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From Leonidas Fegaras <fega...@cse.uta.edu>
Subject Re: MRQL on Flink
Date Thu, 28 Aug 2014 15:13:36 GMT
I neglected to mentioned that this is still work in progress (!). It has 
all the necessary parts to work with Flink but still has bugs and 
obviously needs lots of performance tuning. The reason I announced it 
early is to get feedback and hopefully bug reports from the dev@flink. 
But I must say you already gave me a lot of encouragement. Thanks!
The major component missing in this system is to work with HDFS on 
distributed mode by default. Now, it uses the local file system (which 
is NFS shared by workers) on both local and distributed mode, which is 
terribly inefficient. For local mode, I want to have the local working 
directory as the default for relative paths (I think this works OK). For 
distributed mode, I want the HDFS and the user home on HDFS to be the 
default. I will try to fix this and have a workable system for Yarn by 
the end of this weekend. The local mode works fine now, I think.
It was easy to port the MRQL physical operators to Flink DataSet 
methods; I have done something similar for Spark. The components that 
took me long to develop were the DataSources and the DataSinks. All the 
other MRQL backends use the hadoop HDFS. So I had to copy some of my 
files from my core system that uses HDFS to the Flink backend, change 
their names, and use the Flink filesystem packages (which are very 
similar to Hadoop HDFS). Another problem was that I had heavily used 
Hadoop Sequential files to store results for the other backends. So I 
had to switch to Flink's BinaryOutputFormat. The DataSinks in Flink are 
not very convenient. I wish there was a DataSink that contains an 
Iterator so that we can use the results for purposes other than storing 
them in files. Also, compared to Spark, there are very few ways to send 
results from workers to the master node after execution. Custom 
aggregators still have a bug when the aggregation result is a custom 
class (it's a serialization problem: the class of the deserialized 
result doesn't match the expected class, although they have the same 
name). In general, I encountered some problems with serialization: 
sometimes I couldn't use inner classes for the Flink functional 
parameters and I had to define them as static classes. Another thing 
that took me a couple of days to fix was to dump data from an Iterator 
to a Flink Binary file. Dumping the iterator data into a vector first 
was not feasible because these data may be huge. First, I tried to use 
the fromCollection method, but it required that the Iterator be 
serializable (It doesn't make sense; how do you make an Iterator 
serializable?) Then I used the following hack:

  BinaryOutputFormat of = new BinaryOutputFormat();
  of.setOutputFilePath(path);
  of.open(0,2);
  ...
It took me a while to find that I need to put of.open(0,2) instead of 
of.open(0,1). Why do we need 2 tasks?
So, thanks for your encouragement. I will try to fix some of these bugs 
by Monday and have a system that performs well on Yarn.
Leonidas


On 08/28/2014 03:58 AM, Fabian Hueske wrote:
> That's really cool!
>
> I'm also curious about your experience with Flink. Did you find major
> obstacles that you needed to overcome for the integration?
> Is there some write-up / report available somewhere (maybe in JIRA) that
> discusses the integration? Are you using Flink's full operator set or do
> you compile everything into Map and Reduce?
>
> Best, Fabian
>
>
> 2014-08-28 7:37 GMT+02:00 Aljoscha Krettek <aljoscha@apache.org>:
>
>> Very nice indeed! How well is this tested? Can it already run all the
>> example queries you have? Can you say anything about the performance
>> of the different underlying execution engines?
>>
>> On Thu, Aug 28, 2014 at 12:58 AM, Stephan Ewen <sewen@apache.org> wrote:
>>> Wow, that is impressive!
>>>
>>>
>>> On Thu, Aug 28, 2014 at 12:06 AM, Ufuk Celebi <uce@apache.org> wrote:
>>>
>>>> Awesome, indeed! Looking forward to trying it out. :)
>>>>
>>>>
>>>> On Wed, Aug 27, 2014 at 10:52 PM, Sebastian Schelter <ssc@apache.org>
>>>> wrote:
>>>>
>>>>> Awesome!
>>>>>
>>>>>
>>>>> 2014-08-27 13:49 GMT-07:00 Leonidas Fegaras <fegaras@cse.uta.edu>:
>>>>>
>>>>>> Hello,
>>>>>> I would like to let you know that Apache MRQL can now run queries
on
>>>>> Flink.
>>>>>> MRQL is a query processing and optimization system for large-scale,
>>>>>> distributed data analysis, built on top of Apache Hadoop/map-reduce,
>>>>>> Hama, Spark, and now Flink. MRQL queries are SQL-like but not SQL.
>>>>>> They can work on complex, user-defined data (such as JSON and XML)
>> and
>>>>>> can express complex queries (such as pagerank and matrix
>>>> factorization).
>>>>>> MRQL on Flink has been tested on local mode and on a small Yarn
>>>> cluster.
>>>>>> Here are the directions on how to build the latest MRQL snapshot:
>>>>>>
>>>>>> git clone
>> https://git-wip-us.apache.org/repos/asf/incubator-mrql.git
>>>>> mrql
>>>>>> cd mrql
>>>>>> mvn -Pyarn clean install
>>>>>>
>>>>>> To make it run on your cluster, edit conf/mrql-env.sh and set the
>>>>>> Java, the Hadoop, and the Flink installation directories.
>>>>>>
>>>>>> Here is how to run PageRank. First, you need to generate a random
>>>>>> graph and store it in a file using the MRQL query RMAT.mrql:
>>>>>>
>>>>>> bin/mrql.flink -local queries/RMAT.mrql 1000 10000
>>>>>>
>>>>>> This will create a graph with 1K nodes and 10K edges using the RMAT
>>>>>> algorithm, will remove duplicate edges, and will store the graph
in
>>>>>> the binary file graph.bin. Then, run PageRank on Flink mode using:
>>>>>>
>>>>>> bin/mrql.flink -local queries/pagerank.mrql
>>>>>>
>>>>>> To run MRQL/Flink on a Yarn cluster, first start the Flink container
>>>>>> on Yarn by running the script yarn-session.sh, such as:
>>>>>>
>>>>>> ${FLINK_HOME}/bin/yarn-session.sh -n 8
>>>>>>
>>>>>> This will print the name of the Flink JobManager, which can be used
>> in:
>>>>>> export FLINK_MASTER=name-of-the-Flink-JobManager
>>>>>> bin/mrql.flink -dist -nodes 16 queries/RMAT.mrql 1000000 10000000
>>>>>>
>>>>>> This will create a graph with 1M nodes and 10M edges using RMAT on
>> 16
>>>>>> nodes (slaves). You can adjust these numbers to fit your cluster.
>>>>>> Then, run PageRank using:
>>>>>>
>>>>>> bin/mrql.flink -dist -nodes 16 queries/pagerank.mrql
>>>>>>
>>>>>> The MRQL project page is at: http://mrql.incubator.apache.org/
>>>>>>
>>>>>> Let me know if you have any questions.
>>>>>> Leonidas Fegaras
>>>>>>
>>>>>>


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