CN109976944B - Data processing method and system, storage medium and electronic device - Google Patents
Data processing method and system, storage medium and electronic device Download PDFInfo
- Publication number
- CN109976944B CN109976944B CN201910123537.XA CN201910123537A CN109976944B CN 109976944 B CN109976944 B CN 109976944B CN 201910123537 A CN201910123537 A CN 201910123537A CN 109976944 B CN109976944 B CN 109976944B
- Authority
- CN
- China
- Prior art keywords
- data
- write
- traffic
- group
- flow
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
- G06F11/1469—Backup restoration techniques
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The present disclosure aims to provide a data processing method and system, a storage medium, and an electronic device, so as to solve the problem in the related art that the maintenance difficulty for insurance service data traffic is large. The method is applied to a data processing system, wherein the data processing system comprises a plurality of data groups; the method comprises the following steps: acquiring write flow data of insurance service generated according to upstream service; acquiring a policy number corresponding to the write flow data, wherein the policy number comprises a first identification code for identifying group information of a data group; and writing the write flow data into a target data group corresponding to the first identification code.
Description
Technical Field
The present disclosure relates to the field of storage technologies, and in particular, to a data processing method and system, a storage medium, and an electronic device.
Background
With the enhancement of insurance awareness, the data volume of various insurance services is growing, and the traffic volume of the sale of insurance is rapidly increased. Insurance sales services can be understood as a service provided by other service lines. The core of insurance shipments is associated with HTTP traffic (the traffic required to download a file from a server to a client) relating to the client. Therefore, when a machine room fails, if the machine room fails to cause the upstream service to fail, the insurance flow of the sale type insurance of the related service line can also break down.
In the sale of insurance, most transaction flow and risk are generated according to the upstream service served by the insurance, and the insurance flow has no data of the dimension of user ID. For example, for each take-away order, an insurance may be generated for the rider associated with the order; for the hotel order providing the deposit-free service, corresponding insurance is generated to ensure the benefit of the hotel, and the insurance is not directly associated with the user of the upstream business service, so that the data definition of the dimension of the user ID for selling the insurance is not convenient.
Because the flow of the sale insurance has no data of the dimension of the user ID, if the flow breaking fault occurs, the related data service is difficult to be inquired, and great difficulty is brought to the data maintenance of the sale insurance.
Disclosure of Invention
The present disclosure aims to provide a data processing method and system, a storage medium, and an electronic device, so as to solve the problem in the related art that the maintenance difficulty for insurance service data traffic is large.
To achieve the above object, in a first aspect, the present disclosure provides a data processing method,
the method is applied to a data processing system, wherein the data processing system comprises a plurality of data groups;
the method comprises the following steps:
acquiring write flow data of insurance service generated according to upstream service;
acquiring a policy number corresponding to the write flow data, wherein the policy number comprises a first identification code for identifying group information of a data group;
and writing the write flow data into a target data group corresponding to the first identification code.
Optionally, each of the data sets comprises: a write traffic database and a read traffic database;
the read traffic database includes: the read flow database of the same group corresponding to the write flow database and the read flow database of the different group corresponding to the other data groups;
the writing the write traffic data into the target data group corresponding to the first identification code includes: writing the write flow data into a write flow database in a target data group;
the method further comprises the following steps:
synchronizing the write traffic data to the same set of read traffic databases;
and synchronizing the write flow data to the heterogeneous read flow database of any data group except the target data group.
Optionally, each of the data sets comprises: the write flow databases are the same as the read flow databases in the same group corresponding to each write flow database;
the policy number further comprises a second identification code for identifying group information of the database;
the writing the write traffic data into the target data group corresponding to the first identification code includes:
and writing the write flow data into a target write flow database corresponding to the second identification code in the target data group.
Optionally, a plurality of the data sets are located in at least two different machine rooms;
the method further comprises the following steps:
responding to the query operation of the first machine room, and acquiring a policy number of query data flow;
judging whether a first data group corresponding to the first identification code of the policy number of the query data traffic belongs to a local data group of the first machine room;
if the first data group belongs to a local data group of the first machine room, acquiring data information corresponding to the query data traffic in the same group of read traffic databases of the first data group;
and if the first data group belongs to other machine rooms, acquiring data information corresponding to the query data traffic in the heterogeneous read traffic database of the first data group of the first machine room.
Optionally, a plurality of the data sets are located in at least two different machine rooms;
before writing the write traffic data into the target data group corresponding to the first identification code, the method further includes:
if the target data group does not belong to a second machine room for executing the upstream service, detecting whether a data link of the second machine room and a third machine room where the target data group is located is smooth or not;
if the data link is abnormal, recording the write flow data in an abnormal record form of a local data group of the second machine room;
the writing the write traffic data into the target data group corresponding to the first identification code includes:
writing the write flow data recorded in the abnormal record form into the target data set in response to an event characterizing that the data link is clear to recover.
Optionally, before obtaining the policy number corresponding to the write traffic data, the method further includes:
performing idempotent verification on the write flow data by inquiring a read flow database of a plurality of data groups;
the obtaining of the policy number corresponding to the write traffic data includes:
and if the write flow data passes the idempotent verification, acquiring a policy number corresponding to the write flow data.
Optionally, if the write traffic data is guaranteed traffic data, the method further includes:
searching for a repeated policy with a policy number consistent with the policy number of the insurable traffic data in the read traffic database of the plurality of data sets;
and if the repeated insurance policy exists, sending a request message for requesting to initiate a refund process corresponding to the insurance flow data to an upstream service.
Optionally, the write traffic data is guaranteed traffic data or guaranteed traffic data.
Optionally, the obtaining the policy number corresponding to the write traffic data includes:
acquiring service characteristic information of the upstream service;
determining a target data group used for writing the write flow data and a first identification code used for identifying group information of the target data group according to the service characteristic information and a preset corresponding relation between the service characteristic and the data group;
and generating the policy number of the write flow data at least according to the first identification code.
In a second aspect, the present disclosure provides a data processing system comprising a plurality of data sets;
the system further comprises:
the access module is used for acquiring write flow data of the insurance service generated according to the upstream service;
the numbering module is used for acquiring a policy number corresponding to the write flow data, wherein the policy number comprises a first identification code of group information for identifying a data group;
and the writing module is used for writing the writing flow data into the target data group corresponding to the first identification code.
Optionally, each of the data sets comprises: a write traffic database and a read traffic database;
the read traffic database includes: the read flow database of the same group corresponding to the write flow database and the read flow database of the different group corresponding to the other data groups;
the write module is configured to:
writing the write flow data into a write flow database in a target data group;
synchronizing the write traffic data to the same set of read traffic databases;
and synchronizing the write flow data to the heterogeneous read flow database of any data group except the target data group.
Optionally, each of the data sets comprises: the write flow databases are the same as the read flow databases in the same group corresponding to each write flow database;
the policy number further comprises a second identification code for identifying group information of the database;
the write module is configured to:
and writing the write flow data into a target write flow database corresponding to the second identification code in the target data group.
Optionally, a plurality of the data sets are located in at least two different machine rooms;
the system further comprises a query module for:
responding to the query operation of the first machine room, and acquiring a policy number of query data flow;
judging whether a first data group corresponding to the first identification code of the policy number of the query data traffic belongs to a local data group of the first machine room;
if the first data group belongs to a local data group of the first machine room, acquiring data information corresponding to the query data traffic in the same group of read traffic databases of the first data group;
and if the first data group belongs to other machine rooms, acquiring data information corresponding to the query data traffic in the heterogeneous read traffic database of the first data group of the first machine room.
Optionally, a plurality of the data sets are located in at least two different machine rooms;
the system further comprises a fault handling module for:
if the target data group does not belong to a second machine room executing the upstream service, before writing the write flow data into the target data group corresponding to the first identification code, detecting whether a data link of the second machine room and a third machine room where the target data group is located is smooth;
if the data link is abnormal, recording the write flow data in an abnormal record form of a local data group of the second machine room;
the write module is configured to:
writing the write flow data recorded in the abnormal record form into the target data set in response to an event characterizing that the data link is clear to recover.
Optionally, the system further comprises a verification module for:
before acquiring the policy number corresponding to the write flow data, performing idempotent verification on the write flow data by inquiring a plurality of read flow databases of the data group;
and the numbering module is used for acquiring a policy number corresponding to the write flow data if the write flow data passes the idempotent check.
Optionally, the verification module is configured to:
searching for a repeated policy with a policy number consistent with the policy number of the insurable traffic data in the read traffic database of the plurality of data sets;
and if the repeated insurance policy exists, sending a request message for requesting to initiate a refund process corresponding to the insurance flow data to an upstream service.
Optionally, the write traffic data is guaranteed traffic data or guaranteed traffic data.
Optionally, the numbering module is configured to:
acquiring service characteristic information of the upstream service;
determining a target data group used for writing the write flow data and a first identification code used for identifying group information of the target data group according to the service characteristic information and a preset corresponding relation between the service characteristic and the data group;
and generating the policy number of the write flow data at least according to the first identification code.
In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of any of the data processing methods.
In a fourth aspect, a data processing system is provided, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory, in any of the steps of the data processing method.
The technical scheme can at least achieve the following technical effects:
and acquiring write flow data generated according to the upstream service, acquiring a policy number corresponding to the write flow data, and further writing the write flow data into a target data group corresponding to an identification code in the policy number. Therefore, the assignment of the data group attribution of the write flow data can be completed based on the policy number, and if the data interruption caused by the machine room fault occurs, the data group for processing the write flow data corresponding to the data group attribution can be inquired through the policy number. When the insurance business data management system is used for some insurance business data of the sale class lacking user ID information, the corresponding target data group is also determined through the identification code in the insurance policy number, so that the convenience of insurance business data management and maintenance is improved.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow diagram illustrating a method of data processing according to an exemplary embodiment.
FIG. 2 is a flow diagram illustrating another method of data processing according to an example embodiment.
Fig. 3 is a diagram illustrating a data processing method according to an example embodiment.
FIG. 4 is a schematic diagram illustrating another data processing method according to an example embodiment.
FIG. 5 is a schematic diagram illustrating another data processing method according to an example embodiment.
FIG. 6 is a flow diagram illustrating another method of data processing according to an example embodiment.
FIG. 7 is a schematic diagram illustrating another data processing method according to an example embodiment.
FIG. 8 is a block diagram illustrating a data processing system in accordance with an exemplary embodiment.
FIG. 9 is a block diagram of an electronic device shown in accordance with an example embodiment.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
FIG. 1 is a flow diagram illustrating a method of data processing according to an exemplary embodiment.
The method is applied to a data processing system comprising a plurality of data sets. Wherein the plurality of data groups may be virtual groupings. The multiple data sets may be deployed in the same machine room, or may be deployed in different machine rooms respectively. The specific deployment rules may be assigned based on the geographic location of the business.
For example, a Daxing (DX) machine room deploys a data group a, and a Yongfeng (YF) machine room deploys a data group B. The great rise (DX) computer room comprises a storage resource library A1 library and an A2 library, and the Yongfeng (YF) computer room comprises a storage resource library B1 library and a storage resource library B2 library. Further traffic data, which is allocated to the a1 pool or the a2 pool, can be virtually grouped into a group; traffic data assigned to the B1 pool or the B2 pool can be virtually grouped into B groups.
The method comprises the following steps:
and S11, acquiring write flow data of the insurance service generated according to the upstream service.
Wherein the write traffic data may be guaranteed traffic data or guaranteed traffic data.
For example, the upstream service is to create a takeaway order, and the corresponding takeaway order generates insurance flow data corresponding to the order rider; and the upstream business is the delivery completion of the take-out order, and the corresponding take-out order generates the refund flow data corresponding to the order rider.
S12, acquiring a policy number corresponding to the write flow data, wherein the policy number comprises a first identification code of group information for identifying a data group.
Specifically, the obtaining of the policy number corresponding to the write traffic data includes: acquiring service characteristic information of the upstream service; determining a target data group used for writing the write flow data and a first identification code used for identifying group information of the target data group according to the service characteristic information and a preset corresponding relation between the service characteristic and the data group; and generating the policy number of the write flow data at least according to the first identification code.
The service characteristic information of the upstream service may be geographical location information of a machine room where the upstream service is located, generation time information of the upstream service, insurance type information formulated according to the upstream service, and the like.
Taking the geographical location information of the machine room where the upstream service is located as an example, the preset corresponding relationship between the service features and the data group may be that the machine room where the upstream service is located is consistent with the machine room where the data group is located. For example, if the machine room of the upstream service is a DX machine room, the write traffic data is correspondingly distributed to the data group a group located in the DX machine room; and if the machine room of the upstream service is the YF machine room, correspondingly distributing the write flow data to a data group B group which is located in the YF machine room.
And S13, writing the write flow data into the target data group corresponding to the first identification code.
That is, the data group to which the write traffic data is to be written can be determined based on the policy number corresponding to the write traffic data. For example, please refer to a schematic table (table 1) of the composition of a policy number provided in the embodiments of the present disclosure.
TABLE 1
Wherein the first digit of the policy number is used to indicate the identification code corresponding to the data set. Illustratively, if the corresponding data set is set a, the identification code is 2, 4, 6; the corresponding data set is set B, and the identification codes are 3, 5 and 7.
For example, the time information in the table specifically corresponds to the number of natural days from the flow generation date to 1/1970. The insurance business type specifically refers to whether the insurance policy is a sale type insurance policy or an insurance policy issued in a shopping mall.
The natural sequence of the policy may begin counting at 1000000, where group A begins counting at 10000000 and group B begins counting at 10001000. The specific rule may be grouping interval count, for example, the data range of group A count is 10000000-1000999, 10002000-10002999, 10004000-10004999 …, and the data range of group B count is 10001000-10001999, 10003000-10003999, 10005000-10005999 …. Because the natural sequences of the policy numbers corresponding to different data groups are positioned in the data range which is not crossed with each other, even if the identification codes of other digital bits are lost, the data can be further distributed and traced through the natural sequences, and the management of the flow data is facilitated.
The technical scheme can at least achieve the following technical effects:
and acquiring write flow data generated according to the upstream service, acquiring a policy number corresponding to the write flow data, and further writing the write flow data into a target data group corresponding to an identification code in the policy number. In this way, the assignment of the data group attribution of the write traffic data can be completed based on the policy number, and the write traffic data can be flexibly assigned to the data group based on the requirement. The specific allocation strategy may be a same machine room strategy, a service route strategy, or a set strategy, etc. for the newly added data traffic allocation data set. Subsequent newly added data traffic does not affect the query of previously stored traffic data. The routing storage is performed based on the policy number (or the order number in the specific implementation), so when the order information is queried through the policy number, the order number is analyzed to know in which data group the order is stored. If data cutoff caused by machine room faults occurs, the data group for processing the write flow data corresponding to the data group can be inquired through the policy number. When the insurance business data management system is used for some insurance business data of the sale class lacking user ID information, the corresponding target data group is also determined through the identification code in the insurance policy number, so that the convenience of insurance business data management and maintenance is improved.
As information becomes an increasingly important enterprise asset, the dependence of enterprises on data and information systems is increasing, and the loss of data loss and service interruption caused by disasters is increasing, so that the disaster recovery system plays a vital role in the enterprise in dealing with disasters. Many enterprises are dedicated to directly reducing the risk of machine room downtime (downtime means that a machine stops running or is abnormally shut down and cannot work normally, namely the phenomenon of frequently-called halt) and avoiding the potential influence of service interruption.
In the related art, the current disaster tolerance strategies are as follows:
firstly, a set of systems with the same functions is deployed in different places, and disaster recovery data is kept synchronous with a production machine room in a cold standby mode. When the machine room level fails, the system can be switched to the disaster-tolerant machine room to continue providing service to the outside according to the preset RTO (Recovery Time Objective, which is the Time length of the service interruption tolerable for the enterprise) and RPO (Recovery Point Objective, which is the Time Point corresponding to the recovered data after the service is recovered) indexes.
For the first strategy, the disaster tolerance adopts a cold standby mode, so that the depreciation of disaster tolerance equipment must be accelerated, when a disaster occurs, because no real-time traffic access is available at ordinary times, once a fault occurs, the condition of a cold standby machine room is unknown, and even if the disaster is switched to the cold standby machine room, the disaster takes effect for a long time.
And secondly, a disaster recovery system is deployed in a multi-place and multi-live mode, disaster recovery data are synchronized almost in real time and serve external services at the same time, and when a machine room fails, traffic can be switched away in a short time without influencing user experience. In addition, the cross-machine room data can be assigned based on the message queue, and a complex data consistency checking scheme can be formulated.
For the second strategy, many conventional industries are essentially based on a closed architecture of a small computer and a Storage Area Network (SAN), technically use Continuous Data Protection (CDP) for Data backup and recovery in an enterprise disaster recovery system, and for disaster recovery in a large internet system, the requirements for synchronization and backup of internet big Data are far from being met. Even if the enterprise cost is abandoned, the implementation of the remote multi-activity technology is extremely complex, and a series of problems such as routing consistency, data delay and consistency need to be solved.
However, the time effectiveness of the insurance business for insuring the traffic data is highly required, and in view of various problems in the disaster tolerance strategy in the related art, applying the disaster tolerance strategy in the related art to the maintenance of the insurance business data traffic is also difficult to implement.
In view of this, the present disclosure provides a data processing system to enhance the disaster recovery capability of write traffic data maintenance management.
The data processing system includes a plurality of data sets. Wherein the plurality of data groups may be virtual groupings. The multiple data sets may be deployed in the same machine room, or may be deployed in different machine rooms respectively. The specific deployment rules may be assigned based on the geographic location of the business.
For example, a Daxing (DX) machine room deploys a data group a, and a Yongfeng (YF) machine room deploys a data group B. The great rise (DX) computer room comprises databases A1, A2, A3, A4 and A5, and the Yongfeng (YF) computer room comprises databases B1 and B2. Further traffic data allocated to the a1 pool, the a2 pool, the A3 pool, the a4 pool, or the a5 pool may be virtually grouped into the a group; traffic data assigned to the B1 pool or the B2 pool can be virtually grouped into B groups.
Further, each data set includes a write traffic database and a read traffic database. The write traffic data may specifically be guaranteed traffic data or guaranteed traffic data. The read traffic data may specifically be query traffic data.
In this embodiment, there may be a plurality of write traffic databases and a plurality of read traffic databases for each data group. Taking the DX kiosk described above as an example, both database a1 and database a2 may be configured as write traffic databases. In addition, database A3, database a4, and database a5 of the DX kiosk may be configured as read traffic databases.
In addition, each data set includes a same set of read traffic databases corresponding to each of the write traffic databases. Also for example with a DX farm, both database A1 and database A2 may be configured as write traffic databases. Database A3 may be configured to correspond to the same set of read traffic databases as the a1 library and database a4 may be configured to correspond to the same set of read traffic databases as the a2 library.
In addition, each data set includes a heterogeneous read traffic database. The heterogeneous read traffic database can synchronize write traffic data of other corresponding data groups as slave databases of the master database in the other data groups. Also for example, in a DX farm, the database A5 may be configured as a heterogeneous read traffic database corresponding to the data burst B group, i.e., the write traffic data in the database A5 may be synchronously written to the data burst B group.
Specifically, the database may be a MySQL database. The write traffic data synchronized to the read traffic database may be synchronized by the binlog log. The binlog log function of the MySQL database is used for recording records of contents such as MySQL internal addition, deletion, modification and the like which have update on the MySQL database (for example, modification of write flow data on the database), and read flow data (for example, query select flow data) on the database cannot be recorded by the binlog log; the method is mainly used for master-slave copy and increment recovery of the database.
In the above example, the group a is located in a DX room and the group B is located in an YF room. The process of synchronizing write traffic data to the same set of read traffic databases may be synchronized to the binlog of the computer room. The process of synchronizing write traffic data to the heterogeneous read traffic database may be binlog synchronization across the computer room.
Fig. 2 is a flow chart of another data processing method provided according to an embodiment of the disclosure. The method is applied to the data processing system. The method comprises the following steps:
and S21, acquiring write flow data of the insurance service generated according to the upstream service.
Wherein the write traffic data may be guaranteed traffic data or guaranteed traffic data.
For example, the upstream service is to create a takeaway order, and the corresponding takeaway order generates insurance flow data corresponding to the order rider; and the upstream business is the delivery completion of the take-out order, and the corresponding take-out order generates the refund flow data corresponding to the order rider.
For another example, the upstream service is a train ticket or an air ticket order, and the generated insurance flow data for the personal insurance of the passenger or the insurance flow data for the travel delay risk may be generated. And if the order itinerary accords with the insurance claim settlement conditions, further generating claim settlement flow data.
And S22, acquiring a policy number corresponding to the write flow data, wherein the policy number comprises a first identification code for identifying group information of a data group and a second identification code for identifying group information of a write flow database.
In one example, please refer to a schematic table (table 2) of the composition of a policy number provided in the embodiments of the present disclosure.
TABLE 2
Wherein the number of the 1 st digit of the policy number is used to represent the second identification code corresponding to the database. Illustratively, if the corresponding database is a 2-bank, the identification code is 2; the corresponding database is 3 libraries, and the identification code is 3.
The number at position 6/7 of the policy number is used to indicate the first identification code corresponding to the database. Illustratively, if the corresponding data set is set a, the identification code is 01; the corresponding data set is set B, and the identification code is 03.
The time information in the table specifically corresponds to the natural days from the flow generation date in 1970. The insurance business type specifically refers to whether the insurance policy is a sale type insurance policy or an insurance policy issued in a shopping mall.
The natural sequence of the policy may begin counting at 1000000, where group A begins counting at 10000000 and group B begins counting at 10001000. The specific rule may be grouping interval count, for example, the data range of group A count is 10000000-1000999, 10002000-10002999, 10004000-10004999 …, and the data range of group B count is 10001000-10001999, 10003000-10003999, 10005000-10005999 …. Because the natural sequences of the policy numbers corresponding to different data groups are positioned in the data range which is not crossed with each other, even if the identification codes of other digital bits are lost, the data can be further distributed and traced through the natural sequences, and the management of the flow data is facilitated.
In another example, the first digit in the policy number is not the second identification code, and the 5 th and 6 th digits are the first identification code. In the first identification code, "01" corresponds to the group a of data group, and "02" corresponds to the group B of data group. In the second identification code, "1" corresponds to database 1, and "2" corresponds to database 2. As shown in FIG. 3, the policy number "1 xxx01 xxxxxxxxxxxx" corresponds to the database A1 in data set A group and "2 xxx01 xxxxxxxxxxxx" corresponds to the database A2 in data set A group. Similarly, the policy number "1 xxx02 xxxxxxxxxxxxxx" corresponds to the database B1 in data set B, and "2 xxx02 xxxxxxxxxxxx" corresponds to the database B2 in data set B.
It is worth mentioning that the databases may have a distinction of service types in one data set, such as data access service, policy service, direct connection service, etc. Correspondingly, the data group A comprises an access A1 library, a policy A1 library, a policy A2 library and a direct connection A1 library, and the data group B comprises an access B1 library, a policy B1 library, a policy B2 library and a direct connection B1 library. Further, the write flow data may be allocated to a corresponding service library according to a service specifically corresponding to the write flow data.
In particular implementations, the system code may rely on different service libraries. Table 3 shows the correspondence relationship between system code dependent service libraries.
TABLE 3
Optionally, the obtaining the policy number corresponding to the write traffic data includes: acquiring service characteristic information of the upstream service; determining a target data group used for writing the write flow data and a first identification code used for identifying group information of the target data group according to the service characteristic information and a preset corresponding relation between the service characteristic and the data group; and generating the policy number of the write flow data at least according to the first identification code.
The service characteristic information of the upstream service may be geographical location information of a machine room where the upstream service is located, generation time information of the upstream service, insurance type information formulated according to the upstream service, and the like.
Taking the geographical location information of the machine room where the upstream service is located as an example, the preset corresponding relationship between the service features and the data group may be that the machine room where the upstream service is located is consistent with the machine room where the data group is located. For example, if the machine room of the upstream service is a DX machine room, the write traffic data is correspondingly distributed to the data group a group located in the DX machine room; and if the machine room of the upstream service is the YF machine room, correspondingly distributing the write flow data to a data group B group which is located in the YF machine room.
By taking fig. 4 as an example, the "unified access DX" and the "unified access YF" are respectively a data group and a database of the data group deployed for traffic data received by a DX machine room YF machine room. After the unified access service completes the deployment (deployment configuration group a, deployment configuration group B), a number-taking request (number-taking group a, number-taking group B) may be sent to the serial number service. "service of serial number DX" and "service of serial number YF" return the corresponding policy numbers ("1 xxx01 xxxxxxxxxxxxxx", "2 xxx01 xxxxxxxxxxxxxx", "1 xxx02 xxxxxxxxxxxxxxxx", "2 xxx02 xxxxxxxxxxxxxx"), respectively, according to the different requests for taking the numbers.
And S23, writing the write flow data into the target write flow database corresponding to the second identification code in the target data group corresponding to the first identification code.
And S24, synchronizing the write traffic data to the same group of read traffic databases corresponding to the target write traffic database in the target data group.
S25, synchronizing the write flow data to the heterogeneous read flow database in any data group except the target data group in the data processing system.
It is noted that the present embodiment provides the method steps as shown in the flow chart, but may include more or less steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In practical implementation, the synchronization steps S24 and S25 may be performed before the writing step S23.
To more intuitively show the above technical solution, please refer to a distribution diagram of data traffic and database of digital insurance service shown in fig. 5.
In a specific implementation, a plurality of the data sets are located in at least two different rooms.
Wherein, the dotted line part represents the same group of read flow database and the same group of data synchronization. The dotted line part represents the heterogeneous read traffic database and heterogeneous data synchronization.
Whether the upstream service of the guaranteed/guaranteed traffic data is in a DX (full Duplex switched Ethernet) machine room or a YF (YF machine room), the guaranteed/guaranteed traffic data can be distributed to an A data group of the DX machine room. Specifically, in the unified access service library, the guaranteed/committed traffic data is written into the "access a1 library" of write traffic. Further, synchronizing to the "access a1 library" of the same set of read traffic corresponding to the "access a1 library" of the write traffic; to the "Access A1 pool" of the heterogeneous read traffic corresponding to the "Access A1 pool" of the write traffic.
Specifically, in the policy service library, if the guaranteed/guaranteed traffic data is distributed to the a1 library of the a data group of the DX room, the guaranteed/guaranteed traffic data is written into the "policy a1 library" of the write traffic. Further, synchronizing to the "policy A1 library" of the same set of read traffic corresponding to the "policy A1 library" of the write traffic; synchronized to the "policy A1 bank" of the heterogeneous set of read traffic corresponding to the "policy A1 bank" of the write traffic.
If the guaranteed/guaranteed traffic data is distributed to the A2 bank of the A data set of the DX farm, then the guaranteed/guaranteed traffic data is written to the "policy A2 bank" of write traffic. Further, synchronizing to the "policy A2 library" of the same set of read traffic corresponding to the "policy A2 library" of the write traffic; synchronized to the "policy A2 bank" of the heterogeneous set of read traffic corresponding to the "policy A2 bank" of the write traffic.
In addition, for Eagle (distributed real-time monitoring and early warning framework) cluster service of each computer room, data of two computer rooms can be synchronized by means of databus (database log mining) or MQ (message queue).
In addition, the service libraries respectively corresponding to the access service, the policy service and the direct connection service can be subjected to partition management through a Mafka architecture. The writing and synchronization rules for different service libraries are similar, and are not illustrated herein for economy.
Specifically, in response to the query operation, the following method steps may also be performed:
responding to the query operation of the first machine room, and acquiring a policy number of query data flow; judging whether a first data group corresponding to the first identification code of the policy number of the query data traffic belongs to a local data group of the first machine room; if the first data group belongs to a local data group of the first machine room, acquiring data information corresponding to the query data traffic in the same group of read traffic databases of the first data group; and if the first data group belongs to other machine rooms, acquiring data information corresponding to the query data traffic in the heterogeneous read traffic database of the first data group of the first machine room.
That is, each computer room can query the read traffic database (including the same group of read traffic databases and the different group of read traffic databases) for the full amount of data. For example, when the DX server room receives the traffic data allocated to the DX server room, the same group of read traffic databases can be accessed to read the data. When the DX computer room receives the flow data distributed to the YF computer room by inquiry, the different groups of flow database can be accessed to read the data.
According to the technical scheme of the embodiment, the service flow data are read and written and distinguished, and the databases in the data groups deployed in each machine room are read and written and distinguished, so that effective utilization of hardware resources of the machine rooms is guaranteed. And then, the data are synchronized to the read flow database of the same machine room and the read flow database of a different machine room, so that cross-machine-room flow query is guaranteed, and meanwhile, the operation of cross-machine-room writing is reduced, and therefore the timeliness and the consistency of database management flow data are effectively improved.
Fig. 6 is a flow chart of another data processing method provided according to an embodiment of the disclosure. The method is applied to the data processing system. The method comprises the following steps:
and S61, acquiring the write flow data generated according to the upstream service.
Wherein the write traffic data may be guaranteed traffic data or guaranteed traffic data. In addition, the data can also be the data of claim flow.
For example, the upstream service is to create a takeaway order, and the corresponding takeaway order generates insurance flow data corresponding to the order rider; and the upstream business is the delivery completion of the take-out order, and the corresponding take-out order generates the refund flow data corresponding to the order rider.
For another example, the upstream service is a train ticket or an air ticket order, and the generated insurance flow data for the personal insurance of the passenger or the insurance flow data for the travel delay risk may be generated. And if the order itinerary accords with the insurance claim settlement conditions, further generating claim settlement flow data.
And S62, performing idempotent verification on the write traffic data by inquiring a read traffic database of the plurality of data groups.
And S63, if the write traffic data passes the idempotent verification, acquiring a policy number corresponding to the write traffic data, wherein the policy number comprises a first identification code for identifying group information of a data group and a second identification code for identifying group information of a write traffic database.
S64, detecting whether the data link between the machine room executing the upstream service and the target data group is smooth.
And S65, if the data link is abnormal, recording the write flow data in an abnormal record form of the local data group of the computer room.
That is, before writing the write traffic data into the target data group corresponding to the first identification code, the method further includes: if the target data group does not belong to a second machine room for executing the upstream service, detecting whether a data link of the second machine room and a third machine room where the target data group is located is smooth or not; and if the data link is abnormal, recording the write flow data in an abnormal record form of a local data group of the second computer room.
It should be noted that if the target data group belongs to a room executing an upstream service, the operation of writing the write data traffic is executed in the local room, and this process does not involve writing across rooms. When the data link between the machine rooms is abnormal, the plurality of machine rooms are set, the write flow of each machine room forms a closed loop, the closed-loop read-write flow of the core link can be realized, and the read-write can normally run.
S66, in response to the event representing that the data link is recovered to be smooth, writing the write flow data recorded in the abnormal record form into a target write flow database corresponding to the second identification code in a target data group corresponding to the first identification code.
The event representing the data link is recovered to be unblocked can be that a corresponding signal identifier is received, or that a fault machine room is detected to be restarted and enters a normal operation state.
Optionally, if the write traffic data is guaranteed traffic data, the method further includes: searching a repeated policy with the same policy number in a read flow database of the plurality of data groups; and if the repeated policy exists in the read flow database of the plurality of data groups, sending a request message for requesting to initiate a de-insurance process corresponding to the policy number to an upstream service.
That is, if the application data is repeatedly generated, an un-guarantee request message is sent through the upstream traffic to invalidate the repeated policy.
To more intuitively show the above technical solution, please refer to a distribution diagram of data traffic and database of digital insurance service shown in fig. 7.
Wherein, the dotted line part represents the same group of read flow database and the same group of data synchronization.
For example, upstream traffic for guaranteed/guaranteed traffic data is in the DX closet and guaranteed/guaranteed traffic data is distributed to the a dataset of the DX closet. Specifically, in the unified access service library, the guaranteed/committed traffic data is written into the "access a1 library" of write traffic. Further, synchronizing to the "Access A1 Bank" of the same set of read traffic corresponding to the "Access A1 Bank" of the write traffic.
Specifically, in the policy service library, if the guaranteed/guaranteed traffic data is distributed to the a1 library of the a data group of the DX room, the guaranteed/guaranteed traffic data is written into the "policy a1 library" of the write traffic. Further, to the "policy A1 library" of the same set of read traffic corresponding to the "policy A1 library" of the write traffic.
If the guaranteed/guaranteed traffic data is distributed to the A2 bank of the A data set of the DX farm, then the guaranteed/guaranteed traffic data is written to the "policy A2 bank" of write traffic. Further, to the "policy A2 library" of the same set of read traffic corresponding to the "policy A2 library" of the write traffic.
And when the DX computer room receives the flow data distributed to the DX computer room by inquiry, the same group of read flow database can be accessed to read the data.
Due to the data link between the rooms being abnormal. And if the upstream service of the input/output guarantee flow data is in the YF machine room, recording the input/output guarantee flow data in an abnormal record form of the YF machine room.
And after the data link between the DX room and the YF room is recovered, writing the guarantee-throwing/guarantee-returning flow data recorded in the abnormal record form of the YF room into a target database of a target data group deployed in the DX room.
In addition, for Eagle (distributed real-time monitoring and early warning framework) cluster service of each machine room, under the condition that a data link between the two machine rooms is normal, data of the two machine rooms can be synchronized by means of databus (database log mining) or MQ (message queue). And under the condition that a data link between two machine rooms is normal, the flow data can be recorded in an abnormal record form corresponding to Eagle cluster service of the machine room.
In addition, the service libraries respectively corresponding to the access service, the policy service and the direct connection service can be subjected to partition management through a Mafka architecture. The writing and synchronization rules for different service libraries are similar, and are not illustrated herein for economy.
According to the technical scheme of the embodiment, the service flow data are read and written and distinguished, and the databases in the data groups deployed in each machine room are read and written and distinguished, so that effective utilization of hardware resources of the machine rooms is guaranteed. When a data link between the machine rooms is abnormal, the read flow of the cross machine room is interrupted, the write flow of each machine room forms a closed loop, the closed-loop read-write flow of a core link can be realized, and the multi-machine room is ensured to be alive.
FIG. 8 is a block diagram illustrating a data processing system according to an exemplary embodiment of the present disclosure. The data processing system 800 includes a plurality of data sets (data set a, data set B, data set C …);
the system further comprises:
an access module 810, configured to obtain write flow data of an insurance service generated according to an upstream service;
a numbering module 820, configured to obtain a policy number corresponding to the write traffic data, where the policy number includes a first identifier for identifying group information of a data group;
a writing module 830, configured to write the write traffic data into the target data group corresponding to the first identification code.
The technical scheme can at least achieve the following technical effects:
and acquiring write flow data generated according to the upstream service, acquiring a policy number corresponding to the write flow data, and further writing the write flow data into a target data group corresponding to an identification code in the policy number. In this way, the assignment of the data group attribution of the write traffic data can be completed based on the policy number, and the write traffic data can be flexibly assigned to the data group based on the requirement. The specific allocation strategy may be a same machine room strategy, a service route strategy, or a set strategy, etc. for the newly added data traffic allocation data set. Subsequent newly added data traffic does not affect the query of previously stored traffic data. The routing storage is performed based on the policy number (or the order number in the specific implementation), so when the order information is queried through the policy number, the order number is analyzed to know in which data group the order is stored. If data cutoff caused by machine room faults occurs, the data group for processing the write flow data corresponding to the data group can be inquired through the policy number. When the insurance business data management system is used for some insurance business data of the sale class lacking user ID information, the corresponding target data group is also determined through the identification code in the insurance policy number, so that the convenience of insurance business data management and maintenance is improved.
Optionally, each of the data sets comprises: a write traffic database and a read traffic database;
the read traffic database includes: the read flow database of the same group corresponding to the write flow database and the read flow database of the different group corresponding to the other data groups;
the write module is configured to:
writing the write flow data into a write flow database in a target data group;
synchronizing the write traffic data to the same set of read traffic databases;
and synchronizing the write flow data to the heterogeneous read flow database of any data group except the target data group.
Optionally, each of the data sets comprises: the write flow databases are the same as the read flow databases in the same group corresponding to each write flow database;
the policy number further comprises a second identification code for identifying group information of the database;
the write module is configured to:
and writing the write flow data into a target write flow database corresponding to the second identification code in the target data group.
Optionally, a plurality of the data sets are located in at least two different machine rooms;
the system further comprises a query module for:
responding to the query operation of the first machine room, and acquiring a policy number of query data flow;
judging whether a first data group corresponding to the first identification code of the policy number of the query data traffic belongs to a local data group of the first machine room;
if the first data group belongs to a local data group of the first machine room, acquiring data information corresponding to the query data traffic in the same group of read traffic databases of the first data group;
and if the first data group belongs to other machine rooms, acquiring data information corresponding to the query data traffic in the heterogeneous read traffic database of the first data group of the first machine room.
Optionally, a plurality of the data sets are located in at least two different machine rooms;
the system further comprises a fault handling module for:
if the target data group does not belong to a second machine room executing the upstream service, before writing the write flow data into the target data group corresponding to the first identification code, detecting whether a data link of the second machine room and a third machine room where the target data group is located is smooth;
if the data link is abnormal, recording the write flow data in an abnormal record form of a local data group of the second machine room;
the write module is configured to:
writing the write flow data recorded in the abnormal record form into the target data set in response to an event characterizing that the data link is clear to recover.
Optionally, the system further comprises a verification module for:
before acquiring the policy number corresponding to the write flow data, performing idempotent verification on the write flow data by inquiring a plurality of read flow databases of the data group;
and the numbering module is used for acquiring a policy number corresponding to the write flow data if the write flow data passes the idempotent check.
Optionally, the verification module is configured to:
searching for a repeated policy with a policy number consistent with the policy number of the insurable traffic data in the read traffic database of the plurality of data sets;
and if the repeated insurance policy exists, sending a request message for requesting to initiate a refund process corresponding to the insurance flow data to an upstream service.
Optionally, the write traffic data is guaranteed traffic data or guaranteed traffic data.
Optionally, the numbering module is configured to:
acquiring service characteristic information of the upstream service;
determining a target data group used for writing the write flow data and a first identification code used for identifying group information of the target data group according to the service characteristic information and a preset corresponding relation between the service characteristic and the data group;
and generating the policy number of the write flow data at least according to the first identification code.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The disclosed embodiments provide a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps of any of the data processing methods.
An embodiment of the present disclosure provides a data processing system, including: a memory having a computer program stored thereon; a processor for executing the computer program in the memory, in any of the steps of the data processing method.
Fig. 9 is a block diagram illustrating an electronic device 900 in accordance with an example embodiment. For example, the electronic device 900 may be provided as a server. Referring to fig. 9, the electronic device 900 includes a processor 922, which may be one or more in number, and a memory 932 for storing computer programs executable by the processor 922. The computer programs stored in memory 932 may include one or more modules that each correspond to a set of instructions. Further, the processor 922 may be configured to execute the computer program to perform the data processing method described above.
Additionally, the electronic device 900 may also include a power component 926 and a communication component 950, the power component 926 may be configured to perform power management of the electronic device 900, and the communication component 950 may be configured to enable communication, e.g., wired or wireless communication, of the electronic device 900. The electronic device 900 may also include input/output (I/O) interfaces 958. The electronic device 900 may operate based on an operating system stored in the memory 932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, and the like.
In another exemplary embodiment, there is also provided a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the data processing method described above. For example, the computer readable storage medium may be the memory 932 described above including program instructions that are executable by the processor 922 of the electronic device 900 to perform the data processing method described above.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (10)
1. A data processing method for use in a data processing system, said data processing system comprising a plurality of data sets;
the method comprises the following steps:
acquiring write flow data of insurance service generated according to upstream service;
acquiring a policy number corresponding to the write flow data, wherein the policy number comprises a first identification code for identifying group information of a data group;
writing the write flow data into a target data group corresponding to the first identification code;
each of the data sets includes: a write traffic database and a read traffic database;
the read traffic database includes: the read flow database of the same group corresponding to the write flow database and the read flow database of the different group corresponding to the other data groups;
the writing the write traffic data into the target data group corresponding to the first identification code includes: writing the write flow data into a write flow database in a target data group;
the method further comprises the following steps:
synchronizing the write traffic data to the same set of read traffic databases;
synchronizing the write traffic data to a heterogeneous read traffic database of any data group except the target data group;
a plurality of the data sets are positioned in at least two different machine rooms;
before writing the write traffic data into the target data group corresponding to the first identification code, the method further includes:
if the target data group does not belong to a second machine room for executing the upstream service, detecting whether a data link of the second machine room and a third machine room where the target data group is located is smooth or not;
if the data link is abnormal, recording the write flow data in an abnormal record form of a local data group of the second machine room;
the writing the write traffic data into the target data group corresponding to the first identification code includes:
writing the write flow data recorded in the abnormal record form into the target data set in response to an event characterizing that the data link is clear to recover.
2. The method of claim 1, wherein each of the data sets comprises: the write flow databases are the same as the read flow databases in the same group corresponding to each write flow database;
the policy number further comprises a second identification code for identifying group information of the database;
the writing the write traffic data into the target data group corresponding to the first identification code includes:
and writing the write flow data into a target write flow database corresponding to the second identification code in the target data group.
3. The method of claim 1, wherein a plurality of said data sets are located in at least two different rooms;
the method further comprises the following steps:
responding to the query operation of the first machine room, and acquiring a policy number of query data flow;
judging whether a first data group corresponding to the first identification code of the policy number of the query data traffic belongs to a local data group of the first machine room;
if the first data group belongs to a local data group of the first machine room, acquiring data information corresponding to the query data traffic in the same group of read traffic databases of the first data group;
and if the first data group belongs to other machine rooms, acquiring data information corresponding to the query data traffic in the heterogeneous read traffic database of the first data group of the first machine room.
4. The method of any of claims 1-3, wherein prior to obtaining the policy number corresponding to the write traffic data, the method further comprises:
performing idempotent verification on the write flow data by inquiring a read flow database of a plurality of data groups;
the obtaining of the policy number corresponding to the write traffic data includes:
and if the write flow data passes the idempotent verification, acquiring a policy number corresponding to the write flow data.
5. The method of any of claims 1-3, wherein if the write traffic data is guaranteed traffic data, the method further comprises:
searching for a repeated policy with a policy number consistent with the policy number of the insurable traffic data in the read traffic database of the plurality of data sets;
and if the repeated insurance policy exists, sending a request message for requesting to initiate a refund process corresponding to the insurance flow data to an upstream service.
6. The method of any of claims 1-3, wherein the write traffic data is either guaranteed traffic data or guaranteed traffic data.
7. The method according to any of claims 1-3, wherein said obtaining a policy number corresponding to said write traffic data comprises:
acquiring service characteristic information of the upstream service;
determining a target data group used for writing the write flow data and a first identification code used for identifying group information of the target data group according to the service characteristic information and a preset corresponding relation between the service characteristic and the data group;
and generating the policy number of the write flow data at least according to the first identification code.
8. A data processing system, characterized in that said data processing system comprises a plurality of data sets;
the system further comprises:
the access module is used for acquiring write flow data of the insurance service generated according to the upstream service;
the numbering module is used for acquiring a policy number corresponding to the write flow data, wherein the policy number comprises a first identification code of group information for identifying a data group;
the writing module is used for writing the writing flow data into a target data group corresponding to the first identification code;
each of the data sets includes: a write traffic database and a read traffic database;
the read traffic database includes: the read flow database of the same group corresponding to the write flow database and the read flow database of the different group corresponding to the other data groups;
the write module is configured to:
writing the write flow data into a write flow database in a target data group;
synchronizing the write traffic data to the same set of read traffic databases;
synchronizing the write traffic data to a heterogeneous read traffic database of any data group except the target data group;
a plurality of the data sets are positioned in at least two different machine rooms;
the system further comprises a fault handling module for:
if the target data group does not belong to a second machine room executing the upstream service, before writing the write flow data into the target data group corresponding to the first identification code, detecting whether a data link of the second machine room and a third machine room where the target data group is located is smooth;
if the data link is abnormal, recording the write flow data in an abnormal record form of a local data group of the second machine room;
the write module is configured to:
writing the write flow data recorded in the abnormal record form into the target data set in response to an event characterizing that the data link is clear to recover.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910123537.XA CN109976944B (en) | 2019-02-18 | 2019-02-18 | Data processing method and system, storage medium and electronic device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910123537.XA CN109976944B (en) | 2019-02-18 | 2019-02-18 | Data processing method and system, storage medium and electronic device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109976944A CN109976944A (en) | 2019-07-05 |
| CN109976944B true CN109976944B (en) | 2021-05-07 |
Family
ID=67077115
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910123537.XA Active CN109976944B (en) | 2019-02-18 | 2019-02-18 | Data processing method and system, storage medium and electronic device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109976944B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112348687B (en) * | 2020-11-24 | 2023-11-07 | 泰康保险集团股份有限公司 | Method and device for processing policy data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020194033A1 (en) * | 2001-06-18 | 2002-12-19 | Huff David S. | Automatic insurance data extraction and quote generating system and methods therefor |
| CN101515376A (en) * | 2009-03-06 | 2009-08-26 | 李连海 | Short term personal accident insurance real name electronic statement printing system and method |
| CN105760552A (en) * | 2016-03-25 | 2016-07-13 | 北京奇虎科技有限公司 | Data management method and device |
| CN106372994A (en) * | 2016-08-31 | 2017-02-01 | 北京炎黄新星网络科技有限公司 | Method and system for processing orders |
| CN108985946A (en) * | 2018-06-14 | 2018-12-11 | 中国平安人寿保险股份有限公司 | A kind of processing method and server of declaration form |
-
2019
- 2019-02-18 CN CN201910123537.XA patent/CN109976944B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020194033A1 (en) * | 2001-06-18 | 2002-12-19 | Huff David S. | Automatic insurance data extraction and quote generating system and methods therefor |
| CN101515376A (en) * | 2009-03-06 | 2009-08-26 | 李连海 | Short term personal accident insurance real name electronic statement printing system and method |
| CN105760552A (en) * | 2016-03-25 | 2016-07-13 | 北京奇虎科技有限公司 | Data management method and device |
| CN106372994A (en) * | 2016-08-31 | 2017-02-01 | 北京炎黄新星网络科技有限公司 | Method and system for processing orders |
| CN108985946A (en) * | 2018-06-14 | 2018-12-11 | 中国平安人寿保险股份有限公司 | A kind of processing method and server of declaration form |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109976944A (en) | 2019-07-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2017140131A1 (en) | Data writing and reading method and apparatus, and cloud storage system | |
| US9015164B2 (en) | High availability for cloud servers | |
| US9965364B2 (en) | Fault tolerant listener registration in the presence of node crashes in a data grid | |
| US7490265B2 (en) | Recovery segment identification in a computing infrastructure | |
| US7844856B1 (en) | Methods and apparatus for bottleneck processing in a continuous data protection system having journaling | |
| US7647449B1 (en) | Method, system, and computer readable medium for maintaining the order of write-commands issued to a data storage | |
| US10726042B2 (en) | Replication control using eventually consistent meta-data | |
| JPS6170654A (en) | Resource control system of decentralized processing system | |
| US10423625B2 (en) | Exactly-once semantics for streaming analytics in non-idempotent output operations | |
| US8850074B2 (en) | Data synchronization method | |
| CN109976944B (en) | Data processing method and system, storage medium and electronic device | |
| WO2012101531A1 (en) | Data integrity protection in storage volumes | |
| CN111404737B (en) | Disaster recovery processing method and related device | |
| CN110659303A (en) | Read-write control method and device for database nodes | |
| CN112860746B (en) | Cache reduction-based method, equipment and system | |
| CN112596867A (en) | Suspension transaction processing method and distributed database system | |
| US9197519B2 (en) | Tracking objects within dynamic environments | |
| US10331627B2 (en) | Method and system for unified technological stack management for relational databases | |
| CN111459737A (en) | Problem positioning method and device, computer equipment and storage medium | |
| TW201403362A (en) | Managing large data sets through page based information tracking in multi-master environments | |
| CN113448775B (en) | Multi-source heterogeneous data backup method and device | |
| CN116107803A (en) | Database backup method, system, electronic equipment and storage medium | |
| CN117891680A (en) | Service information management method, system, equipment and storage medium | |
| CN115237890A (en) | Data migration method, device, medium and equipment for database splitting | |
| CN115048399A (en) | Database information processing method and device, storage medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |