPC-DS on SparkSQL (which involves generating
the data, storing it in HDFS, getting all the queries in the right format,
etc.).
Cloudera does have a repo here:
== Short version ==
A recent commit replaces Spark's networking subsystem with one based
on Netty rather than raw sockets. Users running off of master can
disable this change by setting
spark.shuffle.blockTransferService=nio. We will be testing with this
during the QA period for Spark 1.2. The new
