Hey Leonidas, Edward, and Communities! The are some serious efforts going on in Flink to improve the runtime, optimize serialization, and change the way that the API lets you use your types, specify keys, etc.
I believe that will take some more weeks to be in place. After that, it would be really interesting for us to look how higher level languages (like MRQL) could make use of that. And possibly do a before/after comparison. I would like to ping you later with respect to that, if you are interested! Greetings, Stephan On Fri, Aug 29, 2014 at 9:53 AM, Fabian Hueske <[email protected]> wrote: > Hi Edward, > > that sounds very interesting! > Let us know if you have any problems setting up or configuring Flink. We'll > be very happy to help. > > Cheers, Fabian > > > > 2014-08-29 4:30 GMT+02:00 Edward J. Yoon <[email protected]>: > > > Cool! > > > > >>> 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? > > > > Recently I have a plan on benchmark for performance of new Hama > > release. I might be able to generate some comparison table bt spark, > > hama, flink. > > > > On Fri, Aug 29, 2014 at 12:13 AM, Leonidas Fegaras <[email protected]> > > wrote: > > > 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 <[email protected]>: > > >> > > >>> 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 <[email protected]> > > wrote: > > >>>> > > >>>> Wow, that is impressive! > > >>>> > > >>>> > > >>>> On Thu, Aug 28, 2014 at 12:06 AM, Ufuk Celebi <[email protected]> > wrote: > > >>>> > > >>>>> Awesome, indeed! Looking forward to trying it out. :) > > >>>>> > > >>>>> > > >>>>> On Wed, Aug 27, 2014 at 10:52 PM, Sebastian Schelter < > [email protected] > > > > > >>>>> wrote: > > >>>>> > > >>>>>> Awesome! > > >>>>>> > > >>>>>> > > >>>>>> 2014-08-27 13:49 GMT-07:00 Leonidas Fegaras <[email protected] > >: > > >>>>>> > > >>>>>> > > >>>>>>> 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 > > >>>>>>> > > >>>>>>> > > > > > > > > > > > -- > > Best Regards, Edward J. Yoon > > CEO at DataSayer Co., Ltd. > > >
