CN107958080A - A kind of big data report processing method based on ElasticSearch - Google Patents
A kind of big data report processing method based on ElasticSearch Download PDFInfo
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- CN107958080A CN107958080A CN201711342616.7A CN201711342616A CN107958080A CN 107958080 A CN107958080 A CN 107958080A CN 201711342616 A CN201711342616 A CN 201711342616A CN 107958080 A CN107958080 A CN 107958080A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
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Abstract
The invention discloses a kind of big data report processing method based on ElasticSearch, it includes condition modular converter, ElasticSearch.Net modules, result modular converter and result export module.Present invention utilizes PB grades of mass datas of ElasticSearch efficient process and have the characteristics that polymerizable functional come perform complexity business intelligence and data query, overcome traditional distributed data base can only effective search data deficiency polymerizable functional the shortcomings that can not realizing the business intelligences such as report and data analysis, provide a kind of effective ways for taking into account efficiency and synchronism for big data report form processing.
Description
Technical field
At computer communication field, more particularly to a kind of big data report based on ElasticSearch
Reason method.
Background technology
With the arriving of cloud era, data become the core of information system, and the logic of most operation systems returns root
Knot bottom is the operation to data, and big data has also attracted more and more concerns.In common information system, usually it can all carry
For the detailed inquiry of data and report form statistics function.But in face of TB grades even PB grades of mass data, traditional relational data
Helpless, the distributed data base that then can handle big data occurs.Distributed data base either depositing in data
There is larger change in terms of storage and the search of data with relevant database and become more efficient.
But the weakness of distributed data base maximum is just a lack of polymerizable functional, that is, usually said data summarization
Function.However, such function is the basis of report form statistics.Then just there is a kind of method of compromise, in routine duties, together
When the data cleaned are imported into distributed data base and relevant database.Distributed data base makees query function, profit
Make collection work with the powerful aggregate function function of relevant database the mode of result detail record is inquired about and divided for user
Analysis.Due to the performance issue of relevant database, collection work is regularly non real-time.
There is problems with for such way：
1st, the synchronism of data is poor, non real-time due to collecting, and data, relationship type number are often found in distributed data base
Do not collect into also according to storehouse.Cause data result inconsistent.
2nd, efficiency is low.The limitation in relationship type number storehouse, causes the extremely inefficient of collection work.If data volume is big,
Cycle can unconfined extension.
Therefore, those skilled in the art is directed to developing a kind of big data processing method for taking into account efficiency and synchronism.
The content of the invention
In view of the drawbacks described above of the prior art, the technical problem to be solved in the present invention is overcome traditional distributed data
Storehouse can only effective search data deficiency polymerizable functional the shortcomings that can not realizing the business intelligences such as report and data analysis, be big data
Report form processing provides a kind of effective method.
To achieve the above object, the present invention provides a kind of big data report processing method based on ElasticSearch,
Including condition modular converter, ElasticSearch.Net modules, result modular converter and result export module；Including with
Lower step：
S11, is received the input of user's statistical demand by condition modular converter, desired content is parsed；
S12, sends inquiry request to ElasticSearch engines by ElasticSearch.Net modules, is returned
As a result；
S13, converts the result to form by result modular converter and shows；
S14, exports result set huge profit by result export module and uses.
Further, in the step S1, the condition modular converter classifies statistical demand input by user, and
It is JSON by the Content Transformation of classification.
Further, the classification includes querying condition, dimension, index, polymeric type, sequence and result set size.
Further, when it is JSON to carry out the Content Transformation by classification, first the content of each classification is carried out respectively
JSON is changed.
Further, the result of conversion respectively forms one group of JSON character string.
Further, JSON conversions and then the JSON character strings by each classification are carried out respectively in the content to each classification
It is spliced into the querying condition JSON character strings that ElasticSearch can be allowed to receive.
Further, secondary classification is carried out to the querying condition.
Further, in the step S12, the ElasticSearch.Net modules are accessed by api function
Elasticsearch engines.
Further, the api function is PlainElastic API.
Further, in the step S14, the result set is exported as into EXCEL, PDF or flat file data format.
Complexity is performed present invention utilizes PB grades of mass datas of ElasticSearch efficient process and with polymerizable functional
Business intelligence and the characteristics of data query, overcoming traditional distributed data base can only effective search data deficiency polymerization work(
Can the shortcomings that can not realizing the business intelligences such as report and data analysis, for big data report form processing provide one kind take into account efficiency with
The effective ways of synchronism.
It is described further below with reference to the technique effect of design of the attached drawing to the present invention, concrete structure and generation, with
It is fully understood from the purpose of the present invention, feature and effect.
Brief description of the drawings
Fig. 1 is four modules involved by the preferred embodiment of the present invention；
Fig. 2 is the work flow diagram of the preferred embodiment of the present invention.
Embodiment
Multiple preferred embodiments of the present invention are introduced below with reference to Figure of description, make its technology contents more clear and just
In understanding.The present invention can be emerged from by many various forms of embodiments, and protection scope of the present invention not only limits
The embodiment that Yu Wenzhong is mentioned.
In the accompanying drawings, the identical component of structure is represented with same numbers label, everywhere the similar component of structure or function with
Like numeral label represents.The size and thickness of each component shown in the drawings arbitrarily show that the present invention does not limit
The size and thickness of each component.In order to make diagram apparent, the appropriate thickness for exaggerating component in some places in attached drawing.
ElasticSearch is increasing income based on Lucene structures, distributed full-text search engine.It or one
Distribution type file database has High Availabitity and scalability, can extend to hundreds of server storage and processing PB
The data of level.ElasticSearch also possesses business intelligence, and the business intelligence of complexity is performed by its powerful polymerizable functional
With data query.
Using Lucene inverted indexs, inverted index is come from practical application to be needed according to category ElasticSearch
Property value search record, each single item in this concordance list all includes a property value and with changing respectively recording for property value
Address.Due to not being to determine property value by recording, but the position of record is determined by property value, so inverted index is than closing
It is that the B-tree indexed search efficiency of type database wants higher.
ElasticSearch employs the concept of bucket (Buckets), and both bucket and index (Metrics) are implemented in combination with
Polymerizable functional.One bucket is exactly a collection of document for meeting specified conditions, for example, date 2014-10-28 belong to the October this
A bucket.One polymerization is exactly the combination of some barrels and index, each polymerization be simply by one or more bucket, zero or
The multiple indicator combinations of person form.One polymerization can only have a bucket, either an index or every one, sample.In bucket very
Can extremely there are multiple nested buckets.As polymerization is performed, the value in every part of document can be calculated to determine whether they match
The condition of bucket.If successful match, then the document can be placed into the bucket, while polymerization may proceed to perform.Bucket also can
It is nested in other buckets, user can be allowed to complete level or condition divides these demands.
Bucket can allow user to carry out significant division to document, but it is final or need to the document in each bucket into
Certain index of row calculates.It is the means for reaching final purpose to divide bucket：The method divided to document is provided, so as to allow user
The index of needs can be calculated.
Most indexs are only simple mathematical operation (such as min, mean, max and sum), they are used in document
Value calculated.In practical applications, index can allow user to calculate such as averagely wages or highest commercial value.Than
Such as, document can be subjected to a point bucket according to its belonging country, then calculates each barrel its average wages (index).
Because bucket can be nested, we can realize a more complicated converging operation：
(1) document is subjected to a point bucket according to country.(bucket)
(2) and then by each national bucket bucket is divided according still further to gender.(bucket)
(3) and then by the bucket of each gender according to age range a point bucket is carried out.(bucket)
(4) finally, average wages are calculated for each age range.(index)
At this time, just can obtain each<Country, gender, age>The average wages information of combination, can so pass through
One request, data traversal are completed.
The powerful function of two above is based on, ElasticSearch is more suitable for developing magnanimity than relevant database
The reporting system of level data.
Fig. 1 is four modules involved by a preferred embodiment of the present invention, S01 conditions modular converter,
S02ElasticSearch.Net modules, S03 results modular converter and S04 result export modules.Wherein：
S01 condition modular converters, mainly receive the input of user's statistical demand, desired content are parsed.It includes
Following function：
User's input statistical demand is classified.Classification includes：
(1) querying condition, than if desired for count which period, which product, which company data.
(2) dimension, that is, need the content shown, such as time, company, product etc..
(3) index, that is, need which index polymerize,
(4) type of polymerization, including summation, average value, maximum, minimum value, percentage etc..
(5) sort, including the field and the type of sequence to be sorted：Ascending order, descending.
(6) size of result set, i.e., the bar number of the result data once returned.
Wherein querying condition can carry out secondary classification：
Including：Be equal to, be more than, being not less than, being less than, being not more than, between, comprising etc..
Since in ElasticSearch interfaces, most of carried out with the form of JSON, so needing by more than
The Content Transformation of classification is JSON.JSON refers to JavaScript object representation (JavaScript Object
Notation), be the text data exchange format of lightweight, similar to XML, but than XML smaller, faster, be more easy to parse.This
Outside, JSON conversions are first carried out respectively to the content of each classification when conversion, the result of conversion respectively forms one group of JSON character
String.It is to allow in the content progress JSON conversions to each classification and then by the JSON string-concatenations of each classification
The querying condition JSON character strings that ElasticSearch receives.So just complete the conversion work of condition.
S02ElasticSearch.Net modules, ElasticSearch.Net modules be it is all kinds of increase income based on .Net's
It may have access to the dynamic link library of ElasticSearch.These dynamic link libraries all encapsulate a group access ElasticSearch's
Api function.Usual ElasticSearch only provides the api function based on java, and the interface that this kind of module increased income carries turns
Change function and solve the problems, such as that the system under .Net frameworks accesses ElasticSearch.
S03 result modular converters, mainly return the result the conversion collected into row format to statistics.Usually
The result set that ElasticSearch is returned is generally JSON forms.Usual data result integrates as form, so needing to turn
Change, general conversion can be used to lower two ways, and one kind is programming, is parsed using functions such as the partition of character string, interceptions.Separately
A kind of is the JSON crossover tools increased income.This kind of crossover tool carries JSON layout sequences and the class libraries of unserializing.It can incite somebody to action
JSON objects are easily converted to all kinds data display form, including form, list etc..
S04 result export modules, its main function are to export the result set of data to EXCEL, PDF, flat file
Etc. data format, so that data huge profit is used.
Fig. 2 is the work flow diagram of a preferred embodiment of the present invention, specifically includes following steps：
S11, parsed the statistical condition, index and dimension of input by condition modular converter and be converted to JSON lattice
Formula.
S12, by ElasticSearch.Net to ElasticSearch engines send inquiry request, obtain return knot
Fruit.
S13, convert the result to form by result modular converter and show.
S14, export result set huge profit use by result export module.
Flow chart is illustrated with reference to a specific embodiment.
User needs to obtain quantity on order (the not paging, using rolling mould that each customs that the date is 2014-10-1 encodes
Formula).
Step S11, statistical condition, index and dimension are parsed：
Querying condition, date 2014-10-1.
Dimension：Customs encodes.
Index：Quantity of order,
Polymeric type：Summation.
Sequence：Nothing.
The size of result set：It is unlimited.
Form following JSON forms：
S12 sends inquiry request by ElasticSearch.Net to ElasticSearch engines, is returned the result.
Here ElasticSearch.Net can access Elasticsearch engines by the api function that it is carried, and return the result.
S13 converts the result to form by result modular converter and shows.The result that Elasticsearch is returned
Following form：
It is as follows to be converted to form
Date is 2014-10-1, and each customs's coding quantity on order is as follows：
| Customs encodes | Quantity on order |
| 10000000 | 20100 |
| 10000001 | 13001 |
| 10000002 | 30023 |
| …… |
S14 exports result set huge profit by result export module and uses.Data are exported using all kinds of export plug-in units increased income
For data formats such as EXCEL, PDF, flat files.
Preferred embodiment of the invention described in detail above.It should be appreciated that the ordinary skill of this area is without wound
The property made work can conceive according to the present invention makes many modifications and variations.Therefore, all technician in the art
Pass through the available technology of logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Scheme, all should be in the protection domain being defined in the patent claims.
Claims (10)
- A kind of 1. big data report processing method based on ElasticSearch, it is characterised in that including condition modular converter, ElasticSearch.Net modules, result modular converter and result export module；Including following steps：S11, is received the input of user's statistical demand by condition modular converter, desired content is parsed；S12, sends inquiry request to ElasticSearch engines by ElasticSearch.Net modules, obtains and return to knot Fruit；S13, converts the result to form by result modular converter and shows；S14, exports result set huge profit by result export module and uses.
- 2. the big data report processing method based on ElasticSearch as claimed in claim 1, it is characterised in that described In step S11, the condition modular converter classifies statistical demand input by user, and is by the Content Transformation of classification JSON。
- 3. the big data report processing method based on ElasticSearch as claimed in claim 2, it is characterised in that described Classification includes querying condition, dimension, index, polymeric type, sequence and result set size.
- 4. the big data report processing method based on ElasticSearch as claimed in claim 2, it is characterised in that into When the row Content Transformation by classification is JSON, JSON conversions are first carried out respectively to the content of each classification.
- 5. the big data report processing method based on ElasticSearch as claimed in claim 4, it is characterised in that conversion Result respectively formed one group of JSON character string.
- 6. the big data report processing method based on ElasticSearch as claimed in claim 5, it is characterised in that right The content respectively classified carries out JSON conversions and then is to allow by the JSON string-concatenations of each classification respectively The querying condition JSON character strings that ElasticSearch receives.
- 7. the big data report processing method based on ElasticSearch as claimed in claim 3, it is characterised in that to institute State querying condition and carry out secondary classification.
- 8. the big data report processing method based on ElasticSearch as claimed in claim 1, it is characterised in that described In step S12, the ElasticSearch.Net modules access Elasticsearch engines by api function.
- 9. the big data report processing method based on ElasticSearch as claimed in claim 8, it is characterised in that described Api function is PlainElastic API.
- 10. the big data report processing method based on ElasticSearch as claimed in claim 1, it is characterised in that described In step S14, the result set is exported as into EXCEL, PDF or flat file data format.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108664573A (en) * | 2018-04-27 | 2018-10-16 | 厦门南讯软件科技有限公司 | A kind of quick processing system of big data and method with double-channel data library |
| CN109359142A (en) * | 2018-09-29 | 2019-02-19 | 北京明朝万达科技股份有限公司 | A kind of data processing method, data processing equipment, computer equipment and readable storage medium storing program for executing |
| CN110232044A (en) * | 2019-06-17 | 2019-09-13 | 山东浪潮通软信息科技有限公司 | A kind of realization system and method for big data aggregates dispatch service |
| CN110543517A (en) * | 2019-08-26 | 2019-12-06 | 汉纳森（厦门）数据股份有限公司 | Method, device and medium for realizing complex query of mass data based on elastic search |
| CN110688416A (en) * | 2019-09-05 | 2020-01-14 | 深圳市中电数通智慧安全科技股份有限公司 | Data query method and device and electronic equipment |
| CN110688412A (en) * | 2019-09-27 | 2020-01-14 | 杭州有赞科技有限公司 | Mass data statistical method and mass data statistical system based on ES |
| CN111339147A (en) * | 2020-03-06 | 2020-06-26 | 杭州依图医疗技术有限公司 | Medical data processing method, dimension reduction query method and storage medium |
| CN113032436A (en) * | 2021-04-16 | 2021-06-25 | 苏州臻璇数据信息技术有限公司 | Searching method and device based on article content and title |
| CN113407785A (en) * | 2021-06-11 | 2021-09-17 | 西北工业大学 | Data processing method and system based on distributed storage system |
| CN114036158A (en) * | 2021-11-18 | 2022-02-11 | 广州宸祺出行科技有限公司 | Elasticissearch and MySQL combined query method and device |
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| CN107133267A (en) * | 2017-04-01 | 2017-09-05 | 北京京东尚科信息技术有限公司 | Inquire about method, device, electronic equipment and the readable storage medium storing program for executing of elasticsearch clusters |
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Patent Citations (1)
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| CN107133267A (en) * | 2017-04-01 | 2017-09-05 | 北京京东尚科信息技术有限公司 | Inquire about method, device, electronic equipment and the readable storage medium storing program for executing of elasticsearch clusters |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108664573A (en) * | 2018-04-27 | 2018-10-16 | 厦门南讯软件科技有限公司 | A kind of quick processing system of big data and method with double-channel data library |
| CN109359142A (en) * | 2018-09-29 | 2019-02-19 | 北京明朝万达科技股份有限公司 | A kind of data processing method, data processing equipment, computer equipment and readable storage medium storing program for executing |
| CN109359142B (en) * | 2018-09-29 | 2020-11-27 | 北京明朝万达科技股份有限公司 | Data processing method, data processing device, computer equipment and readable storage medium |
| CN110232044A (en) * | 2019-06-17 | 2019-09-13 | 山东浪潮通软信息科技有限公司 | A kind of realization system and method for big data aggregates dispatch service |
| CN110232044B (en) * | 2019-06-17 | 2023-03-28 | 浪潮通用软件有限公司 | System and method for realizing big data summarizing and scheduling service |
| CN110543517B (en) * | 2019-08-26 | 2022-05-10 | 汉纳森（厦门）数据股份有限公司 | Method, device and medium for realizing complex query of mass data based on elastic search |
| CN110543517A (en) * | 2019-08-26 | 2019-12-06 | 汉纳森（厦门）数据股份有限公司 | Method, device and medium for realizing complex query of mass data based on elastic search |
| CN110688416A (en) * | 2019-09-05 | 2020-01-14 | 深圳市中电数通智慧安全科技股份有限公司 | Data query method and device and electronic equipment |
| CN110688412A (en) * | 2019-09-27 | 2020-01-14 | 杭州有赞科技有限公司 | Mass data statistical method and mass data statistical system based on ES |
| CN111339147A (en) * | 2020-03-06 | 2020-06-26 | 杭州依图医疗技术有限公司 | Medical data processing method, dimension reduction query method and storage medium |
| CN113032436B (en) * | 2021-04-16 | 2022-05-31 | 苏州臻璇数据信息技术有限公司 | Searching method and device based on article content and title |
| CN113032436A (en) * | 2021-04-16 | 2021-06-25 | 苏州臻璇数据信息技术有限公司 | Searching method and device based on article content and title |
| CN113407785A (en) * | 2021-06-11 | 2021-09-17 | 西北工业大学 | Data processing method and system based on distributed storage system |
| CN114036158A (en) * | 2021-11-18 | 2022-02-11 | 广州宸祺出行科技有限公司 | Elasticissearch and MySQL combined query method and device |
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Application publication date: 20180424 |