Alternatives to Sanic

One of our top priorities at Pinterest is fostering a safe and trustworthy experience for all Pinners. As Pinterest’s user base and ads business grow, the review volume has been increasing exponentially, and more content types require moderation support. To solve greater engineering and operational challenges at scale, we needed a highly-reliable and performant system to detect, report, evaluate, and act on abusive content and users and so we created Pinqueue.

Pinqueue-3.0 serves as a generic platform for content moderation and human labeling. Under the hood, Pinqueue3.0 is a Flask + React app powered by Pinterest’s very own Gestalt UI framework. On the backend, Pinqueue3.0 heavily relies on PinLater, a Pinterest-built reliable asynchronous job execution system, to handle the requests for enqueueing and action-taking. Using PinLater has significantly strengthened Pinqueue3.0’s overall infra with its capability of processing a massive load of events with configurable retry policies.

Hundreds of millions of people around the world use Pinterest to discover and do what they love, and our job is to protect them from abusive and harmful content. We’re committed to providing an inspirational yet safe experience to all Pinners. Solving trust & safety problems is a joint effort requiring expertise across multiple domains. Pinqueue3.0 not only plays a critical role in responsively taking down unsafe content, it also has become an enabler for future ML/automation initiatives by providing high-quality human labels. Going forward, we will continue to improve the review experience, measure review quality and collaborate with our machine learning teams to solve content moderation beyond manual reviews at an even larger scale.

Hey, so I developed a basic application with Python. But to use it, you need a python interpreter. I want to add a GUI to make it more appealing. What should I choose to develop a GUI? I have very basic skills in front end development (CSS, JavaScript). I am fluent in python. I'm looking for a tool that is easy to use and doesn't require too much code knowledge. I have recently tried out Flask, but it is kinda complicated. Should I stick with it, move to Django, or is there another nice framework to use?

Investigating Tortoise ORM and GINO ORM...

I need to introduce some async ORM with the current stack: Tornado with asyncio loop, AIOHTTP, with PostgreSQL and MSSQL. This project revolves heavily around realtime and due to the realtime requirements, blocking during database access is not acceptable.

Considering that these ORMs are both young projects, I wondered if anybody had some experience with similar stack and these async ORMs?

The 350M API requests we handle daily include many processing tasks such as image enhancements, resizing, filtering, face recognition, and GIF to video conversions.

Tornado is the one we currently use and aiohttp is the one we intend to implement in production in the near future. Both tools support handling huge amounts of requests but aiohttp is preferable as it uses asyncio which is Python-native. Since Python is in the heart of our service, we initially used PIL followed by Pillow. We kind of still do. When we figured resizing was the most taxing processing operation, Alex, our engineer, created the fork named Pillow-SIMD and implemented a good number of optimizations into it to make it 15 times faster than ImageMagick

Thanks to the optimizations, Uploadcare now needs six times fewer servers to process images. Here, by servers I also mean separate Amazon EC2 instances handling processing and the first layer of caching. The processing instances are also paired with AWS Elastic Load Balancing (ELB) which helps ingest files to the CDN.

Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.

Our whole DevOps stack consists of the following tools:

• GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
• Respectively Git as revision control system
• SourceTree as Git GUI
• Visual Studio Code as IDE
• CircleCI for continuous integration (automatize development process)
• Prettier / TSLint / ESLint as code linter
• SonarQube as quality gate
• Docker as container management (incl. Docker Compose for multi-container application management)
• VirtualBox for operating system simulation tests
• Kubernetes as cluster management for docker containers
• Heroku for deploying in test environments
• nginx as web server (preferably used as facade server in production environment)
• SSLMate (using OpenSSL) for certificate management
• Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
• PostgreSQL as preferred database system
• Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

• Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
• Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
• Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
• Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
• Scalability: All-in-one framework for distributed systems.
• Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

We chose AWS because, at the time, it was really the only cloud provider to choose from.

We tend to use their basic building blocks (EC2, ELB, Amazon S3, Amazon RDS) rather than vendor specific components like databases and queuing. We deliberately decided to do this to ensure we could provide multi-cloud support or potentially move to another cloud provider if the offering was better for our customers.

We’ve utilized c3.large nodes for both the Node.js deployment and then for the .NET Core deployment. Both sit as backends behind an nginx instance and are managed using scaling groups in Amazon EC2 sitting behind a standard AWS Elastic Load Balancing (ELB).

While we’re satisfied with AWS, we do review our decision each year and have looked at Azure and Google Cloud offerings.

#CloudHosting #WebServers #CloudStorage #LoadBalancerReverseProxy

When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

We've been happy with nginx as part of our stack. As an open source web application that folks install on-premise, the configuration system for the webserver is pretty important to us. I have a few complaints (e.g. the configuration syntax for conditionals is a pain), but overall we've found it pretty easy to build a configurable set of options (see link) for how to run Zulip on nginx, both directly and with a remote reverse proxy in front of it, with a minimum of code duplication.

Certainly I've been a lot happier with it than I was working with Apache HTTP Server in past projects.

To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

#BigData #AWS #DataScience #DataEngineering

Our whole DevOps stack consists of the following tools:

• GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
• Respectively Git as revision control system
• SourceTree as Git GUI
• Visual Studio Code as IDE
• CircleCI for continuous integration (automatize development process)
• Prettier / TSLint / ESLint as code linter
• SonarQube as quality gate
• Docker as container management (incl. Docker Compose for multi-container application management)
• VirtualBox for operating system simulation tests
• Kubernetes as cluster management for docker containers
• Heroku for deploying in test environments
• nginx as web server (preferably used as facade server in production environment)
• SSLMate (using OpenSSL) for certificate management
• Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
• PostgreSQL as preferred database system
• Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

• Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
• Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
• Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
• Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
• Scalability: All-in-one framework for distributed systems.
• Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

Hi Otensia! I'd definitely recommend using the skills you've already got and building with JavaScript is a smart way to go these days. Most platform services have JavaScript/Node SDKs or NPM packages, many serverless platforms support Node in case you need to write any backend logic, and JavaScript is incredibly popular - meaning it will be easy to hire for, should you ever need to.

My advice would be "don't reinvent the wheel". If you already have a skill set that will work well to solve the problem at hand, and you don't need it for any other projects, don't spend the time jumping into a new language. If you're looking for an excuse to learn something new, it would be better to invest that time in learning a new platform/tool that compliments your knowledge of JavaScript. For this project, I might recommend using Netlify, Vercel, or Google Firebase to quickly and easily deploy your web app. If you need to add user authentication, there are great examples out there for Firebase Authentication, Auth0, or even Magic (a newcomer on the Auth scene, but very user friendly). All of these services work very well with a JavaScript-based application.

For inboxkitten.com, an opensource disposable email service;

We migrated our serverless workload from Cloud Functions for Firebase to CloudFlare workers, taking advantage of the lower cost and faster-performing edge computing of Cloudflare network. Made possible due to our extremely low CPU and RAM overhead of our serverless functions.

If I were to summarize the limitation of Cloudflare (as oppose to firebase/gcp functions), it would be ...

1. <5ms CPU time limit
2. Incompatible with express.js
3. one script limitation per domain

Limitations our workload is able to conform with (YMMV)

For hosting of static files, we migrated from Firebase to CommonsHost

More details on the trade-off in between both serverless providers is in the article

I want to make application like Zomato, #Foodpanda.

Which stack is best for this? As I have expertise in Java and Angular. What is the best stack you will recommend?

Web Micro-service / Mono? Angular / React? Amazon Web Services (AWS) / Google Cloud Platform? DB : SQL or No SQL

Mob Cross-platform: React Native / Flutter

Note: We are a team of 5. what languages do you recommend if I go with microservices?

Thanks

Hello everyone, I would like to start using a cloud service to host my projects, which are web applications. If anyone has enough experience with Microsoft Azure, Amazon Web Services (AWS) or Google Cloud Platform, I would like to know which of these is most recommended to use, depending on the features they have or how used they are. Thank you so much.