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
Code for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in Deep Latent Space"

SRHEN This is a better and simpler implementation for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in

1 Oct 28, 2022
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation, NeurIPS 2021 Spotlight

PCAN for Multiple Object Tracking and Segmentation This is the offical implementation of paper PCAN for MOTS. We also present a trailer that consists

ETH VIS Group 328 Dec 29, 2022
PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Unsupervised_IEPGAN This is the PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer. Ha

25 Oct 26, 2022
Computational inteligence project on faces in the wild dataset

Table of Contents The general idea How these scripts work? Loading data Needed modules and global variables Parsing the arrays in dataset Extracting a

tooraj taraz 4 Oct 21, 2022
Federated Learning Based on Dynamic Regularization

Federated Learning Based on Dynamic Regularization This is implementation of Federated Learning Based on Dynamic Regularization. Requirements Please i

39 Jan 07, 2023
Seg-Torch for Image Segmentation with Torch

Seg-Torch for Image Segmentation with Torch This work was sparked by my personal research on simple segmentation methods based on deep learning. It is

Eren Gölge 37 Dec 12, 2022
Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"

Visually Grounded Bert Language Model This repository is the official implementation of Explainable Semantic Space by Grounding Language to Vision wit

17 Dec 17, 2022
This project is based on RIFE and aims to make RIFE more practical for users by adding various features and design new models

CPM 项目描述 CPM(Chinese Pretrained Models)模型是北京智源人工智能研究院和清华大学发布的中文大规模预训练模型。官方发布了三种规模的模型,参数量分别为109M、334M、2.6B,用户需申请与通过审核,方可下载。 由于原项目需要考虑大模型的训练和使用,需要安装较为复杂

hzwer 190 Jan 08, 2023
Implementation of the paper "Generating Symbolic Reasoning Problems with Transformer GANs"

Generating Symbolic Reasoning Problems with Transformer GANs This is the implementation of the paper Generating Symbolic Reasoning Problems with Trans

Reactive Systems Group 1 Apr 18, 2022
RoboDesk A Multi-Task Reinforcement Learning Benchmark

RoboDesk A Multi-Task Reinforcement Learning Benchmark If you find this open source release useful, please reference in your paper: @misc{kannan2021ro

Google Research 66 Oct 07, 2022
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition

AdaFocusV2 This repo contains the official code and pre-trained models for AdaFo

79 Dec 26, 2022
A Kaggle competition: discriminate gender based on handwriting

Gender discrimination based on handwriting See http://fastml.com/gender-discrimination/ for description. prep_data.py - a first step chunk_by_authors.

Zygmunt Zając 22 Jul 20, 2022
official implemntation for "Contrastive Learning with Stronger Augmentations"

CLSA CLSA is a self-supervised learning methods which focused on the pattern learning from strong augmentations. Copyright (C) 2020 Xiao Wang, Guo-Jun

Lab for MAchine Perception and LEarning (MAPLE) 47 Nov 29, 2022
A simple editor for captions in .SRT file extension

WaySRT A simple editor for captions in .SRT file extension The program doesn't use any external dependecies, just run: python way_srt.py {file_name.sr

Gustavo Lopes 3 Nov 16, 2022
Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network

DeepCDR Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network This work has been accepted to ECCB2020 and was also published in the

Qiao Liu 50 Dec 18, 2022
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course

Deep Learning with TensorFlow 2 and Keras – Notebooks This project accompanies my Deep Learning with TensorFlow 2 and Keras trainings. It contains the

Aurélien Geron 1.9k Dec 15, 2022
[cvpr22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation

PS-MT [cvpr22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation by Yuyuan Liu, Yu Tian, Yuanhong Chen, Fengbei Liu, Vasile

Yuyuan Liu 132 Jan 03, 2023
My implementation of DeepMind's Perceiver

DeepMind Perceiver (in PyTorch) Disclaimer: This is not official and I'm not affiliated with DeepMind. My implementation of the Perceiver: General Per

Louis Arge 55 Dec 12, 2022
Object detection on multiple datasets with an automatically learned unified label space.

Simple multi-dataset detection An object detector trained on multiple large-scale datasets with a unified label space; Winning solution of E

Xingyi Zhou 407 Dec 30, 2022
Yet Another Robotics and Reinforcement (YARR) learning framework for PyTorch.

Yet Another Robotics and Reinforcement (YARR) learning framework for PyTorch.

Stephen James 51 Dec 27, 2022