Concurrency (Fisher Syer S2GX 2010)

Chicago, October 19 - 22, 2010
Concurrent Programming
and Distributed Applications
Mark Fisher, Dave Syer - SpringSource

Goal
●
Demystify concurrent and distributed programming.
●
Identify and help avoid the pitfalls
●
Show how Spring makes it easier to write multi-
threaded and multi-process applications

Agenda
• Concurrency
• Asynchronous execution
• Tasks, schedules and triggers
• Events, Messaging and intra-process communication
• Distributed systems

Concurrency
SpringOne 2GX 2010. All rights reserved. Do not distribute without permission.
Why?
- Performance
- Responsiveness
- Scalability
Where?
- Event-driven Architecture
- Scheduling Tasks

When threads were more esoteric,
concurrency was an "advanced" topic;
now, mainstream developers must be
aware of thread-safety issues.
--Brian Goetz

Thread Safe?
public class Counter {
private boolean active = false;
public boolean isActive() {
return active;
}
public void setActive(boolean active) {
this.active = active;
}
}

Thread Safe?
public class Counter {
private volatile int count = 0;
public int increment() {
return count++;
}
}

Thread Safe?
public class Service {
private volatile Resource resource;
public void process(String data) {
if (this.resource == null) {
this.resource = new Resource();
}
this.resource.process(data);
}
}

Thread Safety
What?
Properly managing concurrent access to mutable state.
How?
a) avoid mutability
b) don't share
c) synchronize

Immutable?
public class StringHolder {
private final int id;
private final String value;
public StringHolder(int id, String value) {
this.id = id;
this.value = value;
}
public void display() {
Sysem.out.println(id + ": " + this.value);
}
}

Immutable?
public class StringList {
private final int id;
private final List<String> strings;
public StringList(int id, List<String> strings) {
this.id = id;
this.strings = strings;
}
Sysem.out.println(id + ": " + this.strings);
}
}

Immutable?
public class MapHolder {
private final Map<Integer, StringValue> map;
public MapHolder(Map<Integer, StringValue> map) {
this.map = new HashMap<Integer, StringValue>(map);
}
System.out.println(this.map);
}
}

Immutable?
}
public static class StringValue {
private String string;
public StringValue(String string) {
this.string = string;
}
public void setString(String string) {
}
}
private String string;
}
public void setString(String string) {
}
}

Immutable?
}
private final String string;
}
}
private final String string;
}
}

Demo

Thread Safety: Confinement
When immutability is not an option, consider not sharing
mutable state
• Stack Confinement
– Method Parameters
– Local Variables
• Thread Confinement
– ThreadLocal
– Custom Scopes in Spring

Synchronization
• The 'synchronized' keyword
• Locks
• wait/notify
• Atomic variables

Lazy Initialization (recap)
if (this.resource == null) {
this.resource = new Resource();
}
}
}

Dependency Injection and Singletons
public void setResource(Resources resource) {
this.resource = resource;
}
}
}

Concurrency
• Background processing and performance optimization
• Java Memory Model: final, volatile, synchronized
• Locks: wait, notify, utility wrappers
• Concurrent collections, immutable wrappers
• Queue, BlockingQueue, DelayQueue
• Deadlock, livelock, starvation
• Immutability, stateless components, "hidden" state, thread
safety
• Typical Spring application concerns
• Stateful components in Spring

Executor and ExecutorService
JDK Functionality
• Configurable thread pools
• Execute Runnables asynchronously
• Submit Callable<V> that returns Future<V>

Async Execution Considerations
• Blocking and timeouts
• Cancellation
• Interruption
• Thread Pool Rejection Policies
• Excessive thread creation
• Context-switching overhead

Asynchronous Execution
• Futures, executors, completion service and thread pools
• @Async
• Lifecycle and SmartLifecycle, cf. InitializingBean
• Gateways with Futures
• Timeouts

Spring Support for Task Management
• TaskExecutor
• TaskScheduler and Trigger
• @Async
• @Scheduled
• File-Polling adapter in Spring Integration
• Message listener containers
– JMS
– AMQP
– Spring Integration
– GemFire
• Spring Batch

Events and Intraprocess Communication
• The observer pattern: application and framework design
• ApplicationEvent and ApplicationListener
• Messaging
• SEDA
• Ordering
• Stateful Messaging patterns

Observer Pattern
Publisher
invoke(“input”)
confirmation
Multicaster
fire(“input”)
observe(“input”)
Observer
Event

ApplicationListener
// This is a listener
public class TransferListener implements
ApplicationListener<TransferEvent> {
public onApplicationEvent(TransferEvent event) {
this.auditLogger.log(event.getTransfer());
}
…
}
ApplicationEvent
java.util.Event

ApplicationEventPublisher
public class TransferService implements
ApplicationEventPublisherAware {
public setApplicationEventPublisher(
ApplicationEventPublisher publisher) {
this.publisher = publisher;
}
public void transfer(Transfer transfer) {
...
this.publisher.publish(new TransferEvent(transfer));
}

The Observer Pattern
Publisher
Multicaster
ObserverObserverObserverObserver
ApplicationListener
ApplicationContext
ApplicationEventPublisher

Messaging
• Decouple Producers and Consumers
• Observer Pattern but often with extra semantics
– header and payload
– publish-subscribe or point-to-point
– acks and nacks and other protocol details
– persistence and quality of service
– once-only or at-least-once delivery
widgets

Staged Event Driven Architecture (SEDA)

Staged Event Driven Architecture (SEDA)
Dispatcher
Queue

Ordering
• Multi-threaded applications in general do not preserve
order
• E.g. Messaging
– Producer sends two widgets 1, 2
– Consumer receives 2, 1
• To preserve order expect some overhead
– Storage (stateful patterns)
– Processing time (wait for out of order messages)

Stateful Messaging Patterns
• Scatter-Gather
• Composed Message Processor
• Claim Check
• Publish-Subscribe Channel
• Recipient List Router
• Splitter
• Multi-valued Router
• Aggregator
• Resequencer

Quality of Service, Transactions
Asynchronous hand off =
potential lost messages

Quality of Service, Transactions
Transactional (receive,
send) = no lost messages

Distributed Applications
• RPC, Spring Remoting
• Messaging
• Middleware: HTTP, JDBC, JMS, AMQP
• Latency
• QoS, transactions, durability and guaranteed delivery
• Distributed data: partitioning and eventual consistency
• Gemfire
• Spring Integration adapters

Distributed Application or System
• Application:
– Multiple JVM process nodes
– Same binaries
– Same release schedule
• System:
– Multiple JVM process nodes
– Different binaries
– Different release schedule per node
• Blurred boundary if Application has to have a rolling
upgrade with no downtime

Patterns of Distributed Transactions
• Full XA with 2PC
• XA with the 1PC Optimisation
• XA and the Last Resource Gambit
• Shared Transaction Resource
• Best Efforts 1PC
• Non-transactional Access
• Wing and a Prayer (anti-pattern)
http://www.javaworld.com/javaworld/jw-01-2009/jw-01-
spring-transactions.html
JTA + Spring
Spring Data +
Oracle
Spring JMS

Remote Procedure Call (RPC)
• RPC is synchronous inter-process communication
• Only evil if the system is not an Application
• Transports:
– RMI
– HTTP
– JMS
– JDBC
– ...
• Spring Remoting helps strategise and configure: defers
important architectural choices
• Spring Integration provides additional options

Messaging
• Messaging is asynchronous inter-process communication
• Can use Messaging to implement RPC
• Transports (some point-to-point only):
– RMI
– HTTP
– JMS
– JDBC
– AMQP
– ...
• Spring Integration: helps strategise and configure: defers
important architectural choices

Latency
• All distributed systems and applications suffer from
latency
– EU-US = 6000km, min network latency 6e6/3e8 = 20ms
– Add marshal/unmarshal overhead (variable)
– Transaction (min 50ms)
• Often much worse, e.g. >100ms even on local network
• Request-reply patterns double the problem
• Over-modularization: each inter-process hop is expensive
• Abstractions (e.g. Spring) are dangerous
• Measure and analyse

Distributed Application and Data

Nosql & Spring Data
• Key value stores
– redis, gemfire
– coherence
– riak, voldemort, memcached
• Document stores
– couchdb, mongodb
• Sparse table or column stores
– cassandra, bigtable
• Graph or object stores
– neo4j
• Distributed filesystem
– hdf

SpringSource Gemfire
• Distributed data cache, datastore and compute grid
• Java (so embeddable)
• Low-level API is java.util.Map
• Many high-level abstractions
– Transactions
– Functions and node affinity
– Events, continuous queries
– Replication
• Spring Gemfire:
http://git.springsource.org/spring-gemfire

Q&A
Source code for demos:
http://git.springsource.org/s2gx-2010/concurrent-programming-
distributed-applications

Distributed Transaction: Sunny Day
1. Start messaging transaction
2. Receive message
3. Start database transaction
4. Update database
5. Commit database transaction
6. Commit messaging transaction

Distributed Transaction: Rollback
2. Receive message
4. Update database, fail!
5. Rollback database transaction
6. Rollback messaging transaction

Distributed Transaction: Partial Failure
2. Receive message
4. Update database
5. Commit database transaction
6. Commit messaging transaction, fail!

Distributed Data: Shared Database
Application
Cache

Distributed Data: Cache Overflow
Cache

Distributed Cache: Database Meltdown

Concurrency (Fisher Syer S2GX 2010)

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