CN115934728A - File import method and device and electronic equipment - Google Patents
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Abstract
The invention discloses a file importing method and device and electronic equipment. Relates to the technical field of big data. Wherein, the method comprises the following steps: acquiring a plurality of files to be imported into a Gaussian database and screening conditions of the files to be imported; determining a target control file based on the screening condition, wherein the target control file is used for controlling the import operation of importing a plurality of files to be imported into a target data table in a Gaussian database; and executing a target database instruction, and concurrently importing a plurality of files to be imported into a target data table of the Gaussian database through the target control file, wherein the target database instruction is a data loading command of the Gaussian database. The method and the device solve the technical problem that a batch framework file import mode based on the time sequence database in the related technology cannot be applied to the Gaussian database.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a file importing method and device and electronic equipment.
Background
The batch framework of the credit system mainly aims at processing, analyzing, processing, summarizing and the like of data in the system, wherein the data in the system is used for transactions in the system and providing files for a peripheral system to keep data synchronization; the batch framework simultaneously receives data file import from the peripheral system and processes the data file import. Data synchronization among different applications is realized by a file transmission mode in a credit system to a great extent at present, namely, an upstream transmits files to a downstream application, and the downstream application firstly imports file data into a temporary table through a technical means and then imports the file data into a formal table after processing the file data from the temporary table.
Data file import in a batch framework of an Oracle (sequential database) based database of a credit system is operated through an sql drr instruction (a data loading tool) in an Oracle, and data import is performed through a file import module in the framework after a control file of an adaptation table is written out. In the large background of the transformation of the production application area, the gaussian database is used as the basis for file import, and there is a similar operation to the sql lldr instruction, namely gs _ loader (database instruction in the gaussian database). In Oracle's sql lldr instruction, there is a parameter that is the use of a control file that in most cases is directly applicable to gs _ loader, however there are three cases that are not used:
1. there are more than two screening conditions, i.e., the while statement.
2. Like the filter (file) filter key in sqlldr, in the gaussian database the filter field is the filter condition.
3. The key serves as a table field name.
The problem that file import in a traditional batch framework cannot be directly used under the background of an existing Gaussian database is solved, and the adaptive file import mode of the Gaussian database is difficult to improve. Meanwhile, after a server of a target region is adopted, the concurrency mode aiming at file import is difficult to be suitable for the background of the Gaussian database.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a file importing method, which at least solves the technical problem that a batch framework file importing method based on a time sequence database in the related art cannot be applied to a Gaussian database.
According to an aspect of an embodiment of the present invention, there is provided a file import method, including: acquiring a plurality of files to be imported into a Gaussian database and screening conditions of the files to be imported; determining a target control file based on the screening condition, wherein the target control file is used for controlling the import operation of importing the files to be imported into a target data table in the Gaussian database; and executing a target database instruction, and concurrently importing the plurality of files to be imported into a target data table of the Gaussian database through the target control file, wherein the target database instruction is a data loading command of the Gaussian database.
Further, determining a target control file based on the screening condition includes: calculating the number of screening conditions in the screening conditions to obtain the number of screening conditions; judging whether the screening condition comprises a first keyword or not to obtain a judgment result, wherein the first keyword is used for filtering a plurality of files to be imported according to the first keyword; acquiring a second keyword in the target data table, wherein the second keyword is one of attribute fields in the Gaussian database; and determining the target control file based on the screening condition number, the judgment result and the second keyword, wherein the number of the control files in the target control file is equal to the screening condition number, and in the case that the judgment result indicates that the screening condition comprises the first keyword, the target control file takes the first keyword as one of the fields in the target data table, and the second keyword in the target control file does not comprise a target escape character.
Further, before the plurality of files to be imported are concurrently imported into a target data table of the Gaussian database through the target control file, establishing a process lock based on directories of file fields of the plurality of files to be imported; and after the files to be imported are concurrently imported into a target data table of the Gaussian database through the target control file, releasing the process lock, and deleting the directories of the file fields of the files to be imported.
Further, executing a target database instruction, and concurrently importing the multiple files to be imported into a target data table of the gaussian database through the target control file, includes: calculating a target concurrent scheduling number of a server based on the resource utilization rate of the server, wherein the server is used for processing the target data table of the Gaussian database imported by the plurality of files to be imported; and executing a target database instruction, and concurrently importing the plurality of files to be imported into the target data table according to the target concurrent scheduling number through the target control file.
Further, concurrently importing the multiple files to be imported into the target data table according to the target concurrent scheduling number through the target control file, including: determining the concurrent scheduling number of the process lock according to the target concurrent scheduling number; and according to the concurrency scheduling number of the process lock, the files to be imported are concurrently imported into the target data table through the target control file.
Further, calculating a target concurrent scheduling number of the server based on resource utilization of the server, comprising: distributing the number of the cores of the central processing unit for each file import task of the file to be imported based on a first distribution rule to obtain a first distribution result; adjusting the first distribution result to obtain a second distribution result based on a residual minimum rule, wherein the residual minimum rule is used for adjusting the number of cores of a central processing unit distributed for each file import task so as to enable the time difference of the execution time of each file import task to be smaller than a preset threshold value; determining the target concurrent scheduling number based on the second allocation result and the resource utilization rate.
Further, determining the target concurrent scheduling number based on the second allocation result and the resource utilization ratio comprises: determining the expected execution time of the file import task of each file to be imported based on the second distribution result; sorting the file import tasks of the plurality of files to be imported according to the estimated execution time of the file import task of each file to be imported to obtain a target sorting result; and determining the target concurrent scheduling number according to the target sequencing result and the resource utilization rate.
Further, determining the target concurrent scheduling number according to the target sorting result and the resource utilization rate includes: according to the target sorting result, sequentially selecting the file import tasks of the files to be imported, and calculating the resource utilization rate of the file import task selected each time; under the condition that the resource utilization rate of each selected file import task reaches a preset resource utilization rate threshold value, taking the number of the selected file import tasks as a first concurrency scheduling number, and calculating the number of idle central processing unit cores of the server and the predicted idle time of the idle central processing unit cores; selecting a target file import task from a plurality of file import tasks according to the target sorting result based on the number of cores of the central processing unit with the server idle and the predicted idle time; and determining the target concurrent scheduling number according to the first concurrent scheduling number and the number of the file import tasks of the target file import task.
According to another aspect of the embodiments of the present invention, there is also provided a file importing apparatus, including: the device comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring a plurality of files to be imported into a Gaussian database and screening conditions of the files to be imported; the determining unit is used for determining a target control file based on the screening condition, wherein the target control file is used for controlling the importing operation of importing the files to be imported into a target data table in the Gaussian database; and the importing unit is used for executing a target database instruction, and importing the plurality of files to be imported into a target data table of the Gaussian database concurrently through the target control file, wherein the target database instruction is a data loading command of the Gaussian database.
Further, the determination unit includes: the first calculating subunit is used for calculating the number of the screening conditions in the screening conditions to obtain the number of the screening conditions; the judging subunit is configured to judge whether the screening condition includes a first keyword, to obtain a judgment result, where the first keyword is used to filter multiple files to be imported according to the first keyword; an obtaining subunit, configured to obtain a second keyword in the target data table, where the second keyword is one of attribute fields in the gaussian database; a determining subunit, configured to determine the target control file based on the number of screening conditions, the determination result, and the second keyword, where the number of control files in the target control file is equal to the number of screening conditions, and in a case that the determination result indicates that the screening conditions include the first keyword, the target control file takes the first keyword as one of the fields in the target data table, and the second keyword in the target control file does not include a target escape character.
Further, the file importing apparatus further includes: the establishing unit is used for establishing a process lock based on directories of file fields of the files to be imported before the files to be imported are concurrently imported into a target data table of the Gaussian database through the target control file; and the releasing unit is used for releasing the process lock and deleting the directories of the file fields of the files to be imported after the files to be imported are concurrently imported into the target data table of the Gaussian database through the target control file.
Further, the importing unit includes: the second calculating subunit is configured to calculate a target concurrent scheduling number of the server based on a resource utilization rate of the server, where the server is configured to process the target data table in which the plurality of files to be imported are imported into the gaussian database; and the importing subunit is used for executing a target database instruction and importing the plurality of files to be imported into the target data table concurrently through the target control file according to the target concurrent scheduling number.
Further, the importing subunit includes: the first determining module is used for determining the concurrent scheduling number of the process lock according to the target concurrent scheduling number; and the import module is used for importing the files to be imported into the target data table through the target control file according to the concurrent scheduling number of the process lock.
Further, the second calculation subunit includes: the distribution module is used for distributing the number of the cores of the central processing unit to the file import task of each file to be imported based on a first distribution rule to obtain a first distribution result; the adjusting module is used for adjusting the first distribution result based on a residual minimum rule to obtain a second distribution result, wherein the residual minimum rule is used for adjusting the number of cores of a central processing unit distributed for each file import task, so that the time difference of the execution time of each file import task is smaller than a preset threshold value; a second determining module, configured to determine the target concurrent scheduling number based on the second allocation result and the resource utilization rate.
Further, the second determining module includes: the determining submodule is used for determining the expected execution time of the file import task of each file to be imported based on the second distribution result; the sorting submodule is used for sorting the file import tasks of the plurality of files to be imported according to the predicted execution time of the file import task of each file to be imported, so that a target sorting result is obtained; and the determining submodule is used for determining the target concurrent scheduling number according to the target sequencing result and the resource utilization rate.
Further, the determining the sub-module includes: the first calculation submodule is used for sequentially selecting the file import tasks of the multiple files to be imported according to the target sorting result and calculating the resource utilization rate of the file import task selected each time; the processing submodule I is used for taking the number of the selected file import tasks as a first concurrent scheduling number under the condition that the resource utilization rate of each selected file import task reaches a preset resource utilization rate threshold value, and calculating the number of idle central processing unit cores of the server and the estimated idle time of the idle central processing unit cores; the first selecting submodule is used for selecting a target file import task from a plurality of file import tasks according to the target sorting result on the basis of the number of idle cores of the central processing unit of the server and the predicted idle time; and the first determining submodule is used for determining the target concurrent scheduling number according to the first concurrent scheduling number and the number of the file import tasks of the target file import task.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to perform the file import method of any of the above via execution of the executable instructions.
In the invention, a plurality of files to be imported into a Gaussian database and screening conditions of the files to be imported are obtained; determining a target control file based on the screening condition, wherein the target control file is used for controlling the import operation of importing a plurality of files to be imported into a target data table in a Gaussian database; and executing a target database instruction, and concurrently importing a plurality of files to be imported into a target data table of the Gaussian database through the target control file, wherein the target database instruction is a data loading command of the Gaussian database. And the technical problem that a batch framework file import mode based on the time sequence database cannot be applied to the Gaussian database in the related technology is solved. In the invention, a plurality of files to be imported are concurrently imported into the target data table of the Gaussian database through the target control file adapted to the Gaussian database, so that the situation that the files to be imported into the Gaussian database cannot be directly imported is avoided, and the technical effects of improving the import efficiency and the import stability of the Gaussian data of the files to be imported are realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a diagram illustrating an alternative file import method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an alternative control file adaptation adjustment according to an embodiment of the present invention;
FIG. 3 is a flow diagram of an alternative concurrent import of files, according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an alternative file importing apparatus according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of description, some terms or nouns related to the present invention will be described below.
Formal form: and function actual look-up tables such as a service table, a parameter table and the like.
Temporary table: the data file is imported into a temporary storage position, the table field is consistent with the formal table, and the data file generally has no main key.
Time block: different time zones are different in different regions of the world, the time for executing the batch is different, and in the credit system, the internal time zone is a time zone group 1.
The field times are as follows: the execution time of the modules executing the batch scheduling in each time zone group is different in different fields.
Controlling a file: is one of the physical files of the database, and is a very small binary file for recording the physical structure of the database.
It should be noted that relevant information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for presentation, analyzed data, etc.) referred to in the present disclosure are information and data that are authorized by the user or sufficiently authorized by various parties. For example, an interface is provided between the system and the relevant user or organization, before obtaining the relevant information, an obtaining request needs to be sent to the user or organization through the interface, and after receiving the consent information fed back by the user or organization, the relevant information is obtained.
Example one
In accordance with a first embodiment of the present invention, there is provided an alternative method embodiment for file importing, it should be noted that the steps illustrated in the flowchart of the drawings may be executed in a computer system such as a set of computer-executable instructions, and that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be executed in an order different from that described herein.
Fig. 1 is a flowchart of an alternative file importing method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
step S101, acquiring a plurality of files to be imported into the Gaussian database and screening conditions of the plurality of files to be imported.
The screening conditions may be screening conditions for screening a plurality of files to be imported, and may include, but are not limited to: the type of the screened file, which data in the screened file, the above multiple files to be imported may be files to be imported into the gaussian database in a batch framework (such as a batch framework of a credit system), and since the coverage range of the credit system may cover multiple time blocks, the above multiple files to be imported may be files from the same time block or files to be imported from different time blocks.
And step S102, determining a target control file based on the screening condition, wherein the target control file is used for controlling the import operation of importing a plurality of files to be imported into a target data table in a Gaussian database.
Since the control file of the Oracle database in the related art is not applicable to the gaussian database, the target control file may be determined according to the screening condition for screening the plurality of files to be imported in this embodiment.
Step S103, executing a target database instruction, and concurrently importing a plurality of files to be imported into a target data table of the Gaussian database through the target control file, wherein the target database instruction is a data loading command of the Gaussian database.
The target database instruction may be a data load command of the gaussian database, such as: the gs _ loader instruction. The target data table may be a temporary table in the gaussian database, and after a plurality of files to be imported are concurrently imported into the temporary table of the gaussian database, the data in the temporary table may also be imported into a formal table in the gaussian database.
Through the steps, in the invention, a plurality of files to be imported are concurrently imported into the target data table of the Gaussian database through the target control file adapted to the Gaussian database, so that the situation that the files to be imported into the Gaussian database cannot be directly imported is avoided, and the technical effects of improving the import efficiency and the import stability of the files to be imported into the Gaussian data are realized. Further, the technical problem that a batch framework file import mode based on a time sequence database cannot be applied to a Gaussian database in the related art is solved.
Optionally, determining the target control file based on the screening condition includes: calculating the number of screening conditions in the screening conditions to obtain the number of the screening conditions; judging whether the screening conditions comprise first keywords or not to obtain a judgment result, wherein the first keywords are used for filtering a plurality of files to be imported according to the first keywords; acquiring a second keyword in the target data table, wherein the second keyword is one of attribute fields in the Gaussian database; and determining a target control file based on the screening condition number, the judgment result and the second keyword, wherein the control file number in the target control file is equal to the screening condition number, the target control file takes the first keyword as one of the fields in the target data table under the condition that the judgment result shows that the screening condition comprises the first keyword, and the second keyword in the target control file does not comprise the target escape character.
In this embodiment, aiming at the problem that the sql drr instruction is not applicable to the gaussian database at present, in combination with the batch framework of the credit system in the related art at present, the adaptation adjustment is performed on the control file (corresponding to the target control file) imported from the file in the context of the gaussian database, and fig. 2 is a schematic diagram of an optional adaptation adjustment of the control file according to an embodiment of the present invention, where the adaptation adjustment is as shown in fig. 2. The method comprises the following specific steps:
there are more than two screening conditions, the while statement. The screening conditions include more than two screening conditions, and data can be directly imported according to the screening conditions by carrying out sqlldr instruction import on the control file in the Oracle-based batch framework. However, the gaussian database gs _ loader instruction (corresponding to the target database instruction) is not adapted to the current multi-screening condition situation, in this embodiment, the multi-file import compatibility of the batch framework itself is utilized, the screening conditions are divided according to the we statements, the structures of the control files are consistent, and each control file has only one we condition screening. When a file is imported, the batch framework takes both the two control files as an importing means for the file, and the particularity of the gs _ loader does not influence the data import from the same file to the same temporary table (corresponding to the target data table) through different control files.
The filter key is a filter condition. Unlike the control file in sqlldr, the filtered field itself can be used as a filter condition, i.e., the filtered field in sqlldr is used as a filter condition. When the value is imported into Oracle, the value is merely used as a filtering condition, and is not actually imported into the temporary table. When importing in the gaussian database, if a similar writing method is directly used, an error is directly reported, so that in this embodiment, the filter field (corresponding to the first key mentioned above) can still be put into the temporary table as a temporary field name. Due to the specificity of the temporary table, it can be made a bit different from the formal table, i.e. dynamically adding a temporary filter field to the temporary table. When the formal table is used, the field is ignored.
The key (second key corresponding to the point) serves as a target data table (temporary table) field name. A key word (e.g. comment) in the database is used as an attribute field in the temporary table, and in the sql drr instruction in Oracle, a quotation mark is added to the field in the control file to carry out escape (corresponding to the target branch character), otherwise an error is reported. But the gaussian database in this embodiment supports keyword field names without quotation marks.
The target control file is determined through the screening condition, the control file suitable for file import of the Gaussian database is provided, and the technical effect of improving the stability of the Gaussian data of the batch frame imported with the file to be imported is achieved.
Optionally, before a plurality of files to be imported are concurrently imported into a target data table of the gaussian database through the target control file, a process lock is established based on directories of file fields of the plurality of files to be imported; and after the plurality of files to be imported are concurrently imported into the target data table of the Gaussian database through the target control file, releasing the process lock, and deleting the directories of the file fields of the plurality of files to be imported.
In the embodiment, in a GRAM (a risk asset management system) batch framework, a gs _ loader multi-process execution is controlled in a manner similar to an object lock, namely a process lock. All gs _ loader statements in the target field are exported, and lock resources are obtained in a competition mode under the condition that the number of working processes (the number of optimal concurrent scheduling) is not exceeded. Fig. 3 is a flowchart illustrating an alternative file concurrent import according to an embodiment of the present invention. Because a file to be imported may be distributed to different time zone groups, but the same control file is used for file import, so that the file import of the same file also has a competitive relationship, that is, a competitive relationship exists among different time zone groups for file import, competitive resources also exist in different fields of the same time zone group for file import, and a competitive relationship also exists among files of the same field for file import.
In this embodiment, how to create a process lock resource for the execution of the gs _ loader statement can ensure that the process locks during resource contention are the same, and at the same time, ensure that the locks are correctly released after the process is finished. Therefore, in consideration of the particularity of the bash command, this embodiment may choose to create a directory as the process lock, where the directory may include a field name of a file field, create the gs _ loader process lock before the process execution starts, and execute the release of the process lock based on a return value after the process ends by using a trap (bash built-in command, which may be used to view a processing manner of a shell (an interaction layer between a user and an operating system) environment signal and setting information) of a Linux (an operating system) system. Other matters are checked and executed in the file import. Therefore, the execution of the process for importing these files can be set to a background execution state, and after the gs _ loader process is finished executing, the directory process lock (lock) is released (directory is deleted). The technical effect of ensuring that files with resource competition are stably and efficiently imported into the target data table is achieved by setting the maximum concurrent number of the directory process locks when controlling the process number of gs _ loader.
Optionally, executing the target database instruction, and concurrently importing a plurality of files to be imported into the target data table of the gaussian database through the target control file, includes: calculating a target concurrent scheduling number of a server based on the resource utilization rate of the server, wherein the server is used for processing a target data table of a plurality of files to be imported into a Gaussian database; and executing the target database instruction, and concurrently importing a plurality of files to be imported into the target data table according to the target concurrent scheduling number through the target control file.
In this embodiment, the resource utilization rate of the server may be a CPU utilization rate, an optimal concurrency scheduling number (corresponding to the target concurrency scheduling data) of the gs _ loader, that is, in an existing IT (internet technology) operation and maintenance environment, adaptive allocation of IT operation and maintenance resources is performed, a prediction model is built by using past file import information in combination with historical job information, and a resource scheduling model of the resource utilization rate is built based on a prediction model basis in combination with a performance change trend of past parallel import. And finally, the optimal concurrency number can be dynamically controlled according to the file import prediction time of different fields so as to meet the requirement that the overall task execution time is the lowest.
Because the number of the CPUs is one of the important factors influencing the execution time, the utilization rate of the CPUs can directly reflect the performance change trend, theoretical basis is provided for reasonably distributing resources, the number of the CPUs is increased, and the utilization rate of the CPUs is gradually reduced when the data scale is the same, a model for parallel computing the execution time is modeled by adopting a power function, table 1 is the explanation of partial letters in a formula related to the CPU utilization rate computing process, and the CPU utilization rate can be obtained in the following mode:
TABLE 1
| Parameter(s) | Description of the invention |
| Tserial | Serial execution time |
| C | CPU utilization |
| Tparal | Parallel execution time |
| s | Data size |
| T | Prediction model |
| p | Number of CPU cores |
T paral = a × b + c (formula 1)
Where equation 1 represents a power function model curve, a is the slope and c is the intercept, the model curve is similar to a power function, where b is a power function of the number of CPU cores p, i.e. b = p n And n is a power of several. The serial computation execution time is specific to the utilization rate of the CPU, and in the serial computation statistics, the data amount and the execution time are mainly related, and the serial computation execution time is directly proportional to the data amount, that is, the serial computation expression may be:
T serial = a × s + b (formula 2)
And combining the two formulas to obtain a prediction model under concurrent scheduling execution:
T＝T serial xs + b (formula 3)
The calculation mode of the CPU utilization rate is that the ratio of the serial execution time to the parallel execution time is adopted for obtaining, and the serial execution time is compared with the parallel execution time to obtain the CPU utilization rate, namely:
T serial /(T paral * P) (formula 4)
That is, as the number of CPUs increases, the closer the total execution time is to the serial calculation time, the higher the CPU utilization rate is.
The technical effect of improving the importing efficiency and stability of the target data table for simultaneously importing a plurality of files to be imported into the Gaussian database is achieved.
In this embodiment, the credit system covers multiple multi-time zone groups, and there are multiple bulk execution sessions within each time zone group, which are executed concurrently, either in the same time zone group or in different time zone groups. It is particularly important to control the concurrent execution of file import operations. After the adaptive target control file is imported for the files in the Gaussian database, what is more important is that gs _ loader statements are concurrently scheduled for server resources, otherwise, the whole time of the batch is affected. If the quantity of the concurrent scheduling is too small, the execution duration of the batch is influenced; however, too many concurrent schedules waste server system resources. Therefore, in this embodiment, when a control file concurrently imports a plurality of files to be imported, a resource scheduling model is introduced, an optimal concurrent scheduling number (corresponding to the target concurrent scheduling number) suitable for the file import currently performed is obtained through statistics of currently used resources and progress conditions in the server and through model prediction calculation and dynamic allocation, and the plurality of files to be imported are concurrently imported into the target data table through the target control file according to the target concurrent scheduling number, so that a technical effect of improving import efficiency and stability of the target data table of the gaussian database concurrently importing the plurality of files to be imported is achieved.
Optionally, concurrently importing a plurality of files to be imported into the target data table according to the target concurrent scheduling number by the target control file, including: determining the concurrent scheduling number of the process lock according to the target concurrent scheduling number; and according to the concurrent scheduling number of the process lock, concurrently importing a plurality of files to be imported into the target data table through the target control file.
In this embodiment, a resource scheduling usage statistical model based on a big data model is provided, where the model obtains an optimal gs _ loader concurrent scheduling number (corresponding to the target concurrent scheduling number) by calculating current resources of a server, such as occupation conditions of a CPU, and the time for importing a field file in historical data, determines the concurrent scheduling number of a process lock according to the target concurrent scheduling number, and further controls the number of processes of gs _ loader target database instructions, so that a target control file is controlled to concurrently import a plurality of files to be imported into a target data table according to the number of processes of the gs _ loader instructions (target database instructions), thereby achieving a technical effect of ensuring stability of importing the plurality of files to be imported into the target data table.
Optionally, calculating a target concurrent scheduling number of the server based on the resource utilization rate of the server, including: distributing the number of the cores of the central processing unit for each file import task of the file to be imported based on a first distribution rule to obtain a first distribution result; adjusting the first distribution result to obtain a second distribution result based on a minimum residual rule, wherein the minimum residual rule is used for adjusting the number of the cores of the central processing unit distributed to each file import task so as to enable the time difference of the execution time of each file import task to be smaller than a preset threshold value; and determining a target concurrent scheduling number based on the second allocation result and the resource utilization rate.
In this embodiment, the concurrent scheduling number is calculated, and through dynamic resource scheduling allocation, the first stage: randomly distributing the number of cores of the CPU (corresponding to the number of cores of the CPU) to each task, and calculating the execution time of the task under the current distributed core according to a time prediction model, wherein the task distribution rule is as follows: (1) and the residual error minimum rule ensures that the execution time of each task is balanced as much as possible. Wherein, the residual error minimum rule: the task running time under each resource is considered by using a minimum residual rule, the total task amount (corresponding to the file import task of the file to be imported) is set as n, and the number of CPUs is set as m.
1. And (3) allocating m/n number of cores for each task, and calculating the running time { t1, t 2.. Multidot.tn } of the current task under the corresponding data quantity according to a time prediction model.
2. Traversing the predicted running time of all tasks, searching a long time-consuming task tmax and an unmarked task tmin with the CPU number >1 and low consumption, recording the predicted time r1= tmax of the long time-consuming task, and executing the step 5 when the tmax = = tmin).
3. And increasing the number of CPUs for the long time-consuming task tmax and reducing the number of CPUs for the bottom time-consuming task tmin to recalculate the predicted running time of the two tasks.
4. Traversing the predicted running time of all tasks, finding a long time-consuming task t2max, recording the predicted time r2= t2max, if r1> r2, clearing all marks, and executing the step 1 again), if r1< = r2, adding one to the number of CPUs of tmin, marking that the CPU is modified, reducing one CPU by tmax, recalculating the predicted running time of the two tasks, and executing the step 1).
5. And calculating the memory required by each task under the current distribution core.
The minimum residual error between the calculation tasks is effectively ensured by comparing the maximum task running time traversed each time with the maximum task time traversed last time, and the execution time balance of each task is ensured.
And calculating the target concurrent scheduling number of the server based on the resource utilization rate of the server, thereby realizing the technical effect of improving the accuracy of calculating the optimal concurrent scheduling number of the server.
Optionally, determining the target concurrent scheduling number based on the second allocation result and the resource utilization rate includes: determining the expected execution time of the file import task of each file to be imported based on the second distribution result; sorting the file import tasks of the plurality of files to be imported according to the estimated execution time of the file import task of each file to be imported to obtain a target sorting result; and determining the target concurrent scheduling number according to the target sequencing result and the resource utilization rate.
In this embodiment, the execution time of all tasks may be sorted first by determining the operation mode of the task (file import task), and the tasks are sequentially placed in the computing resources according to the execution time (from left to right). When the resource upper limit is reached, the running state of calculating the kernel number of the CPU is traversed from bottom to top to obtain the kernel number of the idle CPU and estimate the idle time of the idle CPU, the tasks which can be accommodated under the idle resource are searched, the tasks are put in again according to the sequencing rule of the tasks, and finally the optimal concurrent scheduling number (corresponding to the target concurrent scheduling number) is obtained, so that the technical effect of calculating the optimal concurrent scheduling number is realized.
Optionally, determining the target concurrent scheduling number according to the target sorting result and the resource utilization rate includes: according to the target sorting result, sequentially selecting a plurality of file import tasks of the files to be imported, and calculating the resource utilization rate of the file import task selected each time; under the condition that the resource utilization rate of each selected file import task reaches a preset resource utilization rate threshold value, taking the number of the selected file import tasks as a first concurrent scheduling number, and calculating the number of idle central processing unit cores of the server and the estimated idle time of the idle central processing unit cores; selecting a target file import task from a plurality of file import tasks according to a target sorting result based on the number of cores of the idle central processing unit of the server and the predicted idle time; and determining the target concurrent scheduling number according to the first concurrent scheduling number and the number of the file import tasks of the target file import task.
In this embodiment, the file import tasks in the target sorting result may be sequentially placed into the computing resource according to the execution time of the file import task. When the resource upper limit (corresponding to the preset resource utilization rate threshold) is reached, the running state of the central processing unit kernel is calculated from bottom to top in a traversing mode to obtain the number of idle CPUs and estimate the idle time of the idle CPUs, tasks which can be accommodated under the idle resources are searched, the tasks are put in again according to the sequencing rule of the tasks, and finally the optimal concurrent scheduling number (corresponding to the target concurrent scheduling number) is obtained.
According to the embodiment, the same file adaptation transformation is performed on the oracle database, and meanwhile, a self-adaptive allocation model for server resource scheduling in an operation and maintenance environment is adopted, so that the file concurrent import resource allocation under the existing resource use condition in the server is facilitated. The optimal concurrency number of the file concurrent scheduling can be obtained by calculating in other calculation modes to obtain the resource use condition in the current operation and maintenance environment, so that the file concurrent importing requirement in the server is met, and the technical effects of improving the importing efficiency and the importing stability of the Gaussian data of the file to be imported are achieved.
Example two
The second embodiment of the present application provides an optional file importing apparatus, where each implementation unit in the file importing apparatus corresponds to each implementation step in the first embodiment.
Fig. 4 is a schematic diagram of an alternative file importing apparatus according to an embodiment of the present invention, and as shown in fig. 4, the file importing apparatus includes: an acquisition unit 41, a determination unit 42, and an import unit 43.
An obtaining unit 41, configured to obtain multiple files to be imported into a gaussian database and a screening condition for the multiple files to be imported;
a determining unit 42, configured to determine a target control file based on the screening condition, where the target control file is used to control an import operation of importing a plurality of files to be imported into a target data table in a gaussian database;
and the importing unit 43 is configured to execute a target database instruction, and concurrently import a plurality of files to be imported into a target data table of the gaussian database through the target control file, where the target database instruction is a data loading command of the gaussian database.
In the file importing apparatus provided in the second embodiment of the present application, a plurality of files to be imported into the gaussian database and a screening condition for the plurality of files to be imported may be obtained by the obtaining unit 41, a target control file may be determined by the determining unit 42 based on the screening condition, where the target control file is used to control an importing operation of importing the plurality of files to be imported into a target data table in the gaussian database, and a target database instruction is executed by the importing unit 43, and the plurality of files to be imported are concurrently imported into the target data table in the gaussian database through the target control file, where the target database instruction is a data loading instruction of the gaussian database. And the technical problem that a batch framework file import mode based on the time sequence database cannot be applied to the Gaussian database in the related technology is solved. In the invention, a plurality of files to be imported are concurrently imported into the target data table of the Gaussian database through the target control file adapted to the Gaussian database, so that the situation that the files to be imported into the Gaussian database cannot be directly imported is avoided, and the technical effects of improving the import efficiency and the import stability of the Gaussian data of the files to be imported are realized.
Optionally, in the file importing apparatus provided in embodiment two of the present application, the determining unit 42 includes: the first calculating subunit is used for calculating the number of the screening conditions in the screening conditions to obtain the number of the screening conditions; the judging subunit is configured to judge whether the screening condition includes a first keyword, to obtain a judgment result, where the first keyword is used to filter the multiple files to be imported according to the first keyword; the acquiring subunit is used for acquiring a second keyword in the target data table, wherein the second keyword is one of the attribute fields in the Gaussian database; and the determining subunit is used for determining the target control file based on the screening condition number, the judgment result and the second keyword, wherein the number of the control files in the target control file is equal to the screening condition number, and in the case that the judgment result indicates that the screening condition comprises the first keyword, the target control file takes the first keyword as one of the fields in the target data table, and the second keyword in the target control file does not comprise the target escape character.
Optionally, in the file importing apparatus provided in the second embodiment of the present application, the file importing apparatus further includes: the system comprises an establishing unit, a processing unit and a processing unit, wherein the establishing unit is used for establishing a process lock based on a directory of a file field of a plurality of files to be imported before the plurality of files to be imported are concurrently imported into a target data table of a Gaussian database through a target control file; and the releasing unit is used for releasing the process lock and deleting the directories of the file fields of the plurality of files to be imported after the plurality of files to be imported are concurrently imported into the target data table of the Gaussian database through the target control file.
Optionally, in the file importing apparatus provided in the second embodiment of the present application, the importing unit 43 includes: the second calculation subunit is used for calculating a target concurrent scheduling number of the server based on the resource utilization rate of the server, wherein the server is used for processing a target data table of a plurality of files to be imported into the Gaussian database; and the importing subunit is used for executing the target database instruction and importing a plurality of files to be imported into the target data table concurrently through the target control file according to the target concurrent scheduling number.
Optionally, in the file importing apparatus provided in the second embodiment of the present application, the importing subunit includes: the first determining module is used for determining the concurrent scheduling number of the process lock according to the target concurrent scheduling number; and the import module is used for importing a plurality of files to be imported into the target data table through the target control file according to the concurrent scheduling number of the process lock.
Optionally, in the file importing apparatus provided in the second embodiment of the present application, the second calculating subunit includes: the distribution module is used for distributing the number of the cores of the central processing unit to the file import task of each file to be imported based on a first distribution rule to obtain a first distribution result; the adjusting module is used for adjusting the first distribution result based on a residual minimum rule to obtain a second distribution result, wherein the residual minimum rule is used for adjusting the number of the cores of the central processing unit distributed for each file import task so as to enable the time difference of the execution time of each file import task to be smaller than a preset threshold value; and the second determining module is used for determining the target concurrent scheduling number based on the second distribution result and the resource utilization rate.
Optionally, in the file importing apparatus provided in the second embodiment of the present application, the second determining module includes: the determining submodule is used for determining the expected execution time of the file import task of each file to be imported based on the second distribution result; the sorting submodule is used for sorting the file import tasks of the plurality of files to be imported according to the estimated execution time of the file import task of each file to be imported, so as to obtain a target sorting result; and the determining submodule is used for determining the target concurrent scheduling number according to the target sequencing result and the resource utilization rate.
Optionally, in the file importing apparatus provided in the second embodiment of the present application, the determining sub-module includes: the first calculating submodule is used for sequentially selecting a plurality of file import tasks of files to be imported according to the target sorting result and calculating the resource utilization rate of the file import task selected each time; the processing submodule I is used for taking the number of the selected file import tasks as a first concurrent scheduling number under the condition that the resource utilization rate of each selected file import task reaches a preset resource utilization rate threshold value, and calculating the number of idle central processing unit cores of the server and the predicted idle time of the idle central processing unit cores; the first selection submodule is used for selecting a target file import task from a plurality of file import tasks according to a target sorting result on the basis of the number of cores of the idle central processing unit of the server and the predicted idle time; and the first determining submodule is used for determining the target concurrent scheduling number according to the first concurrent scheduling number and the number of the file import tasks of the target file import task.
The file importing apparatus may further include a processor and a memory, where the obtaining unit 41, the determining unit 42, the importing unit 43, and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.
The processor comprises a kernel, and the kernel calls a corresponding program unit from the memory. The kernel can be set to be one or more than one, and the multiple files to be imported are concurrently imported into the target data table of the Gaussian database through the target control file adapted to the Gaussian database by adjusting the kernel parameters, so that the situation that the files to be imported into the Gaussian database cannot be directly imported is avoided, and the technical effects of improving the import efficiency and the import stability of the Gaussian data of the files to be imported are achieved.
The memory may include volatile memory in a computer readable medium, random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to perform the file import method of any of the above via execution of the executable instructions.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, where a computer program is stored, and when the computer program runs, a device on which the computer-readable storage medium is located is controlled to execute any one of the file import methods described above.
Fig. 5 is a schematic diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 5, an embodiment of the present invention provides an electronic device 50, where the electronic device includes a processor, a memory, and a program stored in the memory and running on the processor, and the processor implements a file importing method according to any one of the above items when executing the program.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described in detail in a certain embodiment.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A file import method, comprising:
acquiring a plurality of files to be imported into a Gaussian database and screening conditions of the files to be imported;
determining a target control file based on the screening condition, wherein the target control file is used for controlling the import operation of importing the files to be imported into a target data table in the Gaussian database;
and executing a target database instruction, and concurrently importing the plurality of files to be imported into a target data table of the Gaussian database through the target control file, wherein the target database instruction is a data loading command of the Gaussian database.
2. The file import method according to claim 1, wherein determining the target control file based on the filtering condition includes:
calculating the number of screening conditions in the screening conditions to obtain the number of screening conditions;
judging whether the screening condition comprises a first keyword or not to obtain a judgment result, wherein the first keyword is used for filtering a plurality of files to be imported according to the first keyword;
acquiring a second keyword in the target data table, wherein the second keyword is one of attribute fields in the Gaussian database;
and determining the target control file based on the screening condition number, the judgment result and the second keyword, wherein the control file number in the target control file is equal to the screening condition number, and in the case that the judgment result indicates that the screening condition comprises the first keyword, the target control file takes the first keyword as one of the fields in the target data table, and the second keyword in the target control file does not comprise a target escape character.
3. The file import method according to claim 1,
before the plurality of files to be imported are concurrently imported into the target data table of the Gaussian database through the target control file, the method comprises the following steps: establishing a process lock based on the directories of the file fields of the files to be imported;
after the plurality of files to be imported are concurrently imported into the target data table of the Gaussian database through the target control file, the method comprises the following steps: and releasing the process lock, and deleting the directories of the file fields of the plurality of files to be imported.
4. The file import method according to claim 3, wherein executing a target database instruction to concurrently import the plurality of files to be imported into the target data table of the Gaussian database via the target control file comprises:
calculating a target concurrent scheduling number of a server based on the resource utilization rate of the server, wherein the server is used for processing the target data table of the Gaussian database imported by the plurality of files to be imported;
and executing a target database instruction, and concurrently importing the plurality of files to be imported into the target data table through the target control file according to the target concurrent scheduling number.
5. The file import method according to claim 4, wherein the concurrently importing, by the target control file, the plurality of files to be imported into the target data table according to the target concurrent scheduling number comprises:
determining the concurrent scheduling number of the process lock according to the target concurrent scheduling number;
and according to the concurrent scheduling number of the process lock, concurrently importing the files to be imported into the target data table through the target control file.
6. The file import method according to claim 4, wherein calculating the target concurrency scheduling number of the server based on the resource utilization rate of the server comprises:
distributing the number of the cores of the central processing unit for each file import task of the file to be imported based on a first distribution rule to obtain a first distribution result;
adjusting the first distribution result to obtain a second distribution result based on a residual minimum rule, wherein the residual minimum rule is used for adjusting the number of cores of a central processing unit distributed for each file import task so as to enable the time difference of the execution time of each file import task to be smaller than a preset threshold value;
determining the target concurrent scheduling number based on the second allocation result and the resource utilization rate.
7. The file import method according to claim 6, wherein determining the target concurrent scheduling number based on the second allocation result and the resource utilization ratio comprises:
determining the expected execution time of the file import task of each file to be imported based on the second distribution result;
sorting the file import tasks of the plurality of files to be imported according to the estimated execution time of the file import task of each file to be imported to obtain a target sorting result;
and determining the target concurrent scheduling number according to the target sequencing result and the resource utilization rate.
8. The file import method according to claim 7, wherein determining the target concurrent scheduling number according to the target sorting result and the resource utilization rate comprises:
according to the target sorting result, sequentially selecting the file import tasks of the files to be imported, and calculating the resource utilization rate of the file import task selected each time;
under the condition that the resource utilization rate of each selected file import task reaches a preset resource utilization rate threshold value, taking the number of the selected file import tasks as a first concurrency scheduling number, and calculating the number of idle central processing unit cores of the server and the predicted idle time of the idle central processing unit cores;
selecting a target file import task from a plurality of file import tasks according to the target sorting result based on the number of cores of the central processing unit with the server idle and the predicted idle time;
and determining the target concurrent scheduling number according to the first concurrent scheduling number and the number of the file import tasks of the target file import task.
9. A file import apparatus, comprising:
the device comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring a plurality of files to be imported into a Gaussian database and screening conditions of the files to be imported;
the determining unit is used for determining a target control file based on the screening condition, wherein the target control file is used for controlling the importing operation of importing the files to be imported into a target data table in the Gaussian database;
and the importing unit is used for executing a target database instruction, and importing the plurality of files to be imported into a target data table of the Gaussian database concurrently through the target control file, wherein the target database instruction is a data loading command of the Gaussian database.
10. An electronic device comprising one or more processors and memory storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the file import method of any of claims 1 to 7.
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