A anime Discovery, Streaming Site Made with Nextjs and TailwindCSS

The open-source anime streaming service made with NextJs and TailwindCSS. It lets you search, watch animes without any ads with a beautiful ui. It can be self hosted or deployed online.

Acknowledgements

AniList and Kitsu are the underlying public free API's that are used for fetching the data about animes.

riimuru/gogoanime-api is used for interacting with GogoAnime and scrape video soures.

Also thanks to these open source libraries Next.js, tailwindCSS, redux-toolkit, graphql etc.

Online Deployment

Following are the recommended online deployment services which are tested to work with this project. If you want to add a deployment service just open an issue.

Docker Deployment

The docker images for this app is available at dockerhub. chiragdroid/animeflix.

Run it easily using this command

docker run -p 3000:3000 ghcr.io/chirag-droid/animeflix

This will start animeflix at port 3000. You can change the port by doing -p <port>:3000,

You can run this as a background service by adding -d flag

Local Deployment

You need to have nodejs and git installed on your pc for following the intructions

First download the repository using

git clone https://github.com/chirag-droid/animeflix

Next make sure you have yarn installed on your system

npm i -g yarn

this step probably requires admin perms

This should download this repository to your computer. Next, to download the dependencies run

yarn install

Now build and start the production build of the site using

yarn build
yarn start

This will start the app on http://localhost:3000

Sponsor this project

"Buy Me A Coffee"

Author: chirag-droid
Source code: https://github.com/chirag-droid/animeflix
License: AGPL-3.0 license

#react #typescript #javascript #react-native #nextjs

A anime Discovery, Streaming Site Made with Nextjs and TailwindCSS

The open-source anime streaming service made with NextJs and TailwindCSS. It lets you search, watch animes without any ads with a beautiful ui. It can be self hosted or deployed online.

Acknowledgements

AniList and Kitsu are the underlying public free API's that are used for fetching the data about animes.

riimuru/gogoanime-api is used for interacting with GogoAnime and scrape video soures.

Also thanks to these open source libraries Next.js, tailwindCSS, redux-toolkit, graphql etc.

Online Deployment

Following are the recommended online deployment services which are tested to work with this project. If you want to add a deployment service just open an issue.

Docker Deployment

The docker images for this app is available at dockerhub. chiragdroid/animeflix.

Run it easily using this command

docker run -p 3000:3000 ghcr.io/chirag-droid/animeflix

This will start animeflix at port 3000. You can change the port by doing -p <port>:3000,

You can run this as a background service by adding -d flag

Local Deployment

You need to have nodejs and git installed on your pc for following the intructions

First download the repository using

git clone https://github.com/chirag-droid/animeflix

Next make sure you have yarn installed on your system

npm i -g yarn

this step probably requires admin perms

This should download this repository to your computer. Next, to download the dependencies run

yarn install

Now build and start the production build of the site using

yarn build
yarn start

This will start the app on http://localhost:3000

Sponsor this project

"Buy Me A Coffee"

Author: chirag-droid
Source code: https://github.com/chirag-droid/animeflix
License: AGPL-3.0 license

#react #typescript #javascript #react-native #nextjs

Gerhard Brink

1622108520

Stateful stream processing with Apache Flink(part 1): An introduction

Apache Flink, a 4th generation Big Data processing framework provides robust **stateful stream processing capabilitie**s. So, in a few parts of the blogs, we will learn what is Stateful stream processing. And how we can use Flink to write a stateful streaming application.

What is stateful stream processing?

In general, stateful stream processing is an application design pattern for processing an unbounded stream of events. Stateful stream processing means a** “State”** is shared between events(stream entities). And therefore past events can influence the way the current events are processed.

Let’s try to understand it with a real-world scenario. Suppose we have a system that is responsible for generating a report. It comprising the total number of vehicles passed from a toll Plaza per hour/day. To achieve it, we will save the count of the vehicles passed from the toll plaza within one hour. That count will be used to accumulate it with the further next hour’s count to find the total number of vehicles passed from toll Plaza within 24 hours. Here we are saving or storing a count and it is nothing but the “State” of the application.

Might be it seems very simple, but in a distributed system it is very hard to achieve stateful stream processing. Stateful stream processing is much more difficult to scale up because we need different workers to share the state. Flink does provide ease of use, high efficiency, and high reliability for the**_ state management_** in a distributed environment.

#apache flink #big data and fast data #flink #streaming #streaming solutions ##apache flink #big data analytics #fast data analytics #flink streaming #stateful streaming #streaming analytics

Teresa Jerde

1597452410

Spark Structured Streaming – Stateful Streaming

Welcome back folks to this blog series of Spark Structured Streaming. This blog is the continuation of the earlier blog “Internals of Structured Streaming“. And this blog pertains to Stateful Streaming in Spark Structured Streaming. So let’s get started.

Let’s start from the very basic understanding of what is Stateful Stream Processing. But to understand that, let’s first understand what Stateless Stream Processing is.

In my previous blogs of this series, I’ve discussed Stateless Stream Processing.

You can check them before moving ahead – Introduction to Structured Streaming and Internals of Structured Streaming

#analytics #apache spark #big data and fast data #ml #ai and data engineering #scala #spark #streaming #streaming solutions #tech blogs #stateful streaming #structured streaming

Ahebwe Oscar

1623192840

Does My Site Work?

EPISODE SUMMARY

On this episode, we will discuss how you can verify that your site works and continues to work. We’re digging into automated testing and how to write tests for your Django apps.

Full show notes are available at https://www.mattlayman.com/django-riffs/13.