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Backend Thinking

Backend Role

Transform business requirements to action, which usually involves:

  • Service:
    • ZaloPay use microservices architecture, mostly written using Go and Java
  • API:
    • HTTP (Client-Server) and GRPC (Server-Server)
  • Database/Cache/Storage/Message Broker
    • MySQL/Redis/S3/Kafka
    • CRUD
  • Docs
    • Mostly design notes and diagrams which show how to implement business requirements

After successfully do all of that, next step is:

  • Testing
    • Unit tests, Integration tests
  • Observation
    • Log
    • Metrics
    • Tracing

In ZaloPay, each team has its own responsibilities/domains, aka many different services.

Ideally each team can choose custom backend techstack if they want, but mostly boils down to Java or Go. Some teams use Python for scripting, data processing, ...

Example: Team UM (User Management) has 10+ Java services and 30+ Go services.

The question is for each new business requirements, what should we do:

  • Create new services with new APIs?
  • Add new APIs to existing services?
  • Update existing APIs?
  • Change configs?
  • Don't do anything?

Example: Business requirements says: Must match/compare user EKYC data with Bank data (name, dob, id, ...).

Technical side

Backend services talk to Frontend, and talk to each other.

How do they communicate?

API

First is through API, this is the direct way, you send a request then you wait for response.

HTTP

  • Use HTTP Method GET/POST/…
  • HTTP responses status code
  • ZaloPay rule
    • Only return code 200
    • Response body is only JSON

GRPC

  • Use proto file as contract
  • GRPC status code
    • OK
    • INVALID_ARGUMENT
    • INTERNAL …

There are no hard rules on how to design APIs, only some best practices, like REST API, ...

Correct answer will always be: "It depends". Depends on:

  • Your audience (android, ios, web client or another internal service)
  • Your purpose (allow to do what?)
  • Your current techstack (technology limitation?)
  • Your team (bias, prefer, ...?)
  • ...

Why do we use HTTP for Client-Server and GRPC for Server-Server?

  • HTTP for Client-Server is pretty standard. Easy for client to debug, ...
  • Before ZaloPay switch to GRPC for Server-Server, we use HTTP. The reason for switch is mainly performance.

Message Broker

Second way is by Message Broker, the most well known is Kafka.

Main idea is decoupling.

Imaging service A need to call services B, C, D, E after doing some action, but B just died. We must handle B errors gracefully if B is not that important (not affect main flow of A). Imaging not only B, but multi B, like B1, B2, B3, ... Bn, this is so depressed to continue.

Message Broker is a way to detach B from A.

Dumb exaplain be like: each time A do something, A produces message to Message Broker, than A forgets about it. Then all B1, B2 can consume A's message if they want and do something with it, A does not know and does not need to know about it.

sequenceDiagram
    participant A
    participant B
    participant C
    participant D
    
    A ->> B: do something
    A ->> C: do something
    A ->> D: do something

sequenceDiagram
    participant A
    participant B
    participant C
    participant D
    
    A ->> B: do something
    A ->> C: do something
    A -x D: do something but failed

sequenceDiagram
    participant A
    participant B
    participant C
    participant D
    participant Kafka
    
    A ->> B: do something
    A ->> C: do something
    A ->> Kafka: produce message
    D ->> Kafka: consume message
    D ->> D: do something

Tip

  • Whatever you design, you stick with it consistently. Don't use different name for same object/value in your APIs.
  • Don't trust client blindly, everything can be fake, everything must be validated. We can not know the request is actually from our client or some hacker computer. (Actually we can but this is out of scope, and require lots of advance work)
  • Don't delete/rename/change old fields because you want and you can, please think it through before do it. Because back compability is very hard, old apps should continue to function if user don't upgrade. Even if we rollout new version, it takes time.

Pro tip: Use proto to define models (if you can) to take advantage of detecting breaking changes.

References

Coding principle

You should know about DRY, SOLID, KISS, YAGNI, Design Pattern. The basic is learning which is which when you read code. Truly understand will be knowing when to use and when to not.

All of these above are industry standard.

Write code that is easy delete

The way business moving is fast, so a feature is maybe implemented today, but gets thrown out of window tomorrow (Like A/B testing, one of them is chosen, the other says bye). So how do we adapt? The problem is to detect, which code/function is likely stable, resisted changing and which is likely to change.

For each service, I often split to 3 layers: handler, service, repository.

  • Handler layer: Handle HTTP/GRPC/Message Broker/...
  • Service layer: All rules, logic goes here.
  • Repository layer: Interact with cache/databases using CRUD and some cache strategy.

Handler layer is likely never changed. Repository layer is rarely changed. Service layer is changed daily, this is where I put so much time on.

The previous question can be asked in many ways:

  • How to move fast without breaking things?
  • How to quickly experiment new code without affecting old code?
  • ...

My answer is, as Message Broker introduce concept decoupling, loosely coupled coding. Which means, 2 functions which do not share same business can be deleted without breaking the other.

For example, we can send noti to users using SMS, Zalo, or noti-in-app (3 providers). They are all independently feature which serves same purpose: alert user about something. What happen if we add providers or remove some? Existing providers keep working as usual, new providers should behave properly too.

So we have send noti abstraction, which can be implement by each provider, treat like a module (think like lego) which can be plug and play right away.

And when we do not need send noti anymore, we can delete whole of it which includes all providers and still not affecting main flow.

Write code that is easy to test

Test is not a way to find bug, but to make sure what we code is actually what we think/expect.

Best case is test with real dependencies (real servives, real Redis, real MySQL, real Kafka, ...). But it's not easy way to setup yourself.

The easier way is to use mocks. Mock all dependencies to test all possible edge cases you can think of.

  • Unit tests is the standard (ZaloPay currently requires 90% test coverage).
    • Easy to test small to medium function which have simple rules, likely single purpose, with table testing technique.
    • For big, complex function, the strategy testing goes from happy case to each single edge case, each single if else path,... try to cover as much as you can.
  • Integration tests is to test your system as a whole package, can be in 2 ways:
    • Locally, which require to run in your computer with fully set up dependencies, is hard to set up.
    • Remotely, use DEV/... env to test full business flow with all possible scenario.

TODO: Show example

How to make code easier to test. Same idea loosely coupled as above.

Some tips:

  • Rely on abstraction/interface to mock
  • Limit variable outside input (global variable, environment variable, ...)
  • If deleting/adding code but tests are still passed, then maybe your tests are wrong/not enough (tests is missing some code path).

References

System Design Concept

Start with basic: getting data from database.

sequenceDiagram
    participant service
    participant database
    
    service ->> database: get (100ms)

Getting data from cache first, then database later.

sequenceDiagram
    participant service
    participant cache
    participant database
    
    service ->> cache: get (5ms)
    alt not exist in cache
        service ->> database: get (100ms)
    end

If data is already in cache, we can get it so fast (5ms), nearly instant. But if not, we hit penalty, must get database after then re-update cache if need (>105ms). The best case is worth even if hitting penalty sometimes.

Basic cache strategy: combine Write Through and Read Through

sequenceDiagram
    participant service
    participant cache
    participant database
    
    note over service,database: Read Through
    service ->> cache: get
    alt not exist in cache
        service ->> database: get
        service ->> cache: set
    end

    note over service,database: Write Through
    service ->> database: set
    service ->> cache: set

References

Bonus