Process Incoming Data on Multiple Threads

Another way to speed up DataPipeline applications is to use multi-threading to process incoming data in parallel. This is where the AsyncTaskReader comes in. AsyncTaskReader provides a convenient facade on top of several DataPipeline classes to make parallelizing tasks a breeze. It runs the same task (a sequential set of operations) on the specified number of threads to operate on data in a divide-and-conquer fashion instead of one-at-a-time.

How to use AsyncTaskReader

A common pipeline might read from a CSV file, perform a few transformations, filters, and conversions before writing to a database.

AsyncTaskReader comes in handy when some of the steps between reading and writing can be sped up by performing them on a partition of data concurrently.

The Read And Transform Data in Parallel example below shows how to perform RenameField and BasicFieldTransformer operations using 4 threads. The key is to supply AsyncTaskReader with a DataReaderDecorator instance that will be used to apply your operations to each of the parallel pipelines.

package com.northconcepts.datapipeline.examples.cookbook;


import com.northconcepts.datapipeline.core.AsyncTaskReader;
import com.northconcepts.datapipeline.core.DataReader;
import com.northconcepts.datapipeline.core.SortingReader;
import com.northconcepts.datapipeline.core.StreamWriter;
import com.northconcepts.datapipeline.csv.CSVReader;
import com.northconcepts.datapipeline.job.DataReaderDecorator;
import com.northconcepts.datapipeline.job.Job;
import com.northconcepts.datapipeline.transform.BasicFieldTransformer;
import com.northconcepts.datapipeline.transform.RenameField;
import com.northconcepts.datapipeline.transform.TransformingReader;

public class ReadAndTransformDataInParallel {

    private static final int THREADS = 4;

    public static void main(String[] args) {

        DataReader reader = new CSVReader(new File("example/data/input/credit-balance-02-1000000.csv"))

        reader = new AsyncTaskReader(reader, createTaskToRunInMultipleThreads(), THREADS); SortingReader(reader).add("Balance"), StreamWriter.newSystemOutWriter());

    private static DataReaderDecorator createTaskToRunInMultipleThreads() {
        return (originalDataReader) -> new TransformingReader(originalDataReader)
                .add(new RenameField("Rating", "AccountRating"))
                .add(new BasicFieldTransformer("Balance").stringToDouble().nullToValue(0.0));


How AsyncTaskReader Works

  1. Behind the scenes, AsyncTaskReader uses SplitWriter to divide and route the incoming data into multiple, independent streams based on the number of threads you specified.
  2. Each stream is decorated with your task using your instance of DataReaderDecorator. This allows it to perform identical work on the divided pipelines.
  3. An AsyncMultiReaderis the used to collect and merge the outgoing data from the parallel streams into a single pipeline.
  4. The AsyncMultiReader is set as the nested source for the outer AsyncTaskReader to supply its downstream readers.
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