CN108664573A - A kind of quick processing system of big data and method with double-channel data library - Google Patents
A kind of quick processing system of big data and method with double-channel data library Download PDFInfo
- Publication number
- CN108664573A CN108664573A CN201810389728.6A CN201810389728A CN108664573A CN 108664573 A CN108664573 A CN 108664573A CN 201810389728 A CN201810389728 A CN 201810389728A CN 108664573 A CN108664573 A CN 108664573A
- Authority
- CN
- China
- Prior art keywords
- data
- module
- data processing
- condition
- converting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012545 processing Methods 0.000 title claims abstract description 148
- 238000000034 method Methods 0.000 title claims description 40
- 238000006243 chemical reaction Methods 0.000 claims description 34
- 238000003672 processing method Methods 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000013497 data interchange Methods 0.000 abstract 2
- 230000009897 systematic effect Effects 0.000 abstract 1
- 230000002776 aggregation Effects 0.000 description 14
- 238000004220 aggregation Methods 0.000 description 14
- 238000006116 polymerization reaction Methods 0.000 description 6
- 230000009977 dual effect Effects 0.000 description 3
- 238000013523 data management Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present invention relates to big data processing technology fields, disclose a kind of quick processing system of big data with double-channel data library, including condition judgment module, the first data processing module, the second data processing module and data interworking module, for wherein condition judgment module for presetting reference value in systems, the data volume that the condition of reference value and user input systems is formed carries out size comparison；First data processing module is used to, when condition data amount is less than reference value, the module be selected to carry out data processing；Second data processing module is used to, when condition data amount is more than reference value, the module be selected to carry out data processing；Data interchange module is converted to the return value of systematic unity identical with the data processed result format of the second data processing module, realizes data interchange for converting the data processed result of the first data processing module.It is switched fast use between present invention realization disparate databases.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a big data rapid processing system with a dual-channel database and a method thereof.
Background
A Database (Database) is a warehouse that organizes, stores and manages data according to a data structure, and as information technology and markets develop, data management is no longer just storing and managing data, but is converted into various data management ways required by users. Databases are of many types, ranging from the simplest tables that store various types of data to large database systems that are capable of mass data storage.
MySQL is a relational database management system that keeps data in different tables instead of putting all the data in one large repository, which increases speed and flexibility. The SQL language used by MySQL is the most common standardized language for accessing databases. MySQL software has the characteristics of small volume, high speed and low total cost of ownership, and particularly has the characteristic of open source codes, and generally MySQL is selected as a website database for developing small and medium websites.
MySQL has its own disadvantages, such as small scale, limited functions, etc., and in the face of increasing data, the traditional relational database has not satisfied the increasing data requirement, is not fast in complex query, and has low efficiency.
The elastic search is an open-source high-expansion distributed full-text search engine which can store and search data in near real time; the system has good expansibility, can be expanded to hundreds of servers, and processes PB-level structured or unstructured data. The development of the ElasticSearch has been rapid in recent years, has surpassed the role of the original pure search engine, and has increased the characteristics of data aggregation analysis and visualization.
And how to balance or be compatible with the connection between big data and the relational database MySQL and the novel database Elasticissearch is an urgent problem to be solved at present.
Disclosure of Invention
In order to solve the defects of the prior art, the invention discloses a big data rapid processing system with a dual-channel database and a method thereof, aiming at realizing the mutual switching use between a balanced relational database MYSQL and a novel database Elasticissearch, returning an execution result rapidly and increasing the competitiveness of a product.
In order to achieve the technical purpose and achieve the technical effect, the invention discloses a big data rapid processing system with a dual-channel database, which comprises a condition judgment module, a first data processing module, a second data processing module and a data intercommunication module, wherein the condition judgment module, the first data processing module, the second data processing module and the data intercommunication module are arranged in the data processing system
The condition judgment module is used for presetting a reference value in the system and comparing the reference value with the data size formed by the conditions input into the system by the user;
the first data processing module is used for selecting the module to process data when the condition data quantity is smaller than the reference value;
the second data processing module is used for selecting the module to process data when the condition data quantity is larger than the reference value;
the data intercommunication module is used for converting the data processing result of the first data processing module into a system uniform return value with the same format as the data processing result of the second data processing module, and realizing data intercommunication.
Furthermore, the device also comprises an emergency processing module,
and the emergency processing module is used for selecting the first data module to process the data when the condition data quantity is larger than the reference value and the second data module fails to process the data.
Furthermore, the data intercommunication module comprises a conversion rule module and a conversion output module,
the conversion rule module is used for storing the rule for converting the data processing result format of the first data module and the data processing result format of the second data module through a data definition conversion rule,
the conversion output module is used for converting the data processing result format of the first data module into the data processing result format of the second data module according to the conversion rule in the conversion rule module, so as to realize data intercommunication of different data processing modules.
The invention also discloses a big data rapid processing method with a dual-channel database, which comprises a condition judgment module, a first data processing module, a second data processing module and a data intercommunication module, and the method comprises the following steps:
s1: presetting a reference value in the system, and comparing the reference value with the data size formed by the conditions input into the system by a user;
s2: according to the judgment result of S1, if the condition data amount is less than the reference value, selecting a first data module for data processing;
s3: according to the judgment result of S1, if the condition data quantity is larger than the reference value, selecting a second data module for data processing;
s4: and converting the data processing result of the S2 into a system uniform return value with the same format as the data processing result of the S3 through a data intercommunication module, thereby realizing data intercommunication.
5. The method for rapidly processing big data with a dual-channel database as claimed in claim 4, further comprising the steps of:
s5: according to the judgment result of the S1, if the condition data amount is larger than the reference value, when the second data module fails and cannot process data, the first data module is selected for data processing.
Further, the specific steps of the conversion described in step S4 are:
s4-1: defining a conversion rule through a piece of data, and storing the rule for converting the data processing result format of the first data module and the data processing result format of the second data module;
s4-2: and according to the conversion rule in the step S4-1, converting the data processing result format of the first data module into the data processing result format of the second data module, thereby implementing data intercommunication between different data processing modules.
Furthermore, the statement adopted by the first data module is an SQL statement executable by MySQL, and the statement adopted by the second data module is a json statement executable by an Elasticissearch.
Further, the condition includes one or more rules set by the user, and a complex rule may be formed among the rules by adopting a union, difference or intersection.
Further, in S1, the user inputs the system condition to form XML data;
in S2, the specific method for processing the data of the first data module is as follows: reading XML data, analyzing the XML data, acquiring data of each node, splitting fields, carrying out condition splicing, then combining all conditions, converting the XML data into SQL statements, and executing data processing;
in S3, the specific method for processing the data of the second data module is as follows: presetting an input and output rule for converting XML data into a json statement, reading the XML data, converting according to the input and output rule, converting the XML data into the json statement, and executing data processing through an elastic search;
in S4, the specific method for implementing data intercommunication includes: defining a MySQL table by a piece of data, and storing a rule for converting the format of the SQL data processing result and the format of the json data processing result; and according to the conversion rule, converting the SQL data processing result format into a json data processing result format, and realizing data intercommunication of different data processing modules.
The invention has the following beneficial effects:
(1) the big data rapid processing system and the big data rapid processing method can rapidly switch the language between the MySQL of the relational database and the Elasticissearch of the novel database, so that barrier-free switching and use can be carried out between the MySQL and the Elasticissearch.
(2) The method can quickly return the execution result, increase the competitiveness of the product, and solve the problem that MySQL cannot return the result in time under the condition of inquiring large data under complex conditions.
Drawings
FIG. 1 is a schematic block diagram of a big data fast processing system with a dual channel database of the present invention.
FIG. 2 is a schematic block diagram of a data interworking module of a big data fast processing system with a dual channel database of the present invention.
FIG. 3 is a schematic flow chart of a big data fast processing method with a dual-channel database according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments.
The present invention provides a big data fast processing system 10 with a dual channel database, as shown in fig. 1, comprising: a condition judging module 101, a first data processing module 102, a second data processing module 103 and a data intercommunication module 104. Wherein,
the condition judgment module 101 is configured to preset a reference value in the system, and compare the reference value with a data size formed by a condition input by a user to the system;
the first data processing module 102 is configured to select the module for data processing when the condition data amount is smaller than the reference value;
the second data processing module 103 is used for selecting the module to perform data processing when the condition data amount is larger than the reference value;
the data interworking module 104 is configured to convert the data processing result of the first data processing module 102 into a system uniform return value having the same format as the data processing result of the second data processing module 103, so as to implement data interworking.
An emergency treatment module 105 is also included and,
the emergency processing module 105 is configured to select the first data module 103 to process data when the condition data amount is greater than the reference value and the second data module 104 fails to process data.
As shown in fig. 2, the data interworking module 104 further includes a conversion rule module 1041 and a conversion output module 1042.
The conversion rule module 1041 is configured to store a rule for converting the data processing result format of the first data module 102 and the data processing result format of the second data module 103 by defining a conversion rule with one piece of data;
the conversion output module 1042 is used for converting the data processing result format of the first data module 102 into the data processing result format of the second data module 103 according to the conversion rule in the conversion rule module, so as to implement data intercommunication between different data processing modules.
According to the system provided by the invention, the invention also provides a big data rapid processing method with a dual-channel database, which comprises a condition judgment module 101, a first data processing module 102, a second data processing module 103 and a data intercommunication module 104.
As shown in fig. 3, the method comprises the steps of:
s1: presetting a reference value in the system, and comparing the reference value with the data size formed by the conditions input into the system by a user;
s2: according to the judgment result of S1, if the condition data amount is less than the reference value, selecting a first data module for data processing;
s3: according to the judgment result of S1, if the condition data quantity is larger than the reference value, selecting a second data module for data processing;
s4: and converting the data processing result of the S2 into a system uniform return value with the same format as the data processing result of the S3 through a data intercommunication module, thereby realizing data intercommunication. Specifically, the conversion comprises the following specific steps:
s4-1: defining a conversion rule through a piece of data, and storing the rule for converting the data processing result format of the first data module and the data processing result format of the second data module;
s4-2: and according to the conversion rule in the step S4-1, converting the data processing result format of the first data module into the data processing result format of the second data module, thereby implementing data intercommunication between different data processing modules.
The method also includes the steps of:
s5: according to the judgment result of S1, if the condition data amount is larger than the reference value, when the second data module fails and cannot process data, the first data module is selected for data processing.
Specifically, in this embodiment, the statement adopted by the first data module is an SQL statement executable by MySQL, and the statement adopted by the second data module is a json statement executable by Elasticsearch.
The conditions of the input system are input individually according to the service requirements of the user, the conditions can comprise one or more rules, and a complex rule condition can be formed among the rules in a mode of solving union set, difference set or intersection set. At this time, the more rules, the more staggered the relationship between the rules, the more complex the finally formed rules, and the slower the speed of using the conventional balanced relational database MySQL.
The conditions that the user enters into the system form XML data. A reference value is preset in the system, and the reference value is compared with the data size formed by the conditions input into the system by a user.
When the data amount of the condition is smaller than the reference value, the data processing is carried out by adopting a balanced relational database MySQL, and the processing speed is not slow. The specific method of data processing is as follows: reading XML data, analyzing the XML data, acquiring data of each node, splitting fields, carrying out condition splicing, then combining all conditions, converting the XML data into SQL sentences, and executing data processing through MySQL.
Specifically, the conversion rule for converting XML into SQL is:
1. in the XML < group > node, finding the attribute es _ table attribute, obtaining the table name under the attribute, and associating the table.
1.1 Multi-Table Association Using EXISTS or not EXISTS connections
2. Finding out an attribute es _ query = 'aggs' in an XML < group > node, and acquiring aggregation information under the attribute
2.1, defining an aggregation point: defining group by and having entries
2.2, defining the polymerization type: corresponding table information
2.3, defining the number of polymerization groups: defining group by and having rules
2.4, defining an aggregation field: group by and having fields
2.5, defining the polymerization conditions: group by and having values
3. Finding out the attribute all _ customer attribute in the XML < group > node, and returning the query result information;
4. finding an attribute es _ field attribute in an XML (extensive makeup language) node, and acquiring field information under the attribute;
5. finding attribute operator attribute in XML (extensive Makeup) node, obtaining condition information under attribute
(5.1) definition [ string character type ] relationship: multi-condition term, equal to term, not equal to most _ not + term, contain like, not contain notLike, empty is null or = "", not empty is not null or = "", prefix match like, suffix match like, prefix mismatch not like, suffix mismatch not like;
(5.2) define [ data time type ] relationship: equal to AND, not equal to OR, earlier than equal to < =, later than equal > =, distance current > = AND;
(5.3) definition of int numerical type relation: equal to =, not equal to! =, greater than >, greater than or equal to > =, less than <, less than or equal to < =;
6. finding out attribute <![ CDATA [1] ] > attribute at XML (group) node, and acquiring numerical value information under the attribute;
7. table association, field and value combination, condition combination and integration into corresponding SQL statements.
When the data volume of the condition is larger than the reference value, the novel database Elasticissearch is selected for data processing, the problem of low processing speed can occur when the large data volume formed by the complex rule is processed by using the balanced relational database MySQL, and the novel database Elasticissearch can quickly return an execution result. The data processing method comprises the following specific steps: presetting an input and output rule for converting XML data into a json statement, reading the XML data, converting according to the input and output rule, converting the XML data into the json statement, and executing data processing through an elastic search. The input and output rules for converting the XML data into the json statement can be realized by using a preset ES plug-in, and the XML format can be converted into the json format by defining the input and output format rules in the ES plug-in.
Specifically, the input and output rules of the ES plug-in for XML conversion to json are:
1. finding an attribute es _ table attribute in an XML < group > node, acquiring a table name under the attribute, and making type association on the table;
2. finding out an attribute ES _ query = 'aggs' attribute at an XML < group > node, acquiring aggregation information under the attribute, and converting the aggregation information into ES aggregation statement grammar
2.1 into aggregation flags aggregations,
2.2 Association of polymerization types
2.3 definition base Cardinal definition aggregation number
2.4 define aggregation field
2.5 definition of script conditions
3. Finding the attribute all _ customer attribute in the XML < group > node, and returning ES result information;
4. finding an attribute ES _ field attribute in an XML (extensive makeup language) node, and acquiring ES field information under the attribute;
5. finding attribute operator attribute in XML (extensive Makeup) node, obtaining ES condition information under attribute
(5.1) definition [ string character type ] relationship: multiple conditions trees [ "0", "1" ], equal to item, not equal to must _ not item, including wildcard, not including must _ not wildcard, being null mut _ not, not being null exists, prefix match wildcard, suffix match wildcard, prefix mismatch mut _ not wildcard, suffix mismatch mut _ notwildcard
(5.2) define [ data time type ] relationship: equal to range from to, not equal to most _ not range from to, earlier than equal to range from to, later than equal to range from to, and from the current range from to
(5.3) definition of int numerical type relation: equal to, not equal to, most _ not, greater than, less than, and less than
6. And combining type, document, field and associated DSL conditions to complete ES statement integration.
And converting the data processing result of the MySQL balanced relational database into a system uniform return value with the same format as the data processing result of the Elasticissearch database through a data intercommunication module, so as to realize data intercommunication. The specific method for realizing data intercommunication is as follows: defining a MySQL table by a piece of data, and storing a rule for converting the format of the SQL data processing result and the format of the json data processing result; and according to the conversion rule, converting the SQL data processing result format into a json data processing result format, and realizing data intercommunication of different data processing modules.
Specifically, the content defined by the MySQL table is as follows:
core xml rules
(1) Defining XML basic node (root > </root >;
(2) defining an XML two-layer node < group > </group >, setting scenes or attributes which the group can add, such as es _ table = (data table), es _ query = (condition information), all _ customer = (returned value):
2.1 defines the aggregation point: es _ query = "aggs"
2.2 definition of aggregation type es _ table = "XXX"
2.3 definition of the polymerization base es _ ags = "carduality"
2.4 definition aggregation field es _ field = "XXX"
2.5 Definitions polymerization conditions operator = "
(3) Defining XML each grouping attribute < term > </term >;
(4) locating the database information of the query, wherein the database information corresponds to the database in MYSQL and the index in the elastic search;
(5) defining the relation < relationship > </relationship > among a plurality of XML same attributes;
(6) splicing database fields and field comparison relationships
(6.1) defining the XML field in es _ field = ";
(6.2) operator = "" for defining XML comparison relationship;
(6.2.1) definition [ string character type ] relationship: multi-condition in, equal to equal, not equal to notEqual, contain like, not contain notLike, be null isNull, not null isNotNull, prefix match preLike, suffix match postLike, prefix mismatch notrelike, suffix mismatch notpostnotpostcake;
(6.2.2) define [ data time type ] relationship: equal to between, not equal to notBetween, earlier than equal to smallerEqual, later than equal to largerEqual, distance current earriernow;
(6.2.3) definition int numerical type relation: equal to equal, not equal to notEqual, greater than larger, greater than or equal to larger Equal, less than smaller, less than or equal to smallerEqual;
(7) an XML storage structure is defined.
The main concept pairs of the MySQL and elasticsera data architectures are listed in table 1.
Table 1 main concept comparison of MySQL and elasticserum data architecture
| MySQL | Elasticserach |
| database | index |
| table | type |
| row | Document |
| column | field |
| schema | mapping |
| index | Everything is index |
| select * from | GET |
| INSERT | PUT |
| UPDATE | _UPDATE |
| DELETE | DEL |
The method also includes the steps of:
when the data quantity of the condition is larger than the reference value, a novel database Elasticissearch is selected to process data; but when the novel database Elasticisearch fails to process data, the system can select the balance relational database MySQL to process the data. Although the data processing speed is reduced, the problem that the system cannot operate under the condition of failure of the elastic search database is avoided, and the stable operation of the system is ensured.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also included in the scope of the present invention.
Claims (9)
1. A big data rapid processing system with a dual-channel database is characterized by comprising a condition judgment module, a first data processing module, a second data processing module and a data intercommunication module, wherein the condition judgment module, the first data processing module, the second data processing module and the data intercommunication module are arranged in the database
The condition judgment module is used for presetting a reference value in the system and comparing the reference value with the data size formed by the conditions input into the system by the user;
the first data processing module is used for selecting the module to process data when the condition data quantity is smaller than the reference value;
the second data processing module is used for selecting the module to process data when the condition data quantity is larger than the reference value;
the data intercommunication module is used for converting the data processing result of the first data processing module into a system uniform return value with the same format as the data processing result of the second data processing module, and realizing data intercommunication.
2. The big data rapid processing system with the dual-channel database as claimed in claim 2, further comprising an emergency processing module,
and the emergency processing module is used for selecting the first data module to process the data when the condition data quantity is larger than the reference value and the second data module fails to process the data.
3. The big data rapid processing system with the dual-channel database as claimed in claim 2, wherein the data intercommunication module comprises a conversion rule module and a conversion output module,
the conversion rule module is used for storing the rule for converting the data processing result format of the first data module and the data processing result format of the second data module through a data definition conversion rule,
the conversion output module is used for converting the data processing result format of the first data module into the data processing result format of the second data module according to the conversion rule in the conversion rule module, so as to realize data intercommunication of different data processing modules.
4. A big data rapid processing method with a dual-channel database is provided with a condition judgment module, a first data processing module, a second data processing module and a data intercommunication module, and is characterized by comprising the following steps:
s1: presetting a reference value in the system, and comparing the reference value with the data size formed by the conditions input into the system by a user;
s2: according to the judgment result of S1, if the condition data amount is less than the reference value, selecting a first data module for data processing;
s3: according to the judgment result of S1, if the condition data quantity is larger than the reference value, selecting a second data module for data processing;
s4: and converting the data processing result of the S2 into a system uniform return value with the same format as the data processing result of the S3 through a data intercommunication module, thereby realizing data intercommunication.
5. The method for rapidly processing big data with a dual-channel database as claimed in claim 4, further comprising the steps of:
s5: according to the judgment result of the S1, if the condition data amount is larger than the reference value, when the second data module fails and cannot process data, the first data module is selected for data processing.
6. The method for fast processing big data with two-channel database as claimed in claim 5, wherein the step of converting in step S4 comprises the following steps:
s4-1: defining a conversion rule through a piece of data, and storing the rule for converting the data processing result format of the first data module and the data processing result format of the second data module;
s4-2: and according to the conversion rule in the step S4-1, converting the data processing result format of the first data module into the data processing result format of the second data module, thereby implementing data intercommunication between different data processing modules.
7. The method for rapidly processing the big data with the dual-channel database as claimed in claim 6, wherein the statement adopted by the first data module is a MySQL executable SQL statement, and the statement adopted by the second data module is an Elasticsearch executable json statement.
8. The method as claimed in claim 7, wherein the condition includes one or more rules set by a user, and the rules may form a complex rule by union, difference or intersection.
9. The method for fast processing big data with two-channel database according to claim 8,
in S1, the user inputs the system condition to form XML data;
in S2, the specific method for processing the data of the first data module is as follows: reading XML data, analyzing the XML data, acquiring data of each node, splitting fields, carrying out condition splicing, then combining all conditions, converting the XML data into SQL statements, and executing data processing;
in S3, the specific method for processing the data of the second data module is as follows: presetting an input and output rule for converting XML data into a json statement, reading the XML data, converting according to the input and output rule, converting the XML data into the json statement, and executing data processing through an elastic search;
in S4, the specific method for implementing data intercommunication includes: defining a MySQL table by a piece of data, and storing a rule for converting the format of the SQL data processing result and the format of the json data processing result; and according to the conversion rule, converting the SQL data processing result format into a json data processing result format, and realizing data intercommunication of different data processing modules.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810389728.6A CN108664573A (en) | 2018-04-27 | 2018-04-27 | A kind of quick processing system of big data and method with double-channel data library |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810389728.6A CN108664573A (en) | 2018-04-27 | 2018-04-27 | A kind of quick processing system of big data and method with double-channel data library |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108664573A true CN108664573A (en) | 2018-10-16 |
Family
ID=63780326
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810389728.6A Pending CN108664573A (en) | 2018-04-27 | 2018-04-27 | A kind of quick processing system of big data and method with double-channel data library |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108664573A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109542963A (en) * | 2018-10-31 | 2019-03-29 | 平安科技（深圳）有限公司 | Hospital data processing method and relevant apparatus based on big data |
| CN110008173A (en) * | 2019-03-07 | 2019-07-12 | 深圳市买买提信息科技有限公司 | A kind of method and device of data storage |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002259257A (en) * | 2001-03-05 | 2002-09-13 | Casio Comput Co Ltd | Document preparing device and method for automatically switching data transfer |
| CN103678609A (en) * | 2013-12-16 | 2014-03-26 | 中国科学院计算机网络信息中心 | Large data inquiring method based on distribution relation-object mapping processing |
| CN106294805A (en) * | 2016-08-15 | 2017-01-04 | 成都九鼎瑞信科技股份有限公司 | Data processing method and device |
| CN106709012A (en) * | 2016-12-26 | 2017-05-24 | 北京锐安科技有限公司 | Method and device for analyzing big data |
| CN107958080A (en) * | 2017-12-14 | 2018-04-24 | 上海特易信息科技有限公司 | A kind of big data report processing method based on ElasticSearch |
-
2018
- 2018-04-27 CN CN201810389728.6A patent/CN108664573A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002259257A (en) * | 2001-03-05 | 2002-09-13 | Casio Comput Co Ltd | Document preparing device and method for automatically switching data transfer |
| CN103678609A (en) * | 2013-12-16 | 2014-03-26 | 中国科学院计算机网络信息中心 | Large data inquiring method based on distribution relation-object mapping processing |
| CN106294805A (en) * | 2016-08-15 | 2017-01-04 | 成都九鼎瑞信科技股份有限公司 | Data processing method and device |
| CN106709012A (en) * | 2016-12-26 | 2017-05-24 | 北京锐安科技有限公司 | Method and device for analyzing big data |
| CN107958080A (en) * | 2017-12-14 | 2018-04-24 | 上海特易信息科技有限公司 | A kind of big data report processing method based on ElasticSearch |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109542963A (en) * | 2018-10-31 | 2019-03-29 | 平安科技（深圳）有限公司 | Hospital data processing method and relevant apparatus based on big data |
| CN109542963B (en) * | 2018-10-31 | 2023-10-24 | 平安科技（深圳）有限公司 | Hospital data processing method and related device based on big data |
| CN110008173A (en) * | 2019-03-07 | 2019-07-12 | 深圳市买买提信息科技有限公司 | A kind of method and device of data storage |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110633292B (en) | Query method, device, medium, equipment and system for heterogeneous database | |
| US11941034B2 (en) | Conversational database analysis | |
| US12038929B2 (en) | Aggregation operations in a distributed database | |
| US11809468B2 (en) | Phrase indexing | |
| US11429607B2 (en) | Machine language query management for low-latency database analysis system | |
| US11748264B1 (en) | Approximate unique count | |
| CN111198898B (en) | Big data query method and big data query device | |
| US11836136B2 (en) | Distributed pseudo-random subset generation | |
| CN108664573A (en) | A kind of quick processing system of big data and method with double-channel data library | |
| US20240119054A1 (en) | Phrase Translation for a Low-Latency Database Analysis System | |
| US20210109974A1 (en) | Query Execution On Compressed In-Memory Data | |
| US11989196B2 (en) | Object indexing | |
| US11803543B2 (en) | Lossless switching between search grammars | |
| CN114138821A (en) | Database query method, system and electronic equipment | |
| US12072890B2 (en) | Visualization data reuse in a data analysis system | |
| CN116737753A (en) | Service data processing method, device, computer equipment and storage medium | |
| CN117609303A (en) | Multi-table joint query method, device, equipment and storage medium | |
| CN114817308A (en) | Method for optimizing execution of multiple percentile _ cont analysis functions in database | |
| CN112785247A (en) | Digital intelligent recruitment management system | |
| CN117609225A (en) | Multi-level associated data searching method, terminal equipment and medium based on mysql |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| CB02 | Change of applicant information |
Address after: 361101 1-3F, Zone D, innovation building, software park, Xiamen Torch High tech Zone, Xiamen City, Fujian Province Applicant after: Xiamen Nanxun Co.,Ltd. Address before: 361008 Fujian province Xiamen software park two sunrise Road No. 22 unit 401 Applicant before: XIAMEN NASCENT SOFTWARE TECHNOLOGY CO.,LTD. |
|
| CB02 | Change of applicant information | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181016 |
|
| RJ01 | Rejection of invention patent application after publication |