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mahmoudelgamal 2016-08-04 00:55:32 +02:00
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docs/interpreter/beam.md
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@ -11,70 +11,29 @@ group: interpreter
<div id="toc"></div>
## Overview
[Apache Beam](http://beam.incubator.apache.org) is an open source, unified programming model that you can use to create a data processing pipeline. You start by building a program that defines the pipeline using one of the open source Beam SDKs. The pipeline is then executed by one of Beam’s supported distributed processing back-ends, which include Apache Flink, Apache Spark, and Google Cloud Dataflow.
Beam is particularly useful for Embarrassingly Parallel data processing tasks, in which the problem can be decomposed into many smaller bundles of data that can be processed independently and in parallel. You can also use Beam for Extract, Transform, and Load (ETL) tasks and pure data integration. These tasks are useful for moving data between different storage media and data sources, transforming data into a more desirable format, or loading data onto a new system.
# Apache Beam Pipeline Runners
The Beam Pipeline Runners translate the data processing pipeline you define with your Beam program into the API compatible with the distributed processing back-end of your choice. When you run your Beam program, you’ll need to specify the appropriate runner for the back-end where you want to execute your pipeline.
* Beam currently supports Runners that work with the following distributed processing back-ends:
- Google Cloud Dataflow
- Apache Flink
- Apache Spark
[Apache Beam](http://beam.incubator.apache.org) is an open source unified platform for data processing pipelines. A pipeline can be build using one of the Beam SDKs.
The execution of the pipeline is done by different Runners . Currently, Beam supports Apache Flink Runner, Apache Spark Runner, and Google Dataflow Runner.
## How to use
Basically, You can write normal java code and determine the runner inside the code.
You should write the main method inside the main class beacuase the interpreter invoke this main to execute pipline.
Each paragraph is considered as separate job, there isn't any relate to any another job, Beacuse the interpreter is a static repl of java, we compile and run each paragraph apart.
Basically, you can write normal Beam java code where you can determine the Runner. You should write the main method inside a class becuase the interpreter invoke this main to execute the pipline. Unlike Zeppelin normal pattern, each paragraph is considered a separate job, there isn't any relation to any other paragraph.
**Example for Flink Runner**
The following is a demonstration of a word count example
```
%beam
import java.util.ArrayList;
import java.util.List;
import org.apache.spark.api.java.*;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.SparkConf;
import org.apache.spark.streaming.*;
import org.apache.spark.SparkContext;
import org.apache.beam.runners.direct.*;
import org.apache.beam.sdk.runners.*;
import org.apache.beam.sdk.options.*;
import org.apache.beam.runners.spark.*;
import org.apache.beam.runners.spark.io.ConsoleIO;
import org.apache.beam.runners.flink.*;
import org.apache.beam.runners.flink.examples.WordCount.Options;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.transforms.Count;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.SimpleFunction;
import org.apache.beam.sdk.values.KV;
// imports are omitted to save space
public class MinimalWordCount {
static List<String> s = new ArrayList<>();
public static void main(String[] args) {
Options options = PipelineOptionsFactory.create().as(Options.class);
options.setRunner(FlinkPipelineRunner.class);
Pipeline p = Pipeline.create(options);
p.apply(TextIO.Read.from("/home/admin/mahmoud/work/bigdata/beam/shakespeare/input/file1.txt"))
.apply(ParDo.named("ExtractWords").of(new DoFn<String, String>() {
@Override
public void processElement(ProcessContext c) {
for (String word : c.element().split("[^a-zA-Z']+")) {
if (!word.isEmpty()) {
c.output(word);
@ -82,11 +41,8 @@ public class MinimalWordCount {
}
}
}))
.apply(Count.<String> perElement())
.apply("FormatResults", ParDo.of(new DoFn<KV<String, Long>, String>() {
@Override
public void processElement(DoFn<KV<String, Long>, String>.ProcessContext arg0)
throws Exception {
@ -94,9 +50,7 @@ public class MinimalWordCount {
}
}));
p.run();
System.out.println("%table word\tcount");
for (int i = 0; i < s.size(); i++) {
System.out.print(s.get(i));