Hi,
I'm not aware of a performance report for this feature. I don't think it is
well known or used a lot.
The classes to check out for prepartitioned / presorted data are
SplitDataProperties [1], DataSource [2], and as an example
PropertyDataSourceTest [3].
[1]
https://github.com/apache/flink/blo
thanks for tip @Stephan.
To [1] , there's a description about "I’ve got sooo much data to join, do
I really need to ship it?" . How to configure Flink to touch that target?
Is there a performance report ?
[1] :
https://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html
Hi!
Be aware that the "Row" and "Record" types are not very high performance
data types. You might be measuring the data type overhead, rather than the
hash table performance. Also, the build measurements include the data
generation, which influences the results.
If you want to purely benchmark t
Thanks for all your enthusiastic response. Yes, My target was to try to
find the best performance in memory. I got that.
On Tue, 16 May 2017 at 4:10 PM Fabian Hueske wrote:
> Hi,
>
> Flink's HashJoin implementation was designed to gracefully handle inputs
> that exceed the main memory.
> It is no
Hi,
Flink's HashJoin implementation was designed to gracefully handle inputs
that exceed the main memory.
It is not explicitly optimized for in-memory processing and does not play
fancy tricks like optimizing cache accesses or batching.
I assume your benchmark is about in-memory joins only. This w
The Flink version is 1.2.0
On Mon, May 15, 2017 at 10:24 PM, weijie tong
wrote:
> @Till thanks for your reply.
>
> My code is similar to HashTableITCase.testInMemoryMutableHashTable()
> . It just use the MutableHashTable class , there's no other Flink's
> configuration. The main code body is
@Till thanks for your reply.
My code is similar to HashTableITCase.testInMemoryMutableHashTable() .
It just use the MutableHashTable class , there's no other Flink's
configuration. The main code body is:
this.recordBuildSideAccessor = RecordSerializer.get();
> this.recordProbeSideAccessor =
Hi Weijie,
it might be the case that batching the processing of multiple rows can give
you an improved performance compared to single row processing.
Maybe you could share the exact benchmark base line results and the code
you use to test Flink's MutableHashTable with us. Also the Flink
configura
I has a test case to use Flink's MutableHashTable class to do a hash join
on a local machine with 64g memory, 64cores. The test case is one build
table with 14w rows ,one probe table with 320w rows ,the matched result
rows is 12 w.
It takes 2.2 seconds to complete the join.The performance seems ba
