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CN117539682A - Big data transaction backup method and system - Google Patents

Big data transaction backup method and system Download PDF

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Publication number
CN117539682A
CN117539682A CN202311380158.1A CN202311380158A CN117539682A CN 117539682 A CN117539682 A CN 117539682A CN 202311380158 A CN202311380158 A CN 202311380158A CN 117539682 A CN117539682 A CN 117539682A
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Prior art keywords
transaction
data
backup
big data
host computer
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Inventor
徐晓清
李喆
卢梅珍
卞羽
王栋弘
林伟
黄登煌
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Fujian Big Data Trading Co ltd
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Fujian Big Data Trading Co ltd
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Priority to CN202311380158.1A priority Critical patent/CN117539682A/en
Publication of CN117539682A publication Critical patent/CN117539682A/en
Withdrawn legal-status Critical Current

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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
    • G06F11/1451—Management of the data involved in backup or backup restore by selection of backup contents
    • 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
    • G06F11/1458—Management of the backup or restore process
    • G06F11/1461—Backup scheduling policy
    • 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
    • G06F11/1458—Management of the backup or restore process
    • G06F11/1464—Management of the backup or restore process for networked environments
    • 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
    • G06F11/16—Error detection or correction of the data by redundancy in hardware
    • G06F11/20—Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements
    • G06F11/202—Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements where processing functionality is redundant
    • G06F11/2023—Failover techniques
    • G06F11/2028—Failover techniques eliminating a faulty processor or activating a spare
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/80—Database-specific techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/505—Clust
    • 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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Abstract

The invention provides a big data transaction backup method and a system in the technical field of big data transaction, wherein the method comprises the following steps: step S10, selecting a server from a server cluster as a host, and taking the rest servers as slaves; step S20, a seller client purchases data samples through the host on-shelf transaction information, and then purchases big data through the transaction information; step S30, the host computer stores generated transaction data in real time to a mysql database of the host computer in the big data transaction process; step S40, the host computer backs up the transaction data to a mysql database of the slave computer, and the backed-up transaction data is notarized through a blockchain; step S50, when the running state of the host computer is a fault, selecting a server from the slaves with normal running state as a new host computer; step S60, performing data recovery operation when the failed server is recovered from the failure. The invention has the advantages that: the reliability of transaction data backup of big data transaction is greatly improved.

Description

Big data transaction backup method and system
Technical Field
The invention relates to the technical field of big data transaction, in particular to a big data transaction backup method and system.
Background
With the rapid development of new generation information technologies such as big data, cloud computing and artificial intelligence, the data becomes basic strategic resources and revolutionary key elements in the digital era, so that the demand of big data transaction is generated.
Because the big data transaction is marked with data and belongs to intangible marked with data, a large amount of transaction data (transaction logs, big data marks and the like) can be generated in the big data transaction process, when software or hardware of a big data transaction system fails, the data is lost, and immeasurable loss is brought to the big data transaction; such as a power failure, a software failure caused by mysql error, an operating system error, an abnormal shutdown, etc., a hardware failure caused by a media failure (e.g., disk crash); thus, it is necessary to back up transaction data of a big data transaction.
For the backup of transaction data generated by big data transaction, conventionally, a method of additionally opening up a storage space in a local machine, periodically backing up the whole transaction data into the storage space and deleting old backup data is adopted, and the following problems exist: when the hardware of the machine fails, the backup is similar to the dummy, and the full-quantity backup method is adopted every time, when the transaction data needing to be backed up is increased, the backup time is prolonged, and the risk of data loss caused by failure in the backup process is increased.
Therefore, how to provide a big data transaction backup method and system to improve the reliability of transaction data backup of big data transaction becomes a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the technical problem of providing a big data transaction backup method and a system thereof, which can improve the reliability of transaction data backup of big data transaction.
In a first aspect, the present invention provides a big data transaction backup method, including the following steps:
step S10, selecting a server from a server cluster as a host, and operating a mysql database by the other servers as slaves;
step S20, a seller client purchases data samples through the host on-shelf transaction information, and purchases corresponding big data after performing fusion test through the host on-shelf transaction information so as to perform big data transaction;
step S30, the host stores generated transaction data to the mysql database of the host in real time in the big data transaction process;
step S40, the host backs up transaction data stored in the mysql database to the mysql database of the slave in a cascading copy mode, and the backed up transaction data is notarized through a blockchain;
step S50, when the running state of the host computer is a fault, selecting a server from the slaves with normal running state as a new host computer, and carrying out big data transaction through the new host computer;
and step S60, when the failed server fails to recover, executing data recovery operation through the backed-up transaction data.
Further, the step S10 specifically includes:
and selecting a server with the minimum load from the server cluster as a host computer through a load balancing technology, and operating a mysql database for storing transaction data by the other servers as slaves.
Further, the step S20 specifically includes:
the seller client side at least comprises big data types, big data quantity, transaction price, right-confirming information and transaction information of seller information through the host computer on-shelf, and the buyer client side firstly purchases data samples through the transaction information on-shelf of the host computer;
the buyer client accesses a data sample through a data sandbox of the host, performs online fusion test on the data sample based on local data so as to perform requirement matching verification, and automatically destroys the data sample immediately after the fusion test is finished;
and the buyer client purchases the corresponding big data after the fusion test is passed so as to conduct big data transaction.
Further, the step S40 specifically includes:
the host computer backs up transaction data stored in the mysql database to the mysql database of the slave computer in a mode of full backup, incremental backup or differential backup at regular time through a cascading copying mode based on a preset backup period;
and each slave machine carries out hash calculation on the backed-up transaction data to obtain a hash value, and the hash value is bound with the backup time and uploaded to a blockchain, so that the backed-up transaction data is subjected to notarization through the blockchain.
Further, the step S50 specifically includes:
and each slave computer monitors the running state of the host computer in real time, when the running state of the host computer is a fault, the host computer is set as the slave computer, one server is selected from the slave computers with normal running states to serve as a new host computer through a load balancing technology, and big data transaction is carried out through the new host computer.
In a second aspect, the present invention provides a big data transaction backup system, including the following modules:
the first master-slave setting module is used for selecting a server from the server cluster as a host, and the other servers are used as slaves, and each server runs a mysql database;
the large data transaction module is used for purchasing data samples through the host on-shelf transaction information by the seller client side, and purchasing corresponding large data after performing fusion test through the host on-shelf transaction information by the buyer client side so as to perform large data transaction;
the transaction data storage module is used for storing generated transaction data to the mysql database of the host in real time in the big data transaction process;
the transaction data backup module is used for backing up transaction data stored in the mysql database by the host computer in a cascade copy mode to the mysql database of the slave computer, and carrying out notarization on the backed-up transaction data by the blockchain;
the second master-slave setting module is used for selecting a server from the slaves with normal running states as a new host when the running states of the hosts are faults, and carrying out big data transaction through the new host;
and the transaction data recovery module is used for executing data recovery operation through the backup transaction data when the failed server fails to recover.
Further, the first master-slave setting module is specifically configured to:
and selecting a server with the minimum load from the server cluster as a host computer through a load balancing technology, and operating a mysql database for storing transaction data by the other servers as slaves.
Further, the big data transaction module is specifically configured to:
the seller client side at least comprises big data types, big data quantity, transaction price, right-confirming information and transaction information of seller information through the host computer on-shelf, and the buyer client side firstly purchases data samples through the transaction information on-shelf of the host computer;
the buyer client accesses a data sample through a data sandbox of the host, performs online fusion test on the data sample based on local data so as to perform requirement matching verification, and automatically destroys the data sample immediately after the fusion test is finished;
and the buyer client purchases the corresponding big data after the fusion test is passed so as to conduct big data transaction.
Further, the transaction data backup module is specifically configured to:
the host computer backs up transaction data stored in the mysql database to the mysql database of the slave computer in a mode of full backup, incremental backup or differential backup at regular time through a cascading copying mode based on a preset backup period;
and each slave machine carries out hash calculation on the backed-up transaction data to obtain a hash value, and the hash value is bound with the backup time and uploaded to a blockchain, so that the backed-up transaction data is subjected to notarization through the blockchain.
Further, the second master-slave setting module is specifically configured to:
and each slave computer monitors the running state of the host computer in real time, when the running state of the host computer is a fault, the host computer is set as the slave computer, one server is selected from the slave computers with normal running states to serve as a new host computer through a load balancing technology, and big data transaction is carried out through the new host computer.
The invention has the advantages that:
1. selecting one server from the server cluster as a host computer, and using the rest servers as slaves; the seller client side purchases data samples through the transaction information to carry out fusion test, and purchases corresponding big data to carry out big data transaction; in the big data transaction process, the host computer stores the generated transaction data in real time to a mysql database of the host computer, the host computer backs up the transaction data stored in the mysql database to a mysql database of the slave computer in a cascading copy mode, and the backed-up transaction data is notarized through a blockchain; when the running state of the host computer is a fault, selecting a server from the slaves with normal running state as a new host computer, and carrying out big data transaction through the new host computer; when the fault of the fault server is recovered, executing data recovery operation through the backup transaction data; the transaction data is backed up through each server in the server cluster at the same time, namely, a multi-machine backup strategy is adopted, when the server serving as a host fails, the server can be subjected to data recovery operation by using a slave machine which operates normally, and the phenomenon that the backup is similar to a dummy when hardware fails due to the fact that the backup is carried out locally is avoided; when the transaction data of the host computer is backed up to the slave computer, a full-volume backup mode, an incremental backup mode or a differential backup mode is adopted according to the needs, namely, the full-volume backup mode is adopted when the data volume is small, and the incremental backup mode or the differential backup mode is adopted when the data volume is large, so that the timeliness of the backup is ensured; the hash value obtained by carrying out hash calculation on the backed-up transaction data is uploaded to the blockchain, so that the hash value is prevented from being tampered, and when the later data is recovered, whether the backed-up transaction data is tampered or not can be checked rapidly through the hash value, and the reliability of transaction data backup of big data transaction is improved greatly finally in combination with timing backup of a backup period.
2. And the server with the minimum load is selected from all servers in the server cluster to be used as a host for big data transaction through a load balancing technology, so that the stability of the big data transaction is greatly ensured.
Drawings
The invention will be further described with reference to examples of embodiments with reference to the accompanying drawings.
FIG. 1 is a schematic block diagram of a big data transaction backup method of the present invention.
Fig. 2 is a schematic structural diagram of a big data transaction backup system according to the present invention.
Detailed Description
According to the technical scheme in the embodiment of the application, the overall thought is as follows: the transaction data is backed up by each server in the server cluster at the same time, when the server serving as the host fails, the server can be recovered by using the slave which operates normally, so that the situation that the backup is similar to the dummy when hardware fails due to the fact that the backup is carried out locally is avoided; when the transaction data of the host computer is backed up to the slave computer, a full-volume backup mode, an incremental backup mode or a differential backup mode is adopted according to the needs, namely, the full-volume backup mode is adopted when the data volume is small, and the incremental backup mode or the differential backup mode is adopted when the data volume is large, so that timeliness of the backup is ensured; the hash value obtained by carrying out hash calculation on the backed-up transaction data is uploaded to the blockchain, so that the hash value is prevented from being tampered, and when the later data is recovered, whether the backed-up transaction data is tampered or not can be checked rapidly through the hash value, and the reliability of transaction data backup of big data transaction is improved by combining the timing backup of the backup period.
Referring to fig. 1 to 2, a preferred embodiment of a big data transaction backup method of the present invention includes the following steps:
step S10, selecting a server from a server cluster as a host, and operating a mysql database by the other servers as slaves;
step S20, a seller client purchases data samples through the host on-shelf transaction information, and purchases corresponding big data after performing fusion test through the host on-shelf transaction information so as to perform big data transaction;
step S30, the host stores generated transaction data to the mysql database of the host in real time in the big data transaction process;
step S40, the host backs up transaction data stored in the mysql database to the mysql database of the slave in a cascading copy mode, and the backed up transaction data is notarized through a blockchain; the pressure of the host can be greatly reduced through cascade copying, so that the reliability of backup is further ensured; the blockchain has the characteristics that the data is difficult to tamper and decentralize, so that the information recorded by the blockchain is more real and reliable, and the problem that people do not trust each other can be solved;
step S50, when the running state of the host computer is a fault, selecting a server from the slaves with normal running state as a new host computer, and carrying out big data transaction through the new host computer;
and step S60, when the failed server fails to recover, executing data recovery operation through the backed-up transaction data.
The step S10 specifically includes:
and selecting a server with the minimum load from the server cluster as a host computer through a load balancing technology, and operating a mysql database for storing transaction data by the other servers as slaves.
The step S20 specifically includes:
the seller client side at least comprises big data types, big data quantity, transaction price, right-confirming information and transaction information of seller information through the host computer on-shelf, and the buyer client side firstly purchases data samples through the transaction information on-shelf of the host computer;
the buyer client accesses a data sample through a data sandbox of the host, performs online fusion test on the data sample based on local data so as to perform requirement matching verification, and automatically destroys the data sample immediately after the fusion test is finished; the data sandbox performs data calculation of plaintext, and the sandbox is destroyed after calculation, so that calculation results are taken away; the data sample is automatically destroyed after the fusion test is finished, so that the leakage of the data sample is avoided;
and the buyer client purchases the corresponding big data after the fusion test is passed so as to conduct big data transaction.
The step S40 specifically includes:
the host computer backs up transaction data stored in the mysql database to the mysql database of the slave computer in a mode of full backup, incremental backup or differential backup at regular time through a cascading copying mode based on a preset backup period; in specific implementation, manual backup can also be performed on the transaction data; in specific implementation, a data volume threshold can be set, when the size of the transaction data does not exceed the data volume threshold, a full-volume backup mode is adopted, otherwise, an incremental backup mode or a differential backup mode is adopted, so that the advantages of full-volume backup, incremental backup and differential backup are combined, the reliability of backup is ensured, and the timeliness of backup is improved;
and each slave machine carries out hash calculation on the backed-up transaction data to obtain a hash value, and the hash value is bound with the backup time and uploaded to a blockchain, so that the backed-up transaction data is subjected to notarization through the blockchain.
Since the hash calculation is irreversible, the subsequent hash calculation is carried out on the transaction data again, and whether the transaction data is tampered or not can be rapidly judged by comparing whether the hash value obtained by calculation is consistent with the hash value stored in the block chain; and carrying out notarization on the hash value through a blockchain, avoiding the hash value from being tampered, carrying out hash check on the transaction data through a trusted hash value, and further guaranteeing the safety.
The step S50 specifically includes:
and each slave computer monitors the running state of the host computer in real time, when the running state of the host computer is a fault, the host computer is set as the slave computer, one server is selected from the slave computers with normal running states to serve as a new host computer through a load balancing technology, and big data transaction is carried out through the new host computer. And the server with the minimum load is selected from all servers in the server cluster to be used as a host for big data transaction through a load balancing technology, so that the stability of the big data transaction is greatly ensured.
The step S60 specifically includes:
and when the fault server recovers from the fault, selecting the backup transaction data based on the fault time point and the backup time, and mirroring the transaction data to the fault recovery server to execute data recovery operation after carrying out integrity verification on the transaction data through the hash value stored by the block chain.
The invention relates to a preferred embodiment of a big data transaction backup system, which comprises the following modules:
the first master-slave setting module is used for selecting a server from the server cluster as a host, and the other servers are used as slaves, and each server runs a mysql database;
the large data transaction module is used for purchasing data samples through the host on-shelf transaction information by the seller client side, and purchasing corresponding large data after performing fusion test through the host on-shelf transaction information by the buyer client side so as to perform large data transaction;
the transaction data storage module is used for storing generated transaction data to the mysql database of the host in real time in the big data transaction process;
the transaction data backup module is used for backing up transaction data stored in the mysql database by the host computer in a cascade copy mode to the mysql database of the slave computer, and carrying out notarization on the backed-up transaction data by the blockchain; the pressure of the host can be greatly reduced through cascade copying, so that the reliability of backup is further ensured; the blockchain has the characteristics that the data is difficult to tamper and decentralize, so that the information recorded by the blockchain is more real and reliable, and the problem that people do not trust each other can be solved;
the second master-slave setting module is used for selecting a server from the slaves with normal running states as a new host when the running states of the hosts are faults, and carrying out big data transaction through the new host;
and the transaction data recovery module is used for executing data recovery operation through the backup transaction data when the failed server fails to recover.
The first master-slave setting module is specifically configured to:
and selecting a server with the minimum load from the server cluster as a host computer through a load balancing technology, and operating a mysql database for storing transaction data by the other servers as slaves.
The big data transaction module is specifically used for:
the seller client side at least comprises big data types, big data quantity, transaction price, right-confirming information and transaction information of seller information through the host computer on-shelf, and the buyer client side firstly purchases data samples through the transaction information on-shelf of the host computer;
the buyer client accesses a data sample through a data sandbox of the host, performs online fusion test on the data sample based on local data so as to perform requirement matching verification, and automatically destroys the data sample immediately after the fusion test is finished; the data sandbox performs data calculation of plaintext, and the sandbox is destroyed after calculation, so that calculation results are taken away; the data sample is automatically destroyed after the fusion test is finished, so that the leakage of the data sample is avoided;
and the buyer client purchases the corresponding big data after the fusion test is passed so as to conduct big data transaction.
The transaction data backup module is specifically configured to:
the host computer backs up transaction data stored in the mysql database to the mysql database of the slave computer in a mode of full backup, incremental backup or differential backup at regular time through a cascading copying mode based on a preset backup period; in specific implementation, manual backup can also be performed on the transaction data; in specific implementation, a data volume threshold can be set, when the size of the transaction data does not exceed the data volume threshold, a full-volume backup mode is adopted, otherwise, an incremental backup mode or a differential backup mode is adopted, so that the advantages of full-volume backup, incremental backup and differential backup are combined, the reliability of backup is ensured, and the timeliness of backup is improved;
and each slave machine carries out hash calculation on the backed-up transaction data to obtain a hash value, and the hash value is bound with the backup time and uploaded to a blockchain, so that the backed-up transaction data is subjected to notarization through the blockchain.
Since the hash calculation is irreversible, the subsequent hash calculation is carried out on the transaction data again, and whether the transaction data is tampered or not can be rapidly judged by comparing whether the hash value obtained by calculation is consistent with the hash value stored in the block chain; and carrying out notarization on the hash value through a blockchain, avoiding the hash value from being tampered, carrying out hash check on the transaction data through a trusted hash value, and further guaranteeing the safety.
The second master-slave setting module is specifically configured to:
and each slave computer monitors the running state of the host computer in real time, when the running state of the host computer is a fault, the host computer is set as the slave computer, one server is selected from the slave computers with normal running states to serve as a new host computer through a load balancing technology, and big data transaction is carried out through the new host computer. And the server with the minimum load is selected from all servers in the server cluster to be used as a host for big data transaction through a load balancing technology, so that the stability of the big data transaction is greatly ensured.
The transaction data recovery module is specifically configured to:
and when the fault server recovers from the fault, selecting the backup transaction data based on the fault time point and the backup time, and mirroring the transaction data to the fault recovery server to execute data recovery operation after carrying out integrity verification on the transaction data through the hash value stored by the block chain.
In summary, the invention has the advantages that:
1. selecting one server from the server cluster as a host computer, and using the rest servers as slaves; the seller client side purchases data samples through the transaction information to carry out fusion test, and purchases corresponding big data to carry out big data transaction; in the big data transaction process, the host computer stores the generated transaction data in real time to a mysql database of the host computer, the host computer backs up the transaction data stored in the mysql database to a mysql database of the slave computer in a cascading copy mode, and the backed-up transaction data is notarized through a blockchain; when the running state of the host computer is a fault, selecting a server from the slaves with normal running state as a new host computer, and carrying out big data transaction through the new host computer; when the fault of the fault server is recovered, executing data recovery operation through the backup transaction data; the transaction data is backed up through each server in the server cluster at the same time, namely, a multi-machine backup strategy is adopted, when the server serving as a host fails, the server can be subjected to data recovery operation by using a slave machine which operates normally, and the phenomenon that the backup is similar to a dummy when hardware fails due to the fact that the backup is carried out locally is avoided; when the transaction data of the host computer is backed up to the slave computer, a full-volume backup mode, an incremental backup mode or a differential backup mode is adopted according to the needs, namely, the full-volume backup mode is adopted when the data volume is small, and the incremental backup mode or the differential backup mode is adopted when the data volume is large, so that the timeliness of the backup is ensured; the hash value obtained by carrying out hash calculation on the backed-up transaction data is uploaded to the blockchain, so that the hash value is prevented from being tampered, and when the later data is recovered, whether the backed-up transaction data is tampered or not can be checked rapidly through the hash value, and the reliability of transaction data backup of big data transaction is improved greatly finally in combination with timing backup of a backup period.
2. And the server with the minimum load is selected from all servers in the server cluster to be used as a host for big data transaction through a load balancing technology, so that the stability of the big data transaction is greatly ensured.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that the specific embodiments described are illustrative only and not intended to limit the scope of the invention, and that equivalent modifications and variations of the invention in light of the spirit of the invention will be covered by the claims of the present invention.

Claims (10)

1. A big data transaction backup method is characterized in that: the method comprises the following steps:
step S10, selecting a server from a server cluster as a host, and operating a mysql database by the other servers as slaves;
step S20, a seller client purchases data samples through the host on-shelf transaction information, and purchases corresponding big data after performing fusion test through the host on-shelf transaction information so as to perform big data transaction;
step S30, the host stores generated transaction data to the mysql database of the host in real time in the big data transaction process;
step S40, the host backs up transaction data stored in the mysql database to the mysql database of the slave in a cascading copy mode, and the backed up transaction data is notarized through a blockchain;
step S50, when the running state of the host computer is a fault, selecting a server from the slaves with normal running state as a new host computer, and carrying out big data transaction through the new host computer;
and step S60, when the failed server fails to recover, executing data recovery operation through the backed-up transaction data.
2. The big data transaction backup method as claimed in claim 1, wherein: the step S10 specifically includes:
and selecting a server with the minimum load from the server cluster as a host computer through a load balancing technology, and operating a mysql database for storing transaction data by the other servers as slaves.
3. The big data transaction backup method as claimed in claim 1, wherein: the step S20 specifically includes:
the seller client side at least comprises big data types, big data quantity, transaction price, right-confirming information and transaction information of seller information through the host computer on-shelf, and the buyer client side firstly purchases data samples through the transaction information on-shelf of the host computer;
the buyer client accesses a data sample through a data sandbox of the host, performs online fusion test on the data sample based on local data so as to perform requirement matching verification, and automatically destroys the data sample immediately after the fusion test is finished;
and the buyer client purchases the corresponding big data after the fusion test is passed so as to conduct big data transaction.
4. The big data transaction backup method as claimed in claim 1, wherein: the step S40 specifically includes:
the host computer backs up transaction data stored in the mysql database to the mysql database of the slave computer in a mode of full backup, incremental backup or differential backup at regular time through a cascading copying mode based on a preset backup period;
and each slave machine carries out hash calculation on the backed-up transaction data to obtain a hash value, and the hash value is bound with the backup time and uploaded to a blockchain, so that the backed-up transaction data is subjected to notarization through the blockchain.
5. The big data transaction backup method as claimed in claim 1, wherein: the step S50 specifically includes:
and each slave computer monitors the running state of the host computer in real time, when the running state of the host computer is a fault, the host computer is set as the slave computer, one server is selected from the slave computers with normal running states to serve as a new host computer through a load balancing technology, and big data transaction is carried out through the new host computer.
6. A big data transaction backup system is characterized in that: the device comprises the following modules:
the first master-slave setting module is used for selecting a server from the server cluster as a host, and the other servers are used as slaves, and each server runs a mysql database;
the large data transaction module is used for purchasing data samples through the host on-shelf transaction information by the seller client side, and purchasing corresponding large data after performing fusion test through the host on-shelf transaction information by the buyer client side so as to perform large data transaction;
the transaction data storage module is used for storing generated transaction data to the mysql database of the host in real time in the big data transaction process;
the transaction data backup module is used for backing up transaction data stored in the mysql database by the host computer in a cascade copy mode to the mysql database of the slave computer, and carrying out notarization on the backed-up transaction data by the blockchain;
the second master-slave setting module is used for selecting a server from the slaves with normal running states as a new host when the running states of the hosts are faults, and carrying out big data transaction through the new host;
and the transaction data recovery module is used for executing data recovery operation through the backup transaction data when the failed server fails to recover.
7. A big data transaction backup system according to claim 6, wherein: the first master-slave setting module is specifically configured to:
and selecting a server with the minimum load from the server cluster as a host computer through a load balancing technology, and operating a mysql database for storing transaction data by the other servers as slaves.
8. A big data transaction backup system according to claim 6, wherein: the big data transaction module is specifically used for:
the seller client side at least comprises big data types, big data quantity, transaction price, right-confirming information and transaction information of seller information through the host computer on-shelf, and the buyer client side firstly purchases data samples through the transaction information on-shelf of the host computer;
the buyer client accesses a data sample through a data sandbox of the host, performs online fusion test on the data sample based on local data so as to perform requirement matching verification, and automatically destroys the data sample immediately after the fusion test is finished;
and the buyer client purchases the corresponding big data after the fusion test is passed so as to conduct big data transaction.
9. A big data transaction backup system according to claim 6, wherein: the transaction data backup module is specifically configured to:
the host computer backs up transaction data stored in the mysql database to the mysql database of the slave computer in a mode of full backup, incremental backup or differential backup at regular time through a cascading copying mode based on a preset backup period;
and each slave machine carries out hash calculation on the backed-up transaction data to obtain a hash value, and the hash value is bound with the backup time and uploaded to a blockchain, so that the backed-up transaction data is subjected to notarization through the blockchain.
10. A big data transaction backup system according to claim 6, wherein: the second master-slave setting module is specifically configured to:
and each slave computer monitors the running state of the host computer in real time, when the running state of the host computer is a fault, the host computer is set as the slave computer, one server is selected from the slave computers with normal running states to serve as a new host computer through a load balancing technology, and big data transaction is carried out through the new host computer.
CN202311380158.1A 2023-10-24 2023-10-24 Big data transaction backup method and system Withdrawn CN117539682A (en)

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