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WO2019148734A1 - Procédé de traitement de groupe de fils uniformes, serveur d'application et support d'informations lisible par ordinateur - Google Patents

Procédé de traitement de groupe de fils uniformes, serveur d'application et support d'informations lisible par ordinateur Download PDF

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Publication number
WO2019148734A1
WO2019148734A1 PCT/CN2018/090909 CN2018090909W WO2019148734A1 WO 2019148734 A1 WO2019148734 A1 WO 2019148734A1 CN 2018090909 W CN2018090909 W CN 2018090909W WO 2019148734 A1 WO2019148734 A1 WO 2019148734A1
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WIPO (PCT)
Prior art keywords
thread pool
thread
queue
parameters
pool
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PCT/CN2018/090909
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English (en)
Chinese (zh)
Inventor
彭捷
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平安科技（深圳）有限公司
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Publication of WO2019148734A1 publication Critical patent/WO2019148734A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/485—Task life-cycle, e.g. stopping, restarting, resuming execution

Definitions

  • the present application relates to the field of data analysis technologies, and in particular, to a unified thread pool processing method, an application server, and a computer readable storage medium.
  • the thread pool has been widely used in Internet technology. By introducing a thread pool, it is possible to effectively manage threads, control the total number of thread ceilings, and reduce the overhead caused by creating and destroying threads.
  • multiple services in the system also use thread pools, such as sensitive log bulk storage errors, re-entry tables, MQCP log receiving queues, and public cleanup services.
  • the role of the thread pool is to limit the number of execution threads in the system. According to the environment of the system, the number of threads can be set automatically or manually to achieve the best effect of the operation; less system resources are wasted, and the system congestion is not high.
  • the thread pool technology faces a new challenge: if the thread pool is not managed uniformly, it is prone to thread confusion or resource consumption in high concurrency, which seriously affects system stability. .
  • the present application provides a unified thread pool processing method, an application server, and a computer readable storage medium to solve the problem that thread turbulence or resource consumption seriously affects system stability under high concurrency conditions.
  • the present application provides a unified thread pool processing method, the method comprising the steps of:
  • Each of the thread pools is managed by the thread pool queue.
  • the present application further provides an application server, including a memory and a processor, where the memory stores a unified thread pool processing system executable on the processor, and the unified thread pool processing system The steps of the unified thread pool processing method as described above are implemented when executed by the processor.
  • the present application further provides a computer readable storage medium storing a unified thread pool processing system, the unified thread pool processing system being executable by at least one processor, The step of causing the at least one processor to perform the unified thread pool processing method as described above.
  • the unified thread pool processing method, the application server, and the computer readable storage medium proposed by the present application can obtain parameters of each thread pool by querying a persistent data table, and according to each thread pool.
  • the parameters respectively create each thread pool object, and realize the unified creation of the thread pool; save the mapping relationship between each thread pool name and each of the thread pool objects by creating a thread pool queue, and implement according to the thread pool queue
  • the unified management of each of the thread pool objects reduces the system resource consumption and improves the stability of the system.
  • 1 is a schematic diagram of an optional hardware architecture of an application server of the present application
  • FIG. 2 is a schematic diagram of a program module of a first embodiment of a unified thread pool processing system of the present application
  • FIG. 3 is a schematic diagram of a program module of a second embodiment of a unified thread pool processing system of the present application
  • FIG. 4 is a schematic flowchart of a first embodiment of a unified thread pool processing method of the present application
  • FIG. 5 is a schematic flowchart of a second embodiment of a unified thread pool processing method of the present application.
  • FIG. 6 is a schematic flowchart diagram of a third embodiment of a unified thread pool processing method according to the present application.
  • FIG. 1 it is a schematic diagram of an optional hardware architecture of the application server 2 of the present application.
  • the application server 2 may include, but is not limited to, the memory 11, the processor 12, and the network interface 13 being communicably connected to each other through a system bus. It is pointed out that Figure 1 only shows the application server 2 with components 11-13, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.
  • the application server 2 may be a computing device such as a rack server, a blade server, a tower server, or a rack server.
  • the application server 2 may be an independent server or a server cluster composed of multiple servers. .
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static Random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.
  • the memory 11 may be an internal storage unit of the application server 2, such as a hard disk or memory of the application server 2.
  • the memory 11 may also be an external storage device of the application server 2, such as a plug-in hard disk equipped on the application server 2, a smart memory card (SMC), and a secure digital number. (Secure Digital, SD) card, flash card, etc.
  • the memory 11 can also include both the internal storage unit of the application server 2 and its external storage device.
  • the memory 11 is generally used to store an operating system installed on the application server 2 and various types of application software, such as program code of the unified thread pool processing system 200. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 12 is typically used to control the overall operation of the application server 2.
  • the processor 12 is configured to run program code or process data stored in the memory 11, such as running the unified thread pool processing system 200 and the like.
  • the network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the application server 2 and other electronic devices.
  • the present application proposes a unified thread pool processing system 200.
  • FIG. 2 it is a program module diagram of the first embodiment of the unified thread pool processing system 200 of the present application.
  • the unified thread pool processing system 200 includes a series of computer program instructions stored in the memory 11, and when the computer program instructions are executed by the processor 12, the unified thread pool of the embodiments of the present application can be implemented. Processing operations.
  • the unified thread pool processing system 200 can be divided into one or more modules based on the particular operations implemented by the various portions of the computer program instructions. For example, in FIG. 2, the unified thread pool processing system 200 can be divided into a query module 201, an acquisition module 202, a thread pool object creation module 203, and a thread pool queue creation module 204. among them:
  • the query module 201 is configured to query a persistent data table.
  • the persistent data table needs to be read from a key-value database.
  • the key-value database may be a Redis database.
  • the persistent data table is used to store parameter information of each thread pool object in the execution queue, and the parameter information includes a thread pool name, a core thread number, a maximum thread number, a maximum queue length, and a queue type.
  • the persistence method periodically performs a snapshot of Redis data in the memory according to a certain saving rule, and synchronizes the snapshot data to the hard disk.
  • the obtaining module 202 is configured to obtain, according to the persistent data table, parameters of each thread pool in the execution queue.
  • each thread pool includes a thread pool name, a core thread number, a maximum thread number, a maximum queue length, and a queue type.
  • the core thread in the core thread number (corePoolSize) will always survive, even if no tasks need to be executed.
  • corePoolSize The core thread in the core thread number
  • the first thread pool when a task needs to be added to the first thread pool, if the number of threads running in the first thread pool is less than the number of core threads, the first thread pool immediately creates a thread to run the task; If the number of threads running in the pool is greater than or equal to the number of core threads, then this task is placed in the task queue of the first thread pool.
  • the maximum number of threads (maximumPoolSize) is used to indicate how many threads can be created in the thread pool.
  • a task queue of the first thread pool is full (that is, the maximum queue length has been reached), and the number of running threads is less than the maximum number of threads, then To create a new thread to run this task;
  • the first thread pool when a task needs to be added to the first thread pool, if the queue is full (ie, the maximum queue length has been reached) and the number of running threads is greater than or equal to the maximum number of threads, the first thread pool will Throw an exception.
  • the queue types include: direct submission queues, unbounded queues, and bounded queues.
  • the direct submission queue may be a SynchronousQueue.
  • the SynchronousQueue can be set as the default option for the work queue, which is submitted directly to the thread without queuing. If there is no thread available to run the task immediately, the direct commit queue fails to queue the task, so a new thread is constructed. This strategy avoids locks when dealing with request sets that may have internal dependencies.
  • the direct commit queue typically requires an unbounded maximum number of threads to avoid rejecting newly submitted tasks. This strategy allows unbounded threads to grow as the command arrives continuously over the average that can be processed by the queue.
  • the bounded queue can be an ArrayBlockingQueue.
  • the ArrayBlockingQueue is a bounded blocking queue based on an array structure. This queue sorts the elements according to the FIFO (first in, first out) principle.
  • the unbounded queue can be a LinkedBlockingQueue.
  • LinkedBlockingQueue is a blocking queue based on linked list structure. This queue sorts elements by FIFO (first in first out), and the throughput is usually higher than ArrayBlockingQueue.
  • the thread pool object creation module 203 is configured to separately create each thread pool object according to the parameters of each thread pool.
  • the thread pool is created through the java.uitl.concurrent.ThreadPoolExecutor class, and the specific creation method is:
  • keepAliveTime indicates that the thread will terminate when there is no task execution.
  • Unit is the time unit of the parameter keepAliveTime; where threadFactory is the thread factory, which is used to create the thread; where the handler is the policy when the task is rejected.
  • the handler when the value of the handler is ThreadPoolExecutor.DiscardPolicy, the task is discarded, but the Rejected Execution Exception is not thrown;
  • the task at the top of the queue is discarded, and then the task is retried (repeating the process);
  • the handler when the handler takes a value of ThreadPoolExecutor.CallerRunsPolicy, the task is processed by the calling thread.
  • the thread pool queue creation module 204 is configured to create a thread pool queue according to the thread pool name and the thread pool object.
  • mapping relationship between the thread pool name and the corresponding thread pool object is saved as the thread pool queue.
  • mapping relationship between the thread pool name and the corresponding thread pool object may be represented as Map ⁇ ThreadPoolName, ThreadPool>, wherein the key of the Map class is the thread pool name, and the value is the thread pool. Object.
  • mapping relationship between the second thread pool name and the corresponding second thread pool object may be added to the thread pool queue by map.put(ThreadPoolName2, ThreadPool2).
  • the unified thread pool processing system 200 includes a query module 201, an obtaining module 202, a thread pool object creating module 203, and a thread pool queue creating module 204 in the first embodiment, and a management module. 205.
  • the management module 205 is configured to manage each of the thread pools through the thread pool queue.
  • the management module 205 is specifically configured to:
  • the management operation may include: acquiring the first thread pool object thread number, submitting a task to the first thread pool object, and closing the first thread pool object.
  • the number of threads currently active by the thread pool object corresponding to the thread pool name may be obtained by using the thread pool name.
  • the number of threads currently active by the thread pool object corresponding to the thread pool name may be obtained by using a private volatile int poolSize.
  • the length of the current execution queue of the thread pool object corresponding to the thread pool name may be obtained by the thread pool name.
  • the task object to be started may be submitted to the specified thread pool object by using a thread pool name, parameter information, and a task object to be started.
  • the task object to be started may be submitted to the specified thread pool object by execute() and submit().
  • the thread pool object after receiving the task object to be started :
  • the creation thread runs this task
  • the task is put into a queue
  • the thread pool throws an exception.
  • the thread pool object corresponding to the thread pool name may be closed by the thread pool name.
  • the thread pool object corresponding to the thread pool name may be closed by using shutdown() and shutdownNow().
  • Shutdown() does not terminate the thread pool immediately, but waits until all the tasks in the task cache queue have been executed, but will not accept the new task again;
  • shutdownNow() terminate the thread pool immediately and try Interrupts the task being executed and clears the task cache queue, returning tasks that have not yet been executed.
  • the unified thread pool processing system of the present application obtains the parameters of each thread pool by querying the persistent data table, and creates each thread pool object according to the parameters of each thread pool, thereby realizing the unified creation of the thread pool;
  • the thread pool queue saves the mapping relationship between each thread pool name and each of the thread pool objects, and implements unified management of each of the thread pool objects according to the thread pool queue, thereby reducing system resource consumption and improving The stability of the system.
  • the present application also proposes a unified thread pool processing method.
  • FIG. 4 it is a schematic flowchart of the first embodiment of the unified thread pool processing method of the present application.
  • the order of execution of the steps in the flowchart shown in FIG. 5 may be changed according to different requirements, and some steps may be omitted.
  • Step S402 querying the persistent data table.
  • the persistent data table needs to be read from a key-value database.
  • the key-value database may be a Redis database.
  • the persistent data table is used to store parameter information of each thread pool object in the execution queue, and the parameter information includes a thread pool name, a core thread number, a maximum thread number, a maximum queue length, and a queue type.
  • the persistence method periodically performs a snapshot of Redis data in the memory according to a certain saving rule, and synchronizes the snapshot data to the hard disk.
  • Step S404 Acquire, according to the persistent data table, parameters of each thread pool in the execution queue, where the parameters of each thread pool include each thread pool name.
  • each thread pool includes a thread pool name, a core thread number, a maximum thread number, a maximum queue length, and a queue type.
  • the core thread in the core thread number (corePoolSize) will always survive, even if no tasks need to be executed.
  • corePoolSize The core thread in the core thread number
  • the first thread pool when a task needs to be added to the first thread pool, if the number of threads running in the first thread pool is less than the number of core threads, the first thread pool immediately creates a thread to run the task; If the number of threads running in the pool is greater than or equal to the number of core threads, then this task is placed in the task queue of the first thread pool.
  • the core thread in the core thread number (corePoolSize) will always survive, and no task needs to be executed in time.
  • the thread pool will preferentially create a new thread for processing.
  • maximumPoolSize The maximum number of threads (maximumPoolSize) is used to indicate how many threads can be created in the thread pool:
  • a task queue of the first thread pool is full (that is, the maximum queue length has been reached), and the number of running threads is less than the maximum number of threads, then To create a new thread to run this task;
  • the first thread pool when a task needs to be added to the first thread pool, if the queue is full (ie, the maximum queue length has been reached) and the number of running threads is greater than or equal to the maximum number of threads, the first thread pool will Throw an exception.
  • the queue types include: direct submission queues, unbounded queues, and bounded queues.
  • the direct submission queue may be a SynchronousQueue.
  • the SynchronousQueue can be set as the default option for the work queue, which is submitted directly to the thread without queuing. If there is no thread available to run the task immediately, the direct commit queue fails to queue the task, so a new thread is constructed. This strategy avoids locks when dealing with request sets that may have internal dependencies.
  • the direct commit queue typically requires an unbounded maximum number of threads to avoid rejecting newly submitted tasks. This strategy allows unbounded threads to grow as the command arrives continuously over the average that can be processed by the queue.
  • the bounded queue can be an ArrayBlockingQueue.
  • the ArrayBlockingQueue is a bounded blocking queue based on an array structure. This queue sorts the elements according to the FIFO (first in, first out) principle.
  • the unbounded queue can be a LinkedBlockingQueue.
  • LinkedBlockingQueue is a blocking queue based on linked list structure. This queue sorts elements by FIFO (first in first out), and the throughput is usually higher than ArrayBlockingQueue.
  • Step S406 respectively creating each thread pool object according to the parameters of each thread pool.
  • the thread pool is created through the java.uitl.concurrent.ThreadPoolExecutor class, and the specific creation method is:
  • corePoolSize represents the core pool size.
  • the thread pool is created, by default, there is no thread in the thread pool, but waiting for a task to arrive to create a thread to execute the task, unless the prestartAllCoreThreads() or prestartCoreThread() method is called.
  • the thread pool is created, the number of threads in the thread pool is 0.
  • a thread is created to execute the task.
  • the number of threads in the thread pool reaches the number of core threads, Put the arriving task in the cache queue;
  • maximumPoolSize represents the maximum number of threads in the thread pool, indicating how many threads can be created in the thread pool;
  • keepAliveTime indicates that the thread will terminate when there is no task execution.
  • the unit is the time unit of the parameter keepAliveTime.
  • the threadFactory is the thread factory and is used to create the thread.
  • the handler is the strategy when the task is rejected.
  • the handler when the value of the handler is ThreadPoolExecutor.DiscardPolicy, the task is discarded, but the Rejected Execution Exception is not thrown;
  • the task at the top of the queue is discarded, and then the task is retried (repeating the process);
  • the handler when the handler takes a value of ThreadPoolExecutor.CallerRunsPolicy, the task is processed by the calling thread.
  • Step S408 creating a thread pool queue according to each thread pool name and each of the thread pool objects.
  • mapping relationship between the thread pool name and the corresponding thread pool object is saved as the thread pool queue.
  • mapping relationship between the thread pool name and the corresponding thread pool object may be represented as Map ⁇ ThreadPoolName, ThreadPool>, wherein the key of the Map class is the thread pool name, and the value is the thread pool. Object.
  • mapping relationship between the second thread pool name and the corresponding second thread pool object may be added to the thread pool queue by map.put(ThreadPoolName2, ThreadPool2).
  • FIG. 5 it is a schematic flowchart of a second embodiment of a unified thread pool processing method of the present application.
  • the steps S502-S508 of the unified thread pool processing method are similar to the steps S402-S408 of the first embodiment, except that the method further includes step S510.
  • the method includes the following steps:
  • Step S510 managing each thread pool through the thread pool queue.
  • FIG. 6 is a schematic flowchart diagram of a third embodiment of a unified thread pool processing method of the present application.
  • the step of managing the thread pool by using the thread pool queue includes:
  • Step 602 Obtain a first thread pool name of a thread pool to be operated.
  • Step 604 Obtain a corresponding first thread pool object from the thread pool queue according to the first thread pool name.
  • the obtained first thread pool name is taken as a variable ThreadPoolName, and is delivered to the thread pool queue.
  • Step 606 Perform a management operation on the first thread pool object.
  • the managing operation may include: acquiring the first thread pool object thread number, submitting the task to the first thread pool object, and closing the first thread pool object.
  • the number of threads currently active by the thread pool object corresponding to the thread pool name may be obtained by using the thread pool name.
  • the number of threads currently active by the thread pool object corresponding to the thread pool name may be obtained by using a private volatile int poolSize.
  • the length of the current execution queue of the thread pool object corresponding to the thread pool name may be obtained by the thread pool name.
  • the task object to be started may be submitted to the specified thread pool object by using a thread pool name, parameter information, and a task object to be started.
  • the task object to be started may be submitted to the specified thread pool object by execute() and submit().
  • the thread pool object after receiving the task object to be started :
  • the creation thread runs this task
  • the task is put into a queue
  • the thread pool throws an exception.
  • the thread pool object corresponding to the thread pool name may be closed by the thread pool name.
  • the thread pool object corresponding to the thread pool name may be closed by using shutdown() and shutdownNow().
  • Shutdown() does not terminate the thread pool immediately, but waits until all the tasks in the task cache queue have been executed, but will not accept the new task again;
  • shutdownNow() terminate the thread pool immediately and try Interrupts the task being executed and clears the task cache queue, returning tasks that have not yet been executed.
  • the unified thread pool processing method of the present application obtains the parameters of each thread pool by querying the persistent data table, and creates each thread pool object according to the parameters of each thread pool, thereby realizing the unified creation of the thread pool;
  • the thread pool queue saves the mapping relationship between each thread pool name and each of the thread pool objects, and implements unified management of each of the thread pool objects according to the thread pool queue, thereby reducing system resource consumption and improving The stability of the system.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better.
  • Implementation Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

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Abstract

La présente invention concerne un procédé de traitement de groupe de fils uniformes, consistant à : interroger une table de données persistante ; obtenir un paramètre de chaque groupe de fils dans une file d'attente d'exécution selon la table de données persistante, le paramètre de chaque groupe de fils comprenant chaque nom de groupe de fils ; créer respectivement chaque objet de groupe de fils en fonction du paramètre de chaque groupe de fils ; créer une file d'attente de groupe de fils selon chaque nom de groupe de fils et chaque objet de groupe de fils ; et gérer chaque groupe de fils au moyen de la file d'attente de groupe de fils. L'invention concerne également un serveur d'application et un support d'informations lisible par ordinateur. Le procédé de traitement de groupe de fils uniformes, le serveur d'application et le support d'informations lisible par ordinateur fournis par la présente invention peuvent mettre en œuvre une gestion uniforme de chaque objet de groupe de fils, réduire la consommation de ressources du système et améliorer la stabilité du système.
PCT/CN2018/090909 2018-02-01 2018-06-12 Procédé de traitement de groupe de fils uniformes, serveur d'application et support d'informations lisible par ordinateur WO2019148734A1 (fr)

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