I have read about std::sync::Mutex but it lock whole array which I don't want. Mine requirement is lock only region or block (certain contiguous indices) which I'm currently modifying and other regions should be available for other threads for write also.
Can someone give hint for what I should look for?
Related
I know I can use RwLock to wait for reading threads to finish reading, although I was wondering if it was possible to write data while the readers were reading non-atomically (I don't really care whether readers get an old copy of the data or a new one, as long as the memory gets updated)
Is this possible in safe (or unsafe rust)?
A little more about my specific problem: I have an object that may take a long time to write to but I want to have readers reading from it constantly.
EDIT: More specifically, I have a Cache that holds different objects. These objects simply hold a byte (u8) array. This byte array needs to be read from different threads as well as being written to (the writing parses a large cumbersome struct and converts various fields into the byte array).
This is a good use-case of the arc-swap crate, which allows you to atomically swap one Arc for another. Every time you wish to create a new version of your data, you create a clone of the data and put the new version in an Arc, swapping out the old Arc. Code that needs to read can get a clone to the Arc currently in the ArcSwap, and the old version is destroyed once there are no more handles to the old version.
If you need to modify the data from multiple places, you should employ the following pattern using a Mutex.
You have an extra version used for updates stored inside the Mutex.
When you wish to update the data, lock the mutex and make your changes.
Make a clone of the object stored in the Mutex and put it into the ArcSwap.
Then unlock the mutex.
Any code that wishes to read the data will take a clone from the ArcSwap and never touch the Mutex. It is important that the mutex is unlocked after swapping the Arc with the new version.
MDB_NOLOCK as described at mdb_env_open() apidoc:
MDB_NOLOCK Don't do any locking. If concurrent access is anticipated, the caller must manage all concurrency itself. For proper operation the caller must enforce single-writer semantics, and must ensure that no readers are using old transactions while a writer is active. The simplest approach is to use an exclusive lock so that no readers may be active at all when a writer begins.
What if an RW txnA intends to modify a set of keys which has no key in common with another set of keys which another RW txnB intends to modify? Couldn't they be sent concurrently?
Isn't the single-writer semantic wasteful for such situations? As one txn is waiting for the previous one to finish, even though they intend to operate in entirely separate regions in an lmdb env.
In an environment opened with MDB_NOLOCK, what if the client app calculates in the domainland, that two write transactions are intending to RW to mutually exclusive set of keys anywhere in an lmdb environment, and sends only such transactions concurrently anyway? What could go wrong?
Could such concurrent writes scale linearly with cores? Like RO txns do? Given the app is able to manage these concurrent writes, in the manner described in 3.
No, since modifying key/value pairs requires also modifying the b-tree structure, and the two transactions would conflict with each other.
You should avoid doing long-running computations in the middle of a write transaction. Try to do as much as possible beforehand. If you can't do this, then LMDB might not be a great fit for you application. Usually you can though.
Very bad stuff. Application crashes and DB corruption.
Writes are generally IO bound, and will not scale with many cores anyway. There are some very hacky things you can do with LMDB's writemap and/or pwrite(2), but you are very much on your own here.
I'm going to assume that writing to the value part of a pre-existing key does not modify the b-tree because you are not modifying the keys. So what Doug Hoyte's comment stands, except possibly point 3:
Key phrase here is "are intending to RW to mutually exclusive set of keys". So assuming that the keys are pre-allocated, and already in the DB, changing the values should not matter. I don't even know if LMDB can store variable sized values, in which case it could matter if the values are different sizes.
So, it should be possible to write with MDB_NOLOCK concurrently as long as you can guarantee to never modify, add, or delete any keys during the concurrent writes.
Empirically I can state that working with LMDB opened with MDB_NO_LOCK (or lock=False in Python) and simply modifying values of pre-existing keys, or even only adding new key/values - seems to work well. Even if LMDB itself is mounted across an NFS like medium and queried from different machines.
#Doug Hoyte - I would appreciate more context as to what specific circumstances might lead to a crash or corruption. In my case there are many small short-lived type of writes to the same DB.
I have recently come across a question based on multi-threading. I was given a situation where there will be variable no of cars constantly changing there locations. Also there are multiple users who are posting requests to get location of any car at any moment. What would be data structure to handle this situation and why?
You could use a mutex (one per car).
Lock: before changing location of the associated car
Unlock: after changing location of the associated car
Lock: before getting location of the associated car
Unlock: after done doing work that relies on that location being up to date
I'd answer with:
Try to make threading an external concept to your system yet make the system as modular and encapsulated as possible at the same time. It will allow adding concurrency at later phase at low cost and in case the solution happens to work nicely in a single thread (say by making it event-loop-based) no time will have been burnt for nothing.
There are several ways to do this. Which way you choose depends a lot on the number of cars, the frequency of updates and position requests, the expected response time, and how accurate (up to date) you want the position reports to be.
The easiest way to handle this is with a simple mutex (lock) that allows only one thread at a time to access the data structure. Assuming you're using a dictionary or hash map, your code would look something like this:
Map Cars = new Map(...)
Mutex CarsMutex = new Mutex(...)
Location GetLocation(carKey)
{
acquire mutex
result = Cars[carKey].Location
release mutex
return result
}
You'd do that for Add, Remove, Update, etc. Any method that reads or updates the data structure would require that you acquire the mutex.
If the number of queries far outweighs the number of updates, then you can do better with a reader/writer lock instead of a mutex. With an RW lock, you can have an unlimited number of readers, OR you can have a single writer. With that, querying the data would be:
acquire reader lock
result = Cars[carKey].Location
release reader lock
return result
And Add, Update, and Remove would be:
acquire writer lock
do update
release writer lock
Many runtime libraries have a concurrent dictionary data structure already built in. .NET, for example, has ConcurrentDictionary. With those, you don't have to worry about explicitly synchronizing access with a Mutex or RW lock; the data structure handles synchronization for you, either with a technique similar to that shown above, or by implementing lock-free algorithms.
As mentioned in comments, a relational database can handle this type of thing quite easily and can scale to a very large number of requests. Modern relational databases, properly constructed and with sufficient hardware, are surprisingly fast and can handle huge amounts of data with very high throughput.
There are other, more involved, methods that can increase throughput in some situations depending on what you're trying to optimize. For example, if you're willing to have some latency in reported position, then you could have position requests served from a list that's updated once per minute (or once every five minutes). So position requests are fulfilled immediately with no lock required from a static copy of the list that's updated once per minute. Updates are queued and once per minute a new list is created by applying the updates to the old list, and the new list is made available for requests.
There are many different ways to solve your problem.
In complier.h, there is a macro define as below:
# define __cond_lock(x,c) ((c) ? ({ __acquire(x); 1; }) : 0)
But here I have a question, that is, where there is a __cond_lock definition, but does not define the corresponding __cond_unlock, then the variable on the release, how to keep consistent between __cond_lock and __cond_unlock?
And I checked the definition of function spin_trylock (), and it is used __cond_lock, but which also used a _spin_trylock function.in _spin_trylock function, after a few calls, it will use to __acquire function in this case, the equivalent of an operation, it carried out two calculations would lead Sparse detection warning message appears, after I wrote the code for an experiment to test my judgment, is indeed a warning message will appear, if I wrote it twice unlock instruction, there is no alarm information, but this is inconsistent as program running.
Protecting critical sections using locking is up to the programmer. That means, if you hold a lock to protect a critical reason, you've must have to release the lock when you're finished.
There are various types of locking primitives inside Linux kernel like. spinlock(), spinlock_irq(), spin_trylock(). They have their own purposes. Now, spin_trylock() using __cond_lock inside of it, it's because to make sure, whether that particular lock is available for locking or it's been already taken. Take a look at few examples of how spin_trylock or __cond_lock is being used. For ex. at kernel/sched/fair.c::rebalance_domain (https://git.kernel.org/cgit/linux/kernel/git/torvalds/linux.git/tree/kernel/sched/fair.c?id=d8dfad3876e4386666b759da3c833d62fb8b2267#n5574) see how the balancing is used, it's been using spin_trylock() to hold the lock and while releasing doing it conditionally. Another example could be found at kernel/posix-timers.c, lock_timer() macro. If you closely look at the uses of lock_timer() you'll find how __cond_lock is being used inside kernel and hopefully your confusion will disappear.
In other words, __cond_lock is used to hold a lock conditionally and not being used directly. It's possible to check a particular lock before releasing the lock and this what has been done so far.
There is nothing in the way the program uses this data which will cause the program to crash if it reads the old value rather than the new value. It will get the new value at some point.
However, I am wondering if reading and writing at the same time from multiple threads can cause problems for the OS?
I am yet to see them if it does. The program is developed in Linux using pthreads.
I am not interested in being told how to use mutexs/semaphores/locks/etc edit: so my program is only getting the new values, that is not what I'm asking.
No.. the OS should not have any problem. The tipical problem is the that you dont want to read the old values or a value that is half way updated, and thus not valid (and may crash your app or if the next value depends on the former, then you can get a corrupted value and keep generating wrong values all the itme), but if you dont care about that, the OS wont either.
Are the kernel/drivers reading that data for any reason (eg. it contains structures passed in to kernel APIs)? If no, then there isn't any issue with it, since the OS will never ever look at your hot memory.
Your own reads must ensure they are consistent so you don't read half of a value pre-update and half post-update and end up with a value that is neither pre neither post update.
There is no danger for the OS. Only your program's data integrity is at risk.
Imagine you data to consist of a set (structure) of values, which cannot be updated in an atomic operation. The reading thread is bound to read inconsistent data at some point (data consisting of a mixture of old and new values). But you did not want to hear about mutexes...
Problems arise when multiple threads share access to data when accessing that data is not atomic. For example, imagine a struct with 10 interdependent fields. If one thread is writing and one is reading, the reading thread is likely to see a struct that is halfway between one state and another (for example, half of it's members have been set).
If on the other hand the data can be read and written to with a single atomic operation, you will be fine. For example, imagine if there is a global variable that contains a count... One thread is incrementing it on some condition, and another is reading it and taking some action... In this case, there is really no intermediate inconsistent state. It's either got the new value, or it has the old value.
Logically, you can think of locking as a tool that lets you make arbitrary blocks of code atomic, at least as far as the other threads of execution are concerned.