a pragmatic view of reactive
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So how do I do a 2PC in Akka then?
Lutz Hühnken (@lutzhuehnken)
A misleading title, really. Answer: You don’t want to
(we’ll get back to that later)
Valuable Experience vs. Bad Habit From Enterprise to Reactive
Transitions …
Lutz Hühnken (@lutzhuehnken)
Other failed naming attempts..
Real, Pragmatic Questions
WAR? Servlet Container?
Library X (which uses
ThreadLocal)?
RDBMS/ JDBC?
How to do 2PC?
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Threads … A supposedly great idea
• Threads are • lightweight processes • easier programming model, no IPC
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Lightweight?
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Slide from John Rose, Java VM Architect, JFokus, Stockholm, February 2015
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The problem with Threads
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They discard the most essential and appealing properties of sequential computation: understandability, predictability, and determinism.
Threads, as a model of computation, are wildly nondeterministic, and the job of the programmer becomes one of pruning that nondeterminism.
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Threads
• not efficient • memory consumption • cost for context switch • bad match modern NUMA architectures • locks lead to contention
• not a good programming model • shared mutual state is difficult to reason about
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What would be better?
Goals • task level (sub-thread) concurrency • share nothing approach • # threads ~ # of cores
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Thread alternatives (successors?)
• Green threads / coroutines / fibres • other granularity, but basically same programming
model • Event-loop (e.g. vert.x, Reactor) • very loose coupling, slightly limited, 1 x n dispatch
• Actors ! • truly share-nothing, flexible, resilience-proven,
m x n dispatch
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Example: Http Request handling
Java EE: Threads. Servlet API: Thread per Request
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Note: This is a snapshot at one point in time, not a sequence of events.
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Example: Http Request handling
Reactive: Sub-Thread. Play: n threads per m requests
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Note: This is a snapshot at one point in time, not a sequence of events.
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Consequences
•We have effectively (even without explicitly using Actors) switched to a task level concurrency model • As a consequence, ThreadLocal becomes an anti-pattern. • Libraries that depend on them need extra work, might better be avoided • What does it mean for (blocking) I/O?
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Pragmatic reactive learnings cont’d
• You’ll be using sub-thread level concurrency. Accept it, embrace it.
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High concurrency matters
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But there’s one thing we can all agree on: At high levels of concurrency (thousands of connections) your server needs to go to asynchronous non-blocking. [..] any part of your server code blocks you’re going to need a thread. And at these levels of concurrency, you can’t go creating threads for every connection.
From https://strongloop.com/strongblog/node-js-is-faster-than-java/
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Blocking I/O
Reactive: Sub-Thread. Play: n threads per m requests
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What does using blocking I/O mean for this?
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Non-blocking
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For task level (sub-thread level) concurrency • each thread is responsible for n tasks • and that n might be pretty big
You don’t want to block such a thread with blocking I/O
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But what if I need this..?
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try { stmt = con.createStatement(); ResultSet rs = stmt.executeQuery(query); while (rs.next()) { String coffeeName = rs.getString("COF_NAME"); int supplierID = rs.getInt("SUP_ID"); float price = rs.getFloat("PRICE"); int sales = rs.getInt("SALES"); int total = rs.getInt("TOTAL"); System.out.println(coffeeName + "\t" + supplierID + "\t" + price + "\t" + sales +
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Isolate!
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vert.x has „worker verticles“ Play/Akka: Configure Dispatchers
Slick 3 takes care of this for you!
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Pragmatic reactive learnings cont’d
• You’ll be using sub-thread level concurrency. Accept it, embrace it. • Use asynchronous I/O. If you really can’t,
isolate.
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But what if I need this..?
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@Transactional public static class GreetingService {
@Inject private JmsTemplate jmsTemplate;
@PersistenceContext private EntityManager entityManager;
public void createGreeting(String name) { Greeting greeting = new Greeting(name); this.entityManager.persist(greeting); this.jmsTemplate.convertAndSend("greetings", greeting); …
Pragmatic Reactive
Avoid…
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@Transactional public static class GreetingService {
@Inject private JmsTemplate jmsTemplate;
@PersistenceContext private EntityManager entityManager;
public void createGreeting(String name) { Greeting greeting = new Greeting(name); this.entityManager.persist(greeting); this.jmsTemplate.convertAndSend("greetings", greeting); …
The example: In fact not really all-or-nothing, but reconciliation.
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Separate (the steps..)
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Claim: Any 2PC can be expressed in terms of asynchronous messaging.
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Life beyond Distributed Transactions
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In general, application developers simply do not implement large scalable applications assuming distributed transactions. When they attempt to use distributed transactions, the projects founder because the performance costs and fragility make them impractical. [..]
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Want Almost-Infinite Scaling• More of everything… Year by year, bigger and bigger• If it fits on your machines, multiply by 10, if that fits, multiply by 1000…• Strive to scale almost linearly (N log N for some big log).
Assumptions(Don’t Have to Prove These… Just Plain Believe Them)
Grown-Ups Don’t Use Distributed Transactions•The apps using distributed transactions become too fragile…• Let’s just consider local transactions. ! Multiple disjoint scopes of serializability
Want Scale-Agnostic Apps • Two layers to the application: scale-agnostic and scale-aware• Consider scale-agnostic API
Scale Agnostic Code
Scale-Aware-Code
Application
Upper Layer
Lower Layer
Scale Agnostic API
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2 PC => messaging
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Item-B Cancellation Tentative Op
Item-A
Restrictions / Requirements
•at-least-once delivery•idempotent messages •tentative operations
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Pragmatic
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Real world solution: Saga Pattern
Source: http://kellabyte.com/2012/05/30/clarifying-the-saga-pattern/
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Pragmatic
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Example (by Caitie McCaffrey)
Source: https://speakerdeck.com/caitiem20/applying-the-saga-pattern
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Distributed Transactions
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Distributed Transactions („2PC“) are a source of unnecessary failure and of contention.
It can usually be avoided. Generally, local transactions and at-least-once delivery can be used instead.
The Saga Pattern provides a pragmatic solution for „all-or-nothing“ in a distributed system.
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Food for thought
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The data storage landscape is changing, moving towards event sourcing and immutability. This is a great match for reactive systems.
The „all-or-nothing“ problems are closely related to wanting a global truth at a point in time. But what is now… the illusion of present… result of series of events..
Pragmatic Reactive
Pragmatic reactive learnings cont’d
• You’ll be using sub-thread level concurrency. Accept it, embrace it. • Use asynchronous I/O. If you really can’t,
isolate. • Do not use distributed transactions. If you
really must, isolate.
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Java EE Application Servers
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Servlet API was developed for thread-per-request, synchronous I/O.
Application servers are not of much use anyway, nobody uses them as containers for multiple applications. So effectively they’re just a library dependency.
The ops convenience can be provided by other tools.
Pragmatic Reactive
Pragmatic reactive learnings cont’d
• You’ll be using sub-thread level concurrency. Accept it, embrace it. • Use asynchronous I/O. If you really can’t,
isolate. • Do not use distributed transactions. If you
really must, isolate. • Don’t use an application server / servlet
container.
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Conclusion
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If
• Threads are the smallest unit of concurrency in your code, or
• You use blocking I/O (without clear separation), or • You use 2-phase-commit across systems, or • You use a Java EE Application Server / Servlet
Container
Then your application is not reactive.
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The Pragmatic Reactive Checklist
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• Task-level (sub-thread level) concurrency
• Non-blocking I/O • Distributed • Containerless