Leslie Lamport: Programming is more than coding
- We should be thinking harder before we start coding. Clear thinking can prevent errors. Fuzzy/wishful thinking can’t.
- How do you think clearly? Write.
- Specifications: help us think clearly.
- Think like a scientist!
In computer science:
- reality is digital systems, processor chips.
- models are turing machines, tc..
- what is a program? code that makes sense to think about itself
- functions in math are not equal to functions in programming languages
- program execution represented as a behavior
- behavior is a sequence of states
- program is a sequence of behaviors
Best place to eliminate code is to think about what you need to do and what you don’t need to do.
How to describe a set of behaviors
- Theorems: behaviors can be described by safety and liveness properties.
- Safety property: something bad doesn’t happen (doesn’t throw exception)
- Liveness property: something good eventually does happen (program eventually returns)
- Errors more likely to occur in safety properties.
current state, next state transition can be described by a formula
- Engineers starting to use TLA+ to describe system behaviors
- debug 6 lines of specs better than debugging 850+ LOC.
- when you write specs, you should write formal specs.
- Before you write code, write spec.
- Thus, you write better programs.
Netflix: RxJava services
- Observables vs Iterables. Push vs Pull.
- Observable is at the core: abstraction of events over sets.
- Prefer Observable over Future because Future is still fragmented in Java ecosystem.
- Using Rx services that aggregate granular APIs.
Cold finite streams
- Shows an example of observable API - multiple network calls
- Instead of blocking APIs, can do async Observable APIs
- Decouples production from consumption. No matter how you implement
- Very cool example: a Collapser can batch up granular network calls in windows and send them off to a REST API.
- This lets you treat network calls like in-process methods, which goes against typical warnings in distributed systems theory
- Retrieval, transformation
- Flow control: You need backpressure when you hop threads. what do you do when you consume slower than producer? Often necessary in UIs
- Hot vs Cold source: Hot: emits whether you’re ready or not. mouse event.Cold: emits when requested: HTTP request.
- Approach: block threads. like iterables.
- Hot streams: use temporal operators: like
- Buffer, debounce, buffer pattern - can group signals by temporal
- Reactive push: hot infinite stream: Buffer by time window, drop some samples if appropriately, do map/reduce on windows
- imperative -> functional
- sync -> async
- pull -> push
- Rx doesn’t trivialize concurrency. You need to reason about what’s going on underneath.
Evolution to Reactive
- OK for small services, startups
- simple at first
- queries are easy
- “if you don’t end up regretting your early tech decisions, you probably overengineered” (so it’s OK!)
Evolve to Microservices
Microservices: loosely-coupled service oriented architecture with bounded contexts
- single purpose
- simple well-defined interface
- modular and independent
- more a grpah of relationships than tiers
- isolated persistence!
- each unit is simple
- helps your company scale. people can hold it in their heads.
- unit is simple
- independent scaling and performance
- independent testing and deployment
- tune performance
- many cooperating units
- many small repos
- requires sophistication around tooling and dep management
- network latency
(Not a panacea for everything.)
- all groups are organized into services: gmail, app engine, bigtable
- self-sufficient and autonomous
- layered on each other – empowers engineers to focus on the domains
- first tenet is to be responsive, fail latencies, async nonblocking calls from client
- resilient: redundancy, timeouts, retries. hystrix
- “release it” book my michael nygard
- elastic: can scale up and down according to load.
- message-driven: message passing
- FRP patterns: actor model
- scala/akka + rxjava
Kixeye service chassis
- Takes a long time to build and deploy a new service
- goal: make it easy to build/deploy new uservice
- load balancing for downstream services
- failiure management for downstream svc
- eureka: service registry
- 15m no code to running service in AWS
How to do this?
- Don’t migrate in one big bang. do it incrementally.
- find your worst scaling bottleneck
- wall it off behind an interface
- replace it
- then do it again
- common chassis
- define service interface. discuss with clients. agree
- prototype: simplest thing that could possibly work. client can integrate with prototype.
- distributed tracing – track a request through multiple services
- network visualization
- dash metrics - scan health
- hystrix and turbine generate nice visualizations
- Can have smaller teams
- teams are autonomous, can make their own technology and implementation choices
- let the team own the HOW
- vendor/customer relationship: friendly, cooperative, but structured
- clear ownership and division of responsibility
- the customer can choose whether to use that service or not. if it’s not the best choice for the client, why are we building/maintaining it?
- We should have market forces inside our companies than outside.
Paypal used to have a fragmented, legacy API ecosystem. Circa 2012, they began an initiative to standardize and modernize their API infrastructure.
Defined a target state:
- API first
- API as a product
- REST first
- Built using facades
- Facades coordinate between internal services (microservices?)
- Tiered approach: experience APIs (facades) and capability APIs (core services)
- How did they assess success? Did a maturity model. Level 1-5
- People got into competitions
- Used DDD - bounded contexts - shared language
- Evolution is more than technology