threadpool

I understand that thread pool priority should/can not be changed by the running process, but is the priority of particular task running on the thread pool somewhat priced-in with the calling process priority? in other words, do all tasks in thread pool run in the same priority regardless the calling process priority? thank you update 1: i should have been more specific, i refer to thread inside Parallel.ForEach Adriano Repetti I understand that thread pool priority should/can not be changed by the running process, That's not exact. You can change Thread Pool's thread priority (inside delegate

We have 2 ExecutionContexts in the system (Scala 2.11.4, Playframework 2.3.7) Primary context - for system operational work (Uses primary Play ExecutionContext). Administrative - for backend related tasks. I've separated those into 2 in application.configurations. play { akka { akka.loggers = ["akka.event.slf4j.Slf4jLogger"] loglevel = DEBUG actor { default-dispatcher = { fork-join-executor { parallelism-min = 20 parallelism-max = 20 } } } } } contexts { admin { fork-join-executor { parallelism-min = 30 parallelism-max = 30 } } } Is there a way to configure ThreadPool priority? There is

I run a java program which is a thread executor program that inserts thousands of documents to a table in mongodb. I get the following error Exception in thread "pool-1-thread-301" com.mongodb.MongoWaitQueueFullException: Too many threads are already waiting for a connection. Max number of threads (maxWaitQueueSize) of 500 has been exceeded. at com.mongodb.PooledConnectionProvider.get(PooledConnectionProvider.java:70) at com.mongodb.DefaultServer.getConnection(DefaultServer.java:73) at com.mongodb.BaseCluster$WrappedServer.getConnection(BaseCluster.java:221) at com.mongodb.DBTCPConnector

Is there any way to limit the number of processors that the ThreadPool object will use? According to the docs, "You cannot set the number of worker threads or the number of I/O completion threads to a number smaller than the number of processors in the computer." So how can I limit my program to not consume all the processors? After some experiments, I think I have just the thing. I've noticed that the ThreadPool considers the number of processors in the system as the number of processors available to the current process . This can work to your advantage. I have 4 cores in my CPU. Trying to

I'm trying to run some sample code of the multiprocessing.pool module of python, found in the web. The code is: def square(x): return x * x if __name__ == '__main__': pool = Pool(processes=4) inputs = [0, 1, 2, 3, 4] outputs = pool.map(square, inputs) But when i try to run it, it never finsh the execution and i have to restart the kernel of my IpythonNotebook notebook. What's the problem? KT. As you may read from the answer pointed out by John in the comments, multiprocessing.Pool , in general, should not be expected to work well within an interactive interpreter. To understand why it is the

I am doing a multiprocessing on a pandas dataframe by splitting it into several dataframes, which are stored as list. And, using Pool.map() I am passing the dataframe to a defined function. My input file is about "300 mb", so small dataframes are roughly "75 mb". But, when the multiprocessing is running the memory consumption increases by 7 GB and each local process consumes about approx. 2 GB of memory. Why is this happening? def main(): my_df = pd.read_table("my_file.txt", sep="\t") my_df = my_df.groupby('someCol') my_df_list = [] for colID, colData in my_df: my_df_list.append(colData) # now

I'm trying to understand the point in specifying separate core and maximum pool sizes for Java 5's ThreadPoolExecutor. My understanding is that the number of threads is only increased once the queue is full, which seems a bit late (at least with larger queues). Isn't it that I'm either happy to allocate a larger number of threads to the tasks, in which case I might just increase the core pool size; or I am not really willing to do so, in which case I should rather have a larger queue? What is a scenario where separate core and maximum pool sizes are useful? sbridges There is a discussion of