Java Concurrency – Understanding the Basics of Threads
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They give you the tools to just say "go away and do these things", which after years of dealing directly with pthreads in C was a breath of fresh air!
which presents threads as the solution to the pain of using futures.
A future internally is just a value or a set of waiters. Whether they are "colored" (ie. can you wait on a future in a non-future context), or based on threads or cooperative concurrency (ie. async) etc. is entirely an implementation detail, with significantly differing trade-offs.
In that sense, you can really only compare future implementations with other concurrency systems.
Executors are the way to go for almost all finite-time concurrency.
New threads should normally only be used for stuff that keeps running until the process quits.
It's old, but the material holds up well since it covers a lot of fundamentals
I think modern Java has better support for it, but if you've got mutable state spread throughout your application you're going to have a hard time no matter what.
How does one become a senior engineer if you don't understand concurrency?
Mutable state is most easily solved by having cpoies of everything, but then that's a tradeoff between performance and infrastructure/resource costs, but I guess that if you're in an all-Java shop that isn't much of an issue.
Best I can find as a source for now is https://www.youtube.com/watch?v=2y5Pv4yN0b0 -- I thought there was a link somewhere to Hickey himself saying this, but can't find it.
The key thing to know, is that threads aren’t a first-class kernel object. In OS kernels, there are only OS processes and memory regions.
To learn about threads, you should just learn about OS processes; and then learn that distinct OS processes can share memory regions between them, often via subprocess-spawn-time inheritance. Learn what fork(2) does on POSIX, and how it manages to be fast.
Starting with that intuition, it’s simple to then absorb what “threads” actually are: a usage pattern for spawning and managing OS processes that share memory; and a set of convenience APIs (that may be in-kernel, as in Windows; or purely in userland, as in Linux) for setting up this usage pattern. Everything these “threading” APIs can do, you can do yourself directly using the process-management and memory-mapping APIs. And those same calls are all that e.g. libpthread is doing.
Studying C, I haven't really come across threads other than trying out the things in `pthread.h`.
Would you recommend just reading the source code of that header for a better understanding?
That's really going to change and simplify JVM concurrency and I think many other things. Delimited continuations can be used to implement algebraic effects, which is exciting for functional programming.
The biggest challenge will be reconciling Futures, Netty and ThreadLocal (FiberLocal) patterns. Think defining a SQL transaction lifetime, a distributed lock, or a OpenTracing span. For Spring Framework people, no big deal. For everyone else, lots of complex decisions to make.
How-so? I thought the type system was the precluding factor for algebraic effects, not the threading model.
Do avoid anything related to the hoary old Java "Future" class, though. CompletionStage or get out!
RxJava feels like an unnatural port from single-threaded mindsets.
1. https://www.amazon.in/Java-Threads-Concurrency-Utilities-Fri...
I have been a long time Java developer, and I have worked a lot with highly concurrent code. Pony really opened my eyes to what was possible.
Unfortunately, the language, standard library and runtime is still pretty immature. It does however have very good 'C' interop. So for some problems it would be a very good fit.
And achieving concurrency without shared memory is impossible in general case. Sure it is possible to isolate such access to a separate layer and make it transparent for the rest of the program but someone still has to program such layer.
And because you’re in production and getting random spurious failures, the panicked (but common) reaction is to wrap every shared resource in a synchronized block. Which makes an incorrect implementation worse but possibly correct.
As for novices: they are called that for reason and supposed to be under supervision rather than allowed running wild.
HN needs to only allow downvotes that have an accompanying explanation comment.
Let's say you wanted to turn a list into a bounded list of 4 elements.
Race-condition insert:
if (sz < 4) {
list.insert(x);
sz++;
}
Safe insert: atomically {
if (sz < 4) {
list.insert(x);
sz++;
}
}
So atomically organises the locking/unlocking/rollback for you such that a fifth element will not be inserted.synchronized locks code, atomically locks data.
It maps to my thinking better, because what I'm interested in is manipulating two or more resources at the same time. I'm not interested in making sure two threads aren't in the same region of code at the same time.
> What error does it protect you against compared to synchronising on list? Synchronized is perfectly fine for my example. I should have picked a better example of two resources (instead of list and size of list), because synchronized gets trickier once you start combining different pieces.
For example, I have two bounded lists, and I want to transfer an element from one to the other. I've already written synchronized versions of remove and insert, so let's try with them:
transfer(otherlist) {
x = otherlist.synchronizedRemove();
this.synchronizedInsert(x);
}
There will be a moment in time where the outside world can't see x in either list. Maybe I crash and x is gone for good. Or maybe the destination list becomes full and I'm left holding the x with nowhere to put it. So what is to be done? I could synchronize transfer but that still wouldn't fix the vanishing x, or the destination filling up. So I paid the performance cost of taking two/three locks and I've still ended up buggy.I think the fix here is to lock each list, then no-one else can access them and it should fix the vanishing x:
transfer(otherlist) {
synchronized(this) {
synchronized(otherlist) {
x = otherlist.synchronizedRemove();
this.synchronizedInsert(x);
}
}
}
I think that's correct? But now I have taken too many locks - I only needed remove and insert not synchronizedRemove and synchronizedInsert. And now I've introduced deadlock possibility - if two transfers are attempted in opposite directions.I can fix the too many locks problem, by exposing non-synchronized remove and insert and have transfer call them instead. But then callers and I will accidentally call the wrong one. I break any pretence of encapsulation by exposing unsafe and safe versions of a method. The deadlock is harder to fix. I'd need to synchronize the lists in some agreed order (and have everyone else obey that ordering in their other methods too!).
Instead, I want my implementation to look something like:
BoundedList {
transactional Int sz;
transactional List list;
transactional insert(x) {
if (sz < 4) {
list.insert(x);
sz++;
}
}
transactional remove() {...}
transactional transfer(otherlist) {
x = otherlist.remove();
this.insert(x);
}
}
> What happens if .insert() also uses atomically{} somehow?A good implementation would throw a compile-time error. A bad implementation could throw a runtime error.
In order to do this, transactional actions would need to be marked as such - to prevent mixing them up. atomically by definition is a non-transactional action (because it's the thing that commits all the steps to the outside world) so if you find an atomically inside a transaction, it's a type error.
You've already used a system like this if you've worked with any decent SQL implementation:
BEGIN TRANSACTION
UPDATE accounts
SET money = money - 50
WHERE accountId = 'src'
UPDATE accounts
SET money = money + 50
WHERE accountId = 'dest'
COMMIT TRANSACTION
I didn't take any 'locks'. I just wrapped two perfectly good individual actions and said 'run them both or not at all'. Though to be fair I am getting a lot of grief in another thread by suggesting that even novices could wrap up their SQL like that without getting it wrong.So, atomically{} is basically like a SQL transaction and would repeat or signify failure if it cannot commit the changes you make inside the code block, similar to a CAS lock-free algorithm. This seems quite limited though, you are basically constrained to writing code within the atomic block that deals with value types only, and with no side-effects. Otherwise how would the compiler or runtime know how to roll it back?
That sounds useful but doesn't seem to cover all the use cases of thread synchronization by a long shot. Isn't it also the case that even knowing how to implement interesting alrogithms in a lock-free manner is an area of significant ongoing research? For example I think only recently someone worked out how to implement a lock-free ring buffer (https://ferrous-systems.com/blog/lock-free-ring-buffer/)
- Intent: both transactions deduct 50 money
- transaction 1: SELECT balance FROM account; // = 100
- transaction 2: SELECT balance FROM account: // = 100
- transaction 1: UPDATE account SET balance = 50
- transaction 1: COMMIT
- transaction 2: UPDATE account SET balance = 50
- transaction 2: COMMIT
- Result: balance is 50, but should be 0
With serializabile transactions (not all databases have this, particularly if you look beyond SQL): - Intent: both transactions deduct 50 money
- transaction 1: SELECT balance FROM account; // = 100
- transaction 2: SELECT balance FROM account: // = 100
- transaction 1: UPDATE account SET balance = 50
- transaction 1: COMMIT
- transaction 2: UPDATE account SET balance = 50
- transaction 2: COMMIT -> Fails, needs to retry
- transaction 2b: SELECT balance FROM account: // = 50
- transaction 2b: UPDATE account SET balance = 0
- transaction 2b: COMMIT -> Ok!
- Result: balance is 0
Because this is needed so frequently, databases have calculated updates, basically atomic operations: - transaction 1: UPDATE account SET balance = balance - 50; // values indeterminate
- transaction 2: UPDATE account SET balance = balance - 50; // values indeterminate
- transactions 1,2: COMMIT
- Result: balance is 0
Or, one could lock the rows, like so: - transaction 1: SELECT FOR UPDATE balance FROM account; // = 100
- transaction 2: SELECT FOR UPDATE balance FROM account: // = transaction 2 is stalled until transaction 1 commits or rollbacks
- transaction 1: UPDATE account SET balance = 50
- transaction 1: COMMIT
// transaction 2 can now continue and gets balance = 50
- transaction 2: UPDATE account SET balance = 00
- transaction 2: COMMIT
- Result: balance is 0
And this is just one simple example of the problems you can have concurrently accessing one table, even while using transactions. Not to speak of the issues you can run into when interacting with systems outside a single database, which don't interact with the transaction semantics of the DB.Concurrency is just very non-trivial regardless the abstraction.
I want to focus entirely on your first example.
Let me ask you: What is it about your first example that makes you call it transactional? If it behaves as badly as you say, shouldn't it be called a 'method' or a 'procedure'? Because my "fix" for it is to actually use transactions. I suspect your fix would be the same.
Why did you choose to interleave its steps like that, when "Isolation ensures that concurrent execution of transactions leaves the database in the same state that would have been obtained if the transactions were executed sequentially."
If you're telling it like it is, then I cannot argue with facts. I guess I'll stop using DBs, at least until they figure this stuff out in 1973.
We have two concurrent tasks both doing exactly the same thing in order to deduct 50 money:
BEGIN TRANSACTION;
SELECT balance FROM account; // = 100
UPDATE account SET balance = 50; // calculated by application as 100-50
COMMIT;
Perhaps I misunderstand you, or you misunderstood the way I presented the example (possibly because I presented it poorly). But in my mind there is hardly a way to describe this code as "not transactional".I merely showed one possible way how these concurrent tasks may execute in practice leading to bugs. Of course, for casual testing this will actually look and work correctly. As one commenter far up the thread said (as an attempt to refute understanding of concurrency as necessary)
> The problem for novices is that a program that behaves correctly looks a lot like a correct program. Until one day it doesn’t.
> And because you’re in production and getting random spurious failures, the panicked (but common) reaction is to wrap every shared resource in a synchronized block. Which makes an incorrect implementation worse but possibly correct.
Then,
> Why did you choose to interleave its steps like that, when "Isolation ensures that concurrent execution of transactions leaves the database in the same state that would have been obtained if the transactions were executed sequentially."
That is only one of the possible ways for transactions to work. Note that IIRC the only database that interprets the SQL standard like this is postgres, while MySQL and Oracle still have (more subtle) serialization issues even on the SERIALIZABLE isolation level (example: https://stackoverflow.com/a/49425872).
Note that you can end up with deadlocks and transaction failures on any level stricter than READ COMMITTED, so the application needs to be able to deal with both of these.
> And because you’re in production and getting random spurious failures, the panicked (but common) reaction is to wrap every shared resource in a synchronized block.
Yep yep - that's the Java + Threads model. It's (relatively) harder to take single-threaded logic and make it behave in a multi-threaded setting. Compared to the SQL model, where it's (relatively) easier to take single-threaded logic, wrap it in BEGIN/END TRANSACTION, and have it perform exactly as expected.
OK I get you now. In saying that SQL concurrency was easy and Java concurrency was hard I didn't think about what would happen if you tried to write a mixed Java/SQL transaction; I didn't realise there was a bunch of Java running between your SQL statements. So what would my fix be? Get rid of the Java and replace it with SQL.
> Note that you can end up with deadlocks and transaction failures on any level stricter than READ COMMITTED, so the application needs to be able to deal with both of these.
That's cool - transactions proceed completely or not at all.
About the "not transactional" thing, I was applying (a => b) => (^b => ^a). That is, since transactions are isolated, and you demonstrated code that wasn't isolated, I can conclude that it wasn't a transaction. Maybe I need to adjust my thinking a bit:
assumption i) Atomicity says "The series of operations cannot be separated with only some of them being executed".
assumption ii) Isolation says "Isolation ensures that concurrent execution of transactions leaves the database in the same state that would have been obtained if the transactions were executed sequentially."
assumption iii) I use transactions because they're atomic and isolated.
A *SELECT balance* was run, passing its value out to the real world before the commit succeeded. This breaks assumptions i and iii.
"That is only one of the possible ways for transactions to work" breaks assumption ii and iii.
So, I can only conclude I should not use transactions.It's notoriously difficult to reason about concurrent programs using intuition. Much more difficult than reasoning about non-concurrent imperative code. This is why there are articles like [0], and why a bug in a Wikipedia article on a fundamental concurrency algorithm went unnoticed until an analysis tool detected the issue, [1] and why lock-free algorithms in particular are so tricky to get right.
[0] https://www.cs.umd.edu/~pugh/java/memoryModel/DoubleCheckedL...
if i had no idea about C10K problem, success of Nginx, Redis, other concurrency success stories on Actors, CSP concept, Concurrency via messages over shared memory, I would say threads are ok when you can use it. But indeed it is so simple and tempting people to design shitty software. Software is a very welcoming medium for it. it is hell.
Can message passing accomplish this at the same level of performance?
although I agree that simplifying resource access should probably be considered before fully shared state.
Let me ask a different question: Why did databases take off in the way they did? Sure they persist stuff to disk, but so do files. What they offer is a concurrency model so good that you almost never think about it. Beginner programmers can competently write large, concurrent systems by writing single-threaded programs which are backed by a central DB, without even knowing the term "race condition".
If beginner database articles told users how to make database Threads, Thread groups, and how to signal and catch interruptions, I don't think databases would have enjoyed nearly as much popularity.
While Threads are fundamental to Java concurrency, I kinda agree with yetkin's point. It introduces the Thread footgun without even paying lip service to the problems of shared, mutable state.
Threads handled well do not need to have race conditions, and race conditions/deadlocks are also very possible in distributed, message-passing systems.
If they don't need to use threads, then good for them. But to dismiss threads and learning material about threads as "uncool" is just silly. The thing that enables that misunderstanding is all the work that's done on them in the first place.