I'm studying event sourcing and command/query segregation and I have a few doubts that I hope someone with more experience will easily answer:
A) should a command handler work with more than one aggregate? (a.k.a. should they coordinate things between several aggregates?)
B) If my command handler generates more than one event to store, how do you guys push all those events atomically to the event store? (how can I garantee no other command handler will "interleave" events in between?)
C) In many articles I read people suggest using optimistic locking to write the new events generated, but in my use case I will have around 100 requests / second. This makes me think that a lot of requests will just fail at huge rates (a lot of ConcurrencyExceptions), how you guys deal with this?
D) How to deal with the fact that the command handler can crash after storing the events in the event store but before publishing them to the event bus? (how to eventually push those "confirmed" events back to the event bus?)
E) How you guys deal with the eventual consistency in the projections? you just live with it? or in some cases people lock things there too? (waiting for an update for example)
I made a sequence diagram to better ilustrate all those questions
(and sorry for the bad english)
If my command handler generates more than one event to store, how do you guys push all those events atomically to the event store?
Most reasonable event store implementations will allow you to batch multiple events into the same transaction.
In many articles I read people suggest using optimistic locking to write the new events generated, but in my use case I will have around 100 requests / second.
If you have lots of parallel threads trying to maintain a complex invariant, something has gone badly wrong.
For "events" that aren't expected to establish or maintain any invariant, then you are just writing things to the end of a stream. In other words, you are probably not trying to write an event into a specific position in the stream. So you can probably use batching to reduce the number of conflicting writes, and a simple retry mechanism. In effect, you are using the same sort of "fan-in" patterns that appear when you have concurrent writers inserting into a queue.
For the cases where you are establishing/maintaining an invariant, you don't normally have many concurrent writers. Instead, specific writers have authority to write events (think "sharding"); the concurrency controls there are primarily to avoid making a mess in abnormal conditions.
How to deal with the fact that the command handler can crash after storing the events in the event store but before publishing them to the event bus?
Use pull, rather than push, as the primary subscription mechanism. Make sure that subscribers can handle duplicate messages safely (aka "idempotent"). Don't use a message subscription that can re-order events when you need events strictly ordered.
How you guys deal with the eventual consistency in the projections? you just live with it?
Pretty much. Views and reports have metadata information in them to let you know at what fixed point in "time" the report was accurate.
Unless you lock out all writers while a report is being consumed, there's a potential for any data being out of date, regardless of whether you are using events vs some other data model, regardless of whether you are using a single data model or several.
It's all part of the tradeoff; we accept that there will be a larger window between report time and current time in exchange for lower response latency, an "immutable" event history, etc.
should a command handler work with more than one aggregate?
Probably not - which isn't the same thing as always never.
Usual framing goes something like this: aggregate isn't a domain modeling pattern, like entity. It's a lifecycle pattern, used to make sure that all of the changes we make at one time are consistent.
In the case where you find that you want a command handler to modify multiple domain entities at the same time, and those entities belong to different aggregates, then have you really chosen the correct aggregate boundaries?
What you can do sometimes is have a single command handler that manages multiple transactions, updating a different aggregate in each. But it might be easier, in the long run, to have two different command handlers that each receive a copy of the command and decide what to do, independently.
Having read the following statement from the official documentation of OrientDB:
In order to guarantee atomicity and consistency, OrientDB acquire an
exclusive lock on the storage during transaction commit.
I am wondering if my understanding of the situation is correct. Here is how I assume this will work:
Thread 1 opens a transaction, and reads records #1:100 to #1:200, some from class A, and some from class B (something which cannot be determined without the transaction coming to a close).
Thread 1 massages the data, maybe even inserting a few records.
Thread 1 starts to commit the data. As the database does not have any way to know which parts of the data might be effected by the open transaction, it will blindly block the whole storage unit and verify the #version to enforce optimistic locking on all possibly affected records.
Thread 2 tries to read record #1:1 (or any other record from the whole database) and is blocked by the commit process, which is aligned, AFAIK with exclusive locking on the storage unit. This block occurs, if I'm not off, regardless of the cluster the original data resides on, since we have multi-master datasets.
Thread 1 ends the commit process and the database becomes consistent, effectively lifting the lock.
At this point, any thread can operate on the dataset, transactionally or otherwise, and will not be bound by the exclusive locking mechanism.
If this is the case, during the exchange highlighted in point 3 the data store, in its entirety is in an effective trance state and cannot be reached to, read from, or interacted with in any meaningful way.
I do so hope that I am missing my guess.
Disclaimer: I have not had the chance to dig into the underlying code from the rather rich OrientDB codebase. As such, this is, at its best, an educated guess and should not be taken as any sort of reference as to how OrientDB actually operates.
Possible Workarounds:
Should worse come to worse and this happens to be the way OrientDB actually works, I would dearly welcome any workarounds to this conundrum. We are looking for meaningful ways that will still keep OrientDB as a viable option for an enterprise, scalable high-end application.
In current release of OrientDB, transactions lock the storage in exclusive mode. Fortunately OrientDB works in optimistic way and this is done "only" at commit() time. So no matter when the transaction is begun.
If this is a showstopper for your use case, you could consider to:
don't use transactions. In this case you'll go in parallel with no locks, but consider using indexes requires the usage of lock at index level. In case the index is a bottleneck, the most common workaround is to create X sub-classes with an index on each. OrientDB will use the index of sub-classes if needed and on CRUD operation only the specific index will be locked
wait for OrientDB 3.0 where this limitation will be removed with real parallel transaction execution
What are some methods for testing concurrent data structures to make sure the data structs behave correctly when accessed from multiple threads ?
All of the other answers have focused on actually testing the code by putting it through its paces and actually running it in one form or another or politely saying "don't do it yourself, use an existing library".
This is great and all, but IMO, the most important (practical tests are important too) test is to look at the code line by line and for every line of code ask "what happens if I get interrupted by another thread here?" Imagine another thread, running just about any of the other lines/functions during this interruption. Do things still stay consistent? When competing for resources, does the other thread[s] block or spin?
This is what we did in school when learning about concurrency and it is a surprisingly effective approach. Bottom line, I feel that taking the time to prove to yourself that things are consistent and work as expected in all states is the first technique you should use when dealing with this stuff.
Concurrent systems are probabilistic and errors are often difficult to replicate. Therefore you need to run various input/output cases, each tested over time (hours, days, etc) in order to detect possible errors.
Tests for concurrent data structure involves examining the container's state before and after expected events such as insert and delete.
Use a pre-existing, pre-tested library that meets your needs if possible.
Make sure that the code has appropriate self-consistency checks (preferably fast sanity checks), and run your code on as many different types of hardware as possible to help narrow down interesting timing problems.
Have multiple people peer review the code, preferably without a pre-explanation of how it's supposed to work. That way they have to grok the code which should help catch more bugs.
Set up a bunch of threads that do nothing but random operations on the data structures and check for consistency at some rate.
Start with the assumption that your calls to access/modify data are not thread safe and use locks to ensure only a single thread can access/modify any part of the data at a time. Only after you can prove to yourself that a specific type of access is safe outside of the lock by multiple threads at once should you move that code outside of the lock.
Assume worst case scenarios, e.g. that your code will stop right in the middle of some pointer manipulation or another critical point, and that another thread will encounter that data in mid-transition. If that would have a bad result, leave it within the lock.
I normally test these kinds of things by interjecting sleep() calls at appropriate places in the distributed threads/processes.
For instance, to test a lock, put sleep(2) in all your threads at the point of contention, and spawn two threads roughly 1 second apart. The first one should obtain the lock, and the second should have to wait for it.
Most race conditions can be tested by extending this method, but if your system has too many components it may be difficult or impossible to know every possible condition that needs to be tested.
Run your concurrent threads for one or a few days and look what happens. (Sounds strange, but finding out race conditions is such a complex topic that simply trying it is the best approach).
Is it ok to check the current thread inside a function?
For example if some non-thread safe data structure is only altered by one thread, and there is a function which is called by multiple threads, it would be useful to have separate code paths depending on the current thread. If the current thread is the one that alters the data structure, it is ok to alter the data structure directly in the function. However, if the current thread is some other thread, the actual altering would have to be delayed, so that it is performed when it is safe to perform the operation.
Or, would it be better to use some boolean which is given as a parameter to the function to separate the different code paths?
Or do something totally different?
What do you think?
You are not making all too much sense. You said a non-thread safe data structure is only ever altered by one thread, but in the next sentence you talk about delaying any changes made to that data structure by other threads. Make up your mind.
In general, I'd suggest wrapping the access to the data structure up with a critical section, or mutex.
It's possible to use such animals as reader/writer locks to differentiate between readers and writers of datastructures but the performance advantage for typical cases usually wont merit the additional complexity associated with their use.
From the way your question is stated, I'm guessing you're fairly new to multithreaded development. I highly suggest sticking with the simplist and most commonly used approaches for ensuring data integrity (most books/articles you readon the issue will mention the same uses for mutexes/critical sections). Multithreaded development is extremely easy to get wrong and can be difficult to debug. Also, what seems like the "optimal" solution very often doesn't buy you the huge performance benefit you might think. It's usually best to implement the simplist approach that will work then worry about optimizing it after the fact.
There is a trick that could work in case, as you said, the other threads will only make changes only once in a while, although it is still rather hackish:
make sure your "master" thread can't be interrupted by the other ones (higher priority, non fair scheduling)
check your thread
if "master", just change
if other, put off scheduling, if needed by putting off interrupts, make change, reinstall scheduling
really test to see whether there are no issues in your setup.
As you can see, if requirements change a little bit, this could turn out worse than using normal locks.
As mentioned, the simplest solution when two threads need access to the same data is to use some synchronization mechanism (i.e. critical section or mutex).
If you already have synchronization in your design try to reuse it (if possible) instead of adding more. For example, if the main thread receives its work from a synchronized queue you might be able to have thread 2 queue the data structure update. The main thread will pick up the request and can update it without additional synchronization.
The queuing concept can be hidden from the rest of the design through the Active Object pattern. The activ object may also be able to publish the data structure changes through the Observer pattern to other interested threads.
I need to manage CPU-heavy multitaskable jobs in an interactive application. Just as background, my specific application is an engineering design interface. As a user tweaks different parameters and options to a model, multiple simulations are run in the background and results displayed as they complete, likely even as the user is still editing values. Since the multiple simulations take variable time (some are milliseconds, some take 5 seconds, some take 10 minutes), it's basically a matter of getting feedback displayed as fast as possible, but often aborting jobs that started previously but are now no longer needed because of the user's changes have already invalidated them. Different user changes may invalidate different computations so at any time I may have 10 different simulations running. Somesimulations have multiple parts which have dependencies (simulations A and B can be seperately computed, but I need their results to seed simulation C so I need to wait for both A and B to finish first before starting C.)
I feel pretty confident that the code-level method to handle this kind of application is some kind of multithreaded job queue. This would include features of submitting jobs for execution, setting task priorities, waiting for jobs to finish, specifying dependencies (do this job, but only after job X and job Y have finished), canceling subsets of jobs that fit some criteria, querying what jobs remain, setting worker thread counts and priorities, and so on. And multiplatform support is very useful too.
These are not new ideas or desires in software, but I'm at the early design phase of my application where I need to make a choice about what library to use for managing such tasks. I've written my own crude thread managers in the past in C (I think it's a rite of passage) but I want to use modern tools to base my work on, not my own previous hacks.
The first thought is to run to OpenMP but I'm not sure it's what I want. OpenMP is great for parallelizing at a fine level, automatically unrolling loops and such. While multiplatform, it also invades your code with #pragmas. But mostly it's not designed for managing large tasks.. especially cancelling pending jobs or specifying dependencies. Possible, yes, but it's not elegant.
I noticed that Google Chrome uses such a job manager for even the most trivial tasks. The design goal seems to be to keep the user interaction thread as light and nimble as possible, so anything that can get spawned off asynchronously, should be. From looking at the Chrome source this doesn't seem to be a generic library, but it still is interesting to see how the design uses asynchronous launches to keep interaction fast. This is getting to be similar to what I'm doing.
There are a still other options:
Surge.Act: a Boost-like library for defining jobs. It builds on OpenMP, but does allow chaining of dependencies which is nice. It doesn't seem to feel like it's got a manager that can be queried, jobs cancelled, etc. It's a stale project so it's scary to depend on it.
Job Queue is quite close to what I'm thinking of, but it's a 5 year old article, not a supported library.
Boost.threads does have nice platform independent synchronization but that's not a job manager. POCO has very clean designs for task launching, but again not a full manager for chaining tasks. (Maybe I'm underestimating POCO though).
So while there are options available, I'm not satisfied and I feel the urge to roll my own library again. But I'd rather use something that's already in existence. Even after searching (here on SO and on the net) I haven't found anything that feels right, though I imagine this must be a kind of tool that is often needed, so surely there's some community library or at least common design.
On SO there's been some posts about job queues, but nothing that seems to fit.
My post here is to ask you all what existing tools I've missed, and/or how you've rolled your own such multithreaded job queue.
We had to build our own job queue system to meet requirements similar to yours ( UI thread must always respond within 33ms, jobs can run from 15-15000ms ), because there really was nothing out there that quite met our needs, let alone was performant.
Unfortunately our code is about as proprietary as proprietary gets, but I can give you some of the most salient features:
We start up one thread per core at the beginning of the program. Each pulls work from a global job queue. Jobs consist of a function object and a glob of associated data (really an elaboration on a func_ptr and void *). Thread 0, the fast client loop, isn't allowed to work on jobs, but the rest grab as they can.
The job queue itself ought to be a lockless data structure, such as a lock-free singly linked list (Visual Studio comes with one). Avoid using a mutex; contention for the queue is surprisingly high, and grabbing mutexes is costly.
Pack up all the necessary data for the job into the job object itself -- avoid having pointer from the job back into the main heap, where you'll have to deal with contention between jobs and locks and all that other slow, annoying stuff. For example, all the simulation parameters should go into the job's local data blob. The results structure obviously needs to be something that outlives the job: you can deal with this either by a) hanging onto the job objects even after they've finished running (so you can use their contents from the main thread), or b) allocating a results structure specially for each job and stuffing a pointer into the job's data object. Even though the results themselves won't live in the job, this effectively gives the job exclusive access to its output memory so you needn't muss with locks.
Actually I'm simplifying a bit above, since we need to choreograph exactly which jobs run on which cores, so each core gets its own job queue, but that's probably unnecessary for you.
I rolled my own, based on Boost.threads. I was quite surprised by how much bang I got from writing so little code. If you don't find something pre-made, don't be afraid to roll your own. Between Boost.threads and your experience since writing your own, it might be easier than you remember.
For premade options, don't forget that Chromium is licensed very friendly, so you may be able to roll your own generic library around its code.
Microsoft is working on a set of technologies for the next Version of Visual Studio 2010 called the Concurrency Runtime, the Parallel Pattern Library and the Asynchronous Agents Library which will probably help. The Concurrency Runtime will offer policy based scheduling, i.e. allowing you to manage and compose multiple scheduler instances (similar to thread pools but with affinitization and load balancing between instances), the Parallel Pattern Library will offer task based programming and parallel loops with an STL like programming model. The Agents library offers an actor based programming model and has support for building concurrent data flow pipelines, i.e. managing those dependencies described above. Unfortunately this isn't released yet, so you can read about it on our team blog or watch some of the videos on channel9 there is also a very large CTP that is available for download as well.
If you're looking for a solution today, Intel's Thread Building Blocks and boost's threading library are both good libraries and available now. JustSoftwareSolutions has released an implementation of std::thread which matches the C++0x draft and of course OpenMP is widely available if you're looking at fine-grained loop based parallelism.
The real challenge as other folks have alluded to is to correctly identify and decompose work into tasks suitable for concurrent execution (i.e. no unprotected shared state), understand the dependencies between them and minimize the contention that can occur on bottlenecks (whether the bottleneck is protecting shared state or ensuring the dispatch loop of a work queue is low contention or lock-free)... and to do this without scheduling implementation details leaking into the rest of your code.
-Rick
Would something like threadpool be useful to you? It's based on boost::threads and basically implements a simple thread task queue that passes worker functions off to the pooled threads.
I've been looking for near the same requirements. I'm working on a game with 4x-ish mechanics and scheduling different parts of what gets done almost exploded my brain. I have a complex set of work that needs to get accomplished at different time resolutions, and to a different degree of actual simulation depending on what system/region the player has actively loaded. This means as the player moves from system to system, I need to load a system to the current high resolution simulation, offload the last system to a lower resolution simulation, and do the same for active/inactive regions of systems. The different simulations are big lists of population, political, military, and economic actions based on profiles of each entity. I'm going to try to describe my issue and my approach so far and I hope it's useful at describe an alternative for you or someone else. The rough outline of the structure I'm building will use the following:
cpp-taskflow (A Modern C++ Parallel Task Programming Library) I'm going to make a library of modules that will be used as job construction parts. Each entry will have an API for initializing and destruction as well as pointers for communication. I'm hoping to write it in a way that they will be nest-able using the cpp-taskflow API to set-up all the dependencies at job creation time, but provide a means of live adjustment and having a kill-switch available. Most of what I'm making will be decision trees of state machines, or state machines of behavior trees so the job data structure will be settings and states of time-resolution tagged data pointing to actual stats and object values.
FlatBuffers I'm looking to use this library to build a "job list entry" as well as an "object wrapper" system. Each entry in the job queues will be a flatbuffer object describing the work needed done(settings for the module), as well as containing the data(or shared pointers to the data) for the work that needs done. The object storage flatbuffers will contain the data that represents entity tables. For me, most of the actual data will me arrays that need deciding/working on. I'm also looking to use flatbuffers as a communication/control channel between threads. I'm torn on making a master "router" thread all the others communicate through, or each one containing their own, and having some mechanism of discovery.
SQLite Since only the active regions/systems need higher resolution work done, some of the background job lists the game will create(for thousands of systems and their entities) will be pretty large and long lived. 100's of thousands - millions of jobs(big in my mind), each requiring an unknown amount of time to complete. In my case, I don't care when they get done, as long as they all do(long campains). I plan on each thread getting a table of an in-memory sqlite db as a job queue. Each entry will contain a blob of flatbuffer work, a pointer to a buffer to notify upon completion, a pointer to a control buffer for updates, and other fields decorating the job item(location, data ranges, priority) that will get filled as the job entry makes new jobs, and as the items are consumed into the database. This give me a way I can create relational ties between jobs and simply construct queries if I need to re-work/update jobs, remove them and their dependencies, or update/re-order priorities or dependencies. All this being used in an sqlite db also means that at any time I can dump the whole thing to disk and reload it later, or switch to attaching to and processing it from disk. Additionally, this gives me access to a lot of search and ordering algorithmic work I'd normally need a bunch of different types of containers for. Being able to use SQL queries gives me a lot of options to process the jobs.
The communication queue(as a db) is what I'm torn as to whether I should make access via the corresponding thread(each thread contains it's own messaging db, and the module API has locks/mutex abstracted for access), or have all updates, adds/removes, and communication via some master router thread into one large db. I have no idea which will give me the least headaches as far as mutexing and locks. I got a few days into making a monster spaghetti beast of shared pointers to sbuffer pools and lookup tables, so each thread had it's own buffer in, and separate out buffers. That's when I decided to just offload the giant list keeping to sqlite. Then I thought, why not just feed the flatbuffer objects of everything else into tables.
Having almost everything in a db means from each module, I can write sql statements that represent the view of the data I need to work on as well as pivot on the fly as to how the data is worked on. Having the jobs themselves in a db means I can do the same for them as well. SQLite has multi-threading access, so using it as a Multithreaded job queue manager shouldn't be too much of a stretch.
In summary, Cpp-Taskflow will allow you to setup complicated nested loops with dependency chaining and job-pool multithreading. Out of the box it comes with most of the structure you need. FlatBuffers will allow you to create job declarations and object wrappers easy to feed into stream-buffers as one unit of work and pass them between job threads, and SQLite will allow you to tag and queue the stream-buffer jobs into blob entries in a way that should allow adding, searching, ordering, updating, and removal with minimal work on your end. It also makes saving and reloading a breeze. Snapshots and roll-backs should also be doable, you just have to keep your mind wrapped around the order and resolution of events for the db.
Edit: Take this with a grain of salt though, I found your question because I'm trying to accomplish what Crashworks described. I'm thinking of using affinity to open long living threads and have the master thread run the majority of the Cpp-Taskflow hierarchy work, feeding jobs to the others. I've yet to use the sqlite meothod of job-queue/control communication, that's just my plan so far.
I hope someone finds this helpful.
You might want to look at Flow-Based Programming - it is based on data chunks streaming between asynchronous components. There are Java and C# versions of the driver, plus a number of precoded components. It is intrinsically multithreaded - in fact the only single-threaded code is within the components, although you can add timing constraints to the standard scheduling rules. Although it may be at too fine-grained a level for what you need, there may be stuff here you can use.
Take a look at boost::future (but see also this discussion and proposal) which looks like a really nice foundation for parallelism (in particular it seems to offer excellent support for C-depends-on-A-and-B type situations).
I looked at OpenMP a bit but (like you) wasn't convinced it would work well for anything but Fortran/C numeric code. Intel's Threading Building Blocks looked more interesting to me.
If it comes to it, it's not too hard to roll your own on top of boost::thread.
[Explanation: a thread farm (most people would call it a pool) draws work from a thread-safe queue of functors (tasks or jobs). See the tests and benchmark for examples of use. I have some extra complication to (optionally) support tasks with priorities, and the case where executing tasks can spawn more tasks into the work queue (this makes knowing when all the work is actually completed a bit more problematic; the references to "pending" are the ones which can deal with the case). Might give you some ideas anyway.]
You may like to look at Intel Thread Building Blocks. I beleave it does what you want and with version 2 it's Open Source.
There's plenty of distributed resource managers out there. The software that meets nearly all of your requirements is Sun Grid Engine. SGE is used on some of the worlds largest supercomputers and is in active development.
There's also similar solutions in Torque, Platform LSF, and Condor.
It sounds like you may want to roll your own but there's plenty of functionality in all of the above.
I don't know if you're looking for a C++ library (which I think you are), but Doug Lea's Fork/Join framework for Java 7 is pretty nifty, and does exactly what you want. You'd probably be able to implement it in C++ or find a pre-implemented library.
More info here:
http://artisans-serverintellect-com.si-eioswww6.com/default.asp?W1
A little late to the punch perhaps, but take a look also at ThreadWeaver:
http://en.wikipedia.org/wiki/ThreadWeaver