CN110895549B - Quantized data retrieval method and system - Google Patents
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Abstract
The invention discloses a quantized data retrieval method and a quantized data retrieval system, which are based on a non-relational database loaded with an SDCDS system, and enable data to be retrieved in a multithread manner through high memory configuration of independent data processing nodes under a distributed scene through a quantized data calculation principle, so that the problem that data cannot be retrieved in a cross-index association manner due to a data index structure in a non-relational structure-based data source is solved. After the invention is used, the performance of cross-index retrieval can be effectively improved by real-time operation in the memory according to the quantized data, thereby realizing the cross-index relation service.
Description
Technical Field
The invention relates to the field of data retrieval, in particular to a quantized data retrieval method and a quantized data retrieval system.
Background
In the background of the current big data industry, a data distribution technology is usually the most effective storage mode and can improve the data retrieval speed by using the cluster capacity, and the data structure determines the data retrieval performance, so that in most cases, data does not have associated retrieval in a relational database, and the associated retrieval has extremely high performance requirements.
The ElasticSearch is a Lucene-based search server. It provides a distributed multi-user capable full-text search engine based on RESTful web interface. The Elasticsearch was developed in the Java language and published as open source under the Apache licensing terms, a popular enterprise level search engine. The ElasticSearch is used in cloud computing, can achieve real-time searching, and is stable, reliable, quick, convenient to install and use. Official clients are available in Java,. NET (C #), PHP, python, apache Groovy, ruby and many other languages.
The SDCDs are one-stop platforms for processing mass machine data, and cover general capabilities of acquisition, storage, search, analysis, alarm, visualization and the like of the machine data. Based on the machine data search platform, a user can quickly acquire all machine data concerned in the domain, and application scenes such as intelligent operation and maintenance, security situation perception, industrial equipment analysis, business analysis and the like are constructed.
In the existing non-relational data source, data cannot be searched in a cross-index association manner due to a data index structure, and cannot be searched and presented in a relational data.
Disclosure of Invention
The invention aims to: the method and the system for retrieving the quantized data solve the problem that cross-index association retrieval of data cannot be realized due to a data index structure in a data source based on a non-relational structure. After the invention is used, the performance of cross-index retrieval can be effectively improved by real-time operation in the memory according to the quantized data, thereby realizing the cross-index relation service.
The technical scheme adopted by the invention is as follows:
a method for retrieving quantized data comprising a non-relational database loaded with SDCDS system, further comprising the steps of:
s1, a user inputs a retrieval condition into an SDCDS system of a non-relational database;
s2, verifying the retrieval conditions by the SDCDS system, and starting a group thread according to a quantized data rule after verification is completed;
s3, retrieving data in the non-relational database by the sub-threads in the group threads according to retrieval conditions;
s4, summarizing the retrieval results of the sub-threads by the group thread;
s5, the SDCDS system judges the business relation according to the summarized data structure and carries out splicing operation in the memory;
and S6, the SDCDS system stores or presents the spliced relation data to at least one operation of a user.
Compared with the traditional data retrieval method, the scheme is mainly realized by the following steps: firstly, data sources are subjected to real-time data acquisition and then are stored in a non-relational database; then, data analysis is carried out on the SDCDS, and the data is retrieved and presented in different modes by utilizing self-service analysis, report form analysis and machine learning; and then the system searches data in the non-relational database according to the search conditions, then the service model judges the service relationship according to the summarized data structure and performs splicing operation in the memory, the keywords of the data relationship are cached for secondary analysis, and finally corresponding data are presented to the data analysis layer in different search modes.
Starting a group thread according to different scenes before retrieving data, retrieving data in the non-relational database by each sub-thread in the group thread respectively, and summarizing the data after the group thread is executed
Further, the SDCDS system in step S2 verifies the service logic of the retrieval condition. The business logic includes objects to execute the business, business rules, data integrity, and workflow.
Further, when the SDCDS system stores the spliced data in step S6, the data relationship key is cached to obtain the cache relationship data.
Further, after the SDCDS system verifies the retrieval condition in step S2, before starting the group thread, the method further includes the following steps:
s201, the SDCDS system carries out quantization processing on the retrieval conditions and checks whether cache relation data exist in the memory;
s202, if the data has the cache relation, the last service paging execution is carried out, whether the corresponding data exists in the memory is judged, and if the corresponding data exists, the data in the memory is directly returned; if the data in the memory does not exist, executing a new business process;
and S203, if the relational data are not cached, starting a group thread according to the quantized data rule, and creating a thread number according to the retrieval rule and the data structure.
And the cache relation data is the data stored by the SDCDS system in the step S6.
Further, the SDCDS system performs searching using Elasticsearch.
Further, the child threads in the group thread in step S3 retrieve the parent-level data and the child-level data in the non-relational database, respectively.
Further, the step of searching the parent-level data and the child-level data in the non-relational database by the child threads in the group thread respectively comprises the following steps:
s301, if the parent-level data retrieval is successful, notifying a child-level thread relation service;
s302, if the sub-level data is successfully retrieved, the parent-level thread relation service is notified.
When data is searched, two threads are distributed to search data at the same time, and because the two threads belong to the same group, one party notifies the other party of the completion of the search when the two threads finish the search, and the notification mode directly uses the in-thread-group notification mode and the thread group is safe.
Further, the method for performing splicing operation by the SDCDS system in step S5 includes the following steps:
s501, judging whether the retrieval service is a parent retrieval or a child retrieval, if the retrieval service is the parent retrieval, entering a step S502, and if the retrieval service is the child retrieval, entering a step S504;
s502, judging whether the parent retrieval has the parent data, and if the parent retrieval has the parent data, entering the step S503; if the parent level searches for no data, directly ending and returning;
s503, splicing the sub-level data with the parent-level data, caching the quantization identifier, and returning the relational data;
s504, judging whether the sublevel retrieval has sublevel data, and if the sublevel retrieval has the sublevel data, entering the step S505; if the sub-level retrieval has no data, directly ending the return;
and S505, the child-level data is spliced with the parent-level data, and the relationship data is returned after the quantization identification is cached.
A quantized data retrieval system comprising a server for performing the quantized data retrieval method of claim 1, said server comprising a processor, a memory, said memory storing computer instructions, said processor reading and executing said computer instructions.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. the invention relates to a method and a system for retrieving quantized data, which aim at solving the problem of relation service. The method is suitable for solving the scene that the non-relational data source realizes the relational service. When the method is used, only the quantization degree of the non-relational data needs to be guaranteed.
2. The invention relates to a method and a system for retrieving quantized data, which adopt the processing advantages of memory and cache and combine the characteristics of elastic search distributed rapid retrieval to retrieve data relationships in parallel, thereby enabling the data relationships to be positioned more rapidly. Meanwhile, the problem that the performance of a non-relational database is reduced or relational data retrieval is not supported due to relational data is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts, wherein:
FIG. 1 is a data retrieval flow diagram of the present invention;
fig. 2 is a system architecture diagram of the present invention.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
The present invention will be described in detail with reference to fig. 1 and 2.
Example 1
A method for retrieving quantized data comprising a non-relational database loaded with SDCDS system, further comprising the steps of:
s1, a user inputs a retrieval condition into an SDCDS system of a non-relational database;
s2, verifying the retrieval conditions by the SDCDS system, and starting a group thread according to a quantized data rule after verification is completed;
s3, retrieving data in the non-relational database by sub-threads in the group threads according to retrieval conditions;
s4, summarizing the retrieval results of the sub-threads by the group thread;
s5, the SDCDS system judges the business relation according to the summarized data structure and carries out splicing operation in the memory;
and S6, the SDCDS system stores or presents the spliced relation data to at least one operation of the user.
Compared with the traditional data retrieval method, the scheme is mainly realized by the following steps: firstly, data sources are subjected to real-time data acquisition and then are stored in a non-relational database; then, data analysis is carried out on the SDCDS, and the data is retrieved and presented in different modes by utilizing self-service analysis, report form analysis and machine learning; and then the system searches data in the non-relational database according to the search condition, then the business model judges the business relation according to the summarized data structure and carries out splicing operation in the memory, the keywords of the data relation are cached for secondary analysis, and finally corresponding data are presented to a data analysis layer in different search modes.
Starting a group thread according to different scenes before retrieving data, retrieving data in the non-relational database by each sub-thread in the group thread respectively, and summarizing the data after the group thread is executed
Example 2
The present embodiment is different from embodiment 1 only in that the SDCDS system verifies the service logic of the retrieval condition in step S2. The business logic includes objects to execute the business, business rules, data integrity, and workflow.
Further, when the SDCDS system stores the spliced data in step S6, the data relationship key words are cached to obtain the cache relationship data.
Further, after the SDCDS system verifies the retrieval condition in step S2, before starting the group thread, the method further includes the following steps:
s201, the SDCDS system carries out quantization processing on the retrieval conditions and checks whether cache relation data exist in the memory;
s202, if the data has the cache relation, the last service paging execution is carried out, whether the corresponding data exists in the memory is judged, and if the corresponding data exists, the data in the memory is directly returned; if the data in the memory does not exist, executing a new business process;
and S203, if the relational data are not cached, starting a group thread according to the quantized data rule, and creating a thread number according to the retrieval rule and the data structure.
And the cache relation data is the data stored by the SDCDS system in the step S6.
Example 3
The present embodiment is different from embodiment 2 only in that the SDCDS system uses an Elasticsearch for searching.
Further, the child threads in the group thread in step S3 retrieve the parent-level data and the child-level data in the non-relational database, respectively.
Further, the step of searching the parent-level data and the child-level data in the non-relational database by the child threads in the group thread respectively further comprises the following steps:
s301, if the parent-level data retrieval is successful, notifying a child-level thread relation service;
s302, if the sub-level data is successfully retrieved, the parent-level thread relation service is notified.
When data is searched, two threads are distributed to search data at the same time, and because the two threads belong to the same group, one party notifies the other party of the completion of the search when the two threads finish the search, and the notification mode directly uses the in-thread-group notification mode and the thread group is safe.
Further, the method for performing splicing operation by the SDCDS system in step S5 includes the following steps:
s501, judging whether the retrieval service is a parent retrieval or a child retrieval, if the retrieval service is the parent retrieval, entering a step S502, and if the retrieval service is the child retrieval, entering a step S504;
s502, judging whether the parent retrieval has the parent data, and if the parent retrieval has the parent data, entering the step S503; if the parent level searches for no data, the return is directly finished;
s503, the parent-level data is spliced with the child-level data, and the relationship data is returned after the quantization identification is cached;
s504, judging whether the sublevel retrieval has sublevel data, and if the sublevel retrieval has the sublevel data, entering the step S505; if the sub-level retrieval has no data, directly ending the return;
and S505, the child-level data is spliced with the parent-level data, and the relationship data is returned after the quantization identification is cached.
Example 4
As shown in fig. 1, this embodiment further illustrates embodiment 3, and according to the principle of elastic search fast retrieval, the present solution performs associated retrieval on data in parallel, so as to provide the data to an external interface for call query. At present, the SDCDS data search platform adopts the technology to carry out relational data retrieval, and the effect is ideal through practical test application.
As shown in FIG. 1: the invention discloses a method for realizing relational service by quantifying data multi-index, which comprises the steps of memory data processing, multithreading concurrent processing, data source plug-in expansion and multi-index service relation.
In this embodiment, the data processing logic is completed by Java code in combination with the Elasticsearch distributed retrieval and Elasticsearch Plugins extensions:
the first step is as follows: a user inputs a retrieval condition;
the second step is that: the retrieval condition enters an SDCDS system retrieval interface;
the third step: verifying the retrieval condition service logic;
the fourth step: the SERVICE module of the SDCDS system carries out quantization processing on the retrieval conditions and checks whether SERVICE-associated cache data exist in the memory:
the SERVICE module carries out quantization processing on the retrieval conditions and checks whether cache relation data exist in the memory;
if the data has the cache relation data, the last service paging execution is carried out, whether the corresponding data exists in the memory is judged, and if the corresponding data exists, the data in the memory is directly returned; if the data in the memory does not exist, executing a new business process;
if no cache relation data exists, starting a group thread according to the quantized data rule, and creating the thread number according to the retrieval rule and the data structure.
The fifth step: the thread group respectively retrieves the parent-level data and the child-level data according to the retrieval conditions;
and a sixth step: the Elasticissearch plug-in retrieves the return data through the service;
if the parent-level data retrieval is successful, informing the child-level thread relation service;
if the sub-level data retrieval is successful, informing the parent-level thread relation service;
the seventh step: summarizing data to a SERVICE module by the thread group;
eighth step: the SERVICE module assembles data according to the SERVICE relation;
if the retrieval is a parent retrieval and no parent data exists in the parent, directly returning;
if the data is retrieved from the parent level and the parent level data exists in the parent level, splicing the child level data and caching the quantization identification in the returned relation data;
if the retrieval is a sublevel retrieval and sublevel data does not exist in the sublevel, directly returning;
if the data is the sub-level retrieval data and the sub-level data exists in the sub-level, the parent-level data is spliced and the quantization identification is cached in the returned relation data;
the default of the structures of the parent-level data and the child-level data is JSON, the structures can also be objects, the JSON is realized according to the requirements of service scenes, and the JSON format is as follows: { "parent": child { \8230 {, }, \8230 { }, and the format description: parent is parent, child is child (child may be greater than one, id is the label);
the ninth step: the relationship data is presented to the user.
Example 5
As shown in fig. 2, a quantized data retrieval system comprising a server for performing the quantized data retrieval method of claim 1, said server comprising a processor, a memory, said memory storing computer instructions, said processor reading and executing said computer instructions.
The memory stores computer instructions comprising a data source and a non-relational database loaded with a SDCDS system comprising:
a data analysis module: the expression mode is used for displaying the relation data spliced by the SDCDS system, and the basic display comprises the following steps: displaying a data source relation, displaying a data source relation chart and displaying a data source relation instrument panel;
a data storage module: a data structure for storing source data and a base value of the data, the source of the data depending on a service;
the data integration module: the system is used for acquiring source data, analyzing, forwarding and combining the source data;
a data management module: the method is used for managing other basic data sources and is mainly embodied in a data management layer;
a management platform module: the system is used for managing the relationship between data and providing relational data for the data analysis module.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules 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. It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A quantized data retrieval method comprises a non-relational database loaded with an SDCDS system, and is characterized in that: further comprising the steps of:
s1, a user inputs a retrieval condition into an SDCDS system of a non-relational database;
s2, verifying the retrieval conditions by the SDCDS system, and starting a group thread according to a quantized data rule after verification is completed;
s3, retrieving data in the non-relational database by sub-threads in the group threads according to retrieval conditions;
s4, summarizing the retrieval results of the sub-threads by the group thread;
s5, the SDCDS system judges the business relation according to the summarized data structure and carries out splicing operation in the memory;
and S6, the SDCDS system stores or presents the spliced relation data to at least one operation of a user.
2. The quantized data retrieval method of claim 1, wherein: in step S2, the SDCDS system verifies the service logic of the retrieval condition.
3. The quantized data retrieval method of claim 1, wherein: and in the step S6, when the SDCDS system stores the spliced data, caching the data relationship key.
4. The quantized data retrieval method of claim 1, wherein: after the SDCDS system verifies the retrieval condition in step S2, before starting the group thread, the method further includes the following steps:
s201, the SDCDS system carries out quantization processing on the retrieval conditions and checks whether cache relation data exist in the memory;
s202, if the data has the cache relation, the last service paging execution is carried out, whether the corresponding data exists in the memory is judged, and if the corresponding data exists, the data in the memory is directly returned; if the data in the memory does not exist, executing a new business process;
and S203, if the relational data are not cached, starting a group thread according to the quantized data rule, and creating a thread number according to the retrieval rule and the data structure.
5. The quantized data retrieval method according to any of claims 1 to 4, wherein: the SDCDS system adopts an elastic search to search.
6. The quantized data retrieval method of claim 5, wherein: and the sub-threads in the group of threads in the step S3 respectively retrieve the parent-level data and the sub-level data in the non-relational database.
7. The quantized data retrieval method of claim 6, wherein: the step of respectively searching the parent-level data and the child-level data in the non-relational database by the child threads in the group thread further comprises the following steps:
s301, if the parent-level data retrieval is successful, notifying a child-level thread relation service;
s302, if the sub-level data is successfully retrieved, the parent-level thread relation service is notified.
8. The quantized data retrieval method of claim 6, wherein: the method for performing splicing operation on the SDCDS system in the step S5 comprises the following steps:
s501, judging whether the retrieval service is a parent retrieval or a child retrieval, if the retrieval service is the parent retrieval, entering a step S502, and if the retrieval service is the child retrieval, entering a step S504;
s502, judging whether the parent retrieval has the parent data, and if the parent retrieval has the parent data, entering the step S503; if the parent level searches for no data, the return is directly finished;
s503, splicing the sub-level data with the parent-level data, caching the quantization identifier, and returning the relational data;
s504, judging whether the sublevel retrieval has sublevel data, and if the sublevel retrieval has the sublevel data, entering the step S505; if the sub-level retrieval has no data, directly ending the return;
and S505, the child-level data is spliced with the parent-level data, and the relationship data is returned after the quantization identification is cached.
9. A quantized data retrieval system comprising a server for performing the quantized data retrieval method of claim 1, said server comprising a processor, a memory, said memory storing computer instructions, said processor reading and executing said computer instructions.
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