CN112822250B - Method and device for decentralized scheduling and execution of big data platform - Google Patents

Method and device for decentralized scheduling and execution of big data platform Download PDF

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CN112822250B
CN112822250B CN202011631395.7A CN202011631395A CN112822250B CN 112822250 B CN112822250 B CN 112822250B CN 202011631395 A CN202011631395 A CN 202011631395A CN 112822250 B CN112822250 B CN 112822250B
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task
scheduling
trigger
zookeeper
module
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CN112822250A (en
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王晟
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Jingling Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H04L67/62—Establishing a time schedule for servicing the requests
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a task scheduling triggering and executing method for decentralization of a big data platform, wherein a plurality of scheduling triggering modules are distributed in a ZooKeeper cluster by taking a process as a unit and run simultaneously, the scheduling triggering modules write META information of a big data calculation task to be scheduled into the ZooKeeper cluster, the task executing modules are distributed in a plurality of ZNOde nodes of the ZooKeeper cluster by taking the process as a unit and run, and the task executing modules sequentially traverse from the ZNOde trees of the ZooKeeper; and when finding that a certain subprocess has an execution condition, locking the task, acquiring task information, executing the task, writing a subprocess execution result and state information of the task into a task history of MySQL, establishing connection of a back-end WEB module by a user through a WebSocket protocol, and mastering the configuration and state information of all tasks recorded in a ZooKeeper tree in real time.

Description

Decentralized scheduling and executing method and device for big data platform
Technical Field
The invention relates to a method and a device for decentralized scheduling and execution of a big data platform.
Background
Task scheduling and execution are important components of an operating system. For the unified scheduling of periodic tasks, in the prior art, scheduling tasks are determined mainly by judging that the time difference between the current time and the last execution starting time of the tasks is greater than a task execution interval, the tasks are realized by a technology of minimum processing data actions in the concept of a data warehouse, and in one data warehouse, the data processing is completed by thousands of various processing tasks. In the current popular data warehouse platform, task scheduling generally realizes task concurrent scheduling according to task dependency relationship, and can set the running concurrency of tasks and the priority of the tasks, and the higher the priority is, the task can be triggered and executed preferentially. However, in some practical use scenarios, various problems still exist, such as: due to the fact that the computing resources of the data platform are insufficient, the whole platform is delayed; the triggering time and the completion time of the task do not meet the requirements of the business side, and the like.
Disclosure of Invention
The invention aims to: the invention aims to provide a decentralized scheduling and executing method of a large data platform, which can be used for instantiating and starting a Quartz service by the rest scheduling Trigger modules by releasing the control right defined by Trigger where any scheduling Trigger module is down and can not cause service interruption due to the down of the progress of a certain scheduling Trigger module.
Another object of the present invention is to provide a large data platform decentralized scheduling and executing apparatus.
The technical scheme is as follows: in order to achieve the above purpose, a task scheduling triggering and executing method for decentralization of a big data platform is suitable for a distributed big data platform, and comprises the following steps:
(1) The scheduling trigger modules are distributed in the ZooKeeper cluster by taking a process as a unit and run simultaneously, and have the same position with each other;
(2) The scheduling triggering module writes the META information of the scheduled big data computing task into the ZooKeeper cluster;
(3) The method comprises the following steps that a task execution module takes a process as a unit, runs in a plurality of ZNOde nodes of a ZooKeeper cluster in a distributed mode, and has the same status;
(4) The task execution module sequentially traverses from a ZNOde tree of the ZooKeeper by taking a subprocess of a calculation task as a unit; when a certain subprocess is found to have an execution condition, adding a temporary node under a ZNOde node of the task to lock the task;
(5) Acquiring task information from the context of the current subprocess of the computing task, executing the task, and writing the subprocess execution result and state information of the task into the task history of MySQL; meanwhile, the task execution module needs to synchronously wait for the execution of the task subprocess until the completion or abnormal exit;
(6) For the completed task subprocess or abnormal exit, the task execution module releases the locking of the task ZeNode node and judges whether the subprocess is the last subprocess of the whole task; if yes, deleting the ZNOde node of the task from the ZooKeeper tree, and indicating that the task instance is completed or fails to execute;
(7) A user opens a front-end UI of the task monitoring module and establishes connection of a rear-end WEB module through a WebSocket protocol; and the WEB module is used for mastering the configuration and state information of all tasks recorded in the ZooKeeper tree in real time through a dispatcher monitor of the ZooKeeper, and actively broadcasting and pushing the configuration and state information to a user front end UI (user interface) through a WebSocket bidirectional communication protocol.
The scheduling method comprises the following steps:
(1) The Trigger automatically triggers any task by using a Quartz clock expression to start a task process;
(2) The Trigger management module writes Trigger instance information which is persistent and has a Quartz clock expression legal in a MySQL library into a ZooKeeper cluster at regular time, and each Trigger instance occupies one ZNOde node;
(3) The scheduling trigger modules are distributed in the ZooKeeper cluster by taking a process as a unit and run simultaneously, and have the same position with each other;
(4) The scheduling triggering module traverses ZNOde nodes defined by each Trigger from the ZooKeeper cluster and operates through a distributed lock provided by the ZooKeeper;
(5) Each scheduling trigger module process starts all trigger tasks in the ZooKeeper cluster in sequence according to the step (4); after any scheduling triggering module is down, the temporary ZNOE written in the ZooKeeper cluster by the current scheduling module automatically fails, and the control right defined by the Trigger where the temporary ZNOE is located is released; and the released Trigger definition is defined and is started by the rest scheduling Trigger modules in turn for instantiation of the Quartz service.
If the ZNode node does not establish the Quartz service, creating a Quartz service instance according to the expression of the clock and the task parameters, and configuring the information of the service instance under the ZNode in a temporary ZNode form; and after the operation is finished, the operation of traversing the Trigger ZNOde is carried out again.
The trigger instance definition is persisted in MySQL by the trigger management module.
And the step (2) comprises execution state, task starting time and next subprocess ID dynamic information.
And 4, storing the sub-process execution context information on the ZooKeeper tree.
And (5) the task information comprises a certain Spark task or a section of SQL, scala and Python script.
A large data platform decentralized scheduling and executing device comprises a scheduling triggering module, a scheduling processing module and a scheduling processing module, wherein the scheduling triggering module is used for periodically triggering the scheduling processing module to execute scheduling processing operation according to a scheduling period;
the task execution module is used for scheduling the processing module and is configured for executing scheduling processing operation;
and the task monitoring module is used for mastering the configuration and state information of all tasks recorded in the ZooKeeper tree in real time through the watchdog listener of the ZooKeeper.
The method comprises a task detection module, a task execution module and a task execution module, wherein the task detection module is configured to judge whether all trigger tasks in a ZooKeeper cluster are distributed or executed; and ends the task scheduling when all tasks in the task pool are allocated or executed.
Has the advantages that: compared with the prior art, the method and the device for decentralized scheduling and execution of the big data platform have the following advantages:
1. the scheduling Trigger module provided by the invention traverses ZNOde nodes defined by each Trigger from the ZooKeeper cluster, and operates through the distributed lock provided by the ZooKeeper, thereby avoiding synchronous conflict of a plurality of scheduling Trigger modules.
2. According to the scheduling method provided by the invention, after a certain scheduling Trigger module is down, the temporary ZNOde written in the ZooKeeper cluster by the scheduling module automatically fails, and the control right defined by the Trigger where the temporary ZNOde is located is released; the released Trigger definitions are instantiated and started by the remaining scheduling Trigger modules in sequence by Quartz service, so that the decentralized triggering of scheduling tasks is realized, service interruption caused by the crash of a certain scheduling Trigger module process is avoided, and the satisfaction degree of users is improved;
3. the task execution module of the invention takes the subprocess of the calculation task as a unit, sequentially traverses from the ZNOde tree of the ZooKeeper cluster, and when finding that a certain subprocess has execution conditions, adds a temporary node under the ZNOde of the task for locking the task, thereby avoiding the task instance from being scheduled by other task execution modules; the execution efficiency is ensured;
4. the invention comprises a task monitoring module, a task monitoring module and a task processing module, wherein the task monitoring module establishes the connection of a rear-end WEB module through a WebSocket protocol; configuration and state information of all tasks recorded in a ZooKeeper tree are mastered in real time through a dispatcher listener of the ZooKeeper in the WEB module, and are actively broadcasted and pushed to a user UI through a two-way communication protocol of WebSocket, so that the user can monitor the execution condition of task scheduling in real time on the UI, a client can conveniently know the progress, and the use feeling is better.
Drawings
FIG. 1 is a schematic structural diagram of a scheduling method according to the present invention;
FIG. 2 is a flow diagram of a method for decentralized scheduling and execution of a big data platform.
Detailed Description
The present invention is further described in detail below with reference to the drawings and examples, which should be construed as merely illustrative and not a limitation of the scope of the present invention.
Example 1
The large data platform decentralized scheduling and executing method shown in fig. 2 comprises the following steps:
(1) The Trigger automatically triggers the task once by using a Quartz clock expression; all Trigger instances are defined and persisted in MySQL by a Trigger management module;
MySQL contains support for Trigger. Trigger is a database object related to table operations, and is called when a specified event occurs on the table where Trigger is located.
(2) The Trigger management module is used for timing, for example, 5 minutes, as shown in fig. 2, key information of Trigger instances which are persistent in the MySQL library and in which a Quartz clock expression is legal is written into the ZooKeeper cluster, and each Trigger instance occupies one ZNode node;
(3) The scheduling trigger modules are distributed in the cluster by taking a process as a unit and run simultaneously, have the same position with each other and do not have centralized nodes;
(4) And traversing ZNOde nodes defined by each Trigger from the ZooKeeper cluster by the scheduling Trigger module, and operating through a distributed lock provided by the ZooKeeper to avoid synchronous conflict of a plurality of scheduling Trigger modules. If the ZNode node does not establish the Quartz service, creating a Quartz service instance according to the clock expression and the task parameters, and hanging the information of the service instance under the ZNode in the form of a temporary ZNode. After the operation is finished, the operation enters the dormancy for 10 seconds, and then the operation of traversing Trigger ZNOde is carried out again;
(5) And (5) each scheduling trigger module process starts all trigger tasks in the whole cluster in sequence according to the operation flow in the step (4). When a certain scheduling Trigger module is down, the temporary ZNOde written in the ZooKeeper cluster by the scheduling module automatically fails, which is equal to the release of the control right defined by the Trigger. The Trigger definitions released next are gradually instantiated and started by the Quartz service of the rest scheduling Trigger modules in sequence, so that decentralized triggering of scheduling tasks is realized, and service interruption caused by the crash of a certain scheduling Trigger module process is avoided;
(6) The scheduling triggering module writes META information of a scheduled big data computing task into a ZooKeeper cluster, and besides the task definition, a plurality of dynamic information such as execution states, task starting time, a next subprocess ID and the like are added;
(7) The task execution module is distributed in a plurality of nodes of the cluster to operate by taking a process as a unit, the positions of the task execution modules are the same, and no centralized node exists;
(8) And the task execution module sequentially traverses from a ZNOde tree of the ZooKeeper by taking the subprocess of the calculation task as a unit. When a certain subprocess is found to have an execution condition (the subprocess execution context information is also stored in the ZooKeeper tree), a temporary node is added under the ZNOde of the task for locking the task, so that the task instance is prevented from being scheduled by other task execution modules;
(9) Acquiring task information (for example, specifying a certain spare task or a segment of SQL, scala, python script, etc.) from the context of the current subprocess of the computing task, executing the task, and writing the execution result and state information of the subprocess of the task into the task history of MySQL. At the moment, the task execution module needs to synchronously wait for the execution of the task subprocess until the completion or abnormal exit;
(10) For a task sub-process that has completed (or an exception exits), the task execution module releases the lock on the task ZNode and determines if the sub-process is the last sub-process of the entire task. If so, the ZNOde (including all child nodes) for the task is removed from the ZooKeeper tree, indicating that the task instance has completed (or failed) execution;
(11) And opening a UI (user interface) of the task monitoring module by the user, and establishing the connection of the rear-end WEB module through a WebSocket protocol. The WEB module controls the configuration and state information of all tasks recorded in a ZooKeeper tree in real time through a dispatcher monitor of the ZooKeeper, and actively broadcasts and pushes the configuration and state information to a user UI (user interface) through a two-way communication protocol such as WebSocket, so that the user can monitor the execution condition of task scheduling in real time on the UI.
A large data platform decentralized scheduling and executing device comprises a scheduling triggering module, a scheduling processing module and a scheduling processing module, wherein the scheduling triggering module is used for periodically triggering the scheduling processing module to execute scheduling processing operation according to a scheduling period;
the task execution module is used for scheduling the processing module and is configured for executing scheduling processing operation;
and the task monitoring module is used for controlling the configuration and state information of all tasks recorded in the ZooKeeper tree in real time through the watch monitor of the ZooKeeper.
The method comprises a task detection module, a task execution module and a task execution module, wherein the task detection module is configured to judge whether all trigger tasks in a ZooKeeper cluster are distributed or executed; and ends task scheduling when all tasks in the task pool are allocated or executed.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. A large data platform decentralized scheduling and executing method is suitable for a distributed large data platform and is characterized in that: the method comprises the following steps:
step 1: the Trigger automatically triggers any task by using a Quartz clock expression to start a task process;
and 2, step: the Trigger management module writes Trigger instance information which is persistent and has a Quartz clock expression legal in a MySQL library into a ZooKeeper cluster at regular time, and each Trigger instance occupies one ZNOde node;
and 3, step 3: the scheduling trigger modules are distributed in the ZooKeeper cluster by taking a process as a unit and run simultaneously, and the positions of the scheduling trigger modules are the same;
and 4, step 4: the scheduling Trigger module traverses ZNOde nodes defined by Trigger triggers from the ZooKeeper cluster and operates through distributed locks provided by the ZooKeeper;
and 5: each scheduling trigger module process starts all trigger tasks in the ZooKeeper cluster in sequence according to the step 4; after any scheduling Trigger module is down, automatically disabling a temporary ZNOde written in a ZooKeeper cluster by a current scheduling module, and releasing the control right defined by the Trigger cluster in which the temporary ZNOde is located; defining the released Trigger, and sequentially instantiating and starting the Quartz service by the rest scheduling Trigger modules;
step 6: the scheduling triggering module writes META information of the scheduled big data calculation task into a ZooKeeper cluster;
and 7: the task execution module is distributed in a plurality of ZNOde nodes of the ZooKeeper cluster to operate by taking a process as a unit, and the positions of the task execution modules are the same;
and step 8: the task execution module sequentially traverses from a ZNOde tree of the ZooKeeper by taking a subprocess of a calculation task as a unit; when a certain subprocess is found to have an execution condition, adding a temporary node under a ZNOde node of the task to lock the task;
and step 9: acquiring task information from the context of the current subprocess of the computing task, executing the task, and writing the subprocess execution result and state information of the task into the task history of MySQL; meanwhile, the task execution module needs to synchronously wait for the execution of the task subprocess until the completion or abnormal exit;
step 10: for the completed task subprocess or abnormal exit, the task execution module releases the locking of the task ZeNode node and judges whether the subprocess is the last subprocess of the whole task; if yes, deleting the ZNOde node of the task from the zooKeeper tree, and indicating that the task instance is completed or fails to execute;
step 11: a user opens a front-end UI of the task monitoring module and establishes connection of a rear-end WEB module through a WebSocket protocol; and the WEB module is used for mastering the configuration and state information of all tasks recorded in the ZooKeeper tree in real time through a dispatcher monitor of the ZooKeeper, and actively broadcasting and pushing the configuration and state information to a user front end UI (user interface) through a WebSocket bidirectional communication protocol.
2. The big data platform decentralized scheduling and execution method according to claim 1, wherein: in step 4, if the ZNode node does not establish the Quartz service, creating a Quartz service instance according to the expression of the clock and the task parameters, and configuring the information of the service instance under the ZNode in the form of a temporary ZNode; and after the operation is finished, the operation of traversing Trigger ZNOde is carried out again.
3. The big data platform decentralized scheduling and execution method according to claim 1, wherein: the definition of the trigger instance is persisted in MySQL by the trigger management module.
4. The big data platform decentralized scheduling and execution method according to claim 1, wherein: in step 6, the scheduling trigger module writes the execution state of the scheduled big data computing task, the task start time and the dynamic information of the next subprocess ID into the ZooKeeper cluster.
5. The big data platform decentralized scheduling and execution method according to claim 1, wherein: in step 8, the sub-process execution context information is stored on the ZooKeeper tree.
6. The big data platform decentralized scheduling and execution method according to claim 1, wherein: in step 9, the task information includes a specific Spark task or a segment of SQL or Scala or Python script.
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