EP61: The journey of a Slack message

This week’s system design refresher:

• Amazon Prime Video Ditches AWS Serverless (Youtube video)

• The journey of a Slack Message

• How GraphQL works in the real world

• Different cloud services

• Bytebytego Collective

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Amazon Prime Video ditches AWS Serverless, saves 90%

Why did Amazon Prime Video monitoring move from serverless to monolithic? How can it save 90% cost?

In this video, we will talk about:

• What is Amazon Prime Video Monitoring Service

• What is the problem with the old serverless architecture

• How the monolithic architecture saves 90% cost

• What did Amazon leaders say about this

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What is the journey of a Slack message?

In a recent technical article, Slack explains how its real-time messaging framework works. Here is my short summary:

• Because there are too many channels, the Channel Server (CS) uses consistent hashing to allocate millions of channels to many channel servers.

• Slack messages are delivered through WebApp and Admin Server to the correct Channel Server.

• Through Gate Server and Envoy (a proxy), the Channel Server will push messages to message receivers.

• Message receivers use WebSocket, which is a bi-directional messaging mechanism, so they are able to receive updates in real-time.

A Slack message travels through five important servers:

• WebApp: define the API that a Slack client could use

• Admin Server (AS): find the correct Channel Server using channel ID

• Channel Server (CS): maintain the history of message channel

• Gateway Server (GS): deployed in each geographic region. Maintain WebSocket channel subscription

• Envoy: service proxy for cloud-native applications

Over to you: The Channel Servers could go down. Since they use consistent hashing, how might they recover?

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How does GraphQL work in the real world?

The diagram below shows how LinkedIn adopts GraphQL.

The overall workflow after adopting GraphQL has 3 parts:

• Part 1 - Edit and Test a Query
  Steps 1-2: The client-side developer develops a query and tests with backend services.

• Part 2 - Register a Query
  Steps 3-4: The client-side developer commits the query and publishes the query to the query registry.

• Part 3 - Use in Production
  Step 5: The query is released together with the client code.
  
  Steps 6-7: The routing metadata is included with each registered query. The metadata is used at the traffic routing tier to route the incoming requests to the correct service cluster.
  
  Step 8: The registered queries are cached at service runtime.
  
  Step 9: The sample query goes to the identity service first to retrieve members and then goes to the organization service to retrieve company information.

• LinkedIn doesn’t deploy a GraphQL gateway for two reasons:
  1. Prevent an additional network hop
  2. Avoid single point of failure

Over to you: How are GraphQL queries managed in your project?

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A nice cheat sheet of different cloud services

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