Text To Avro Data File using Mapreduce

We have already seen how to convert a text file to Avro data file using a simple java program. In this blog we will see how to process a text file and store the result in avro data file. To run map reduce jobs on Avro data files see this blog.

Text to Avro Example
Text to Avro Example
Input File Format :

Our input file is a movie database, contains the following information

serial number :: movie name (year)::tag1|tag2

example records

1::Toy Story (1995)::Animation|Children's|Comedy
2::Jumanji (1995)::Adventure|Children's|Fantasy
3::Grumpier Old Men (1995)::Comedy|Romance
4::Waiting to Exhale (1995)::Comedy|Drama
5::Father of the Bride Part II (1995)::Comedy
6::Heat (1995)::Action|Crime|Thriller
7::Sabrina (1995)::Comedy|Romance
8::Tom and Huck (1995)::Adventure|Children's

Avro Schema for output file :

we can ignore the serial number,we can store the tags in array.Check this blog to know more about the supported types. The resulting schema is







we could have stored the year in the integer format as well.

The map function will extract the different fields from the input record and constructs a generic record. The reduce function will simply write it’s key to the output, no processing is done in reducer.

Program Code

public class MRTextToAvro extends Configured implements Tool{ 
 public static void main(String[] args) throws Exception {
 int exitCode=ToolRunner.run(new MRTextToAvro(),args );
 System.out.println("Exit code "+exitCode); 

 public int run(String[] arg0) throws Exception {
 Job job= Job.getInstance(getConf(),"Text To Avro");
 FileInputFormat.setInputPaths(job, new Path(arg0[0]));
 FileOutputFormat.setOutputPath(job, new Path(arg0[1]));
 Schema.Parser parser = new Schema.Parser();
 Schema schema=parser.parse(getClass().getResourceAsStream("movies.avsc"));
 job.setMapOutputValueClass( NullWritable.class);
 AvroJob.setOutputKeySchema(job, schema);

 return job.waitForCompletion(true)?0:1;

 public static class TextToAvroMapper extends Mapper<LongWritable ,Text,AvroKey<GenericRecord>,NullWritable>{
 Schema schema;
 protected void setup(Context context) throws IOException, InterruptedException {
 Schema.Parser parser = new Schema.Parser();

 public void map(LongWritable key,Text value,Context context) throws IOException,InterruptedException{
 GenericRecord record=new GenericData.Record(schema);
 String inputRecord=value.toString();
 record.put("movieName", getMovieName(inputRecord));
 record.put("year", getMovieRelaseYear(inputRecord));
 record.put("tags", getMovieTags(inputRecord));
 context.write(new AvroKey(record), NullWritable.get());
 public String getMovieName(String record){
 String movieName=record.split("::")[1];
 return movieName.substring(0, movieName.lastIndexOf('(' )).trim();
 public String getMovieRelaseYear(String record){
 String movieName=record.split("::")[1];
 return movieName.substring( movieName.lastIndexOf( '(' )+1,movieName.lastIndexOf( ')' )).trim();
 public String[] getMovieTags(String record){
 return (record.split("::")[2]).split("\\|");

 public static class TextToAvroReduce extends Reducer<AvroKey<GenericRecord>,NullWritable,AvroKey<GenericRecord>,NullWritable>{
 public void reduce(AvroKey<GenericRecord> key,Iterable<NullWritable> value,Context context) throws IOException, InterruptedException{
 context.write(key, NullWritable.get());

To run the job we need to put the avro related lib on class path

export HADOOP_CLASSPATH=/path/to/targets/avro-mapred-1.7.4-hadoop2.jar

yarn jar HadoopAvro-1.0-SNAPSHOT.jar MRTextToAvro -libjars avro-mapred-1.7.4-hadoop2.jar /input/path output/path

The resulting Avro file


Full project is available on github


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