-Bits-and-Bytes.html
>
> Probably if re-ran the benchmarks with 1.5/tungsten line would close the
> gap a bit(or a lot) with spark moving towards similar style off-heap memory
> mgmt, more planning optimizations
>
>
> *From:* Jerry Lam [mailto:chiling...@gmail.com ]
> *Sent:* Sun
t;> a bit(or a lot) with spark moving towards similar style off-heap memory
>> mgmt, more planning optimizations
>>
>>
>> From: Jerry Lam [mailto:chiling...@gmail.com <mailto:chiling...@gmail.com>]
>> Sent: Sunday, July 5, 2015 6:28 PM
>> To: Ted Yu
&g
anning optimizations
>
>
> From: Jerry Lam [mailto:chiling...@gmail.com]
> Sent: Sunday, July 5, 2015 6:28 PM
> To: Ted Yu
> Cc: Slim Baltagi; user
> Subject: Re: Benchmark results between Flink and Spark
>
> Hi guys,
>
> I just read the paper too. There is no much i
,
more planning optimizations
From: Jerry Lam [mailto:chiling...@gmail.com]
Sent: Sunday, July 5, 2015 6:28 PM
To: Ted Yu
Cc: Slim Baltagi; user
Subject: Re: Benchmark results between Flink and Spark
Hi guys,
I just read the paper too. There is no much information regarding why Flink
Hi guys,
I just read the paper too. There is no much information regarding why Flink
is faster than Spark for data science type of workloads in the benchmark.
It is very difficult to generalize the conclusion of a benchmark from my
point of view. How much experience the author has with Spark is in
There was no mentioning of the versions of Flink and Spark used in
benchmarking.
The size of cluster is quite small.
Cheers
On Sun, Jul 5, 2015 at 10:24 AM, Slim Baltagi wrote:
> Hi
>
> Apache Flink outperforms Apache Spark in processing machine learning &
> graph
> algorithms and relational q
