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https://issues.apache.org/jira/browse/HAMA-983?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15730085#comment-15730085
]
JongYoon Lim commented on HAMA-983:
-----------------------------------
Hi, Edward. First of all, sorry for long delay.
This is process for testing beam-hama-runner.
1. Define testing ParDo, for example, as below.
{code}
PCollection<KV<Text, LongWritable>> output = input.apply("test",
ParDo.of(new DoFn<KV<Text, LongWritable>, KV<Text, LongWritable>>() {
@ProcessElement
public void processElement(ProcessContext c) {
for (String word : c.element().toString().split("[^a-zA-Z']+")) {
if (!word.isEmpty()) {
c.output(KV.of(new Text(word), new LongWritable(11)));
}
}
}
}));
{code}
2. For translation of ParDo, I can pass the ParDo to DoFnFunction which is a
subclass of Superstep and has OldDoFn.ProcessContext. Here, I'd like to create
dofn instance in hama cluster after finishing all translation. And I'm not sure
how I can do it easily...
{code}
private static <InputT, OutputT> TransformTranslator<ParDo.Bound<InputT,
OutputT>> parDo() {
return new TransformTranslator<ParDo.Bound<InputT, OutputT>>() {
@Override
public void translate(final ParDo.Bound<InputT, OutputT> transform,
TranslationContext context) {
// context.addSuperstep(TestSuperStep.class);
DoFnFunction dofn = new DoFnFunction((OldDoFn<KV, KV>)
transform.getFn());
// context.addSuperstep(dofn.getClass());
}
};
}
{code}
> 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.
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