99 lines
3.8 KiB
Markdown
99 lines
3.8 KiB
Markdown
---
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title: Flink Batch Example JAVA
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---
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## Flink Batch Example JAVA
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Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities.
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### Prerequisites
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* Unix-like environment (Linux, Mac OS X, Cygwin)
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* git
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* Maven (we recommend version 3.0.4)
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* Java 7 or 8
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* IntelliJ IDEA or Eclipse IDE
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```
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git clone https://github.com/apache/flink.git
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cd flink
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mvn clean package -DskipTests # this will take up to 10 minutes
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```
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### Datasets
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For the batch processing data we'll be using the datasets in here: [datasets](http://files.grouplens.org/datasets/movielens/ml-latest-small.zip)
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In this example we'll be using the movies.csv and the ratings.csv, create a new java project and put them in a folder in the application base.
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### Example
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We're going to make an execution where we retrieve the average rating by movie genre of the entire dataset we have.
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**Environment and datasets**
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First create a new Java file, I'm going to name it AverageRating.java
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The first thing we'll do is to create the execution environment and load the csv files in a dataset. Like this:
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```
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ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
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DataSet<Tuple3<Long, String, String>> movies = env.readCsvFile("ml-latest-small/movies.csv")
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.ignoreFirstLine()
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.parseQuotedStrings('"')
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.ignoreInvalidLines()
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.types(Long.class, String.class, String.class);
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DataSet<Tuple2<Long, Double>> ratings = env.readCsvFile("ml-latest-small/ratings.csv")
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.ignoreFirstLine()
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.includeFields(false, true, true, false)
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.types(Long.class, Double.class);
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```
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There, we are making a dataset with a <Long, String, String> for the movies, ignoring errors, quotes and the header line, and a dataset with <Long, Double> for the movie ratings, also ignoring the header, invalid lines and quotes.
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**Flink Processing**
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Here we will process the dataset with flink. The result will be in a List of String, Double tuples. where the genre will be in the String and the average rating will be in the double.
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First we'll join the ratings dataset with the movies dataset by the moviesId present in each dataset.
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With this we'll create a new Tuple with the movie name, genre and score.
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Later, we group this tuple by genre and add the score of all equal genres, finally we divide the score by the total results and we have our desired result.
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```
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List<Tuple2<String, Double>> distribution = movies.join(ratings)
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.where(0)
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.equalTo(0)
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.with(new JoinFunction<Tuple3<Long, String, String>,Tuple2<Long, Double>, Tuple3<StringValue, StringValue, DoubleValue>>() {
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private StringValue name = new StringValue();
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private StringValue genre = new StringValue();
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private DoubleValue score = new DoubleValue();
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private Tuple3<StringValue, StringValue, DoubleValue> result = new Tuple3<>(name,genre,score);
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@Override
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public Tuple3<StringValue, StringValue, DoubleValue> join(Tuple3<Long, String, String> movie,Tuple2<Long, Double> rating) throws Exception {
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name.setValue(movie.f1);
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genre.setValue(movie.f2.split("\\|")[0]);
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score.setValue(rating.f1);
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return result;
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}
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})
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.groupBy(1)
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.reduceGroup(new GroupReduceFunction<Tuple3<StringValue,StringValue,DoubleValue>, Tuple2<String, Double>>() {
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@Override
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public void reduce(Iterable<Tuple3<StringValue,StringValue,DoubleValue>> iterable, Collector<Tuple2<String, Double>> collector) throws Exception {
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StringValue genre = null;
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int count = 0;
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double totalScore = 0;
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for(Tuple3<StringValue,StringValue,DoubleValue> movie: iterable){
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genre = movie.f1;
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totalScore += movie.f2.getValue();
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count++;
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}
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collector.collect(new Tuple2<>(genre.getValue(), totalScore/count));
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}
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})
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.collect();
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```
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With this you'll have a working batch processing flink application. Enjoy!.
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