CN109800239B - Redis-based distributed architecture data sharing method - Google Patents
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Abstract
The invention discloses a distributed architecture data sharing method based on Redis, and relates to the field of data processing. The invention comprises the following steps: step S1, initializing cache data: after each node of the distributed system is started, loading data in redis into a memory of each node; step S2, deploying subscription listening: defining a subscription callback method, subscribing a redis channel by each node, and deploying monitoring; step S3, data synchronization: and after the data change is monitored, updating the memory data of each node. The invention uses the redis as a cache data synchronization manager in the distributed architecture system, and utilizes the releasing and subscribing mechanism of the redis to synchronize the cache data of the distributed multi-nodes in real time, thereby realizing the data consistency among the multi-nodes, simultaneously, the cache data in the redis can be self-defined according to the actual requirement of the service system, and the configurable releasing and subscribing functions of the cache data are realized, and the invention is not limited by the specific service system and has strong flexibility.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a distributed architecture data sharing method based on Redis.
Background
Redis is a fully open source free high performance key-value in-memory database. Various data storage structures such as character strings (string), lists (list), sets (set), hash tables (hash) and the like are supported. Redis has a publish-subscribe (pub/sub) messaging mode and is therefore useful in scenarios such as caching, data publishing and subscribing, high-speed message queuing, etc.
CAP theory in distributed domain: any distributed system cannot simultaneously meet the three basic requirements of Consistency, Availability and Partition tolerance. In a distributed architecture, high availability is realized by adopting a multi-node deployment mode, partition fault tolerance is one of the most basic requirements, and how to balance consistency and availability is a main difficult problem facing the distributed architecture. Therefore, the BASE theory arises from the end that the BASE theory is based on the CAP theory, and the result of balancing consistency and availability in the CAP theory is summarized through the distributed practice of a large-scale internet system.
In the specific solution process of realizing the data consistency, a data consistency model is derived, wherein the data consistency model is strong consistency, weak consistency and final consistency. The strong consistency means that after the updating operation is completed, the access of any plurality of subsequent operations returns the latest updated value, and the data acquired each time is forced to be the latest data, so that the accuracy of the data is ensured. The weak consistency means that after the updating operation is completed, the system does not ensure that the access of the subsequent operation returns the latest updated value, and does not ensure that the latest value can be accessed after a long time, and a compensation measure of data consistency is not taken in the implementation; the final consistency is a special form of weak consistency, which means that a system ensures that a value of the last updating operation is finally returned on the premise of no subsequent updating operation, and two implementation schemes of message queue real-time triggering data compensation and timing task triggering data compensation are mainly provided. The existing scheme mainly has the following defects:
(1) the two-stage submission protocol is a synchronous blocking type, the performance of a distributed system is greatly limited, a single-point fault problem exists, a perfect fault-tolerant mechanism is not designed, and the failure of any node can cause the failure of the whole transaction;
(2) although the three-stage submission protocol solves the problem of blocking and single-point failure of the two-stage submission protocol, when partition inconsistency is introduced, the nodes cannot communicate, and data inconsistency is affected;
(3) although strong consistency implementation schemes such as a TCC (transmission control center) protocol and a Paxos algorithm can ensure strong consistency of data, the performance is greatly influenced during concurrency, and the strong consistency is not the best solution scheme in a distributed system;
(4) the data compensation scheme triggered by the message queue is influenced by the performance of the message queue, and once the performance of the message queue is reduced, the data cannot be synchronized in real time, so that the data consistency is influenced;
(5) the timing task data compensation scheme can frequently carry out data synchronization operation when the timing time interval is too small, and thus the system performance is influenced; when the timing time interval is too large, data synchronization delay is caused, and data consistency is affected.
Disclosure of Invention
The invention aims to provide a Redis-based distributed architecture data sharing method, which is characterized in that through using Redis as a cache data synchronization manager in a distributed architecture system, the cache data of distributed multiple nodes are synchronized in real time by using a releasing and subscribing mechanism of the Redis, so that the data consistency among the multiple nodes is realized, and the problems that the existing node data is inconsistent and the system performance is influenced by data compensation are solved.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a data sharing method of a distributed architecture based on Redis, which comprises the following steps:
step S1, initializing cache data: after each node of the distributed system is started, loading data in redis into a memory of each node;
step S2, deploy subscription listening: defining a subscription callback method, subscribing a redis channel by each node, and deploying monitoring;
step S3, data synchronization: after monitoring the data change, updating the memory data of each node;
in step S2, the specific steps of deploying subscription listening are as follows:
step S21: defining a callback method, and automatically calling the callback method to process subscribed data when data is published;
step S22: when subscription data are published, adding the data into a built-in message queue, polling the message queue, and taking out the data in the message queue for processing when the data exist;
step S23: subscribing a configured redis channel by using a data subscription method of the redis, and monitoring on a defined callback method;
step S24: after the subscription monitoring is deployed, each node deploys the subscription of the redis channel and sets monitoring;
in step S3, the specific data synchronization method includes the following steps:
step S31: when data of an application system related to cache data in a node memory is added or deleted, the data is updated into redis;
step S32: the data are issued to the configured redis channel by using a data issuing method of the redis;
step S33: each node in step S23 will monitor the data issue, and call a defined callback method, and add the data to the built-in message queue in the callback method;
step S34: the built-in message queue monitors that data exist in the queue, takes out the data in the queue, acquires the latest data from the redis, and updates the corresponding memory data in the node;
step S35: executing a monitoring method for the change of the node memory data;
step S36: and finishing data synchronization.
Preferably, in step S1, the specific steps of initializing the cache data are as follows:
step S11: determining service data to be loaded into a memory according to system service requirements;
step S12: after the distributed nodes are started, firstly loading configuration parameters, and then initializing a connection pool of redis according to the configured redis addresses;
step S13: acquiring a connection instance of the redis from the redis connection pool, and performing regular matching according to the configured key prefix of the redis to query all matched key lists in the redis;
step S14: traversing the key list, acquiring data from the redis, and storing the acquired entity object data into a memory of the node;
step S15: executing a monitoring method for the change of the node memory data;
step S16: and after the initialization of the cache data is completed, each node stores a piece of data mirror image of redis in a memory after being started.
Preferably, in step S12, the loaded configuration parameters include: the prefix of key in the redis, the name of the redis channel, the connecting address of the redis, and the entity types subscribed and published.
The invention has the following beneficial effects:
(1) the invention achieves the final consistency of the data of the distributed system by combining the service characteristics of the distributed system under the guidance of the CAP theory and the BASE theory and adopting a redis-based mode, thereby having theoretical and practical basis.
(2) According to the invention, each node in the distributed architecture system subscribes data in the same channel by using a redis publishing and subscribing mechanism, when the data changes, the redis publishes the data change, each node subscribing the channel can acquire the data change and update the data in real time, the data consistency among each node is maintained, the performance of the distributed architecture system and the data consistency are ensured, and the data access speed is improved.
(3) The cache data in the redis can be customized according to the actual requirements of the service system, and the configurable publishing and subscribing functions of the cache data are realized, so that the method is not limited by the specific service system and has strong flexibility.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a Redis-based distributed architecture data sharing method according to the present invention;
FIG. 2 is a detailed flow chart of cache data initialization;
FIG. 3 is a detailed flow diagram of deployment subscription monitoring;
fig. 4 is a detailed flow chart of data synchronization.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is a method for data sharing based on a Redis distributed architecture, including the following steps:
step S1, initializing cache data: after each node of the distributed system is started, loading data in the redis into a memory of each node;
step S2, deploy subscription listening: defining a subscription callback method, subscribing a redis channel by each node, and deploying monitoring;
step S3, data synchronization: when the data change is monitored, updating the memory data of each node, and keeping the data consistency;
referring to fig. 3, in step S2, the specific steps of deploying subscription listening are as follows:
step S21: defining a callback method, and automatically calling the callback method to process subscribed data when data is published;
step S22: in the callback method, in order to decouple the processing logic of different subscription data, a built-in message queue mode is adopted, when the subscription data is published, the data is added into the built-in message queue, the message queue is polled at the same time, and the data in the message queue is taken out for processing when the data exists;
step S23: subscribing a configured redis channel by using a data subscription method of the redis, and monitoring on a defined callback method;
step S24: after the subscription monitoring is deployed, each node deploys the subscription of the redis channel and sets monitoring;
referring to fig. 4, in step S3, the specific data synchronization method includes the following steps:
step S31: when data of an application system associated with cache data in a node memory is subjected to add, delete and modify operations, updating and synchronizing the memory data of each node, and updating the data into redis;
step S32: after data updating in the redis is finished, releasing data to a configured redis channel by using a data release method of the redis;
step S33: each node in step S23 will monitor the data issue, and call a defined callback method, and add the data to the built-in message queue in the callback method;
step S34: the built-in message queue monitors that data exist in the queue, takes out the data in the queue, acquires the latest data from the redis, and updates the corresponding memory data in the node;
step S35: executing a monitoring method for the change of the node memory data;
step S36: and finishing data synchronization, and when the data changes, updating the memory data in each node to keep data consistency.
Referring to fig. 2, in step S1, the specific steps of initializing the cache data are as follows:
step S11: determining which common service data of the system need to be cached according to the service requirements of the system, and determining the service data which needs to be loaded into a memory;
step S12: after the distributed node is started, firstly loading configuration parameters, and then initializing a connection pool of the redis according to the configured redis address;
step S13: acquiring a connection instance of the redis from the redis connection pool, and performing regular matching according to the configured key prefix of the redis to query all matched key lists in the redis;
step S14: traversing the key list, acquiring data from the redis, and storing the acquired entity object data into a memory of the node;
step S15: the monitoring method for executing the change of the memory data of the nodes comprises the following steps that entity beans are reloaded when the attribute of a real object changes, database connection is created again when the attribute of database connection changes, and the like;
step S16: and after the initialization of the cache data is completed, each node stores a piece of data mirror image of redis in a memory after being started.
In step S12, the loaded configuration parameters include: a prefix of a key in a redis, a redis channel name, a redis connection address, a type of entity subscribed and published, and the like.
It should be noted that, in the foregoing system embodiment, each unit included is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it is understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (2)
1. A data sharing method of a Redis-based distributed architecture is characterized by comprising the following steps:
step S1, initializing cache data: after each node of the distributed system is started, loading data in the redis into a memory of each node;
step S2, deploy subscription listening: defining a subscription callback method, subscribing a redis channel by each node, and deploying monitoring;
step S3, data synchronization: after the data change is monitored, updating the memory data of each node;
in step S2, the specific steps of deploying subscription listening are as follows:
step S21: defining a callback method, and automatically calling the callback method to process subscribed data when data is published;
step S22: when subscription data are published, adding the data into a built-in message queue, polling the message queue, and taking out the data in the message queue for processing when the data exist;
step S23: subscribing the configured redis channel by using a data subscription method of the redis, and monitoring on a defined callback method;
step S24: after the subscription monitoring is deployed, each node deploys the subscription of the redis channel and sets monitoring;
in step S3, the specific data synchronization method includes the following steps:
step S31: when data of an application system related to cache data in a node memory is added or deleted, the data is updated into redis;
step S32: the data are issued to the configured redis channel by using a data issuing method of the redis;
step S33: each node in step S23 will monitor the data issue, and call a defined callback method, and add the data to the built-in message queue in the callback method;
step S34: the built-in message queue monitors that data exist in the queue, takes out the data in the queue, acquires the latest data from the redis, and updates the corresponding memory data in the node;
step S35: executing a monitoring method for the change of the node memory data;
step S36: finishing data synchronization;
in step S1, the specific steps of initializing the cache data are as follows:
step S11: determining service data to be loaded into a memory according to system service requirements;
step S12: after the distributed nodes are started, firstly loading configuration parameters, and then initializing a connection pool of redis according to the configured redis addresses;
step S13: acquiring a connection instance of the redis from the redis connection pool, and performing regular matching according to the configured key prefix of the redis to query all matched key lists in the redis;
step S14: traversing the key list, acquiring data from the redis, and storing the acquired entity object data into a memory of the node;
step S15: executing a monitoring method for the change of the node memory data;
step S16: and after the initialization of the cache data is completed, each node stores a piece of data mirror image of redis in a memory after being started.
2. The Redis-based distributed architecture data sharing method according to claim 1, wherein in the step S12, the loaded configuration parameters include: the prefix of key in the redis, the name of the redis channel, the connecting address of the redis, and the entity types subscribed and published.
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| CN110968439B (en) * | 2019-11-28 | 2020-12-15 | 蜂助手股份有限公司 | Inter-system message notification method, device, server, system and storage medium |
| CN111194012A (en) * | 2019-12-30 | 2020-05-22 | 广东飞企互联科技股份有限公司 | A development process and processing method of a redis-based timing timeout service |
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| CN112286950A (en) * | 2020-11-20 | 2021-01-29 | 四川长虹电器股份有限公司 | Method for keeping consistency of valid data in distributed cache |
| CN112597197B (en) * | 2020-12-17 | 2022-07-26 | 杭州当虹科技股份有限公司 | Distributed cache system based on memory |
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