CN117331755A - High availability system and method for master-slave backup and fragmentation strategy of vector database - Google Patents

High availability system and method for master-slave backup and fragmentation strategy of vector database Download PDF

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CN117331755A
CN117331755A CN202311354536.9A CN202311354536A CN117331755A CN 117331755 A CN117331755 A CN 117331755A CN 202311354536 A CN202311354536 A CN 202311354536A CN 117331755 A CN117331755 A CN 117331755A
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database
query
data
vector
vector database
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刘斌
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Shanghai Shuheng Information Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/14—Error detection or correction of the data by redundancy in operation
    • G06F11/1402—Saving, restoring, recovering or retrying
    • G06F11/1446—Point-in-time backing up or restoration of persistent data
    • G06F11/1448—Management of the data involved in backup or backup restore
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2228—Indexing structures
    • G06F16/2237—Vectors, bitmaps or matrices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2453—Query optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G06F16/275—Synchronous replication
    • 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
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
    • 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
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5083—Techniques for rebalancing the load in a distributed system

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Quality & Reliability (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention relates to the technical field of big data processing, in particular to a high availability system and a method for a master-slave backup and fragmentation strategy of a vector database, wherein the system comprises the following components: the monitoring system is used for monitoring and managing the vector database, helping a user to comprehensively and real-timely monitor the vector database and ensuring the stability and efficiency of the database; the vector database high-availability clusters realize data backup and load balancing by physically constructing a plurality of database nodes, and when one node fails, the node is automatically switched to other nodes to continue providing services; the vector database high-availability cluster comprises a main/standby mechanism unit, a load balancing unit, a heartbeat mechanism unit, a data copying unit and a fault transfer unit; the invention solves the problem of unstable vector database, can realize stable data and stable connection of the vector database, improves the data security of the vector database, and simultaneously realizes the improvement of the query speed and concurrency capability of the vector database.

Description

High availability system and method for master-slave backup and fragmentation strategy of vector database
[ technical field ]
The invention relates to the technical field of big data processing, in particular to a high-availability system and a method for a master-slave backup and fragmentation strategy of a vector database.
[ background Art ]
Currently, the existing vector database mainly comprises a single-machine mode and a cluster mode. The single vector database is not stable enough and cannot be used, so that a master-slave vector database is generally required to be built. However, the vector database itself does not support master-slave synchronization, the SDK of the vector database may be used to read master library data and to synchronize data in the SDK to the slave library. Meanwhile, the vector database is fragmented and high availability is realized through an autonomously developed program. The method mainly has the problems of influencing query speed and concurrency capacity, is low in stability, and even causes the phenomena of losing data and the like when a server is abnormal.
[ summary of the invention ]
The invention aims to solve the defects and provide a high-availability system of a master-slave backup and fragmentation strategy of a vector database, which solves the problem that a vector database is unstable, can realize stable data and stable connection of the vector database, improves the data security of the vector database, and simultaneously realizes the improvement of the query speed and concurrency capacity of the vector database.
In one aspect of the present invention, a high availability system for a master-slave backup and sharding strategy of a vector database is provided, comprising:
the monitoring system is used for monitoring and managing the vector database, helping a user to comprehensively and real-timely monitor the vector database and ensuring the stability and efficiency of the database;
the vector database high-availability cluster realizes data backup and load balancing by physically constructing a plurality of database nodes, and when one node fails, the node is automatically switched to other nodes to continue providing services.
As an embodiment, the vector database high availability cluster comprises:
the main/standby mechanism unit is used for setting a main database and a standby database, writing data into the main database and synchronizing the data into the standby database, and if the main database fails, immediately taking over service by the standby database so as to ensure the continuous availability of the data;
load balancing unit: the method is used for reasonably distributing the access request to each database node through a load balancing technology so as to avoid overload of a single node;
heartbeat mechanism unit: the method comprises the steps that the method is used for detecting the running state of each other between database nodes through heartbeat, and once a certain node is detected to lose response, a switching program is automatically started to ensure continuous service provision;
a data copying unit: for keeping the data of each node consistent, and recovering from other nodes once the data of one node is lost or damaged;
and a failover unit: when the main server fails, the user request is automatically transferred to the standby server, and the main server is restored, and after the main server is restored to normal, the user request is transferred back to the main server.
As one embodiment, the high availability system of the vector database master-slave backup and sharding strategy of the present invention specifically comprises:
query interface: receiving a query request, executing a query operation, and returning a query result;
data synchronization: the data synchronization between the master database and the slave database ensures that one data exists in a plurality of servers; the data of the main database are synchronized to the auxiliary database periodically or in real time, and when the main database fails, the auxiliary database provides service;
and (3) a monitoring system: the method comprises the steps of monitoring the running state of a vector database in real time, including the health condition, performance index and the use condition of system resources, and informing a self-research system;
autonomous distribution: the method is a key mechanism when processing massive query requests, and the aim of autonomous distribution is to distribute query tasks to various database nodes according to a certain strategy so as to disperse query pressure and improve query execution efficiency.
As one embodiment, an external system programmatically retrieves and manipulates data in a vector database by invoking a query interface of the vector database; the query interface supports vector similarity queries and vector distance computation for vector data, supports communication protocols including but not limited to HTTP, RPC, and data formats including but not limited to JSON, XML, and supports complex query statements including but not limited to combined query, range query, ordering functions.
As an embodiment, the monitoring system comprises:
a) System health monitoring: checking the running state of the database, and monitoring factors which possibly influence the performance of the database in real time;
b) Database performance index monitoring: monitoring core performance metrics of the database, including but not limited to query response time, transaction speed, connection number, lock status, cache hit rate;
c) Notification and alarm: when the monitoring system detects the abnormal state of the database, an alarm notification is sent to an administrator or a related system for processing.
As an embodiment, the autonomous distribution comprises:
a) Load balancing: determining which server to distribute a new query request to according to the processing capacity of each server and the current load condition;
b) High availability and failover: if a certain server fails, the autonomous distribution automatically switches the query task of the server to other servers so as to ensure the persistence of the query service;
c) Weight distribution: the query task is assigned according to the weight of each server.
In another aspect of the present invention, a high availability method for a master-slave backup and sharding strategy of a vector database is provided, comprising the steps of:
the system or the user stores unstructured data in a vector database cluster, and submits a request to a request interface of the self-research system according to the need;
the mass queries are distributed to different vector databases according to the self-research system weight strategy to perform data query, the query priority is set, and query requests with higher priority are queried preferentially;
and timely expanding the vector database fragments according to the monitoring of the health state of the vector database by the monitoring system.
In a third aspect of the present invention, a computer-readable storage medium is presented, the computer-readable storage medium comprising a stored program, the program performing the above-described method.
In a fourth aspect, the present invention provides a computer device, comprising: a processor, a memory, and a bus; the processor is connected with the memory through the bus; the memory is used for storing a program, and the processor is used for running the program, and the program runs to execute the method.
Compared with the prior art, the invention has the following advantages:
(1) Vector database stability improves: the invention solves the unstable situation of the vector database by the self-research system, and can completely realize the stable data and stable connection of the vector database;
(2) Vector database data security improves: according to the invention, through the self-research system, data in one part of vector database is stored on different servers, so that the data is prevented from being lost when the servers are abnormal;
(3) Vector database query speed and concurrency improve: the self-research system of the invention has the function of automatic distribution, so that the vector database has higher query speed and concurrency.
[ description of the drawings ]
FIG. 1 is a functional block diagram of the present invention;
FIG. 2 is a schematic view of the general construction of the present invention;
FIG. 3 is a schematic diagram of the construction of the monitoring system and autonomous distribution of the present invention;
FIG. 4 is a schematic diagram showing the specific composition of the present invention;
fig. 5 is a schematic flow chart of the method of the present invention.
Detailed description of the preferred embodiments
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the invention. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described below with reference to the accompanying drawings and specific embodiments:
as shown in the drawings, the invention provides a high availability system of a master-slave backup and fragmentation strategy of a vector database, which comprises a monitoring system and a high availability cluster of the vector database, wherein,
(1) And (3) a monitoring system: the vector database monitoring system is a system specially used for monitoring and managing the vector database, and can help a user to comprehensively and real-timely monitor the database so as to ensure the stability and the efficiency of the database.
The monitoring system has the following main characteristics:
a) Real-time performance: the condition of the database, such as the running state, performance index and the like of the database, can be monitored in real time.
b) Comprehensively: not only basic information of the database but also detailed operations of the database, such as query sentences, error information, etc., can be monitored.
c) Ease of use: the interface is friendly, the operation is simple and convenient, and the user can set personalized monitoring items and alarm mechanisms according to the requirements.
d) Early warning function: when the database is abnormal, early warning can be sent out in time, so that a user can find problems as early as possible, and the system risk is reduced.
e) Performance optimization: the collected data can help a database manager to perform performance optimization, and the database operation efficiency is improved.
(2) Vector database high availability clusters: the vector database high-availability cluster is a system for realizing continuous and stable operation of database services through reasonable architecture design and technical means. The backup and load balancing of data are realized by physically constructing a plurality of database nodes, and when a certain node fails, the system can be automatically switched to other nodes to continue providing services, so that the stability and continuity of the services are ensured to the greatest extent.
As a further embodiment, the vector database high availability cluster mainly comprises the following parts:
a) Master/slave mechanism unit: a master database (master) and a backup database (slave) are set, and data is written into the master database and synchronized to the backup database. If the main database fails, the backup database can immediately take over service, and the continuous availability of data is ensured.
b) Load balancing unit: through a load balancing technology, access requests are reasonably distributed to all database nodes, overload of a single node is avoided, and stability of system performance is guaranteed.
c) Heartbeat mechanism unit: the running state of the other party is detected among the database nodes through heartbeat, and once the fact that a certain node loses response is detected, a switching program is automatically started, so that continuous service provision is ensured.
d) A data copying unit: the data of each node is kept consistent, and once the data of one node is lost or damaged, the data can be recovered from other nodes.
e) And a failover unit: when the main server fails, the user request is automatically transferred to the standby server, the main server is restored, and the user request is transferred back to the main server after the main server is restored to be normal.
As another embodiment of the present invention, specifically, it includes:
(1) Query Interface (Query Interface): different systems perform data queries through the vector database query interface. The main functions are to receive a query request, to perform a query operation, and to return a query result. In a vector database, a query interface typically supports a series of special query operations for vector data, such as vector similarity queries, vector distance calculations, and the like. External systems (e.g., applications, other database systems, large data platforms, etc.) may programmatically retrieve and manipulate data in the database by invoking the query interface of the vector database. Such interfaces typically support a variety of communication protocols (e.g., HTTP, RPC, etc.) and data formats (e.g., JSON, XML, etc.) to facilitate data interactions with a variety of different systems. To improve query efficiency, the query interface typically supports complex query statements, including combining query, range query, ranking, and the like. In addition, in order to meet the requirement of big data processing, a part of advanced query interfaces may also support functions such as distributed query and parallel processing;
(2) Data synchronization (Data Synchronization): and data synchronization is performed between the master library and the slave library, so that one data is ensured to be stored in a plurality of servers. The master-slave synchronization is a traditional database backup strategy, data of a master library can be synchronized into a slave library periodically or in real time, and when the master library fails, the slave library can provide service, so that high availability of the system is ensured. At the same time, the master-slave architecture may also improve read performance because read requests may be distributed to multiple slave library processes;
(3) Monitoring System (Monitor System): the monitoring system monitors the running state of the vector database in real time, including the system health condition, performance index, system resource use condition, and notify the self-research system;
(4) Autonomous distribution (Autonomous Distribution): is a key mechanism in processing massive query requests, especially in a distributed database system. The autonomous distribution aims to distribute the query task to each database node according to a certain strategy (such as query load of a server, processing capacity of the server and the like) so as to disperse query pressure and improve query execution efficiency. Autonomous distribution is of great importance in a large-scale and distributed query environment, and can improve the expansibility and robustness of the system, realize the most effective utilization of resources and improve the performance and availability of query services.
As a further example, the monitoring System (Monitor System) of the present invention is specifically as follows:
a) System health monitoring: the operation state of the database, such as CPU utilization rate, memory utilization rate, disk IO, network IO and the like, is checked, and the factors which possibly influence the performance of the database are monitored in real time.
b) Database performance index monitoring: core performance metrics of the database, such as query response time, transaction speed, number of connections, lock status, cache hit rate, etc., are monitored.
c) Notification and alarm: when the monitoring system detects an abnormal state of the database (e.g., too high CPU usage, insufficient memory, too long response time, etc.), an alarm notification may be sent to an administrator or related system for processing.
As a further embodiment, the autonomous distribution (Autonomous Distribution) of the present invention is specifically as follows:
a) Load balancing: autonomous distribution may perform load balancing, i.e., deciding to which server to distribute a new query request based on the processing power of each server and the current load situation.
b) High availability and failover: if a certain server fails, the autonomous distribution can automatically switch the query task of the server to other servers so as to ensure the persistence of the query service.
c) Weight distribution: in some cases, it may be desirable to assign query tasks according to the weights of the servers. The weights may be based on a variety of factors such as hardware configuration of the server (e.g., CPU, memory), network quality, data locality, etc.
The invention is further illustrated by the following examples in conjunction with one embodiment:
it is assumed that an enterprise has massive unstructured data, such as pictures, videos, voices and texts, and can store the unstructured data in a vector database cluster, so that data security is ensured, and required data can be queried quickly and stably.
Enterprises adopt vector database cluster systems to store and query various unstructured data. The system work flow is specifically as follows:
(1) The system or the user stores unstructured data in a vector database cluster, and submits a request to a request interface of the self-research system according to the need.
(2) The massive queries are distributed to different vector databases according to the self-research system weight strategy to perform data query, and the query priority can be set to perform priority query on the query request with higher priority.
(3) And timely expanding the vector database fragments according to the monitoring of the health state of the vector database by the monitoring system.
It can be found that the stability and speed of query data can be improved and the data security can be improved through an independently developed vector database system.
In addition, the invention also provides a computer readable storage medium, and the computer readable storage medium comprises a stored program, and the program executes the high availability method of the master-slave backup and the fragmentation strategy of the vector database.
Further, the invention also provides a computer device, which comprises a processor, a memory and a bus; the processor is connected with the memory through a bus, the memory is used for storing programs, the processor is used for running the programs, and the high-availability method of the master-slave backup and the fragmentation strategy of the vector database is executed when the programs run.
The functions of the methods of the embodiments of the present invention, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer device readable storage medium. Based on such understanding, a part of the present invention that contributes to the prior art or a part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a mobile computing device or a network device, etc.) to perform all or part of the steps of the method described in the various embodiments of the present invention; the storage medium includes various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory, a random access memory, a magnetic disk, or an optical disk.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limited thereto; the technical features of the above embodiments or in different embodiments may also be combined under the idea of the invention, the steps may be implemented in any order, and many other variations exist in different aspects of the invention as described above; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
The present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principles of the invention should be made in the equivalent manner and are included in the scope of the invention.

Claims (9)

1. A high availability system for a master-slave backup and sharding strategy for a vector database, comprising:
the monitoring system is used for monitoring and managing the vector database, helping a user to comprehensively and real-timely monitor the vector database and ensuring the stability and efficiency of the database;
the vector database high-availability cluster realizes data backup and load balancing by physically constructing a plurality of database nodes, and when one node fails, the node is automatically switched to other nodes to continue providing services.
2. The system of claim 1, wherein the vector database high availability cluster comprises:
the main/standby mechanism unit is used for setting a main database and a standby database, writing data into the main database and synchronizing the data into the standby database, and if the main database fails, immediately taking over service by the standby database so as to ensure the continuous availability of the data;
load balancing unit: the method is used for reasonably distributing the access request to each database node through a load balancing technology so as to avoid overload of a single node;
heartbeat mechanism unit: the method comprises the steps that the method is used for detecting the running state of each other between database nodes through heartbeat, and once a certain node is detected to lose response, a switching program is automatically started to ensure continuous service provision;
a data copying unit: for keeping the data of each node consistent, and recovering from other nodes once the data of one node is lost or damaged;
and a failover unit: when the main server fails, the user request is automatically transferred to the standby server, and the main server is restored, and after the main server is restored to normal, the user request is transferred back to the main server.
3. The system as recited in claim 1, comprising:
query interface: receiving a query request, executing a query operation, and returning a query result;
data synchronization: the data synchronization between the master database and the slave database ensures that one data exists in a plurality of servers; the data of the main database are synchronized to the auxiliary database periodically or in real time, and when the main database fails, the auxiliary database provides service;
and (3) a monitoring system: the method comprises the steps of monitoring the running state of a vector database in real time, including the health condition, performance index and the use condition of system resources, and informing a self-research system;
autonomous distribution: the method is a key mechanism when processing massive query requests, and the aim of autonomous distribution is to distribute query tasks to various database nodes according to a certain strategy so as to disperse query pressure and improve query execution efficiency.
4. A system as claimed in claim 3, wherein: the external system obtains and operates the data in the vector database in a programming mode by calling a query interface of the vector database; the query interface supports vector similarity queries and vector distance computation for vector data, supports communication protocols including but not limited to HTTP, RPC, and data formats including but not limited to JSON, XML, and supports complex query statements including but not limited to combined query, range query, ordering functions.
5. The system of claim 3, wherein the monitoring system comprises:
a) System health monitoring: checking the running state of the database, and monitoring factors which possibly influence the performance of the database in real time;
b) Database performance index monitoring: monitoring core performance metrics of the database, including but not limited to query response time, transaction speed, connection number, lock status, cache hit rate;
c) Notification and alarm: when the monitoring system detects the abnormal state of the database, an alarm notification is sent to an administrator or a related system for processing.
6. The system of claim 3, wherein the autonomous distribution comprises:
a) Load balancing: determining which server to distribute a new query request to according to the processing capacity of each server and the current load condition;
b) High availability and failover: if a certain server fails, the autonomous distribution automatically switches the query task of the server to other servers so as to ensure the persistence of the query service;
c) Weight distribution: the query task is assigned according to the weight of each server.
7. A high availability method of a high availability system of a vector database master-slave backup and sharding strategy according to any of claims 1 to 6, comprising the steps of:
the system or the user stores unstructured data in a vector database cluster, and submits a request to a request interface of the self-research system according to the need;
the mass queries are distributed to different vector databases according to the self-research system weight strategy to perform data query, the query priority is set, and query requests with higher priority are queried preferentially;
and timely expanding the vector database fragments according to the monitoring of the health state of the vector database by the monitoring system.
8. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored program, which performs the method of claim 7.
9. A computer device, comprising: a processor, a memory, and a bus; the processor is connected with the memory through the bus; the memory is used for storing a program, and the processor is used for executing the program, and the program executes the method of claim 7.
CN202311354536.9A 2023-10-18 2023-10-18 High availability system and method for master-slave backup and fragmentation strategy of vector database Pending CN117331755A (en)

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