Introductory talk for everybody who wants to become a distributed systems programmer. By Matko Medenjak, Brno Java Meetup, 23rd March 2019

1. Fundamental truths about
distributed systems
Or: so you want to be a distributed systems programmer?
Matko Medenjak
Hazelcast IMDG core team lead

4. Why distributed?
- Increasing resources becomes cost-prohibitive
- Increasing performance / lowering latency

5. Distributed system properties
- Performance
- Latency, throughput, resource usage
- Scalability
- Size, geographic, administrative
- Availability/Fault tolerance
- Redundancy
- Define what faults you expect

6. private final Semaphore workPermits;
private final Queue<Runnable> workQueue;
private final AtomicBoolean stop;
public void run() {
while (!stop.get()) {
if (workPermits.tryAcquire()) {
Runnable job = workQueue.poll();
execute(job); // exceptions are handled, does not fail
workPermits.release();
} else {
idle();
}
}
}

7. private final DistributedSemaphore workPermits;
private final DistributedQueue workQueue;
private final AtomicBoolean stop;
public void run() {
while (!stop.get()) {
if (workPermits.tryAcquire()) {
Runnable job = workQueue.poll();
execute(job); // exceptions are handled, does not fail
workPermits.release();
} else {
idle();
}
}
}

9. Reality
- Unreliable network
- Network failure = Node failure
- No shared memory
- Stale information
- No shared clock
- Partial failures

10. Technical papers vs books
- Technical papers
- Efficient solutions to specific problems
- What is possible, impossible and minimum cost of correct implementation

11. Defining a model
- Abstractions, models and assumptions about time, order, …
- Strong model vs weak model
- System model (environment, communication links, time and order)
- Failure model (crash-fail, partitions, byzantine…)
- Consistency model (strong, eventual, …)

12. Defining a design objective
- Application-level / high-level goal
- Performance, consistency
- Do we need to maintain the illusion of single system?
- What kind of failures are expected and need to be handled? What kind of
failures are acceptable?
- Can we gain performance by exposing more details about internals?

13. Solutions
- Replication
- Partitioning
- Failure detection
- Concensus
- Leader election, Mutual exclusion, Global snapshots, Concurrency control,
Transactional memory, Data synchronization, Atomic broadcast, atomic
commit, Byzantine fault tolerance, Clock synchronization, ...

14. Replication

15. Partitioning

16. Partitioning + replication

17. Resources
- Designing Data-Intensive Applications - Martin Kleppmann
(https://dataintensive.net/)
- Distributed systems (Maarten van Steen) - https://www.distributed-
systems.net/
- https://www.the-paper-trail.org/post/2014-08-09-distributed-systems-theory-
for-the-distributed-systems-engineer/
- http://alvaro-videla.com/2013/12/failure-modes-in-distributed-systems.html
- https://en.wikipedia.org/wiki/Fallacies_of_distributed_computing