Building Scalable, Highly Concurrent & Fault-Tolerant Systems: Lessons Learned

Table of Contents

1 It's All Trade-offs

  • How do I know if I have a performance problem? If your system is slow for a single user
  • How do I know if I have a scalability problem? If your system is fast for a single user but slow under heavy load
  • Latency vs Throughput You should strive for maximal throughput with acceptable latency
  • Availability vs Consistency

2 Go Concurrent

  • Shared mutable state Together with threads 线程使用共享可修改状态使得代码不稳定
    • code that is totally INDETERMINISTIC
    • and the root of all EVIL
  • The problem with locks 锁带来的问题
    • Locks do not compose 锁不能够进行组合
    • Locks breaks encapsulation 破坏封装
    • Taking too few locks
    • Taking too many locks
    • Taking the wrong locks
    • Taking locks in the wrong order 错误顺序
    • Error recovery is hard 错误恢复处理
  • You deserve better tools 高并发更好的工具和做法
    • Dataflow Concurrency 基于数据流的并发
      • Deterministic
      • Declarative
      • Data-driven
  • Threads are suspended until data is available
  • Lazy & On-demand

    • No difference between
      • Concurrent &
      • Sequential code
    • Actors 轻量线程模式,传递消息方式进行通信
      • Share NOTHING
      • Isolated lightweight event-based processes
      • Each actor has a mailbox (message queue)
      • Communicates through asynchronous and non-blocking message passing
      • Examples: Akka, Erlang
    • Software Transactional Memory (STM) 软件事务内存,更新内存是原子操作,类似DB的transaction实现
      • See the memory as a transactional dataset
      • Similar to a database
      • Transactions are retried automatically upon collision
      • Rolls back the memory on abort
    • Agents 相当于worker角色,做一些异步操作的工作

3 Go Fault-Tolerant

  • Failure management in Java/C/C# etc 在独立的线程里面必须进行错误处理,否则外部没有办法发现错误。这样在线程里面整个错误处理贯穿于逻辑本身
    • You are given a SINGLE thread of control
    • If this thread blows up you are screwed
    • So you need to do all explicit error handling WITHIN this single thread
    • To make things worse - errors do not propagate between threads so there is NO WAY OF EVEN FINDING OUT that something have failed
    • This leads to DEFENSIVE programming with:
      • Error handling TANGLED with business logic
      • SCATTERED all over the code base
  • The right way 正确的方式是每个线程都有独立的监控线程,所有的错误都会发送到这个监控线程,然后由这个监控线程进行处理。在语义上来说remote和local是一样的,这点可能更容易做错误控制
    • Isolated Processes (Units of Computation)
    • Process Supervision
      • Each running process has a supervising process
      • Errors are sent to the supervisor
      • Supervisor manages the failure
    • Same semantics local as remote
    • For example the Actor Model solves it nicely

4 Go Faster & Go More

  • Never block
    • …unless you really have to
    • Blocking kills scalability (and performance)
    • Never sit on resources you don’t use
    • Use non-blocking IO
  • Go Async
    • Use asynchronous message passing
    • Design reactive event-driven systems
    • Use push not pull or poll #note: 可靠性是个问题
    • Don’t use explicit thread management
  • How fast is fast enough?
    • Measure, measure and measure
    • Start with a baseline
    • Define “good enough”
    • Beware of micro-benchmarks

5 Go Distributed

Werner Vogels’ Misconceptions about Reliable Distributed Computing

  1. Transparency is the ultimate goal
  2. Automatic object replication is desirable
  3. All replicas are equal and deterministic

Worth keeping an eye on

  • The CALM Conjecture
  • Could be the future of Distributed Computing
  • Declarative
  • Deterministic
  • Removes TIME, i.e. the need for ordering
  • Check out the BLOOM language

6 Go Big

6.1 Data

  • Imperative OO programming (a la Hadoop) doesn't cut it
    • Object-Mathematics Impedance Mismatch
    • We need functional processing, transformations etc.
    • Examples:Crunch/Scrunch,Cascading,Cascalog, Scalding, Scala Parallel Collections
    • Is the assembly language of MapReduce programming
    • Watch “Why Big Data Needs To Be Functional” by Dean Wampler
  • Batch processing (a la Hadoop) doesn't cut it
    • We need real-time data processing
    • Examples:Spark,Storm,GridGain,Akkaetc.

6.2 DB

  • Scaling reads to a RDBMS is hard
  • Scaling writes to a RDBMS is impossible