I'm using the Java Thick Client of Apache Ignite. I manually acquire locks and release them afterwards. It might happen for whatever reason that code which releases the lock is never reached and thus the lock would persist in the Grid forever.
Is there a way to limit the time a lock may exist in Apache Ignite? I tried with the created expiry policy but the locks were not removed.
It is advised to use Ignite.reentrantLock() instead.
You can close() an IgniteLock (I assume you do not have to own it), so you can "GC" them.
Related
I have an Apache Geode setup, connected with external Postgres datasource. I have a scenario where I define an expiration time for a key. Let's say after T time the key is going to expire. Is there a way so that the keys which are going to expire can make a call to an external datasource and update the value incase the value has been changed? I want a kind of automatic syncing for my keys which are there in Apache Geode. Is there any interface which i can implement and get the desired behavior?
I am not sure I fully understand your question. Are you saying that the values in the cache may possibly be more recent than what is currently stored in the database?
Whether you are using Look-Aside Caching, Inline Caching, or even Near Caching, Apache Geode combined with Spring would take care of ensuring the cache and database are kept in sync, to some extent depending on the caching pattern.
With Look-Aside Caching, if used properly, the database (i.e. primary System of Record (SOR), e.g. Postgres in your case) should always be the most current. (Look-Aside) Caching is secondary.
With Synchronous Inline Caching (using a CacheLoader/CacheWriter combination for Read/Write-Through) and in particular, with emphasis on CacheWriter, during updates (e.g. Region.put(key, value) cache operations), the DB is written to first, before the entry is stored (or overwritten) in the cache. If the DB write fails, then the cache entry is not written or updated. This is true each time a value for a key is updated. If the key has not be updated recently, then the database should reflect the most recent value. Once again, the database should always be the most current.
With Asynchronous Inline Caching (using AEQ + Listener, for Write-Behind), the updates for a cache entry are queued and asynchronously written to the DB. If an entry is updated, then Geode can guarantee that the value is eventually written to the underlying DB regardless of whether the key expires at some time later or not. You can persist and replay the queue in case of system failures, conflate events, and so on. In this case, the cache and DB are eventually consistent and it is assumed that you are aware of this, and this is acceptable for your application use case.
Of course, all of these caching patterns and scenarios I described above assume nothing else is modifying the SOR/database. If another external application or process is also modifying the database, separate from your Geode-based application, then it would be possible for Geode to become out-of-sync with the database and you would need to take steps to identify this situation. This is rather an issue for reads, not writes. Of course, you further need to make sure that stale cache entries does not subsequently overwrite the database on an update. This is easy enough to handle with optimistic locking. You could even trigger a cache entry remove on an DB update failure to have the cache refreshed on read.
Anyway, all of this is to say, if you applied 1 of the caching patterns above correctly, the value in the cache should already be reflected in the DB (or will be in the Async, Write-Behind Caching UC), even if the entry eventually expires.
Make sense?
I am using Weblogic 10.3.6 with 8 managed servers configured with session timeout as 600 seconds. I have an issue with my application that when a session gets timed out in 600 seconds(I am receiving as STUCK alerts which is also configured) I am facing slowness in my application. My question is,
Will all threads be impacted because of one STUCK thread(STUCK thread
was due to DB transaction timeout)
I assume it will not be, but wanted to confirm.
Depends on your application. In general no, but if for example the stuck thread is holding a lock on an object (database, file, etc.) called by other requests, these may be affected too. Also, depending on what the stuck thread is doing, it may use excessive resources (cpu, memory, disk, etc.). I suggest to investigate why the thread is taking so long and if it's possible to
I'm using AcquireLock method from ServiceStack Redis when updating and getting the key/value like this:
public virtual void Set(string key, T entity)
{
using (var client = ClientManager.GetClient())
{
using (client.AcquireLock(key + ":locked", DefaultLockingTimeout, DefaultLockExpire))
{
client.Set(key, entity);
}
}
}
I've extended AcqurieLock method to accept extra parameter for expiration of the lock key. So I'm wondering that if I need AcquireLock at all or not? My class uses AcquireLock in every operation like Get<>, GetAll<>, ExpireAt, SetAll<>, etc..
But this approach doesn't work everytime. For example, if the operating in the lock throws an exception, then the key remains locked. For this situation I've added DefaultLockExpire parameter to AcquireLock method to expire the "locked" key.
Is there any better solution, or when do we need acquiring locks like "lock" blocks in multi-thread programming.
As The Real Bill answer has said, you don't need locks for Redis itself. What the ServiceStack client offers in terms of locking is not for Redis, but for your application. In a C# application, you can lock things locally with lock(obj) so that something cannot happen concurrently (only one thread can access the locked section at a time), but that only works if you have one webserver. If you want to prevent something happening concurrently, you need a locking mechanism living outside of the webserver. Redis is a good fit for this.
We have a case where it is checked if a customer has a shopping cart already and if not, create it. Between checking and creating it, there's a time where another request could have also found out that cart doesn't exist and might also proceed to create one. That's a classical case for locking but a simple lock wouldn't work here as the request may have arrived from an entirely different web-server. So for this, we use the ServiceStack Redis client (with some abstraction) to lock using Redis and only allow one request at a time to enter the "create a cart" section.
So to answer your actual question: no, you don't need a lock for getting/setting values to Redis.
I wouldn't use locks for get/set operations. Redis will do those actions atomically, so there is no chance of it getting "changed underneath you" when setting or getting. I've built systems where hundreds of clients are updating/operating on values concurrently and never needed a lock to do those operations (especially an expire).
I don't know how Service Stack redis implements the locking it has so I can't say why it is failing. However, I'm not sure I'd trust it given there is no true locking needed on the Redis side for data operations. Redis is single-threaded so locking there doesn't make sense.
If you are doing complex operations where you get a value, operate on things based on it, then update it after a while and can't have the value change in the meantime I'd recommend reading and groking http://redis.io/topics/transactions to see if what you want is what Redis is good for, whether your code needs refactored to eliminate the problem, or at the least find a better way to do it.
For example, SETNX may be the route you need to get what you want, but without details I can't say it will work.
As #JulianR says, the locking in ServiceStack.Redis is only for application-level distributed locks (i.e. to replace using a DB or an empty .lock file on a distributed file system) and it only works against other ServiceStack.Redis clients in other process using the same key/API to acquire the lock.
You would never need to do this for normal Redis operations since they're all atomic. If you want to ensure a combination of redis operations happen atomically than you would combine them within a Redis Transaction or alternatively you can execute them within a server-side Lua script - both allow atomic execution of batch operations.
We're using siteCore 6.5 and each time we start to publish items, users who are browsing the website will get server 500 errors which end up being
Transaction (Process ID ##) was deadlocked on lock resources with
another process and has been chosen as the deadlock victim. Rerun the
transaction.
How can we setup SQL Server to give priority to a specific application? We cannnot modify any queries or code so it has to be done via SQL Server (or connection string)
I've seen "deadlock victim" in transaction, how to change the priority? and looked at http://msdn.microsoft.com/en-us/library/ms186736(v=SQL.105).aspx but these seem to be per session, not globally.
I don't care if it's a fix/change to SiteCore or a SQL solution.
I don't think you can set the deadlock priority globally - it's a session-only setting. There are not any connection string settings that I know of. The list of possible SqlConnection string settings can be found here.
It sounds to me like you're actually having a problem with the cache and every time you publish, it's clearing the cache and thus you're getting deadlocks with all these calls made at the same time. I haven't seen this sort of thing happen with 6.5 so you might also want to check into your caching. It would help a lot to look at your Sitecore logs and see if this is happening when caches are being created. Either way, check the caching guide on the SDN and see if that helps.
I have a set of resources each of which has a unique identifier, and each resource element must be locked before it is used, and unlocked afterwards. The logic of the application is:
lock any one element;
if (none locked) then
exit with error;
else
get resource-id from lock
use resource
unlock resource
end
Zookeeper looks like a good candidate for managing these locks, being fast and resilient, and it seems quite simple to recover from client failure.
Can anyone think how I could use Zookeeper to achieve this ?
How about this-
you have resources in the a directory (say /locks)
each process which needs to lock, lists all the children of this directory
and then creates an ephemeral node called /locks/resource1/lock depending on
which resource it wants to lock. It could be randomized on the set of resources.
This ephemeral node will be deleted by the process as soon as its done using
the resource. A process should only use to resource_{i} if its been able to
create /locks/resource_{i}/locks.
Would that work?
Thanks
mahadev