DoubleSummaryStatistics summaryStats = mTransactionSet.stream()
.mapToDouble(this::getPrice).summaryStatistics();
I have above code but javadoc says that DoubleSummaryStatistics is not thread safe.
So how do i ensure that multiple threads when act on mTransactionSet will work properly ?
Any pointers would be much appreciated . Thank you
DoubleSummaryStatistics is intended to be used in streams, in particular in code of form:
DoubleSummaryStatistics stats = stream.collect(Collectors.summarizingDouble());
or, when you have a DoubleStream:
DoubleSummaryStatistics stats = stream.summaryStatisitics();
This works for both sequential and parallel streams. The parallel streams will not use a single DoubleSummaryStatistics object to collect the data, it will use a different instance in each thread and later combine the data with DoubleSummaryStatistics::combine.
If you want to use DoubleSummaryStatistics in a stream, you don't have to think about all this stuff. Java does it for you. If you want to use it yourself in a multithreaded environment, use the same approach as parallel streams: each thread should use its own DoubleSummaryStatistics object, and in the end, all data are combined.
Or you synchronize all access to the object of course, but that will probably will be very slow.
I have two Futures, the second of which starts after the first ended. Both write to the same ArrayBuffer instance, but since they are executed serially (not at the same time), I consider them not acting concurrently.
However, I know there is the #volatile annotation for variables shared among two or more threads (#volatile disables caching).
Since after the first thread finishes, inside the ArrayBuffer instance, there might be some caching going on that makes it impossible for the second thread to see the ArrayBuffer's real state: I am not sure whether it is safe to use ArrayBuffer this way.
Is it true that caching might be a problem in my situation, and if this is the case: Is there a recommended way to make ArrayBuffer use #volatile internally?
It should be fine iff (if-and-only-if) you propagate it [the array] through the future:
val futureA = Future {
val buf = ArrayBuffer(…)
update(buf)
buf
}
val futureB = futureA map {
buf => moreUpdates(buf); buf
}
futureB foreach println // print the result of the transformations
This is OK from a memory safety point of view because the completion of futureA happens-before the onComplete (virtually all transformations on Future is implemented on top of onComplete) callback is invoked. In this case map.
The problem is not caching, per se, but the fact that an ArrayBuffer is a composite, with several subfields that have to be updated in concert to assure correct operation. You will need to use thread synchronization tools to ensure this.
class ArrayBufferWrapper[T](ab: ArrayBuffer[T]) {
def add(item: T) = {
this.synchronized {
ab.add(item)
}
}
}
By wrapping the ArrayBuffer, the components are properly realized into the current thread, and you ensure thread-safe add operations.
No, it is not safe.
This is exactly the reason why they invented functional programming. If you are using scala anyway, might as well take advantage of the paradigm it offers.
Avoid using mutable structures, or, at least, in the rare cases when you have to use them, do not let them escape the local scope. Then you won't ever have to deal with problems like this. They just will not exist anymore.
Tell us more about what you are trying to do, and i am sure someone will suggest a design or two, not involving two threads mutating the same structure.
I'm having problem using MQSeries Perl module in multi-threading environment. Here what I have tried:
create two handle in different thread with $mqMgr = MQSeries::QueueManager->new(). I thought this would give me two different connection to MQ, but instead I got return code 2219 on the second call to MQOPEN(), which probably means I got the same underling connection to mq from two separate call to new() method.
declare only one $mqMgr as global shared variable. But I can't assign reference to an MQSeries::QueueManager object to $mqMgr. The reason is "Type of arg 1 to threads::shared::share must be one of [$#%] (not subroutine entry)"
declare only one $mqMgr as global variable. Got same 2219 code.
Tried to pass MQCNO_HANDLE_SHARE_NO_BLOCK into MQSeries::QueueManager->new(), so that a single connection can be shared across thread. But I can not find a way to pass it in.
My question is, with Perl module MQSeries
How/can I get separate connection to MQ queue manager from different thread?
How/can I share a connection to MQ queue manager across different thread?
I have looked around but with little luck, Any info would be appreciated.
related question:
C++ - MQ RC Code 2219
Update 1: add a example that two local MQSeries::QueueManager object in two thread cause MQ error code 2219.
use threads;
use Thread::Queue;
use MQSeries;
use MQSeries::QueueManager;
use MQSeries::Queue;
# globals
our $jobQ = Thread::Queue->new();
our $resultQ = Thread::Queue->new();
# ----------------------------------------------------------------------------
# sub routines
# ----------------------------------------------------------------------------
sub worker {
# fetch work from $jobQ and put result to $resultQ
# ...
}
sub monitor {
# fetch result from $resultQ and put it onto another MQ queue
my $mqQMgr = MQSeries::QueueManager->new( ... );
# different queue from the one in main
# this would cause error with MQ code 2219
my $mqQ = MQSeries::Queue->new( ... );
while (defined(my $result = $resultQ->dequeue())) {
# create an mq message and put it into $mqQ
my $mqMsg = MQSeries::Message->new();
$mqQ->put($mqMsg);
}
}
# main
unless (caller()) {
# create connection to MQ
my $mqQMgr = MQSeries::QueueManager->new( ... );
my $mqQ = MQSeries::Queue->new( ... );
# create worker and monitor thread
my #workers;
for (1 .. $nThreads) {
push(#workers, threads->create('worker'));
}
my $monitor = threads->create('monitor');
while (True) {
my $mqMsg = MQSeries::Message->new ();
my $retCode = $mqQ->get(
Message => $mqMsg,
GetMsgOptions => $someOption,
Wait => $sometime
);
die("error") if ($retCode == 0);
next if ($retCode == -1); # no message
# not we have some job to do
$jobQ->enqueue($mqMsg->Data);
}
}
There is a very real danger when trying to multithread with modules that the module is not thread safe. There's a bunch of things that can just break messily because of the way threading works - you clone the current process state, and that includes things like file handles, sockets, etc.
But if you try and use them in an asynchronous/threaded way, they'll act really weird because the operations aren't (necessarily) atomic.
So whilst I can't answer your question directly, because I have no experience of the particular module:
Unless you know otherwise, assume you can't share between threads. It might be thread safe, it might not. If it isn't, it might still look ok, until one day you get a horrifically difficult to find bug as a result of a race condition in concurrent conditions.
A shared scalar/list is explicitly described in threads::shared as basically safe (and even then, you can still have problems with non-atomicity if you're not locking).
I would suggest therefore that what you need to do is either:
have a 'comms' thread, that does all the work related to the module, and make the other threads use IPC to talk to it. Thread::Queue can work nicely for this.
treat each thread as entirely separate for purposes of the module. That includes loading it (with require and import - not use because that acts earlier) and instantiating. (You might get away with 'loading' the module before threads start, but instantiating does things like creating descriptors, sockets etc.)
lock stuff when there's any danger of interruption of an atomic operation.
Much of the above also applies to fork parallelism too - but not in quite the same way, as fork makes "sharing" stuff considerably harder, so you're less likely to trip over it.
Edit:
Looking at the code you've posted, and crossreferencing against the MQSeries source:
There is a BEGIN block, that sets up some stuff with the MQSeries at the point at which you use it.
Whilst I can't say for sure that this is your problem, it makes me very wary - because bear in mind that when it does that, it sets up some stuff - and then when your threads start, they inherit non-shared copies of "whatever it did" during that "BEGIN" block.
So in light of what I suggested earlier on - I would recommend you try (because I can't say for sure, as I don't have a reference implementation):
require MQSeries;
MQSeries->import;
Put this in your code - in lieu of use - after thread start. E.g. after you do the creates and within the thread subroutine.
This is a continuation from here: Golang: Shared communication in async http server
Assuming I have a hashmap w/ locking:
//create async hashmap for inter request communication
type state struct {
*sync.Mutex // inherits locking methods
AsyncResponses map[string]string // map ids to values
}
var State = &state{&sync.Mutex{}, map[string]string{}}
Functions that write to this will place a lock. My question is, what is the best / fastest way to have another function check for a value without blocking writes to the hashmap? I'd like to know the instant a value is present on it.
MyVal = State.AsyncResponses[MyId]
Reading a shared map without blocking writers is the very definition of a data race. Actually, semantically it is a data race even when the writers will be blocked during the read! Because as soon as you finish reading the value and unblock the writers - the value may not exists in the map anymore.
Anyway, it's not very likely that proper syncing would be a bottleneck in many programs. A non-blocking lock af a {RW,}Mutex is probably in the order of < 20 nsecs even on middle powered CPUS. I suggest to postpone optimization not only after making the program correct, but also after measuring where the major part of time is being spent.
Coding in Lua, I have a triply nested loop that goes through 6000 iterations. All 6000 iterations are independent and can easily be parallelized. What threads package for Lua compiles out of the box and gets decent parallel speedups on four or more cores?
Here's what I know so far:
luaproc comes from the core Lua team, but the software bundle on luaforge is old, and the mailing list has reports of it segfaulting. Also, it's not obvious to me how to use the scalar message-passing model to get results ultimately into a parent thread.
Lua Lanes makes interesting claims but seems to be a heavyweight, complex solution. Many messages on the mailing list report trouble getting Lua Lanes to build or work for them. I myself have had trouble getting the underlying "Lua rocks" distribution mechanism to work for me.
LuaThread requires explicit locking and requires that communication between threads be mediated by global variables that are protected by locks. I could imagine worse, but I'd be happier with a higher level of abstraction.
Concurrent Lua provides an attractive message-passing model similar to Erlang, but it says that processes do not share memory. It is not clear whether spawn actually works with any Lua function or whether there are restrictions.
Russ Cox proposed an occasional threading model that works only for C threads. Not useful for me.
I will upvote all answers that report on actual experience with these or any other multithreading package, or any answer that provides new information.
For reference, here is the loop I would like to parallelize:
for tid, tests in pairs(tests) do
local results = { }
matrix[tid] = results
for i, test in pairs(tests) do
if test.valid then
results[i] = { }
local results = results[i]
for sid, bin in pairs(binaries) do
local outcome, witness = run_test(test, bin)
results[sid] = { outcome = outcome, witness = witness }
end
end
end
end
The run_test function is passed in as an argument, so a package can be useful to me only if it can run arbitrary functions in parallel. My goal is enough parallelism to get 100% CPU utilization on 6 to 8 cores.
Norman wrote concerning luaproc:
"it's not obvious to me how to use the scalar message-passing model to get results ultimately into a parent thread"
I had the same problem with a use case I was dealing with. I liked lua proc due to its simple and light implementation, but my use case had C code that was calling lua, which was triggering a co-routine that needed to send/receive messages to interact with other luaproc threads.
To achieve my desired functionality I had to add features to luaproc to allow sending and receiving messages from the parent thread or any other thread not running from the luaproc scheduler. Additionally, my changes allow using luaproc send/receive from coroutines created from luaproc.newproc() created lua states.
I added an additional luaproc.addproc() function to the api which is to be called from any lua state running from a context not controlled by the luaproc scheduler in order to set itself up with luaproc for sending/receiving messages.
I am considering posting the source as a new github project or contacting the developers and seeing if they would like to pull my additions. Suggestions as to how I should make it available to others are welcome.
Check the threads library in torch family. It implements a thread pool model: a few true threads (pthread in linux and windows thread in win32) are created first. Each thread has a lua_State object and a blocking job queue that admits jobs added from the main thread.
Lua objects are copied over from main thread to the job thread. However C objects such as Torch tensors or tds data structures can be passed to job threads via pointers -- this is how limited shared memory is achieved.
This is a perfect example of MapReduce
You can use LuaRings to accomplish your parallelization needs.
Concurrent Lua might seem like the way to go, but as I note in my updates below, it doesn't run things in parallel. The approach I tried was to spawn several processes that execute pickled closures received through the message queue.
Update
Concurrent Lua seems to handle first-class functions and closures without a hitch. See the following example program.
require 'concurrent'
local NUM_WORKERS = 4 -- number of worker threads to use
local NUM_WORKITEMS = 100 -- number of work items for processing
-- calls the received function in the local thread context
function worker(pid)
while true do
-- request new work
concurrent.send(pid, { pid = concurrent.self() })
local msg = concurrent.receive()
-- exit when instructed
if msg.exit then return end
-- otherwise, run the provided function
msg.work()
end
end
-- creates workers, produces all the work and performs shutdown
function tasker()
local pid = concurrent.self()
-- create the worker threads
for i = 1, NUM_WORKERS do concurrent.spawn(worker, pid) end
-- provide work to threads as requests are received
for i = 1, NUM_WORKITEMS do
local msg = concurrent.receive()
-- send the work as a closure
concurrent.send(msg.pid, { work = function() print(i) end, pid = pid })
end
-- shutdown the threads as they complete
for i = 1, NUM_WORKERS do
local msg = concurrent.receive()
concurrent.send(msg.pid, { exit = true })
end
end
-- create the task process
local pid = concurrent.spawn(tasker)
-- run the event loop until all threads terminate
concurrent.loop()
Update 2
Scratch all of that stuff above. Something didn't look right when I was testing this. It turns out that Concurrent Lua isn't concurrent at all. The "processes" are implemented with coroutines and all run cooperatively in the same thread context. That's what we get for not reading carefully!
So, at least I eliminated one of the options I guess. :(
I realize that this is not a works-out-of-the-box solution, but, maybe go old-school and play with forks? (Assuming you're on a POSIX system.)
What I would have done:
Right before your loop, put all tests in a queue, accessible between processes. (A file, a Redis LIST or anything else you like most.)
Also before the loop, spawn several forks with lua-posix (same as the number of cores or even more depending on the nature of tests). In parent fork wait until all children will quit.
In each fork in a loop, get a test from the queue, execute it, put results somewhere. (To a file, to a Redis LIST, anywhere else you like.) If there are no more tests in queue, quit.
In the parent fetch and process all test results as you do now.
This assumes that test parameters and results are serializable. But even if they are not, I think that it should be rather easy to cheat around that.
I've now built a parallel application using luaproc. Here are some misconceptions that kept me from adopting it sooner, and how to work around them.
Once the parallel threads are launched, as far as I can tell there is no way for them to communicate back to the parent. This property was the big block for me. Eventually I realized the way forward: when it's done forking threads, the parent stops and waits. The job that would have been done by the parent should instead be done by a child thread, which should be dedicated to that job. Not a great model, but it works.
Communication between parent and children is very limited. The parent can communicate only scalar values: strings, Booleans, and numbers. If the parent wants to communicate more complex values, like tables and functions, it must code them as strings. Such coding can take place inline in the program, or (especially) functions can be parked into the filesystem and loaded into the child using require.
The children inherit nothing of the parent's environment. In particular, they don't inherit package.path or package.cpath. I had to work around this by the way I wrote the code for the children.
The most convenient way to communicate from parent to child is to define the child as a function, and to have the child capture parental information in its free variables, known in Lua parlances as "upvalues." These free variables may not be global variables, and they must be scalars. Still, it's a decent model. Here's an example:
local function spawner(N, workers)
return function()
local luaproc = require 'luaproc'
for i = 1, N do
luaproc.send('source', i)
end
for i = 1, workers do
luaproc.send('source', nil)
end
end
end
This code is used as, e.g.,
assert(luaproc.newproc(spawner(randoms, workers)))
This call is how values randoms and workers are communicated from parent to child.
The assertion is essential here, as if you forget the rules and accidentally capture a table or a local function, luaproc.newproc will fail.
Once I understood these properties, luaproc did indeed work "out of the box", when downloaded from askyrme on github.
ETA: There is an annoying limitation: in some circumstances, calling fread() in one thread can prevent other threads from being scheduled. In particular, if I run the sequence
local file = io.popen(command, 'r')
local result = file:read '*a'
file:close()
return result
the read operation blocks all other threads. I don't know why this is---I assume it is some nonsense going on within glibc. The workaround I used was to call directly to read(2), which required a little glue code, but this works properly with io.popen and file:close().
There's one other limitation worth noting:
Unlike Tony Hoare's original conception of communicating sequential processing, and unlike most mature, serious implementations of synchronous message passing, luaproc does not allow a receiver to block on multiple channels simultaneously. This limitation is serious, and it rules out many of the design patterns that synchronous message-passing is good at, but it's still find for many simple models of parallelism, especially the "parbegin" sort that I needed to solve for my original problem.