I am struck with a question and thus seeking your help.
The maxThread count value in tomcat controls the max number of concurrent request which a tomcat can serve(my understanding), which means max number of threads working.
Now suppose I set maxThread =2 and there are two threads working simultaneously currently. If one of the thread creates 2 child threads(calling some other api).
My Question: If maxThread=2 can 4 threads(two parent + 2 child) exists?
If yes what maxThread controls?
If not what will happen in such case?
Pardon if I am difficult to explain myself.
For each HTTP request made on one of your connector, Tomcat creates one thread (the one named http-8080-thread1 for instance).
The maxThreads value controls those threads. It is totally possible that several other threads exists or can be created in the JVM when maxThreads is reached.
Also, take into account there is an acceptCount which sets the number of requests queued when all requests threads are busy.
More details in tomcat documentation
Related
I would like to cache a large amount of data in a Flask application. Currently it runs on K8S pods with the following unicorn.ini
bind = "0.0.0.0:5000"
workers = 10
timeout = 900
preload_app = True
To avoid caching the same data in those 10 workers I would like to know if Python supports a way to multi-thread instead of multi-process. This would be very easy in Java but I am not sure if it is possible in Python. I know that you can share cache between Python instances using the file system or other methods. However it would be a lot simpler if it is all share in the same process space.
Edited:
There are couple post that suggested threads are supported in Python. This comment by Filipe Correia, or this answer in the same question.
Based on the above comment the Unicorn design document talks about workers and threads:
Since Gunicorn 19, a threads option can be used to process requests in multiple threads. Using threads assumes use of the gthread worker.
Based on how Java works, to shared some data among threads, I would need one worker and multiple threads. Based on this other link
I know it is possible. So I assume I can change my gunicorn configuration as follows:
bind = "0.0.0.0:5000"
workers = 1
threads = 10
timeout = 900
preload_app = True
This should give me 1 worker and 10 threads which should be able to process the same number of request as current configuration. However the question is: Would the cache still be instantiated once and shared among all the threads? How or where should I instantiate the cache to make sure is shared among all the threads.
would like to ... multi-thread instead of multi-process.
I'm not sure you really want that. Python is rather different from Java.
workers = 10
One way to read that is "ten cores", sure.
But another way is "wow, we get ten GILs!"
The global interpreter lock must be held
before the interpreter interprets a new bytecode instruction.
Ten interpreters offers significant parallelism,
executing ten instructions simultaneously.
Now, there are workloads dominated by async I/O, or where
the interpreter calls into a C extension to do the bulk of the work.
If a C thread can keep running, doing useful work
in the background, and the interpreter gathers the result later,
terrific. But that's not most workloads.
tl;dr: You probably want ten GILs, rather than just one.
To avoid caching the same data in those 10 workers
Right! That makes perfect sense.
Consider pushing the cache into a storage layer, or a daemon like Redis.
Or access memory-resident cache, in the context of your own process,
via mmap or shmat.
When running Flask under Gunicorn, you are certainly free
to set threads greater than 1,
though it's likely not what you want.
YMMV. Measure and see.
Hy,
I tried to modelize a threads pool usage with the following scenario:
Request a db to retrieve a set of instances
Create a set of threads, each one doing the same calls set
start a loop for all retrieved instances
wait that one thread is availablle
Provides the instance to the available thread that executes its two tasks
enf of loop
I see this concurrency question but it does not modelize a pool.
Any idea ? A fork is like to create a thread so i can not figure how to modelize my goal.
Maybe something like, but this is not correct because StructuredAction does not get a isSynchronous attribute (i did not find one in StructuredAction generalizations)
The schema provided in the question is in fact my answer.
my problem is quite complex.
The matter is to test our web site answers to an increasing amount of requests from different users.
So I can take users/passwords from a CSV Data and launch an HTTP request (with variables readen from the file).
But I don't want to run the thread with all users at same time, but to loop and add at every iteration an other user from the file to the running thread groups (after some delay).
It seems very difficult to do so with jMeter. Perhaps I's need to call a custom java class ?
If I understand you correctly, you just should use Rump up. This parameter control how fast your test will reach maximum threads count.
As explained in JMeter documentation,
The ramp-up period tells JMeter how long to take to "ramp-up" to the
full number of threads chosen. If 10 threads are used, and the ramp-up
period is 100 seconds, then JMeter will take 100 seconds to get all 10
threads up and running. Each thread will start 10 (100/10) seconds
after the previous thread was begun. If there are 30 threads and a
ramp-up period of 120 seconds, then each successive thread will be
delayed by 4 seconds.
Also may be this Throughput Shaping Timer may be helpful for you. You can schedule duration of request with it.
As Jay stated, you can use ramp up to try to control this, though I am not sure the result will be what you are after...though it will add the startup delay. If you have a single thread then each row of the CSV will be processed one at a time, in order.
You can set the thread group to 1 thread and loop forever. In the CSV config you can set a single pass and to terminate the thread on EOF.
CSV Data Set Config-->Recycle on EOF = False
CSV Data Set Config-->Stop thread on EOF = True
Thread Group-->Loop Count = Forever
Also keep in mind that by using BSF and Beanshell you can exact a great deal of control over JMeter.
You should check out UltimateThreadGroup from jmeter-plugins.
Summary:
I am interested in knowing what's the best practice for high throughput applications that have bulk messages trying to update the same row and get oracle deadlock errors. I know you cannot avoid those errors but how do you recover from them gracefully without getting bogged down by such deadlock errors happening over and over again.
Details:
We are building a high throughput JMS messaging application. Production environment will be two weblogic 11g nodes (running 6 MDB listener instances each). We were getting Oracle deadlock errors (ORA-00060) when we get around 1000 messages all trying to update the same row in oracle database. Java synchronization across nodes is not possible in standard java threading API (unless there's no other solution we don't want to use any 3rd party solutions like terracotta etc).
We were hoping Oracle "select for update WAIT n secs" statement will help because that will essentially make the competing threads (for the same row) wait few seconds before the first thread (who got the lock on the row first) gets done with it.
First issue with "SELECT FOR UPDATE WAIT n" is it doesn't allow using milliseconds for wait times. This starts negatively affecting our application's throughput because putting 1 sec WAIT (least wait time) causes delays on the messages.
Second thing we are fiddling with weblogic queue re-delivery delay parameter (30 secs in our case). Whenever a thread bounces back because of the deadlock error, it will wait 30 seconds before being re-tried.
In our experience 1000 competing messages, in a lot of situations take forever to get processed because the deadlock keeps on happening over and over.
I understand that with the current architecture we are supposed to get deadlock errors regardless ( in case of 1000 competing messages) but application should be resilient enough to recover from these errors after retrying the looping messages.
Any idea what we are missing here ? anybody who has dealt with similar issues before?
I am looking for some design ideas that can make this work resiliently so that it recovers from this deadlock situation and eventually processes all messages in reasonable amount of time without using much additional hardware.
COMPUTATION DETAILS:
These 1000 messages will EACH create 4 objects of 4 different position types each having a quantity associated with it. These quantities will have to merged into those 4 different slots (depending on the position type). The deadlock is happening when those 4 individual slots are being updated by each individual thread. We have already ordered those individual updates in a specific order before being applied to the database rows to avoid any possible race conditions.
A deadlock implies that each thread is trying to update multiple rows in a single transaction and that those updates are being done in a different order across threads. The simplest possible answer, therefore, would be to modify the code so that messages within the same transaction are applied in some defined order (i.e. in order of the primary key). That would ensure that you would never get a deadlock though you'd still get blocking locks while one thread waits for another thread to commit its transaction.
Taking a step back, though, it seems unlikely that you would really want many threads updating the same row in a table when you can't predict the order of the updates. It seems highly likely that would lead to lots of lost updates and some rather unpredictable behavior. What, exactly, is your application doing that would make this sort of thing sensible? Are you doing something like updating aggregate tables after inserting rows into a detail table (i.e. updating the count of the number of views a post has in addition to logging information about a particular view)? If so, do those operations really need to be synchronous? Or could you update the view count periodically in another thread by aggregating the views over the past N second?
As for the MDB
Let it consume the messages, and update instance variables which contain the delta of the quantities of the processed messages (an MDB can carry state in its instance variables across multiple messages).
A #Schedule method in the same MDB persists the quantities in a single database transaction using a single SQL statement every second (for example)
update x set q1 = q1 + delta1, q2 = q2 + delta2, ...
I have done some tests:
It takes 6s to create 1000 messages (JBoss 7 using HornetQ)
During that time, 840 messages were already persisted.
It takes another 2s to persist the remaining ones (the scheduled method ran every second)
This required seven SQL update commands in seven DB transcations
The load is completely caused by creating the messages; there is not real load on the DB
Notes
You need another #PreDestroy method to persist the pending deltas to make sure that nothing gets lost
If you must guarantee transactional correctness, this approach is not suitable. In that case I suggest using a normal queue receiver (= no MDB), transacted session and receive(timeout) to collect 100 - 10000 messages (or until a timeout), do one DB transaction, and right after that the commit on the queue session. This is better, but it's still not XA transactional. If you need this, both commits need to be coordinated by a single XA transaction.
This is advanced question but looks simple - see code (one global variable which increment by each http handler call).
some_global_var = 0
class DebugCgiHandler(webapp.RequestHandler):
def get(self):
some_global_var++
self.response.out.write(some_global_var)
Q1. The question is what will happen in Python 2.5 and Python 2.7 in single thread mode if variable is set to 1 and concurrent call happen with another request so variable will be incremented in second request before self.response.out.write(some_global_var). What will be displayed in 1st request and what in 2nd?
Q2. The second question is what will happen in the same situation if Python 2.7 will be run with multithread mode?
Q3. How to avoid variables changes during whole request processing to keep same value til the end protected from other threads?
Q1: In single thread mode you won't have a concurrent request call come in so you don't have to worry about the thread safety.
Q2: With multithreaded on, you may get unpredictable behavior as the different threads could potentially increment some_global_var and display them at different times. For example, if the timing is happens in a particular way, some_global_var might only get incremented once.
Q3: Use threading.Lock()
Now I haven't answered what results you'll get, because with App Engine, this question is broken as it stands. some_global_var will be "global" in a particular instance, but you have no control over what instances your requests go to. So even in a single threaded environment, your two requests may hit two separate instances, in which case you'll see 1 for both requests. Or they may hit the same instance and you'll see 2. Your threading issues will apply on top of that.