Cheerz,
Basically the data is stored in CSV format. The flatMap which I have
implemented does:
String[] tokens = value.split(",");
out.collect(new Tuple2<String, Double>(tokens[0], Double.valueOf(tokens[2])));
The result calculation looks like:
DataSet<Tuple2<String, String>> statistics = rawData.flatMap(new
VariableParser()).groupBy(0).reduceGroup(new ReduceStats());
ReduceStats implements GroupReduceFunction, iterates and addes values into
DescriptiveStatistics and at the end output min, max and avg.
I ran the new experiments with suggested configuration and what I have noticed
is only one task slot is being occupied. Something I am doing is wrong..
3
Task Managers
21
Task Slots
20
Available Task Slots
Best regards,
Serhiy.
From: Robert Metzger [mailto:rmetz...@apache.org]
Sent: 13 May 2016 15:26
To: user@flink.apache.org
Subject: Re: Flink performance tuning
Hi,
Can you try running the job with 8 slots, 7 GB (maybe you need to go down to 6
GB) and only three TaskManagers (-n 3) ?
I'm suggesting this, because you have many small JVMs running on your machines.
On such small machines you can probably get much more use out of your available
memory by running a few big task managers (which can share all the common
management infra).
Another plus of running a few JVMs is that you are deducing network overhead,
because communication can happen within the process, and less network transfer
is required.
Another big factor for performance are the datatypes used. How do you represent
your data in Flink? (Are you using the TupleX types? or POJOs?)
How do you select the key for the grouping?
Regards,
Robert
On Fri, May 13, 2016 at 11:25 AM, Serhiy Boychenko
<serhiy.boyche...@cern.ch<mailto:serhiy.boyche...@cern.ch>> wrote:
Hey,
I have successfully integrated Flink into our very small test cluster (3
machines with 8 cores, 8GBytes of memory and 2x1TB disks). Basically I am
started the session to use YARN as RM and the data is being read from HDFS.
/yarn-session.sh -n 21 -s 1 -jm 1024 -tm 1024
My code is very simple, flatMap is being done on the CSV data, so I extract the
signal name and value, I group by signal name and performing group reduce on
the data in order to calculate max, min and average on the collected values.
I have observed on 3 nodes, the average processing rate is around
11Mbytes/second. I have compared the results with MR execution(without any kind
of tuning) and I am quite surprised, since the performance of Hadoop is
85Mybtes/second when executing the same query on the same data. I have read few
reports claiming that Flink is better in comparison to MR and other tools. I am
wondering what is wrong? Any clue?
The processing rate is calculated according to the following formula:
Overall processing rate = sum of total amount of data read per job/sum of total
time the job was running (including staging periods)
Best regards,
Serhiy.
