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? >>>>>> >>>>> >>>>> >>>> >>> >> >
