WO2021093461A1

WO2021093461A1 - Method and apparatus for aggregation calculation in blockchain-type ledger, and device

Info

Publication number: WO2021093461A1
Application number: PCT/CN2020/116810
Authority: WO
Inventors: 杨新颖; 殷山; 张渊; 孙勇
Original assignee: 蚂蚁区块链科技(上海)有限公司
Priority date: 2019-11-11
Filing date: 2020-09-22
Publication date: 2021-05-20
Also published as: CN113434550A; CN111046069B; CN111046069A

Abstract

A method and apparatus for aggregation calculation in a blockchain-type ledger, and a device. The method comprises: when aggregation calculation is performed in a blockchain-type ledger, performing iteration processing on a calculation target; each time iteration calculation is performed, performing integrity verification on a currently used data record; and after verification is passed, performing aggregation calculation, and if verification fails, terminating aggregation calculation at any time.

Description

Method, device and equipment for aggregation calculation in block chain ledger

Technical field

The embodiments of this specification relate to the field of information technology, and in particular, to an aggregate calculation method, device, and equipment in a block chain ledger.

Background technique

In the field of database, aggregation calculation is a kind of more common calculation methods, including summation, averaging, counting and so on. In this process, the data is usually directly aggregated based on the obtained data, and the authenticity of the data and the accuracy of the aggregated result cannot be guaranteed.

Based on this, the embodiments of this specification provide a more accurate aggregation calculation method.

Summary of the invention

The purpose of the embodiments of the present application is to provide a more accurate aggregation calculation method in a blockchain ledger.

In order to solve the above technical problems, the embodiments of the present application are implemented by the following methods.

An aggregation calculation method in a block chain ledger includes: determining a collection including multiple data records used in the aggregation calculation, setting the initial value of the aggregation result to 0, and performing the following method on the collection and the aggregation result Iterative processing until the preset iterative requirement is reached: select a data record from the set, determine the data block where the data record is located; verify the integrity of the data record in the data block, and if the verification passes, the The data record is removed from the set, and the data record and the current aggregation result are aggregated to obtain the updated set and aggregation result; if the verification fails, the aggregation calculation is terminated, and the position of the current data record that fails the verification in the ledger is returned. Information; at the end of the iteration, the current aggregation result is output as the target result of the aggregation calculation.

Correspondingly, the embodiment of the present specification also provides an aggregation calculation device in a block chain ledger, including: a set determination module, which determines a set including multiple data records used in the aggregation calculation, and sets the initial value of the aggregation result to 0 ; Iterative calculation module, select a data record from the set, determine the data block in which the data record is located; verify the integrity of the data record in the data block, and if the verification passes, remove the data record from the set Cancellation, aggregate the data record and the current aggregation result to obtain the updated collection and aggregation result; iteration termination module, terminate the aggregation calculation, and return the location information of the current data record that has failed verification in the ledger; output module, iteration At the end, output the current aggregation result as the target result of the aggregation calculation.

Through the solution provided in the embodiment of this specification, when performing aggregation calculation in the blockchain ledger, the calculation target is iteratively processed. In each iteration calculation, the integrity verification of the currently used data record is performed, and the verification is passed. Perform aggregation calculation, and terminate the aggregation calculation at any time if the verification fails, thereby ensuring that the data record is not tampered with or lost, and the result of the aggregation calculation is more accurate.

It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the embodiments of this specification.

In addition, any one of the embodiments of the present specification does not need to achieve all the above-mentioned effects.

Description of the drawings

In order to more clearly describe the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some of the embodiments described in the embodiments of this specification. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

Figure 1 is a schematic diagram of the system architecture involved in an embodiment of the specification;

Fig. 2 is a schematic diagram of a process for generating a block chain ledger provided by an embodiment of the specification;

FIG. 3 is a schematic diagram of a block header of a data block provided by an embodiment of this specification;

FIG. 4 is a schematic flowchart of a method for creating an index of data records according to an embodiment of the specification;

FIG. 5 is a schematic flowchart of the aggregation calculation method in the block chain ledger provided by the embodiment of the specification; FIG.

6 is a schematic structural diagram of an aggregation computing device in a blockchain ledger provided by an embodiment of this specification;

Fig. 7 is a schematic structural diagram of a device for configuring the method of the embodiment of this specification.

Detailed ways

In order to enable those skilled in the art to better understand the technical solutions in the embodiments of this specification, the technical solutions in the embodiments of this specification will be described in detail below in conjunction with the drawings in the embodiments of this specification. Obviously, the described implementation The examples are only a part of the embodiments in this specification, not all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art should fall within the scope of protection.

First, the block chain ledger involved in the embodiment of this specification will be described.

Blockchain ledger is a centralized data storage structure applied in the database. Its service objects are often various institutions, and each institution can communicate between them and third-party users (including other institutions or individuals). The generated data records are stored in the database server. As shown in FIG. 1, FIG. 1 is a schematic diagram of a system architecture involved in an embodiment of the specification. In this schematic diagram, an enterprise organization can face multiple users, and each user can query the database service provider through its corresponding enterprise organization.

For example, the organization connected to the database server is a financial product company, and the data records can be individual users’ financial management records in the financial product company; or, the connected organization can be a government department, where the data records are what the government department has for the department. Expense details of the managed public project; or, the database server is connected to a hospital, and the data record is the patient’s medical record; or the database server is connected to a third-party payment agency, and the data record can be an individual user Payment records through the agency, and so on.

On the centralized database server, the block chain ledger is generated in the following manner, as shown in FIG. 2, which is a schematic diagram of the process of generating a block chain ledger provided by the embodiment of this specification, including the following steps.

S201: Receive a data record to be stored, and determine a hash value of each data record, where the data record contains business attributes.

The data records to be stored here can be various consumption records of individual users of the client, or can be business results, intermediate states, and operation records generated when the application server executes business logic based on user instructions. Specific business scenarios can include consumption records, audit logs, supply chains, government supervision records, medical records, and so on.

In each organization that interfaces with the database server, the business attribute is generally unique in the interface organization. The business attributes are based on different business scenarios and can include user names, user ID numbers, and driver’s licenses. Number, mobile phone number, project unique number, type of data record (such as financial package number), etc.

For example, for a third-party payment institution, the data record is the user's consumption record, and the business attribute at this time is the user ID (including mobile phone number, ID number, user name, etc.), or the user ID is hashed The hash value obtained by the algorithm; or, for government agencies, the data record is the overhead flow of multiple public projects, then the business attribute at this time can be a unique number for each project.

Business attributes can be stored in a specified location in the data record, such as the head or tail of the data record.

S203: When a preset block forming condition is reached, determine each data record to be written in the data block, and generate an Nth data block including the hash value of the data block and the data record.

The preset blocking conditions include: the number of data records to be stored reaches the number threshold, for example, every time one thousand data records are received, a new data block is generated and one thousand data records are written into the block; or , The time interval from the last block formation time reaches the time threshold, for example, every 5 minutes, a new data block is generated, and the data records received within these 5 minutes are written into the block.

Here, N refers to the serial number of the data block. In other words, in the embodiment of this specification, the data block is in the form of a block chain, which is arranged sequentially based on the order of the block time, and has strong timing characteristics. Among them, the block height of the data block increases monotonically based on the sequence of the block time. The block height can be a sequence number. At this time, the block height of the Nth data block is N; the block height can also be generated in other ways, for example, the block time symmetric encryption is converted into large integer data (for example, 12-bit or 15-bit Integer) as the block height.

When N=1, that is, the data block at this time is the initial data block. The hash value and block height of the initial data block are given based on a preset method. For example, if the initial data block does not contain data records, the hash value is any given hash value, and the block height blknum=0; for another example, the generation trigger condition of the initial data block is consistent with the trigger conditions of other data blocks. However, the hash value of the initial data block is determined by hashing all the contents in the initial data block.

When N>1, since the content and hash value of the previous data block have been determined, at this time, the current data block (the first data block) can be generated based on the hash value of the previous data block (that is, the N-1th data block). For example, a feasible way is to determine the hash value of each data record to be written in the Nth block, and generate a Merck according to the order in the block. In the Er tree, the root hash value of the Merkel tree and the hash value of the previous data block are spliced together, and the hash algorithm is used again to generate the hash value of the current block. For another example, it is also possible to splice according to the order of the data records in the block and obtain the hash value of the entire data record by hashing, splicing the hash value of the previous data block and the hash value of the entire data record, and combining the result The string is hashed to generate the hash value of the data block.

The data block generated in the above manner may include two parts: a block header and a block body. The block body can be used to store the plaintext of the spliced data, or the hash value of the spliced data, etc.; the block header can be used to store metadata about the data block, for example, the version number of the ledger, the hash of the previous data block Value, the root hash value of the Merkel tree composed of the spliced data in the own data block, the hash value of the own data block, the state array used to record the operated state of the spliced data, and so on. As shown in FIG. 3, FIG. 3 is a schematic diagram of a block header of a data block provided by an embodiment of this specification.

After the user uploads the data successfully, the hash value of the corresponding data record and the hash value of the data block can be obtained and saved, and integrity verification can be initiated based on the hash value. The specific verification method is to recalculate the hash value of the data record itself and the hash value of the data block in the database, and compare with the locally stored hash value.

Through the foregoing method of generating data blocks, each data block is determined by a hash value, and the hash value of the data block is determined by the content and order of the data records in the data block and the hash value of the previous data block. The user can initiate verification based on the hash value of the data block at any time. Any modification of the content of the data block (including the modification of the data record content or sequence in the data block) will result in the hash value of the data block calculated during verification. The hash value of the data block is inconsistent when it is generated, which causes the verification to fail, thus realizing the immutability under centralization.

When performing integrity verification on data blocks in a blockchain ledger, generally speaking, a segment of data block is designated for continuous integrity verification, or continuous integrity verification is performed from the initial data block. The verification method is to obtain the hash value of the previous data block, and use the same algorithm as when generating the hash value of the data block, and recalculate its own data according to its own data record and the hash value of the previous data block. The hash value of the block for verification.

In one implementation, if the user needs to verify the integrity of a data record locally, the information in the block header can be pre-downloaded to the local for storage, so that when verification is needed, only the database server needs to be verified. Just query the "Merkel path", and calculate the hash value of the data record locally, and calculate the root hash of the Merkel tree according to the "Merkel path", and compare it with the locally saved block header information. The root hash is compared for consistency.

Based on the foregoing method, a block chain ledger that is difficult to tamper with can be obtained in the database server for storing data records. For the convenience of querying data, the embodiment of this specification also provides an index creation method for the block chain ledger. As shown in FIG. 4, FIG. 4 is a schematic flowchart of a method for creating an index for data records provided by an embodiment of this specification, and the process specifically includes the following steps.

S401: In the block chain ledger, for any data record, obtain business attributes included in the data record.

The specific location and acquisition method of the business attributes can be negotiated in advance by the database server and the docking organization. For example, when the data record provided by the docking organization is a standard structured data record, the business attributes can be obtained from the specified offset in the data record, or the start and end positions can be identified by specific characters; or, the docking organization provides When the data record of is unstructured data, the header containing the business attribute can be directly spliced at the beginning of each data record when uploading by the docking agency, and the database server can directly obtain the business attribute of each data record from the header .

S403: Determine location information of the data record in the ledger, where the location information includes the block height of the data block where the data record is located, and the offset in the data block where the data record is located.

As mentioned earlier, a block-chain ledger is composed of multiple data blocks, and at the same time, a data block usually contains multiple data records. Therefore, in the embodiment of this specification, the location information specifically refers to which data block in the ledger is located when a data record is saved, and where it is in the data block.

In the data blocks provided in the embodiments of this specification, there can be multiple ways to identify different data blocks, including the hash value or block height of the data block.

The hash value of the data block is a hash value obtained by hash calculation based on the previous block's hash value and its own data record, which can be used to uniquely and unambiguously identify a data block. In a block chain ledger, usually the block height of the first data block is 0, and the block height is increased by 1 for each additional data block; or, the block time of the data block can be converted into a large monotonic increase Integer data (usually 12 to 15 bits) sequence, as the block height of the data block. Therefore, a data block usually has a clear block height.

For another example, in a data block that has been determined to be written to the database, the order of the data records has also been fixed, so the serial number of a data record in the data block is also clear. When the length of the data record is a fixed unit, The sequence number can also be used to clarify the location information of the data record in the data block in which it is located. That is, the sequence number can also be used to indicate the offset of the data recorded in the data block.

At the same time, in a data block, since multiple data records are usually included, the address offset of each data record in the data block can also be used to identify the data records in the data block respectively. Obviously, in the same data block, the address offset of each data record is not the same.

Of course, because in the method provided by the embodiment of this specification, the specific format of the data block can be customized (for example, the metadata information and remark information contained in the block header of the data block, and the block height of the data block is adopted Format, etc.), in different formats, the content of the location information will also be different, which does not constitute a limitation to this solution.

S405: Establish a corresponding relationship between the business attribute and location information, and write an index with the business attribute as the main key.

That is, the index is an inverted index. In this index, the primary key is the business attribute contained in the data record. The specific writing method is: when the primary key in the index does not include the designated identification field, an index record with the designated identification field as the primary key is created in the index table.

When the primary key in the index includes the designated identification field, the location information is written into the index record where the designated identification field is located. It should be noted that the writing here is not an overwriting writing, but the location information is added to the value of the index record, and it is stored in the index record alongside other location information.

As shown in Table 1, Table 1 is an exemplary index table provided in the embodiment of this specification. The Key is the specific value of the business attribute, and each array in the Value part is a piece of position information. The first part of each array is the block height, and the latter part is the serial number of the data recorded in the data block, passing the block height and serial number That is, a data record can be uniquely determined. It is easy to understand that in the index table, a key can correspond to multiple location information.

Table 1

KeyKey	ValueValue
0X1234560X123456	(2,08)，(2,10)，(300,89)，(300,999)(2,08), (2,10), (300,89), (300,999)
344X0001344X0001	(5,01)，(8,22)(5,01), (8,22)
……...	……...

Through the solution provided by the embodiment of this specification, for the data records written into the ledger, the business attributes of the data records and the storage location in the ledger are determined, the corresponding relationship between the two is established, and the business attribute is created as the main key. Sort index, without knowing the user's business details, from the index, you can perform corresponding statistics on data records based on business attributes, as well as subsequent query and verification.

In the index table, since one business attribute can correspond to multiple location information (that is, one index record can contain multiple location information, or one business attribute can correspond to multiple data records), when writing the location information In the index, the location information can also be arranged in sequence according to the order in which the data is recorded in the ledger, which is conducive to the user's query and verification. The sequence of data records in the ledger can be reflected by the timestamp when the data record is written into the ledger (that is, the block timestamp of the data block), and the sequence of data records in the same data block can be reflected in the The order in the data block is reflected one after another. By sorting the location information, it is convenient to obtain corresponding data records in sequence when querying, acquiring, and reading data records, thereby improving user experience.

In practical applications, aggregation calculations for a certain business attribute are often initiated, including summation, average, maximum/minimum, count, standard deviation, variance, and so on. For example, when the user ID is used as the business attribute, the docking agency needs to query the number of data records uploaded by several users it serves in the past 12 months. This process needs to ensure that the data records of each user in the ledger are not tampered with or omitted, otherwise the accuracy of the aggregation results cannot be guaranteed.

Based on this, an embodiment of this specification provides an aggregation calculation method in a block chain ledger, as shown in FIG. 5, which is a schematic flow chart of the aggregation calculation method in a block chain ledger provided by the embodiment of this specification, including The following steps.

S501: Determine a set including multiple data records used in the aggregation calculation, and set the initial value of the aggregation result to 0.

Specifically, when the user or the docking structure initiates the aggregation calculation, it can be sent to the database server through the corresponding aggregation instruction. For example, Count (ID, Blknum), where "Count" is the count, "ID" is the business attribute, and Blknum is the block height. Through this instruction, calculate the data record of the user "ID" before the block height "Blknum" Number of articles.

In one embodiment, based on the uniqueness of the business attribute in the ledger, the database server receives the instruction, that is, it can use the traversal method to query the entire ledger to determine the data records that contain the business attribute in the ledger, so as to obtain the inclusion A collection of N data records.

In another implementation manner, if the database server has established an index on business attributes and location information, the location information corresponding to the business attributes can be queried from the index, and the location information obtained from the query can be obtained based on the location information obtained. Multiple data records of the business attribute to generate the set. The initial aggregation result is regarded as 0, which will not affect the aggregation calculation.

The preset iteration requirement is generally that all data records in the set have been traversed. In the embodiment of this specification, the set may be iterated to an empty set.

After determining the initial set, the initial aggregation result, and the end condition of the iteration, the following method is used to iterate the set and aggregation result.

S503: Select a data record from the set, determine the data block in which the data record is located, and verify the integrity of the data record in the data block.

In the aggregation operation, the iterative sequence of data records can be random or ordered. The random selection of data records does not affect the aggregation results. Of course, it can also be selected in a certain order, for example, based on the generation time or upload time of the data record.

Specifically, if the aforementioned set is obtained by traversing the ledger from the initial data block, the sequence can be determined according to the block height of the data recorded in the ledger, and the data records can be selected for iteration in the order of block height.

If the aforementioned set is obtained by querying the location information corresponding to the business attribute in the index, at this time, correspondingly, if the values in the index record (ie, multiple pieces of location information) are sorted according to the timestamp, then at this time, the same The data in the collection is sorted in the same order according to the position information, so that the data records can be iterated in sequence. Data recording based on time sequence (the order of block height can also be considered as a time sequence) can be connected with the user's reality, and the iteration based on the time sequence can meet the needs of the user's actual application.

In the block chain ledger, when verifying the integrity of data records, one way can be: determine the data block where the data record is located, and build a Merkel tree based on the data records in the data block, and calculate the current The hash value of the Merkel tree can be compared with the hash value of the Merkel tree stored in the block header. If it is inconsistent, it means that the data record has been tampered with.

Another way is to store a backup copy of the data record or the hash value of the backup copy when the user or the docking organization uploads the data record to the server, so that the current hash value of the data record can be calculated. And compare it with the hash value of the backup copy to achieve integrity verification.

It should be noted that the hash value of the backup copy may be returned by the database server, and the centralized database server may digitally sign the hash value to ensure that the hash value is recognized by the database server.

For example, when the user uploads data, the server has already determined the hash value of each data record, and at this time, the hash value can be digitally signed and returned to the user. The user only needs to save the hash value with the digital signature. Therefore, integrity verification can be performed at any time based on the hash value and the corresponding data record.

The specific aggregation result S(i) is related to the way of aggregation calculation, 1≤i≤N. For example, when the first data record is selected for calculation, if the aggregate calculation is the sum SUM or the maximum value, S(1) is equal to the relevant value in the first data record; if the aggregate calculation is the count COUNT, then S(1)=1, and so on.

S505: If the verification is passed, remove the data record from the set, aggregate the data record and the current aggregation result, and obtain an updated set and aggregation result.

After the initial verification is passed, for the i-th data record used in the i-th iteration, the aggregation calculation is performed again according to the i-th data record and the current aggregation result S(i-1), and the aggregation result is updated to obtain S (i).

At the same time, after each successful verification and calculation, the data record is removed from the current set (including the first one) to avoid repeated calculations, and an updated set containing data records is obtained. It is easy to understand that the data record in the collection is reduced by one after each update.

For example, when i=2, if the aggregation calculation is the sum SUM, then S(2) is equal to the sum of S(1) and the related value in the second data record; if the aggregation calculation is the count COUNT, then S( 1)=1 and when the second data record is counted, S(2)=2 is obtained, and so on.

It is easy to understand that if each verification succeeds, the value of i starts from 2. After each iteration, the value of i is automatically increased by 1 until i is equal to N. In this process, only one data record will be obtained during each iteration and integrity verification is performed. If each data record passes the integrity verification, the iteration will not be terminated until i=N, and the final aggregation will be output. The result is S(N).

S507: If the verification fails, the aggregation calculation is terminated, and the location information of the current data record in the ledger that fails the verification is returned.

If the integrity verification of any data record fails, the iteration is immediately terminated, and the database server obtains the location information of the data record that fails the verification, and returns the location information of the ith data record that failed the verification in the ledger to the aggregate calculation instruction The initiator of the aggregation instruction shall be notified of the "incorrect data record" for further follow-up.

S509: When the iteration ends, output the current aggregation result as the target result of the aggregation calculation.

The end of the iteration here means that the verification has been successful until the set is empty, and does not include the aforementioned termination of the aggregation calculation caused by the verification failure.

Through the solution provided by the embodiment of this specification, when performing aggregation calculation in the blockchain ledger, the calculation target is iteratively processed, and the integrity of the currently used data record is verified for each iteration calculation. Perform aggregation calculation, and terminate the aggregation calculation at any time if the verification fails, thereby ensuring that the data record is not tampered with or lost, and the result of the aggregation calculation is more accurate.

Correspondingly, an embodiment of this specification also provides an aggregation computing device in a blockchain ledger, as shown in FIG. 6, which is a schematic structural diagram of an aggregation computing device in a blockchain ledger provided by an embodiment of this specification , Including the following modules.

The set determining module 601 determines the set including multiple data records used in the aggregation calculation, and sets the initial value of the aggregation result to 0.

The iterative calculation module 603 selects a data record from the set to determine the data block where the data record is located; verifies the integrity of the data record in the data block, and if the verification passes, removes the data record from the set If the data record and the current aggregation result are aggregated, the updated collection and aggregation result are obtained.

The iteration termination module 605 terminates the aggregation calculation, and returns the location information of the current data record in the ledger that has failed the verification.

The output module 607, at the end of the iteration, outputs the current aggregation result as the target result of the aggregation calculation.

The embodiments of this specification also provide a computer device, which at least includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the aggregation shown in FIG. 5 when the program is executed. Calculation method.

FIG. 7 shows a more specific hardware structure diagram of a computing device provided by an embodiment of this specification. The device may include a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input/output interface 1030, and the communication interface 1040 realize the communication connection between each other in the device through the bus 1050.

The processor 1010 may be implemented by a general CPU (Central Processing Unit, central processing unit), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc., for execution related Program to realize the technical solutions provided in the embodiments of this specification.

The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, related program codes are stored in the memory 1020 and called and executed by the processor 1010.

The input/output interface 1030 is used to connect an input/output module to realize information input and output. The input/output/module can be configured in the device as a component (not shown in the figure), or it can be connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and an output device may include a display, a speaker, a vibrator, an indicator light, and the like.

The communication interface 1040 is used to connect a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.), or through wireless means (such as mobile network, WIFI, Bluetooth, etc.).

The bus 1050 includes a path to transmit information between various components of the device (for example, the processor 1010, the memory 1020, the input/output interface 1030, and the communication interface 1040).

It should be noted that although the above device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include the equipment necessary for normal operation. Other components. In addition, those skilled in the art can understand that the above-mentioned devices may also include only the components necessary to implement the solutions of the embodiments of the present specification, and not necessarily include all the components shown in the figures.

The embodiment of the present specification also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the aggregate calculation method shown in FIG. 5 is implemented.

Computer-readable media include permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, Magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices. According to the definition in this article, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

From the description of the foregoing implementation manners, it can be understood that those skilled in the art can clearly understand that the embodiments of this specification can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solutions of the embodiments of this specification can be embodied in the form of software products, which can be stored in storage media, such as ROM/RAM, A magnetic disk, an optical disk, etc., include several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute the methods described in the various embodiments or some parts of the embodiments of this specification.

The systems, methods, modules, or units explained in the above embodiments may be implemented by computer chips or entities, or implemented by products with certain functions. A typical implementation device is a computer. The specific form of the computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email receiving and sending device, and a game control A console, a tablet computer, a wearable device, or a combination of any of these devices.

The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, as for the method embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiment. The method embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separated. When implementing the solutions of the embodiments of this specification, the functions of the modules may be in the same Or multiple software and/or hardware implementations. It is also possible to select some or all of the modules according to actual needs to achieve the objectives of the solutions of the embodiments. Those of ordinary skill in the art can understand and implement it without creative work.

The above are only specific implementations of the embodiments of this specification. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the embodiments of this specification, several improvements and modifications can be made. These Improvement and retouching should also be regarded as the protection scope of the embodiments of this specification.

Claims

An aggregation calculation method in a blockchain ledger, including:

Determine the set including multiple data records used in the aggregation calculation, set the initial value of the aggregation result to 0, and perform iterative processing on the set and the aggregation result in the following manner until the preset iteration requirement is reached;

Select a data record from the set, determine the data block where the data record is located, and verify the integrity of the data record in the data block;

If the verification is passed, remove the data record from the collection, aggregate the data record and the current aggregation result, and obtain an updated collection and aggregation result;

If the verification fails, terminate the aggregation calculation, and return the location information of the current data recorded in the ledger for the failed verification;

At the end of the iteration, the current aggregation result is output as the target result of the aggregation calculation.
The method according to claim 1, determining a set including a plurality of data records used in the aggregation calculation includes:

Receiving an aggregate calculation instruction, where the aggregate calculation instruction includes a business attribute;

Traverse the ledger, determine multiple data records containing the business attributes, and generate the set; or query the location information corresponding to the business attributes from a pre-built index, and obtain information that contains information based on the query location information. Multiple data records of the business attribute to generate the set.
The method according to claim 1, wherein the block chain ledger is pre-generated in the following manner:

Receive the data records to be stored and determine the hash value of each data record, where the data record contains business attributes;

When the preset blocking condition is reached, each data record to be written in the data block is determined, and the Nth data block containing the hash value of the data block and the data record is generated, which specifically includes:

When N=1, the hash value and block height of the initial data block are given based on a preset method;

When N>1, determine the hash value of the Nth data block according to the data records in the data block to be written and the hash value of the N-1th data block, and generate a hash containing the Nth data block Value and the Nth data block of each data record.
The method according to claim 3, wherein the preset blocking condition comprises:

The number of data records to be stored reaches the number threshold; or, the time interval from the last block time reaches the time threshold.
The method according to claim 2, wherein the index is pre-established in the following manner:

In the blockchain ledger, for any data record, obtain the business attributes contained in the data record;

Determine the location information of the data record in the ledger, where the location information includes the block height of the data block where the data record is located, and the offset in the data block where the data record is located;

The corresponding relationship between the business attribute and the location information is established, and an index with the business attribute as the main key is written.
The method according to claim 5, writing an index with the business attribute as the primary key, comprising:

Determine the time stamp of the data record; in the same index record, according to the order of the time stamp, write the position information of the data record into the value of the index record in order;

Correspondingly, traversing other data records to perform iterative processing includes: sequentially performing iterative processing on the data records according to the order of the position information in the index record.
An aggregation computing device in a block chain ledger, including:

The set determination module determines the set including multiple data records used in the aggregation calculation, and sets the initial value of the aggregation result to 0;

The iterative calculation module selects a data record from the set and determines the data block where the data record is located; verifies the integrity of the data record in the data block, and if the verification passes, removes the data record from the set Eliminate, aggregate the data record and the current aggregation result, and obtain the updated collection and aggregation result;

The iterative termination module terminates the aggregation calculation, and returns the location information of the current data record in the ledger that fails the verification;

The output module, at the end of the iteration, outputs the current aggregation result as the target result of the aggregation calculation.
7. The device of claim 7, wherein the set determination module receives an aggregate calculation instruction, wherein the aggregate calculation instruction contains a business attribute; traverse the ledger to determine a plurality of data records containing the business attribute, and generate The set; or, query the location information corresponding to the business attribute from a pre-established index, obtain multiple data records containing the business attribute according to the query location information, and generate the set.
The device according to claim 7, further comprising a data block generation module, which receives the data records to be stored and determines the hash value of each data record, wherein the data record contains the business attribute; when the preset block condition is reached , Determine each data record in the data block to be written, and generate the Nth data block containing the hash value of the data block and the data record, which specifically includes:

When N=1, the hash value and block height of the initial data block are given based on a preset method;

When N>1, determine the hash value of the Nth data block according to the data records in the data block to be written and the hash value of the N-1th data block, and generate a hash containing the Nth data block Value and the Nth data block of each data record.
9. The device of claim 9, wherein the preset blocking condition comprises:

The number of data records to be stored reaches the number threshold; or, the time interval from the last block time reaches the time threshold.
The device according to claim 9, further comprising an index creation module, in the block chain ledger, for any data record, obtain the business attributes contained in the data record; determine the position of the data record in the ledger Information, the location information includes the block height of the data block where the data record is located, and the offset in the data block where the data record is located; the corresponding relationship between the service attribute and the location information is established, and the service attribute is written in the service The index of the attribute as the primary key.
The device according to claim 11, wherein the index creation module determines the time stamp of the data record; in the same index record, according to the order of the time stamp, the position information of the data record is sequentially written into the value of the index record; correspondingly Yes, traversing other data records to perform iterative processing includes: sequentially performing iterative processing on the data records according to the order of the position information in the index record.
A computer device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program as described in any one of claims 1 to 6 method.