High performance distributed data processing and machine learning.

dpe provides a high-level API in Javascript and an optimized parallel execution engine on top of NodeJS.

Features

• Pure javascript implementation of a Spark like engine
• Multiple data sources: filesystems, databases, cloud (S3, azure)
• Multiple data formats: CSV, JSON, Columnar (Parquet)...
• 50 high level operators to build parallel apps
• Machine learning: scalable classification, regression, clusterization
• Run interactively in a nodeJS REPL shell
• Docker ready, simple local mode or full distributed mode
• Very fast, see benchmark

Quickstart

npm install dpe

Word count example:

var sc = require('@praveensastry/dpe').context();

sc.textFile('/my/path/*.txt')
  .flatMap(line => line.split(' '))
  .map(word => [word, 1])
  .reduceByKey((a, b) => a + b, 0)
  .count(function (err, result) {
    console.log(result);
    sc.end();
  });

Local mode

In local mode, worker processes are automatically forked and communicate with app through child process IPC channel. This is the simplest way to operate, and it allows to use all machine available cores.

To run in local mode, just execute your app script:

node my_app.js

or with debug traces:

dpe_DEBUG=2 node my_app.js

Distributed mode

In distributed mode, a cluster server process and worker processes must be started prior to start app. Processes communicate with each other via raw TCP or via websockets.

To run in distributed cluster mode, first start a cluster server on server_host:

./bin/server.js

On each worker host, start a worker controller process which connects to server:

./bin/worker.js -H server_host

Then run your app, setting the cluster server host in environment:

dpe_HOST=server_host node my_app.js

The same with debug traces:

dpe_HOST=server_host dpe_DEBUG=2 node my_app.js

License

Apache-2.0

Credits