WO2022140936A1

WO2022140936A1 - Metering method and device for data query workload in blockchain, and terminal

Info

Publication number: WO2022140936A1
Application number: PCT/CN2020/140246
Authority: WO
Inventors: 李伟; 邱炜伟; 张珂杰; 黄方蕾; 胡麦芳
Original assignee: 杭州趣链科技有限公司
Priority date: 2020-12-28
Filing date: 2020-12-28
Publication date: 2022-07-07

Abstract

The present application is applicable to the technical field of blockchains and provides a metering method and device for data query workload in a blockchain, and a terminal. The method comprises: executing an intelligent contract according to a blockchain transaction generated by a user, and generating an SQL query statement corresponding to the intelligent contract; inputting the SQL query statement into a built-in SQL query engine, and executing the SQL query statement by means of the SQL query engine; obtaining an execution operation of the SQL query engine from an SQL query statement execution process; matching, on the basis of the execution operation, workload corresponding to the execution operation from a preset workload table; and accumulating, on the basis of the workload, the workload in the process of executing the SQL query statement by the SQL query engine. The scheme can adapt to a workload metering mechanism of a blockchain, and fusion between SQL query and a blockchain platform is realized.

Description

Method, device and terminal for measuring data query workload in blockchain

technical field

The present application belongs to the technical field of blockchain, and in particular relates to a method, device and terminal for measuring the workload of data query in the blockchain.

Background technique

The blockchain network adopts the Proof-of-Work (PoW) mechanism, which requires all nodes in the blockchain network to perform calculations according to the preset Proof-of-Work method, and the node that first obtains the result has the The right to package blockchain transaction information and receive rewards.

In the existing blockchain network, the smart contract execution engine needs to execute the smart contract to read data from the blockchain ledger. When executing the smart contract, it needs to execute several different instructions according to the logical bottom layer of the smart contract, such as data operations, data read. Each time an instruction is executed, a certain workload is generated, and the combination of the workload and the instruction is easy to perform quantitative statistics.

Limited by the application of the workload proof mechanism, in the existing blockchain network, the way of reading data from the blockchain ledger based on smart contracts, compared with other database applications, the data structure that can be directly used is relatively Limited, lack of support for relational data reading, and low data query efficiency, so it is proposed to introduce existing SQL (Structured Query Language, Structured Query Language) queries into the blockchain platform for application.

The execution of the existing SQL query is a complex logic, and the workload cannot be measured from the dimension of the instruction. The execution engine of smart contracts cannot adapt to the workload measurement mechanism of the blockchain when applying SQL queries, and cannot integrate SQL queries with the blockchain platform.

technical problem

In view of this, the embodiments of the present application provide a method, device and terminal for measuring the workload of data query in the blockchain, so as to solve the problem that the execution engine of the smart contract in the prior art cannot adapt to the blockchain when applying SQL query. The workload measurement mechanism cannot integrate SQL queries with the blockchain platform.

technical solutions

A first aspect of the embodiments of the present application provides a method for measuring data query workload in a blockchain, including:

According to the blockchain transaction generated by the user, execute the smart contract, and generate the SQL query statement corresponding to the smart contract;

The SQL query statement is input into the built-in SQL query engine, and the SQL query statement is executed by the SQL query engine;

From the execution process of the SQL query statement, obtain the execution operation of the SQL query engine;

Based on the execution operation, matching the workload corresponding to the execution operation from a preset workload table;

Based on the workload, the workload in the process of executing the SQL query statement by the SQL query engine is accumulated.

A second aspect of the embodiments of the present application provides a device for measuring data query workload in a blockchain, including:

A generation module, used for executing the smart contract according to the blockchain transaction generated by the user, and generating the SQL query statement corresponding to the smart contract;

A query module, for inputting the SQL query statement into a built-in SQL query engine, and executing the SQL query statement through the SQL query engine;

an acquisition module, for acquiring the execution operation of the SQL query engine from the execution process of the SQL query statement;

a matching module, configured to match the workload corresponding to the execution operation from a preset workload table based on the execution operation;

A workload calculation module, configured to accumulate the workload in the process of executing the SQL query statement by the SQL query engine based on the workload.

A third aspect of the embodiments of the present application provides a terminal, including a memory, a processor, and a computer program stored in the memory and running on the processor, which is implemented when the processor executes the computer program The steps of the method according to the first aspect.

A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

A fifth aspect of the present application provides a computer program product, which, when the computer program product runs on a terminal, causes the terminal to execute the steps of the method in the above-mentioned first aspect.

beneficial effect

As can be seen from the above, in the embodiment of the present application, the smart contract is executed according to the blockchain transaction generated by the user, the SQL query statement corresponding to the smart contract is generated, the SQL query statement is input into the built-in SQL query engine, and the SQL query statement is executed. The engine executes the SQL query statement, obtains the execution operation of the SQL query engine from the execution process of the SQL query statement, and based on the execution operation, matches the workload corresponding to the execution operation from the preset workload table, and based on the workload, Accumulate the workload in the process of executing the SQL query statement by the SQL query engine. In this process, the SQL query statement corresponding to the smart contract is generated when the smart contract is executed, the SQL query statement is executed through the built-in SQL query engine, and the execution process is monitored, and the SQL query engine is obtained when the SQL query statement is executed. Corresponding execution operations, and match these execution operations in the preset workload table to obtain the workload corresponding to these execution operations based on the record content in the workload table, and accumulate them. From the query process The dimension of the execution operation itself involved measures the workload, adapts to the workload measurement mechanism of the blockchain, and realizes the integration between the SQL query and the blockchain platform.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the accompanying 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 for the present application. In some embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

1 is a flow chart 1 of a method for measuring data query workload in a blockchain provided by an embodiment of the present application;

2 is a second flowchart of a method for measuring data query workload in a blockchain provided by an embodiment of the present application;

3 is a structural example diagram of a layered executor provided by an embodiment of the present application;

4 is a structural diagram of a device for measuring data query workload in a blockchain provided by an embodiment of the present application;

FIG. 5 is a structural diagram of a terminal provided by an embodiment of the present application.

Embodiments of the present invention

In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

It is to be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described feature, integer, step, operation, element and/or component, but does not exclude one or more other features , whole, step, operation, element, component and/or the presence or addition of a collection thereof.

In order to illustrate the technical solutions described in the present application, the following specific embodiments are used for description.

Referring to FIG. 1 , FIG. 1 is a flowchart 1 of a method for measuring data query workload in a blockchain provided by an embodiment of the present application. As shown in Figure 1, a method for measuring the workload of data query in blockchain, the method includes the following steps:

Step 101: Execute the smart contract according to the blockchain transaction generated by the user, and generate a SQL query statement corresponding to the smart contract.

Among them, after obtaining the blockchain transaction generated by the user, the code of the corresponding smart contract needs to be obtained and executed, and the corresponding SQL query statement is generated based on the contract logic during the execution process. The SQL query statement is used to read the target data from the blockchain ledger, and the target data is used to implement the execution of the smart contract.

In the specific implementation, the implementation process of this step 101 is specifically:

Analyze the blockchain transaction generated by the user to obtain input parameters; read the smart contract code to be executed from the blockchain ledger according to the input parameters; initialize the smart contract virtual machine based on the input parameters and the smart contract code; The control smart contract virtual machine generates SQL query statements according to the contract logic of the smart contract in the smart contract code.

The input parameters are, for example, data content to be modified, transaction information to be added, and the like.

The smart contract code to be executed is the smart contract code corresponding to the input parameters parsed in the blockchain transaction.

When generating the SQL query statement, input parameters may be brought into the query statement frame to obtain the query statement. For example: select * from T where B=”aa” and C>100.

Specifically, in the process of parsing the blockchain transaction generated by the user and obtaining the input parameters, it is necessary to judge the legality of the blockchain transaction, and when judging that it is legal, obtain the input parameters and perform subsequent operations.

In the specific implementation process, the blockchain system receives the blockchain transaction generated by the user, parses the blockchain transaction, verifies the legality of the format, and obtains the input parameters after several preprocessing. Preprocessing includes but is not limited to parameter parsing and consensus packaging.

The smart contract execution engine reads the smart contract code to be executed from the blockchain ledger according to the input parameters, and uses the above code and parameters to initialize the smart contract virtual machine. Start executing the smart contract through the smart contract virtual machine, first run some contract logic, accumulate some workload, generate SQL query statement according to the contract logic, and input it into the SQL query engine built in the smart contract execution engine.

Step 102: Input the SQL query statement into the built-in SQL query engine, and execute the SQL query statement through the SQL query engine.

When the SQL query engine executes an SQL query statement, it can preprocess the SQL query statement, for example, disassemble it to obtain query conditions, and then generate a corresponding executor based on the disassembled query conditions, and execute the block execution through the executor. Data query action in the chain ledger.

Specifically, the method of matching data from the blockchain ledger can be to construct a specific primary key according to the contract logic and data structure in the smart contract, and match the target data with the target primary key and meet the set matching conditions from the blockchain ledger.

Step 103: Acquire the execution operation of the SQL query engine from the execution process of the SQL query statement.

The execution operation is multiple.

Wherein, the execution operation of the SQL query engine is, for example: the aforementioned operation of preprocessing the SQL query statement, and the data execution operation executed by the matching executor, and the data execution operation may be a string comparison operation, an integer comparison operation, a data execution operation read operations, etc.

Step 104 , based on the execution operation, match the workload corresponding to the execution operation from a preset workload table.

The workload table is table structure data used to measure the workload consumed by the SQL query engine in the process of executing the SQL query statement. Specifically, the table structure data is used to measure the workload consumed by the preprocessing and executor of the SQL query engine in the process of executing the SQL query statement. In the workload table, the workload of processing input data of a certain size is recorded for each preprocessor, and the workload of processing input data of a certain size is recorded for each executor.

Each time the SQL query engine executes an SQL query statement, the total workload is accumulated by looking up the table according to the actual processing process of processing the current SQL query statement.

In order to ensure the consistency of each node of the blockchain, the workload table needs to be stored on the blockchain ledger and loaded when the SQL query engine is initialized. When some workloads set in the workload table need to be changed, unified changes need to be made in the form of blockchain transactions. If the work sheet is not recorded on the blockchain ledger, the same default configuration is required between blockchain nodes.

Step 105: Based on the workload, the workload in the process of executing the SQL query statement by the SQL query engine is accumulated.

In the above process, a workload measurement mechanism based on the processing process is constructed in the SQL query engine, so that the smart contract virtual machine with the workload mechanism can build this SQL query engine, and then provide the SQL query function for the smart contract.

Specifically, the workload is set for the processing processes such as scanning, conditional filtering, and connection involved in the execution of the SQL query statement, and is recorded in the workload table in advance. When executing the SQL query statement during the execution of the smart contract , according to the specific execution operations and the number of calls, the total workload is accumulated, so that the execution engine of the smart contract can be embedded with an efficient native SQL query engine while maintaining the original workload mechanism, thereby improving the reading of smart contract data. The friendliness of the interface improves the efficiency of blockchain contract development.

Further, after accumulating the workload in the process of executing the SQL query statement by the SQL query engine based on the workload, the method further includes:

When the SQL query engine executes the SQL query statement, if the total amount of work corresponding to the execution operation exceeds the threshold, the SQL query engine is controlled to output the data query result.

The setting of the workload mechanism is mainly to solve the problem of downtime, avoid the situation that the contract call cannot be terminated, and also avoid excessive system resources consumed by a single execution.

The upper limit of the workload of the SQL query engine needs to be set. The upper limit of the workload that can be used to query data using the built-in SQL query engine is specified by the parameter. The workload cap can be an input or a unified configuration made in a user blockchain transaction.

When the SQL query task is processed, or the accumulated workload of the SQL query engine reaches the upper limit, the data is returned in a readable data structure in the smart contract for the contract logic.

In the embodiment of the present application, the smart contract is executed according to the blockchain transaction generated by the user, the SQL query statement corresponding to the smart contract is generated, the SQL query statement is input into the built-in SQL query engine, and the SQL query engine is used to execute the SQL Query statement, obtain the execution operation of the SQL query engine from the execution process of the SQL query statement, based on the execution operation, match the workload corresponding to the execution operation from the preset workload table, and based on the workload, the SQL query engine The workload in the process of executing the SQL query statement is accumulated. In this process, the SQL query statement corresponding to the smart contract is generated when the smart contract is executed, the SQL query statement is executed through the built-in SQL query engine, and the execution process is monitored, and the SQL query engine is obtained when the SQL query statement is executed. Corresponding execution operations, and match these execution operations in the preset workload table to obtain the workload corresponding to these execution operations based on the record content in the workload table, and accumulate them. From the query process The dimension of the execution operation itself involved measures the workload, adapts to the workload measurement mechanism of the blockchain, and realizes the integration between the SQL query and the blockchain platform.

The embodiments of the present application also provide different implementations of the method for measuring the workload of data query in the blockchain.

Referring to FIG. 2 , FIG. 2 is a second flowchart of a method for measuring data query workload in a blockchain provided by an embodiment of the present application. As shown in Figure 2, a method for measuring the workload of data query in blockchain, the method includes the following steps:

Step 201: Execute the smart contract according to the blockchain transaction generated by the user, and generate a SQL query statement corresponding to the smart contract.

The implementation process of this step is the same as the implementation process of step 101 in the foregoing embodiment, and details are not repeated here.

Step 202: Input the SQL query statement into the built-in SQL query engine, and execute the SQL query statement through the SQL query engine.

The implementation process of this step is the same as the implementation process of step 102 in the foregoing embodiment, and details are not repeated here.

Step 203, from the process of executing the SQL query statement, obtain the preprocessing operation of the SQL query statement by the SQL query engine, and obtain the SQL query engine to preprocess the SQL query statement to generate a hierarchical executor, and pass the hierarchical executor During the hierarchical execution of the preprocessed SQL query statement, the data of each executor performs operations.

That is, during the execution of the SQL query statement, the acquired execution operations of the SQL query engine specifically include: preprocessing operations on the SQL query statement and data execution operations of each executor in the hierarchical executors.

In the specific application process, the SQL query engine first uses the preprocessor to preprocess the SQL query statement, and then generates a hierarchical executor based on the preprocessing result. Preprocessing includes, but is not limited to, parsing of SQL query statements, index selection, and execution optimization. Executor is a general term for the logical unit of sub-processes in the SQL query execution process, including but not limited to data operations, comparisons, and connections. Hierarchical refers to the relationship between the executor and the executor. The output of the lower executor is used as the input of the upper executor. The browser is connected to the SQL query interface provided by the SQL query engine to the smart contract, and returns data query results through this interface. The data acquisition in the smart contract can be regarded as a recursive call from the top-level executor to the bottom-level executor, and the data in the execution result of the lower-level executor is returned to the upper-level executor for further matching processing. Called and returns a certain amount of data.

After the SQL query engine receives the SQL query statement from the SQL query interface, it starts to call down to acquire and process data.

Call down through the upper executor, the lowermost executor reads the data of the set number of rows from the blockchain ledger, and the lowermost executor takes the data as the input of the adjacent upper executor, and the adjacent The upper-layer executor performs the data comparison operation that has been assigned to the executor of this layer, and obtains the data that meets the corresponding query conditions based on the comparison result, and continues to input the data to the adjacent upper-layer executor for the corresponding query. The data is subjected to another data comparison and judgment until the execution of the top-level executor is completed, and the final qualified data is output as the result of the SQL query.

Step 204 , based on the execution operation, match the workload corresponding to the execution operation from a preset workload table.

Specifically, in the preset workload table, the recorded content includes: the workload consumed by each preprocessor for processing a unit amount of input data, the amount of work consumed by each preprocessor processing a unit amount of input data workload. The unit amount is, for example, one byte.

Among them, different preprocessors correspond to different preprocessing methods, and different executors perform different data processing operations.

The execution operations include: preprocessing operations on SQL query statements and data execution operations on each executor in the hierarchical executors.

During the implementation process of step 204, the steps include: acquiring the first operation data amount when the preprocessing operation is performed; acquiring the first processing work corresponding to the unit operation data amount of the preprocessing operation from the preset workload table The first workload of the preprocessing operation is obtained according to the first operation data volume and the first processing workload.

Further, in the implementation process of this step 204, it also includes: according to the execution sequence of the data execution operations of the executors, every time an executor is detected to execute the data execution operation, acquiring the second data execution operation of the current executor when the data execution operation is performed. operation data volume; from the preset workload table, obtain the second processing workload of the unit operation data volume corresponding to the data execution operation of the current executor; according to the second operation data volume and the second processing workload, Get the second workload of the current executor.

Realize the acquisition of the workload of different execution operations in the process of executing the SQL query statement by the SQL query engine.

Step 205: Based on the workload, the workload in the process of executing the SQL query statement by the SQL query engine is accumulated.

Correspondingly, the implementation of this step is specifically: accumulating the first workload in the preceding steps and the second workload obtained in sequence.

Among them, the workload of each pair of executors is obtained once according to the method in the preceding steps, and a corresponding second workload is obtained. According to the execution order of the data operations performed by the executors in the multi-layer executors, different executors are sequentially obtained. The second workload corresponding to the executor is accumulated with the first workload, and the total workload in the process of executing the SQL query statement by the SQL query engine is obtained.

Here, the above implementation process is illustrated with an example:

1) Set the processing workload of the unit operation data volume corresponding to different execution operations in the workload table through blockchain transactions. The details are shown in Table 1:

预处理器/执行器名称 preprocessor/executor name	处理输入数据所消耗的单位工作量/字节 Unit effort/byte consumed by processing input data
SQL解析预处理器 SQL parsing preprocessor	10 10
... ...	... ...
字符串比较执行器 String comparison executor	1 1
整数比较执行器 Integer comparison executor	1 1
区块链账本读取执行器 Blockchain Ledger Reader	2 2
... ...	... ...

2) The relational data in the following table 2 is pre-stored on the blockchain ledger through blockchain transactions, which is called table T, where A is the primary key, columns A and C are 8-byte integers, and column B is each A 1-byte string of characters. Only the primary key index.

A A	B B	C C
1 1	“aa” "aa"	100 100
2 2	“aa” "aa"	100 100
3 3	“aa” "aa"	200 200
4 4	“ba” "ba"	200 200
5 5	“ba” "ba"	300 300

The encoding results of the above table are stored in the form of key-value pairs in the blockchain ledger. The data in the above table is stored in the blockchain ledger with a length of 14 bytes.

3) Invoke smart contracts through blockchain transactions.

4) When the smart contract is executed, the SQL query statement select * from T where B=”aa” and C>100, input the SQL query statement to the SQL query engine, and limit the workload to 1000.

The SQL query engine uses the SQL parsing preprocessor to parse the SQL query statement. The input of the query statement is 38 bytes. According to the preset workload table (Table 1), a total of 380 units of workload are generated, and the required amount of work to execute the query statement is generated. Hierarchical executors, each executor returns 5 pieces of data at a time and makes recursive calls. A hierarchical structure is shown in Figure 3.

The structure of the layered executor includes: the uppermost string comparison executor, which is connected with the SQL engine interface to realize the acquisition of SQL query statements; the lowermost blockchain ledger reading executor, which is connected with the blockchain ledger , which implements data reading from the blockchain; an integer comparison executor located in the middle layer.

5) The bottom-level blockchain ledger reading executor constructs the primary key row by row according to the primary key range, reads the data in Table 2, a total of 5 rows of data and 90 bytes, and parses it into an integer returned to the upper layer in the corresponding format Comparing the actuators and combining the data in Table 1, it can be concluded that 180 units of workload will be consumed, and the cumulative consumption of 560 units of workload at this time.

6) The integer comparison executor compares the processing data in column C in Table 2. It needs to process the 5 rows of data returned by the blockchain ledger reading executor, and perform 5 comparisons of 8-byte integers. Combined with the data in Table 1, we get The output will consume 40 units of workload, and the cumulative consumption of 600 units of workload at this time. Filter out the rows that satisfy the condition C>100, and get 3 rows of data whose primary keys are 3, 4, and 5. At this time, less than 5 pieces of data need to be returned, continue to call the lower-level executor (that is, the blockchain ledger reading executor). If the data provided by the lower-level is not received, the 3 pieces of data are directly returned to the upper-level string comparison. Actuator.

7) The string comparison executor compares the processing data in column B in Table 2. It needs to process the 3 rows of data returned by the integer comparator, and perform 3 comparisons of 2-byte strings. Combined with the data in Table 1, it will consume 6 units of workload, and the cumulative consumption of 606 units of workload at this time. Filter out the rows that satisfy the condition B=”aa”, and get 2 rows of data whose primary keys are 3 and 4. If there are less than 5 pieces of returned data, continue to call the lower-level executor (that is, the integer comparison executor). If the data provided by the lower-level is not received, the processed 2 rows of data are returned to the upper-level, that is, from the SQL engine interface to the intelligent contract logic. In the smart contract, the data is read line by line, the subsequent logic is executed, and the execution result of the smart contract is generated.

The process in this embodiment allows the blockchain smart contract execution engine to embed a SQL query engine compatible with the workload mechanism, and allows the blockchain contract to read relational data in a more flexible, efficient and standard manner.

Referring to FIG. 4 , FIG. 4 is a structural diagram of a device for measuring data query workload in a blockchain provided by an embodiment of the present application. For convenience of description, only parts related to the embodiment of the present application are shown.

The measuring device 400 of the data query workload in the blockchain includes:

The generating module 401 is used for executing the smart contract according to the blockchain transaction generated by the user, and generating the SQL query statement corresponding to the smart contract;

A query module 402, configured to input the SQL query statement into a built-in SQL query engine, and execute the SQL query statement through the SQL query engine;

An obtaining module 403, configured to obtain the execution operation of the SQL query engine from the execution process of the SQL query statement;

a matching module 404, configured to match the workload corresponding to the execution operation from a preset workload table based on the execution operation;

The workload calculation module 405 is configured to accumulate the workload in the process of executing the SQL query statement by the SQL query engine based on the workload.

Wherein, the acquisition module 403 is specifically used for:

From the process of executing the SQL query statement, obtain the preprocessing operation of the SQL query statement by the SQL query engine, and obtain the SQL query engine after preprocessing the SQL query statement to generate a layered executor , and during the hierarchical execution process of the preprocessed SQL query statement by the hierarchical executor, the data of each executor performs operations.

Wherein, the matching module 404 is specifically used for:

obtaining the first operation data amount when the preprocessing operation is performed;

obtaining, from the preset workload table, a first processing workload corresponding to the preprocessing operation per unit of operation data;

According to the first operation data amount and the first processing workload, the first workload of the preprocessing operation is obtained.

Wherein, the matching module 404 is also specifically used for:

According to the execution sequence of the data execution operations of the executors, each time an executor is detected to execute the data execution operation, obtain the second operation data amount of the current executor when the data execution operation is performed;

From the preset workload table, obtain the second processing workload of the unit operation data volume corresponding to the data execution operation of the current executor;

According to the second operation data amount and the second processing workload, the second workload of the current executor is obtained.

Among them, the workload calculation module 405 is specifically used for:

The first workload and the sequentially acquired second workload are accumulated.

Wherein, the generation module 401 is specifically used for:

Analyze the blockchain transaction generated by the user to obtain the input parameters;

According to the input parameters, read the smart contract code to be executed from the blockchain ledger;

Based on the input parameters and the smart contract code, initialize a smart contract virtual machine;

The smart contract virtual machine is controlled to generate an SQL query statement according to the contract logic of the smart contract in the smart contract code.

The device also includes: an output module for:

During the process of executing the SQL query statement by the SQL query engine, if the total amount of work corresponding to the execution operation exceeds a threshold, the SQL query engine is controlled to output a data query result.

The device for measuring the workload of data query in the blockchain provided by the embodiment of the present application can realize the various processes of the above-mentioned embodiments of the method for measuring the workload of data query in the blockchain, and can achieve the same technical effect. In order to avoid repetition, I won't go into details here.

FIG. 5 is a structural diagram of a terminal provided by an embodiment of the present application. As shown in this figure, the terminal 5 of this embodiment includes: at least one processor 50 (only one is shown in FIG. 5 ), a memory 51 , and a memory 51 stored in the memory 51 and available on the at least one processor 50 The running computer program 52, when the processor 50 executes the computer program 52, implements the steps in any of the foregoing method embodiments.

The terminal 5 may be a computing device such as a desktop computer, a notebook, a palmtop computer and a cloud server. The terminal 5 may include, but is not limited to, a processor 50 and a memory 51 . Those skilled in the art can understand that FIG. 5 is only an example of the terminal 5, and does not constitute a limitation on the terminal 5. It may include more or less components than the one shown in the figure, or combine some components, or different components, such as The terminal may also include input and output devices, network access devices, buses, and the like.

The processor 50 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 51 may be an internal storage unit of the terminal 5 , such as a hard disk or a memory of the terminal 5 . The memory 51 may also be an external storage device of the terminal 5, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card equipped on the terminal 5, Flash card (Flash Card) and so on. Further, the memory 51 may also include both an internal storage unit of the terminal 5 and an external storage device. The memory 51 is used to store the computer program and other programs and data required by the terminal. The memory 51 can also be used to temporarily store data that has been output or will be output.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit, and the above-mentioned integrated units may adopt hardware. It can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above-mentioned system, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps of the foregoing method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable media Electric carrier signals and telecommunication signals are not included.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the above-mentioned embodiments, those of ordinary skill in the art should understand that: it can still be used for the above-mentioned implementations. The technical solutions described in the examples are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the application, and should be included in the within the scope of protection of this application.

Claims

A method for measuring data query workload in a blockchain, characterized by comprising:

According to the blockchain transaction generated by the user, execute the smart contract, and generate the SQL query statement corresponding to the smart contract;

The SQL query statement is input into the built-in SQL query engine, and the SQL query statement is executed by the SQL query engine;

From the execution process of the SQL query statement, obtain the execution operation of the SQL query engine;

Based on the execution operation, matching the workload corresponding to the execution operation from a preset workload table;

Based on the workload, the workload in the process of executing the SQL query statement by the SQL query engine is accumulated.
The measurement method according to claim 1, wherein the obtaining the execution operation of the SQL query engine from the execution process of the SQL query statement comprises:

From the process of executing the SQL query statement, obtain the preprocessing operation of the SQL query statement by the SQL query engine, and obtain the SQL query engine after preprocessing the SQL query statement to generate a layered executor , and during the hierarchical execution process of the preprocessed SQL query statement by the hierarchical executor, the data of each executor performs operations.
The metering method according to claim 2, wherein, based on the execution operation, matching the workload corresponding to the execution operation from a preset workload table, comprising:

obtaining the first operation data amount when the preprocessing operation is performed;

obtaining, from the preset workload table, a first processing workload corresponding to the preprocessing operation per unit of operation data;

According to the first operation data amount and the first processing workload, the first workload of the preprocessing operation is obtained.
The measurement method according to claim 3, wherein after obtaining the first workload of the preprocessing operation according to the first operation data volume and the first processing workload, the method further comprises:

According to the execution sequence of the data execution operations of the executors, each time an executor is detected to execute the data execution operation, obtain the second operation data amount of the current executor when the data execution operation is performed;

From the preset workload table, obtain the second processing workload of the unit operation data volume corresponding to the data execution operation of the current executor;

According to the second operation data amount and the second processing workload, the second workload of the current executor is obtained.
The measurement method according to claim 4, wherein, based on the workload, accumulating the workload in the process of executing the SQL query statement by the SQL query engine, comprising:

The first workload and the sequentially acquired second workload are accumulated.
The measurement method according to claim 1, wherein, executing a smart contract according to a blockchain transaction generated by a user, and generating an SQL query statement corresponding to the smart contract, comprising:

Analyze the blockchain transaction generated by the user to obtain the input parameters;

According to the input parameters, read the smart contract code to be executed from the blockchain ledger;

Based on the input parameters and the smart contract code, initialize a smart contract virtual machine;

The smart contract virtual machine is controlled to generate an SQL query statement according to the contract logic of the smart contract in the smart contract code.
The measurement method according to claim 1, wherein, after accumulating the workload in the process of executing the SQL query statement by the SQL query engine based on the workload, the method further comprises:

During the process of executing the SQL query statement by the SQL query engine, if the total amount of work corresponding to the execution operation exceeds a threshold, the SQL query engine is controlled to output a data query result.
A measuring device for data query workload in a blockchain, characterized in that it includes:

A generation module, used for executing the smart contract according to the blockchain transaction generated by the user, and generating the SQL query statement corresponding to the smart contract;

A query module, for inputting the SQL query statement into a built-in SQL query engine, and executing the SQL query statement through the SQL query engine;

an acquisition module, for acquiring the execution operation of the SQL query engine from the execution process of the SQL query statement;

a matching module, configured to match the workload corresponding to the execution operation from a preset workload table based on the execution operation;

A workload calculation module, configured to accumulate the workload in the process of executing the SQL query statement by the SQL query engine based on the workload.
The metering device according to claim 8, wherein the acquisition module is specifically used for:

From the process of executing the SQL query statement, obtain the preprocessing operation of the SQL query statement by the SQL query engine, and obtain the SQL query engine after preprocessing the SQL query statement to generate a layered executor , and during the hierarchical execution process of the preprocessed SQL query statement by the hierarchical executor, the data of each executor performs operations.
The metering device according to claim 9, wherein the matching module is specifically used for:

obtaining the first operation data amount when the preprocessing operation is performed;

obtaining, from the preset workload table, a first processing workload corresponding to the preprocessing operation per unit of operation data;

According to the first operation data amount and the first processing workload, the first workload of the preprocessing operation is obtained.
The metering device according to claim 10, wherein the matching module is further specifically used for:

According to the execution sequence of the data execution operations of the executors, each time an executor is detected to execute the data execution operation, obtain the second operation data amount of the current executor when the data execution operation is performed;

From the preset workload table, obtain the second processing workload of the unit operation data volume corresponding to the data execution operation of the current executor;

According to the second operation data amount and the second processing workload, the second workload of the current executor is obtained.
The metering device according to claim 11, wherein the workload calculation module is specifically used for:

The first workload and the sequentially acquired second workload are accumulated.
The metering device according to claim 8, wherein the generating module is specifically used for:

Analyze the blockchain transaction generated by the user to obtain the input parameters;

According to the input parameters, read the smart contract code to be executed from the blockchain ledger;

Based on the input parameters and the smart contract code, initialize a smart contract virtual machine;

The smart contract virtual machine is controlled to generate an SQL query statement according to the contract logic of the smart contract in the smart contract code.
The metering device according to claim 8, characterized in that, the device further comprises:

An output module, configured to control the SQL query engine to output a data query result if the total amount of work corresponding to the execution operation exceeds a threshold during the process of executing the SQL query statement by the SQL query engine.
A terminal, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, when the processor executes the computer program, the implementation of claims 1 to 7 The steps of any one of the methods.
A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.