Mailing-List: contact dev-help@hama.apache.org; run by ezmlm
Precedence: bulk
Reply-To: dev@hama.apache.org
Date: Mon, 19 Sep 2016 01:27:20 +0000 (UTC)
From: "Edward J. Yoon (JIRA)" <jira@apache.org>
To: dev@hama.apache.org
Message-ID: <JIRA.12939160.1455496684000.601167.1474248440596@Atlassian.JIRA>
In-Reply-To: <JIRA.12939160.1455496684000@Atlassian.JIRA>
References: <JIRA.12939160.1455496684000@Atlassian.JIRA> <JIRA.12939160.1455496684705@arcas>
Subject: [jira] [Commented] (HAMA-983) Hama runner for DataFlow
MIME-Version: 1.0
Content-Type: text/plain; charset=utf-8
Content-Transfer-Encoding: 7bit
archived-at: Mon, 19 Sep 2016 01:27:22 -0000


    [ https://issues.apache.org/jira/browse/HAMA-983?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15501973#comment-15501973 ] 

Edward J. Yoon commented on HAMA-983:
-------------------------------------

https://cloud.google.com/dataflow/examples/wordcount-example

This page is well-described about beam concept. The flow is like below:

{code}
    Creating the Pipeline
    Applying transforms to the Pipeline
        Reading input (in this example: reading text files)
        Applying ParDo transforms
        Applying SDK-provided transforms (in this example: Count)
        Writing output (in this example: writing to Google Cloud Storage)
    Running the Pipeline
{code}

Once we created Hama pipeline we should able to run the program like below:

{code}
  public static void main(String[] args) {
    // Create a pipeline parameterized by commandline flags.
    Pipeline p = Pipeline.create(PipelineOptionsFactory.fromArgs(arg));

    p.apply(TextIO.Read.from("gs://..."))   // Read input.
     .apply(new CountWords())               // Do some processing.
     .apply(TextIO.Write.to("gs://..."));   // Write output.

    // Run the pipeline.
    p.run();
  }
{code}

For I/O operations, you can refer this https://github.com/apache/incubator-beam/blob/master/runners/spark/src/main/java/org/apache/beam/runners/spark/io/hadoop/HadoopIO.java (instead of org.apache.hadoop.mapreduce.lib.input.FileInputFormat you should use https://github.com/apache/hama/blob/master/core/src/main/java/org/apache/hama/bsp/FileInputFormat.java)

{quote}BSP for dataflow could be similar to SuperstepBSP{quote}

I think so. GroupByKey seems a built-in processor that groups records by key. We should implement it using a superstep.


> Hama runner for DataFlow
> ------------------------
>
>                 Key: HAMA-983
>                 URL: https://issues.apache.org/jira/browse/HAMA-983
>             Project: Hama
>          Issue Type: Bug
>            Reporter: Edward J. Yoon
>              Labels: gsoc2016
>
> As you already know, Apache Beam provides unified programming model for both batch and streaming inputs.
> The APIs are generally associated with data filtering and transforming. So we'll need to implement some data processing runner like https://github.com/dapurv5/MapReduce-BSP-Adapter/blob/master/src/main/java/org/apache/hama/mapreduce/examples/WordCount.java
> Also, implementing similarity join can be funny. According to http://www.ruizhang.info/publications/TPDS2015-Heads_Join.pdf, Apache Hama is clearly winner among Apache Hadoop and Apache Spark.
> Since it consists of transformation, aggregation, and partition computations, I think it's possible to implement using Apache Beam APIs.


--
This message was sent by Atlassian JIRA
(v6.3.4#6332)