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

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Jonas Bonér
Jonas Bonér

The document discusses lessons learned from building scalable, highly concurrent, and fault-tolerant systems, noting that shared mutable state and threads can lead to non-deterministic code, and recommending approaches like dataflow concurrency, actors, and software transactional memory to avoid these issues. It also advocates designing systems to be reactive by using asynchronous messaging and non-blocking I/O, and to be fault-tolerant using failure recovery mechanisms like those enabled by actor models.

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Building Scalable, Highly Concurrent & Fault-Tolerant Systems: Lessons Learned Jonas Bonér CTO Typesafe Twitter: @jboner
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I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Lessons I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Learned I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again through... I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again
I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Lessons I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Learned I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again through... I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Agony I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again
I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Lessons I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Learned I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again through... I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Agony and Pain I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again lots of Pain I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again
Agenda • It’s All Trade-offs • Go Concurrent • Go Reactive • Go Fault-Tolerant • Go Distributed • Go Big
It’s all Trade-offs
Performance vs Scalability
Latency vs Throughput
Availability vs Consistency
Go Concurrent
Shared mutable state
Shared mutable state Together with threads...
Shared mutable state Together with threads... ...leads to
Shared mutable state Together with threads... ...code that is totally INDETERMINISTIC ...leads to
Shared mutable state Together with threads... ...code that is totally INDETERMINISTIC ...leads to ...and the root of all EVIL
Shared mutable state Together with threads... ...code that is totally INDETERMINISTIC ...leads to ...and the root of all EVIL Please, avoid it at all cost
Shared mutable state B L E T U ! A Together with threads... M e!! IM at EVIL ...code that is totally INDETERMINISTIC e t ...leads to s s ...and the root of all U avoid it at all cost Please,
The problem with locks • Locks do not compose • Locks break encapsulation • Taking too few locks • Taking too many locks • Taking the wrong locks • Taking locks in the wrong order • Error recovery is hard
You deserve better tools • Dataflow Concurrency • Actors • Software Transactional Memory (STM) • Agents
Dataflow Concurrency • Deterministic • Declarative • Data-driven • Threads are suspended until data is available • Lazy & On-demand • No difference between: • Concurrent code • Sequential code • Examples: Akka & GPars
Actors •Share NOTHING •Isolated lightweight event-based processes •Each actor has a mailbox (message queue) •Communicates through asynchronous and non-blocking message passing •Location transparent (distributable) •Examples: Akka & Erlang
STM • See the memory as a transactional dataset • Similar to a DB: begin, commit, rollback (ACI) • Transactions are retried upon collision • Rolls back the memory on abort • Transactions can nest and compose • Use STM instead of abusing your database with temporary storage of “scratch” data • Examples: Haskell, Clojure & Scala
Agents • Reactive memory cells (STM Ref) • Send a update function to the Agent, which 1. adds it to an (ordered) queue, to be 2. applied to the Agent asynchronously • Reads are “free”, just dereferences the Ref • Cooperates with STM • Examples: Clojure & Akka
If we could start all over...
If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core
If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming
If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow
If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed
If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed • Actor/Agent-based Programming
If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed • Actor/Agent-based Programming 3. Add Mutability selectively - only where needed
If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed • Actor/Agent-based Programming 3. Add Mutability selectively - only where needed • Protected by Transactions (STM)
If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed • Actor/Agent-based Programming 3. Add Mutability selectively - only where needed • Protected by Transactions (STM) 4. Finally - only if really needed
If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed • Actor/Agent-based Programming 3. Add Mutability selectively - only where needed • Protected by Transactions (STM) 4. Finally - only if really needed • Add Monitors (Locks) and explicit Threads
Go Reactive
Never block • ...unless you really have to • Blocking kills scalability (and performance) • Never sit on resources you don’t use • Use non-blocking IO • Be reactive • How?
Go Async Design for reactive event-driven systems 1. Use asynchronous message passing 2. Use Iteratee-based IO 3. Use push not pull (or poll) • Examples: • Akka or Erlang actors • Play’s reactive Iteratee IO • Node.js or JavaScript Promises • Server-Sent Events or WebSockets • Scala’s Futures library
Go Fault-Tolerant
Failure Recovery in Java/C/C# etc.
Failure Recovery in Java/C/C# etc. • You are given a SINGLE thread of control
Failure Recovery in Java/C/C# etc. • You are given a SINGLE thread of control • If this thread blows up you are screwed
Failure Recovery in Java/C/C# etc. • 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
Failure Recovery in Java/C/C# etc. • 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
Failure Recovery in Java/C/C# etc. • 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:
Failure Recovery in Java/C/C# etc. • 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
Failure Recovery in Java/C/C# etc. • 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
Failure Recovery in Java/C/C# etc. • You are given a SINGLE thread of control d o • If this thread blows up you are screwed n !! • So you need to do all explicit error handling a ! c r WITHIN this single thread e te • To make things worse - errors do not W et propagate between threads so there is NO WAY OF EVEN FINDING OUT that b something have failed • This leads to DEFENSIVE programming with: • Error handling TANGLED with business logic • SCATTERED all over the code base
Just Let It Crash
The right way 1. Isolated lightweight processes 2. Supervised processes • Each running process has a supervising process • Errors are sent to the supervisor (asynchronously) • Supervisor manages the failure • Same semantics local as remote • For example the Actor Model solves it nicely
Go Distributed
Performance vs Scalability
How do I know if I have a performance problem?
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?
How do I know if I have a scalability problem? If your system is fast for a single user but slow under heavy load
(Three) Misconceptions about Reliable Distributed Computing - Werner Vogels 1. Transparency is the ultimate goal 2. Automatic object replication is desirable 3. All replicas are equal and deterministic Classic paper: A Note On Distributed Computing - Waldo et. al.
Fallacy 1 Transparent Distributed Computing • Emulating Consistency and Shared Memory in a distributed environment • Distributed Objects • “Sucks like an inverted hurricane” - Martin Fowler • Distributed Transactions • ...don’t get me started...
Fallacy 2 RPC • Emulating synchronous blocking method dispatch - across the network • Ignores: • Latency • Partial failures • General scalability concerns, caching etc. • “Convenience over Correctness” - Steve Vinoski
Instead
Instead Embrace the Network Use it i th w Asynchronous don e Message be d an Passing
Guaranteed Delivery Delivery Semantics • No guarantees • At most once • At least once • Once and only once
It’s all lies.
It’s all lies.
The network is inherently unreliable and there is no such thing as 100% guaranteed delivery It’s all lies.
Guaranteed Delivery
Guaranteed Delivery The question is what to guarantee
Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network?
Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network? 2. The message is - received by the receiver host’s NIC?
Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network? 2. The message is - received by the receiver host’s NIC? 3. The message is - put on the receiver’s queue?
Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network? 2. The message is - received by the receiver host’s NIC? 3. The message is - put on the receiver’s queue? 4. The message is - applied to the receiver?
Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network? 2. The message is - received by the receiver host’s NIC? 3. The message is - put on the receiver’s queue? 4. The message is - applied to the receiver? 5. The message is - starting to be processed by the receiver?
Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network? 2. The message is - received by the receiver host’s NIC? 3. The message is - put on the receiver’s queue? 4. The message is - applied to the receiver? 5. The message is - starting to be processed by the receiver? 6. The message is - has completed processing by the receiver?
Ok, then what to do? 1. Start with 0 guarantees (0 additional cost) 2. Add the guarantees you need - one by one
Ok, then what to do? 1. Start with 0 guarantees (0 additional cost) 2. Add the guarantees you need - one by one Different USE-CASES Different GUARANTEES Different COSTS
Ok, then what to do? 1. Start with 0 guarantees (0 additional cost) 2. Add the guarantees you need - one by one Different USE-CASES Different GUARANTEES Different COSTS For each additional guarantee you add you will either: • decrease performance, throughput or scalability • increase latency
Just
Just Use ACKing
Just Use ACKing and be done with it
Latency vs Throughput
You should strive for maximal throughput with acceptable latency
Go Big
Go Big Data
Big Data Imperative OO programming doesn't cut it • Object-Mathematics Impedance Mismatch • We need functional processing, transformations etc. • Examples: Spark, Crunch/Scrunch, Cascading, Cascalog, Scalding, Scala Parallel Collections • Hadoop have been called the: • “Assembly language of MapReduce programming” • “EJB of our time”
Big Data Batch processing doesn't cut it • Ala Hadoop • We need real-time data processing • Examples: Spark, Storm, S4 etc. • Watch“Why Big Data Needs To Be Functional” by Dean Wampler
Go Big DB
When is a RDBMS not good enough?
Scalingreads to a RDBMS is hard
Scalingwrites to a RDBMS is impossible
Do we really need a RDBMS?
Do we really need a RDBMS? Sometimes...
Do we really need a RDBMS?
Do we really need a RDBMS? But many times we don’t
Atomic Consistent Isolated Durable
Availability vs Consistency
Brewer’s CAP theorem
You can only pick 2 Consistency Availability Partition tolerance At a given point in time
Centralized system • In a centralized system (RDBMS etc.) we don’t have network partitions, e.g. P in CAP • So you get both: Consistency Availability
Distributed system • In a distributed (scalable) system we will have network partitions, e.g. P in CAP • So you get to only pick one: Consistency Availability
Basically Available Soft state Eventually consistent
Think about your data Then think again • When do you need ACID? • When is Eventual Consistency a better fit? • Different kinds of data has different needs • You need full consistency less than you think
How fast is fast enough? • Never guess: Measure, measure and measure • Start by defining a baseline • Where are we now? • Define what is “good enough” - i.e. SLAs • Where do we want to go? • When are we done? • Beware of micro-benchmarks
...or, when can we go for a beer? • Never guess: Measure, measure and measure • Start by defining a baseline • Where are we now? • Define what is “good enough” - i.e. SLAs • Where do we want to go? • When are we done? • Beware of micro-benchmarks
To sum things up... 1. Maximizing a specific metric impacts others • Every strategic decision involves a trade-off • There's no "silver bullet" 2. Applying yesterday's best practices to the problems faced today will lead to: • Waste of resources • Performance and scalability bottlenecks • Unreliable systems
SO
GO
...now home and build yourself Scalable, Highly Concurrent & Fault-Tolerant Systems
Thank You Email: jonas@typesafe.com Web: typesafe.com Twitter: @jboner

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

  • 1. Building Scalable, Highly Concurrent & Fault-Tolerant Systems: Lessons Learned Jonas Bonér CTO Typesafe Twitter: @jboner
  • 2. I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again
  • 3. I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Lessons I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Learned I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again through... I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again
  • 4. I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Lessons I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Learned I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again through... I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Agony I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again
  • 5. I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Lessons I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Learned I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again through... I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again Agony and Pain I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again lots of Pain I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again I will never use distributed transactions again
  • 6. Agenda • It’s All Trade-offs • Go Concurrent • Go Reactive • Go Fault-Tolerant • Go Distributed • Go Big
  • 7.
  • 9. Performance vs Scalability
  • 10. Latency vs Throughput
  • 11. Availability vs Consistency
  • 14. Shared mutable state Together with threads...
  • 15. Shared mutable state Together with threads... ...leads to
  • 16. Shared mutable state Together with threads... ...code that is totally INDETERMINISTIC ...leads to
  • 17. Shared mutable state Together with threads... ...code that is totally INDETERMINISTIC ...leads to ...and the root of all EVIL
  • 18. Shared mutable state Together with threads... ...code that is totally INDETERMINISTIC ...leads to ...and the root of all EVIL Please, avoid it at all cost
  • 19. Shared mutable state B L E T U ! A Together with threads... M e!! IM at EVIL ...code that is totally INDETERMINISTIC e t ...leads to s s ...and the root of all U avoid it at all cost Please,
  • 20. The problem with locks • Locks do not compose • Locks break encapsulation • Taking too few locks • Taking too many locks • Taking the wrong locks • Taking locks in the wrong order • Error recovery is hard
  • 21. You deserve better tools • Dataflow Concurrency • Actors • Software Transactional Memory (STM) • Agents
  • 22. Dataflow Concurrency • Deterministic • Declarative • Data-driven • Threads are suspended until data is available • Lazy & On-demand • No difference between: • Concurrent code • Sequential code • Examples: Akka & GPars
  • 23. Actors •Share NOTHING •Isolated lightweight event-based processes •Each actor has a mailbox (message queue) •Communicates through asynchronous and non-blocking message passing •Location transparent (distributable) •Examples: Akka & Erlang
  • 24. STM • See the memory as a transactional dataset • Similar to a DB: begin, commit, rollback (ACI) • Transactions are retried upon collision • Rolls back the memory on abort • Transactions can nest and compose • Use STM instead of abusing your database with temporary storage of “scratch” data • Examples: Haskell, Clojure & Scala
  • 25. Agents • Reactive memory cells (STM Ref) • Send a update function to the Agent, which 1. adds it to an (ordered) queue, to be 2. applied to the Agent asynchronously • Reads are “free”, just dereferences the Ref • Cooperates with STM • Examples: Clojure & Akka
  • 26. If we could start all over...
  • 27. If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core
  • 28. If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming
  • 29. If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow
  • 30. If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed
  • 31. If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed • Actor/Agent-based Programming
  • 32. If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed • Actor/Agent-based Programming 3. Add Mutability selectively - only where needed
  • 33. If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed • Actor/Agent-based Programming 3. Add Mutability selectively - only where needed • Protected by Transactions (STM)
  • 34. If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed • Actor/Agent-based Programming 3. Add Mutability selectively - only where needed • Protected by Transactions (STM) 4. Finally - only if really needed
  • 35. If we could start all over... 1. Start with a Deterministic, Declarative & Immutable core • Logic & Functional Programming • Dataflow 2. Add Indeterminism selectively - only where needed • Actor/Agent-based Programming 3. Add Mutability selectively - only where needed • Protected by Transactions (STM) 4. Finally - only if really needed • Add Monitors (Locks) and explicit Threads
  • 37. Never block • ...unless you really have to • Blocking kills scalability (and performance) • Never sit on resources you don’t use • Use non-blocking IO • Be reactive • How?
  • 38. Go Async Design for reactive event-driven systems 1. Use asynchronous message passing 2. Use Iteratee-based IO 3. Use push not pull (or poll) • Examples: • Akka or Erlang actors • Play’s reactive Iteratee IO • Node.js or JavaScript Promises • Server-Sent Events or WebSockets • Scala’s Futures library
  • 40. Failure Recovery in Java/C/C# etc.
  • 41. Failure Recovery in Java/C/C# etc. • You are given a SINGLE thread of control
  • 42. Failure Recovery in Java/C/C# etc. • You are given a SINGLE thread of control • If this thread blows up you are screwed
  • 43. Failure Recovery in Java/C/C# etc. • 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
  • 44. Failure Recovery in Java/C/C# etc. • 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
  • 45. Failure Recovery in Java/C/C# etc. • 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:
  • 46. Failure Recovery in Java/C/C# etc. • 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
  • 47. Failure Recovery in Java/C/C# etc. • 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
  • 48. Failure Recovery in Java/C/C# etc. • You are given a SINGLE thread of control d o • If this thread blows up you are screwed n !! • So you need to do all explicit error handling a ! c r WITHIN this single thread e te • To make things worse - errors do not W et propagate between threads so there is NO WAY OF EVEN FINDING OUT that b something have failed • This leads to DEFENSIVE programming with: • Error handling TANGLED with business logic • SCATTERED all over the code base
  • 50.
  • 51. The right way 1. Isolated lightweight processes 2. Supervised processes • Each running process has a supervising process • Errors are sent to the supervisor (asynchronously) • Supervisor manages the failure • Same semantics local as remote • For example the Actor Model solves it nicely
  • 53. Performance vs Scalability
  • 54. How do I know if I have a performance problem?
  • 55. How do I know if I have a performance problem? If your system is slow for a single user
  • 56. How do I know if I have a scalability problem?
  • 57. How do I know if I have a scalability problem? If your system is fast for a single user but slow under heavy load
  • 58. (Three) Misconceptions about Reliable Distributed Computing - Werner Vogels 1. Transparency is the ultimate goal 2. Automatic object replication is desirable 3. All replicas are equal and deterministic Classic paper: A Note On Distributed Computing - Waldo et. al.
  • 59. Fallacy 1 Transparent Distributed Computing • Emulating Consistency and Shared Memory in a distributed environment • Distributed Objects • “Sucks like an inverted hurricane” - Martin Fowler • Distributed Transactions • ...don’t get me started...
  • 60. Fallacy 2 RPC • Emulating synchronous blocking method dispatch - across the network • Ignores: • Latency • Partial failures • General scalability concerns, caching etc. • “Convenience over Correctness” - Steve Vinoski
  • 62. Instead Embrace the Network Use it i th w Asynchronous don e Message be d an Passing
  • 63. Guaranteed Delivery Delivery Semantics • No guarantees • At most once • At least once • Once and only once
  • 66. The network is inherently unreliable and there is no such thing as 100% guaranteed delivery It’s all lies.
  • 68. Guaranteed Delivery The question is what to guarantee
  • 69. Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network?
  • 70. Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network? 2. The message is - received by the receiver host’s NIC?
  • 71. Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network? 2. The message is - received by the receiver host’s NIC? 3. The message is - put on the receiver’s queue?
  • 72. Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network? 2. The message is - received by the receiver host’s NIC? 3. The message is - put on the receiver’s queue? 4. The message is - applied to the receiver?
  • 73. Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network? 2. The message is - received by the receiver host’s NIC? 3. The message is - put on the receiver’s queue? 4. The message is - applied to the receiver? 5. The message is - starting to be processed by the receiver?
  • 74. Guaranteed Delivery The question is what to guarantee 1. The message is - sent out on the network? 2. The message is - received by the receiver host’s NIC? 3. The message is - put on the receiver’s queue? 4. The message is - applied to the receiver? 5. The message is - starting to be processed by the receiver? 6. The message is - has completed processing by the receiver?
  • 75. Ok, then what to do? 1. Start with 0 guarantees (0 additional cost) 2. Add the guarantees you need - one by one
  • 76. Ok, then what to do? 1. Start with 0 guarantees (0 additional cost) 2. Add the guarantees you need - one by one Different USE-CASES Different GUARANTEES Different COSTS
  • 77. Ok, then what to do? 1. Start with 0 guarantees (0 additional cost) 2. Add the guarantees you need - one by one Different USE-CASES Different GUARANTEES Different COSTS For each additional guarantee you add you will either: • decrease performance, throughput or scalability • increase latency
  • 78. Just
  • 80. Just Use ACKing and be done with it
  • 81. Latency vs Throughput
  • 82. You should strive for maximal throughput with acceptable latency
  • 85. Big Data Imperative OO programming doesn't cut it • Object-Mathematics Impedance Mismatch • We need functional processing, transformations etc. • Examples: Spark, Crunch/Scrunch, Cascading, Cascalog, Scalding, Scala Parallel Collections • Hadoop have been called the: • “Assembly language of MapReduce programming” • “EJB of our time”
  • 86. Big Data Batch processing doesn't cut it • Ala Hadoop • We need real-time data processing • Examples: Spark, Storm, S4 etc. • Watch“Why Big Data Needs To Be Functional” by Dean Wampler
  • 88. When is a RDBMS not good enough?
  • 89. Scalingreads to a RDBMS is hard
  • 90. Scalingwrites to a RDBMS is impossible
  • 92. Do we really need a RDBMS? Sometimes...
  • 94. Do we really need a RDBMS? But many times we don’t
  • 96. Availability vs Consistency
  • 98. You can only pick 2 Consistency Availability Partition tolerance At a given point in time
  • 99. Centralized system • In a centralized system (RDBMS etc.) we don’t have network partitions, e.g. P in CAP • So you get both: Consistency Availability
  • 100. Distributed system • In a distributed (scalable) system we will have network partitions, e.g. P in CAP • So you get to only pick one: Consistency Availability
  • 102. Think about your data Then think again • When do you need ACID? • When is Eventual Consistency a better fit? • Different kinds of data has different needs • You need full consistency less than you think
  • 103. How fast is fast enough? • Never guess: Measure, measure and measure • Start by defining a baseline • Where are we now? • Define what is “good enough” - i.e. SLAs • Where do we want to go? • When are we done? • Beware of micro-benchmarks
  • 104. ...or, when can we go for a beer? • Never guess: Measure, measure and measure • Start by defining a baseline • Where are we now? • Define what is “good enough” - i.e. SLAs • Where do we want to go? • When are we done? • Beware of micro-benchmarks
  • 105. To sum things up... 1. Maximizing a specific metric impacts others • Every strategic decision involves a trade-off • There's no "silver bullet" 2. Applying yesterday's best practices to the problems faced today will lead to: • Waste of resources • Performance and scalability bottlenecks • Unreliable systems
  • 106. SO
  • 107. GO
  • 108. ...now home and build yourself Scalable, Highly Concurrent & Fault-Tolerant Systems
  • 109. Thank You Email: jonas@typesafe.com Web: typesafe.com Twitter: @jboner