For the Python API, there is a pending pull request: https://github.com/apache/incubator-flink/pull/202 It is still work in progress, but feedback is, as always, appreciated.
On Fri, Dec 26, 2014 at 3:41 PM, Samarth Mailinglist < [email protected]> wrote: > Thanks a lot Márton and Gyula! > > On Fri, Dec 26, 2014 at 2:42 PM, Márton Balassi <[email protected]> > wrote: > >> Hey, >> >> You can find some ml examples like LinerRegression [1, 2] or KMeans [3, >> 4] in the examples package in both java and scala as a quickstart. >> >> [1] >> https://github.com/apache/incubator-flink/blob/release-0.8/flink-examples/flink-java-examples/src/main/java/org/apache/flink/examples/java/ml/LinearRegression.java >> [2] >> https://github.com/apache/incubator-flink/blob/release-0.8/flink-examples/flink-scala-examples/src/main/scala/org/apache/flink/examples/scala/ml/LinearRegression.scala >> [3] >> https://github.com/apache/incubator-flink/blob/release-0.8/flink-examples/flink-java-examples/src/main/java/org/apache/flink/examples/java/clustering/KMeans.java >> [4] >> https://github.com/apache/incubator-flink/blob/release-0.8/flink-examples/flink-scala-examples/src/main/scala/org/apache/flink/examples/scala/clustering/KMeans.scala >> >> On Fri, Dec 26, 2014 at 7:31 AM, Samarth Mailinglist < >> [email protected]> wrote: >> >>> Thank you the answers, folks. >>> Can anyone provide me a link for any implementation of an ML algorithm >>> on Flink? >>> >>> On Thu, Dec 25, 2014 at 8:07 PM, Gyula Fóra <[email protected]> wrote: >>> >>>> Hey, >>>> >>>> 1-2. As for failure recovery, there is a difference how the Flink batch >>>> and streaming programs handle failures. The failed parts of the batch jobs >>>> currently restart upon failures but there is an ongoing effort on fine >>>> grained fault tolerance which is somewhat similar to sparks lineage >>>> tracking. (so technically this is exactly once semantic but that is >>>> somewhat meaningless for batch jobs) >>>> >>>> For streaming programs we are currently working on fault tolerance, we >>>> are hoping to support at least once processing guarantees in the 0.9 >>>> release. After that we will focus our research efforts on an high >>>> performance implementation of exactly once processing semantics, which is >>>> still a hard topic in streaming systems. Storm's trident's exaclty once >>>> semantics can only provide very low throughput while we are trying hard to >>>> avoid this issue, as our streaming system is capable of much higher >>>> throughput than storm in general as you can see on some perf measurements. >>>> >>>> 3. There are already many ml algorithms implemented for Flink but they >>>> are scattered all around. We are planning to collect them in a machine >>>> learning library soon. We are also implementing an adapter for Samoa which >>>> will provide some streaming machine learning algorithms as well. Samoa >>>> integration should be ready in January. >>>> >>>> 4. Flink carefully manages its memory use to avoid heap errors, and >>>> utilizing memory space as effectively as it can. The optimizer for batch >>>> programs also takes care of a lot of optimization steps that the user would >>>> manually have to do in other system, like optimizing the order of >>>> transformations etc. There are of course parts of the program that still >>>> needs to modified for maximal performance, for example parallelism settings >>>> for some operators in some cases. >>>> >>>> 5. As for the status of the Python API I personally cannot say very >>>> much, maybe someone can jump in and help me with that question :) >>>> >>>> Regards, >>>> Gyula >>>> >>>> On Thu, Dec 25, 2014 at 11:58 AM, Samarth Mailinglist < >>>> [email protected]> wrote: >>>> >>>>> Thank you for your answer. I have a couple of follow up questions: >>>>> 1. Does it support 'exactly once semantics' that Spark and Storm >>>>> support? >>>>> 2. (Related to 1) What happens when an error occurs during processing? >>>>> 3. Is there a plan for adding Machine Learning support on top of >>>>> Flink? Say Alternative Least Squares, Basic Naive Bayes? >>>>> 4. When you say Flink manages itself, does it mean I don't have to >>>>> fiddle with number of partitions (Spark), number of reduces / happers >>>>> (Hadoop?) to optimize performance? (In some cases this might be needed) >>>>> 5. How far along is the Python API? I don't see the specs in the >>>>> Website. >>>>> >>>>> On Thu, Dec 25, 2014 at 4:31 AM, Márton Balassi <[email protected]> >>>>> wrote: >>>>> >>>>>> Dear Samarth, >>>>>> >>>>>> Besides the discussions you have mentioned [1] I can recommend one of >>>>>> our recent presentations [2], especially the distinguishing Flink section >>>>>> (from slide 16). >>>>>> >>>>>> It is generally a difficult question as both the systems are rapidly >>>>>> evolving, so the answer can become outdated quite fast. However there are >>>>>> fundamental design features that are highly unlikely to change, for >>>>>> example >>>>>> Spark uses "true" batch processing, meaning that intermediate results are >>>>>> materialized (mostly in memory) as RDDs. Flink's engine is internally >>>>>> more >>>>>> like streaming, forwarding the results to the next operator asap. The >>>>>> latter can yield performance benefits for more complex jobs. Flink also >>>>>> gives you a query optimizer, spills gracefully to disk when the system >>>>>> runs >>>>>> out of memory and has some cool features around serialization. For >>>>>> performance numbers and some more insight please check out the >>>>>> presentation >>>>>> [2] and do not hesitate to post a follow-up mail here if you come across >>>>>> something unclear or extraordinary. >>>>>> >>>>>> [1] >>>>>> http://apache-flink-incubator-mailing-list-archive.1008284.n3.nabble.com/template/NamlServlet.jtp?macro=search_page&node=1&query=spark >>>>>> [2] http://www.slideshare.net/GyulaFra/flink-apachecon >>>>>> >>>>>> Best, >>>>>> >>>>>> Marton >>>>>> >>>>>> On Tue, Dec 23, 2014 at 6:19 PM, Samarth Mailinglist < >>>>>> [email protected]> wrote: >>>>>> >>>>>>> Hey folks, I have a noob question. >>>>>>> >>>>>>> I already looked up the archives and saw a couple of discussions >>>>>>> <http://apache-flink-incubator-mailing-list-archive.1008284.n3.nabble.com/template/NamlServlet.jtp?macro=search_page&node=1&query=spark> >>>>>>> about Spark and Flink. >>>>>>> >>>>>>> I am familiar with spark (the python API, esp MLLib), and I see many >>>>>>> similarities between Flink and Spark. >>>>>>> >>>>>>> How does Flink distinguish itself from Spark? >>>>>>> >>>>>> >>>>>> >>>>> >>>> >>> >> >
