Apache Flink

Overview

Apache Flink

Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities.

Learn more about Flink at https://flink.apache.org/

Features

  • A streaming-first runtime that supports both batch processing and data streaming programs

  • Elegant and fluent APIs in Java and Scala

  • A runtime that supports very high throughput and low event latency at the same time

  • Support for event time and out-of-order processing in the DataStream API, based on the Dataflow Model

  • Flexible windowing (time, count, sessions, custom triggers) across different time semantics (event time, processing time)

  • Fault-tolerance with exactly-once processing guarantees

  • Natural back-pressure in streaming programs

  • Libraries for Graph processing (batch), Machine Learning (batch), and Complex Event Processing (streaming)

  • Built-in support for iterative programs (BSP) in the DataSet (batch) API

  • Custom memory management for efficient and robust switching between in-memory and out-of-core data processing algorithms

  • Compatibility layers for Apache Hadoop MapReduce

  • Integration with YARN, HDFS, HBase, and other components of the Apache Hadoop ecosystem

Streaming Example

case class WordWithCount(word: String, count: Long)

val text = env.socketTextStream(host, port, '\n')

val windowCounts = text.flatMap { w => w.split("\\s") }
  .map { w => WordWithCount(w, 1) }
  .keyBy("word")
  .window(TumblingProcessingTimeWindow.of(Time.seconds(5)))
  .sum("count")

windowCounts.print()

Batch Example

case class WordWithCount(word: String, count: Long)

val text = env.readTextFile(path)

val counts = text.flatMap { w => w.split("\\s") }
  .map { w => WordWithCount(w, 1) }
  .groupBy("word")
  .sum("count")

counts.writeAsCsv(outputPath)

Building Apache Flink from Source

Prerequisites for building Flink:

  • Unix-like environment (we use Linux, Mac OS X, Cygwin, WSL)
  • Git
  • Maven (we recommend version 3.2.5 and require at least 3.1.1)
  • Java 8 or 11 (Java 9 or 10 may work)
git clone https://github.com/apache/flink.git
cd flink
mvn clean package -DskipTests # this will take up to 10 minutes

Flink is now installed in build-target.

NOTE: Maven 3.3.x can build Flink, but will not properly shade away certain dependencies. Maven 3.1.1 creates the libraries properly. To build unit tests with Java 8, use Java 8u51 or above to prevent failures in unit tests that use the PowerMock runner.

Developing Flink

The Flink committers use IntelliJ IDEA to develop the Flink codebase. We recommend IntelliJ IDEA for developing projects that involve Scala code.

Minimal requirements for an IDE are:

  • Support for Java and Scala (also mixed projects)
  • Support for Maven with Java and Scala

IntelliJ IDEA

The IntelliJ IDE supports Maven out of the box and offers a plugin for Scala development.

Check out our Setting up IntelliJ guide for details.

Eclipse Scala IDE

NOTE: From our experience, this setup does not work with Flink due to deficiencies of the old Eclipse version bundled with Scala IDE 3.0.3 or due to version incompatibilities with the bundled Scala version in Scala IDE 4.4.1.

We recommend to use IntelliJ instead (see above)

Support

Don’t hesitate to ask!

Contact the developers and community on the mailing lists if you need any help.

Open an issue if you found a bug in Flink.

Documentation

The documentation of Apache Flink is located on the website: https://flink.apache.org or in the docs/ directory of the source code.

Fork and Contribute

This is an active open-source project. We are always open to people who want to use the system or contribute to it. Contact us if you are looking for implementation tasks that fit your skills. This article describes how to contribute to Apache Flink.

About

Apache Flink is an open source project of The Apache Software Foundation (ASF). The Apache Flink project originated from the Stratosphere research project.

Owner
The Apache Software Foundation
The Apache Software Foundation
[CVPR 2021] "Multimodal Motion Prediction with Stacked Transformers": official code implementation and project page.

mmTransformer Introduction This repo is official implementation for mmTransformer in pytorch. Currently, the core code of mmTransformer is implemented

DeciForce: Crossroads of Machine Perception and Autonomy 232 Dec 31, 2022
A Python multilingual toolkit for Sentiment Analysis and Social NLP tasks

pysentimiento: A Python toolkit for Sentiment Analysis and Social NLP tasks A Transformer-based library for SocialNLP classification tasks. Currently

298 Jan 07, 2023
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W

Jungbeom Lee 81 Dec 16, 2022
Detect roadway lanes using Python OpenCV for project during the 5th semester at DHBW Stuttgart for lecture in digital image processing.

Find Line Detection (Image Processing) Identifying lanes of the road is very common task that human driver performs. It's important to keep the vehicl

LMF 4 Jun 21, 2022
Tensorflow-seq2seq-tutorials - Dynamic seq2seq in TensorFlow, step by step

seq2seq with TensorFlow Collection of unfinished tutorials. May be good for educational purposes. 1 - simple sequence-to-sequence model with dynamic u

Matvey Ezhov 1k Dec 17, 2022
PyTorch implementations of the paper: "DR.VIC: Decomposition and Reasoning for Video Individual Counting, CVPR, 2022"

DRNet for Video Indvidual Counting (CVPR 2022) Introduction This is the official PyTorch implementation of paper: DR.VIC: Decomposition and Reasoning

tao han 35 Nov 22, 2022
An implementation of paper `Real-time Convolutional Neural Networks for Emotion and Gender Classification` with PaddlePaddle.

简介 通过PaddlePaddle框架复现了论文 Real-time Convolutional Neural Networks for Emotion and Gender Classification 中提出的两个模型,分别是SimpleCNN和MiniXception。利用 imdb_crop

8 Mar 11, 2022
Auto White-Balance Correction for Mixed-Illuminant Scenes

Auto White-Balance Correction for Mixed-Illuminant Scenes Mahmoud Afifi, Marcus A. Brubaker, and Michael S. Brown York University Video Reference code

Mahmoud Afifi 47 Nov 26, 2022
A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation(DANN), support Office-31 and Office-Home dataset

DANN A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation Prerequisites Linux or OSX NVIDIA GPU + CUDA (may CuDNN) and corre

8 Apr 16, 2022
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

SeaLion is designed to teach today's aspiring ml-engineers the popular machine learning concepts of today in a way that gives both intuition and ways of application. We do this through concise algori

Anish 324 Dec 27, 2022
Code for the paper "Improved Techniques for Training GANs"

Status: Archive (code is provided as-is, no updates expected) improved-gan code for the paper "Improved Techniques for Training GANs" MNIST, SVHN, CIF

OpenAI 2.2k Jan 01, 2023
Face Recognition and Emotion Detector Device

Face Recognition and Emotion Detector Device Orange PI 1 Python 3.10.0 + Django 3.2.9 Project's file explanation Django manage.py Django commands hand

BootyAss 2 Dec 21, 2021
The source code and dataset for the RecGURU paper (WSDM 2022)

RecGURU About The Project Source code and baselines for the RecGURU paper "RecGURU: Adversarial Learning of Generalized User Representations for Cross

Chenglin Li 17 Jan 07, 2023
[SIGIR22] Official PyTorch implementation for "CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space".

CORE This is the official PyTorch implementation for the paper: Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao. CORE: Simple and Effective Sess

RUCAIBox 26 Dec 19, 2022
Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly

Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly Code for this paper Ultra-Data-Efficient GAN Tra

VITA 77 Oct 05, 2022
Python PID Tuner - Based on a FOPDT model obtained using a Open Loop Process Reaction Curve

PythonPID_Tuner Step 1: Takes a Process Reaction Curve in csv format - assumes data at 100ms interval (column names CV and PV) Step 2: Makes a rough e

6 Jan 14, 2022
Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis"

GRAF This repository contains official code for the paper GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. You can find detailed usage i

349 Dec 29, 2022
Reproducing-BowNet: Learning Representations by Predicting Bags of Visual Words

Reproducing-BowNet Our reproducibility effort based on the 2020 ML Reproducibility Challenge. We are reproducing the results of this CVPR 2020 paper:

6 Mar 16, 2022
Simple reference implementation of GraphSAGE.

Reference PyTorch GraphSAGE Implementation Author: William L. Hamilton Basic reference PyTorch implementation of GraphSAGE. This reference implementat

William L Hamilton 861 Jan 06, 2023
Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Hurdles to Progress in Long-form Question Answering This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hur

Kalpesh Krishna 41 Nov 08, 2022