CN110232044A - A kind of realization system and method for big data aggregates dispatch service - Google Patents
A kind of realization system and method for big data aggregates dispatch service Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 20
- 150000003839 salts Chemical class 0.000 claims abstract description 20
- 238000006116 polymerization reaction Methods 0.000 claims abstract description 19
- 230000005540 biological transmission Effects 0.000 claims abstract description 6
- 230000002159 abnormal effect Effects 0.000 claims description 14
- 239000010453 quartz Substances 0.000 claims description 14
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 14
- 238000004220 aggregation Methods 0.000 claims description 4
- 230000002776 aggregation Effects 0.000 claims description 4
- 230000003542 behavioural effect Effects 0.000 description 4
- 230000010354 integration Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
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- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
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- 238000002955 isolation Methods 0.000 description 1
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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
- G06F16/10—File systems; File servers
- G06F16/11—File system administration, e.g. details of archiving or snapshots
- G06F16/122—File system administration, e.g. details of archiving or snapshots using management policies
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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
- G06F16/10—File systems; File servers
- G06F16/13—File access structures, e.g. distributed indices
- G06F16/137—Hash-based
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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
- G06F16/10—File systems; File servers
- G06F16/14—Details of searching files based on file metadata
- G06F16/148—File search processing
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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
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/182—Distributed file systems
- G06F16/1824—Distributed file systems implemented using Network-attached Storage [NAS] architecture
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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
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
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Abstract
The invention discloses a kind of realization system and methods of big data aggregates dispatch service, belong to big data technical field of memory.The realization system of big data aggregates dispatch service of the invention includes cloud server terminal and local server, cloud server terminal provides cloud the Web API and document data bank ElasticSearch that Hash adds salt to encrypt, local server provides local scheduling service and local relevant database, cloud server terminal calls document data bank ElasticSearch to carry out polymerization and Querying by group to the data of customer relationship, local scheduling service carries out aggregates dispatch service by way of setup time interval, and what local relevant database reception local scheduling service transmission returned summarizes data.The classification that the realization system of the big data aggregates dispatch service of the invention is suitable for big data polymerize aggregate query, solves the related question between Cloud Server, between big data and relation data, has good application value.
Description
Technical field
The present invention relates to big data technical field of memory, specifically provide a kind of realization system of big data aggregates dispatch service
And method.
Background technique
As the user behavior data that system generates is continuously increased, a large amount of behavioral data is no longer appropriate for manual read or looks into
It sees, therefore, analysis and arrangement often is carried out to existing subscriber's behavior using some technologies, with chart or other sides for being easy to read
Formula shows user data information.
Collect the extension and diversity choice of the behavioral data storage mode come, it is intended that the behavioral data of collection can
To be output on more multimedium.The experience of real-time retrieval behavioral data enhances, and more and more users propose to magnanimity behavior
The demand of data quick-searching.The arriving in personalized chart epoch is capable of providing friendly web interface, enhances user experience
Demand is increasing.In order to meet the above demand, Elastic Search becomes our preferred document databases.
Currently in order to alleviating the pressure of big data transmission, reverse proxy and disk queue can be set on Cloud Server,
And access the cluster processing data of multinode.Therefore it uses Nginx as reverse proxy tool, uses Apache Kafka magnetic
Disk queue cache request has been deployed to our essential deployment schemes using the clustering of ElasticSearch.Based on when
Between stab plus salt Hash hash WebAPI enhance public network call API safety and industry a kind of standard.For reality
Now the scheduling of configurable automatically working, Quartz dispatch service have obtained the consistent approval of industry.In conclusion being based on
ElasticSearch, reliable Web API and Quartz component, we guarantee user experience, reliability, safety with
A kind of big data aggregates dispatch service is developed while robustness also just to become a reality.
Summary of the invention
Technical assignment of the invention is in view of the above problems, to provide a kind of classification polymerization remittance suitable for big data
Total inquiry, solves the big data aggregates dispatch service of the related question between Cloud Server, between big data and relation data
Realization system.
The further technical assignment of the present invention is to provide a kind of implementation method of big data aggregates dispatch service.
To achieve the above object, the present invention provides the following technical scheme that
A kind of realization system of big data aggregates dispatch service, including cloud server terminal and local server, the cloud service
End provides cloud the Web API and document data bank ElasticSearch that Hash adds salt to encrypt, and local server provides local adjust
Degree service and local relevant database, cloud server terminal call document data bank ElasticSearch to the data of customer relationship
Polymerization and Querying by group are carried out, local scheduling service carries out aggregates dispatch service by way of setup time interval, local to close
Be type database receive local scheduling service transmission return summarize data.
The local relevant database is defaulted as MicroSoft Sql Server, and alternate data library is MySQL,
PosgreSQL。
Preferably, the local scheduling service that local server provides, according to the time interval being locally configured, timing to
The cloud Web API of cloud server terminal sends Http request, by the collecting index data of return, is inserted into local relevant database
In.
Cipher mode is worth by local server based on timestamp and salt figure, passes through MD5 and hashes plucking for 16 systems of return
Will be as safe foundation be judged whether, cloud server terminal is judged according to the salt figure transmitted with hashed value, if cloud server terminal
The hash code and local server of generation are transmitted through the hash code come always, then message safety.
Preferably, the achievement data that cloud server terminal provides is that PC number, abnormal number and active users, received parameter are
Period parameter and pointer type parameter.
Preferably, the document data bank ElasticSearch to the PV details of user, abnormal details by polymerization with
Date grouping obtains the PV number, abnormal number and active users of user's needs.
Preferably, the local scheduling service, which is based on open source scheduling storehouse Quartz, realizes customized scheduler task.
Scheduling storehouse Quartz increase income according to the time interval being locally configured, timing is sent to the Web API of cloud server terminal
Http request, by the collecting index data of return, relevant index is inserted into local relevant database.
A kind of implementation method of big data aggregates dispatch service, the packet aggregation inquiry including big data, cloud Web API
And local scheduling service is aggregated into local relevant database, specifically includes the following steps:
S1, local server start local scheduling service, periodically send Http to the cloud Web API of cloud server terminal and ask
It asks；
S2, it is requested according to Http, cloud server terminal carries out safe judgement to the data of return, after confirming safety, fetches；
S3, according to step S2 safety determine and Web API request data category, cloud server terminal call document data bank
ElasticSearch carries out polymerization to the data that user is concerned about and looks into grouping；
S4, cloud server terminal return to achievement data；
S5, local server receive the achievement data that cloud server terminal returns and store collecting index data.
Preferably, the parameter of addition salt figure and MD5 hashed value is safe for identification in Http request in step S1.
Local server starts local scheduling service, between the mainly flexible acquisition time for freely configuring acquisition achievement data
Every, pass through Quartz component obtain the customized time interval configuration section of user.Relative to common timer component, Quartz
More complicated time interval configuration can be completed, such as daily operation peak period interval N can be defined according to user demand
Minute collects once, is spaced 4N minutes and is collected once during inoperative.Quartz can be convenient with Springboot is integrated answers
With, therefore this dispatch service establishes the form of micro services publication in production.Method is by Springboot and Quartz group
It is packaged into Docker mirror image after part integration, Docker mirror image is issued into Docker container on local host, is realized micro-
The deployment of service.
Arrange to be grouped polymerization to the data summarized preferably, requiring and fetching according to polymerization in step S3, polymerize
Require to include time grouping, enterprise's grouping or user grouping, the agreement of access includes PV number, abnormal number or number of users.
The safety certification of Web API.The essential information of its HTTP message body includes a salt value parameter, a MD5 digest
Message parameter, an achievement data list parameter.Salt value parameter in message be by by current time stamp, a random number and
Two sections of public tab character strings synthesize, and the MD5 digest message in message is led to after being merged by public tab character string with salt figure
Cross 16 binary message data of MD5 digest generation.Achievement data list in message is the specific targets data (PV of user's request
Number, abnormal number or number of users).
Preferably, local server receives the achievement data that cloud server terminal returns in step S5, pass through the JSON of return
Data are deserialized as needing to store the data object list to local relevant database, corresponding to relevant database insertion
Achievement data.
Compared with prior art, the implementation method of big data aggregates dispatch service of the invention has following prominent beneficial
Effect: the implementation method of the big data aggregates dispatch service depends on document data bank ElasticSearch,
ElasticSearch is the open source big data search engine based on Apache Lucene, and it is more that it provides a distribution
The big data of user capability stores and full-text search engine.The present invention provides based on open source search engine ElasticSearch
Packet aggregation query interface, issued the Web service that Hash adds salt to encrypt, realized and make by oneself based on open source scheduling storehouse Quartz
Adopted scheduler task has carried out security isolation to the inquiry of search engine database by the safety certification that Hash plus salt encrypt, from
And having reached the highly reliable data summarization dispatch service of high safety, the classification suitable for big data polymerize aggregate query, solves
Related question between Cloud Server, between big data and relation data has good application value.
Detailed description of the invention
Fig. 1 is the architecture diagram of the realization system of big data aggregates dispatch service of the present invention.
Specific embodiment
Below in conjunction with drawings and examples, the realization system and method for big data aggregates dispatch service of the invention is made
It is further described.
Embodiment
As shown in Figure 1, the realization system of big data aggregates dispatch service of the invention includes cloud server terminal and local service
Device.
Cloud server terminal provides cloud the Web API and document data bank ElasticSearch that Hash adds salt to encrypt, local to take
Device offer local scheduling service be engaged in local relevant database, cloud server terminal calls ElasticSearch pairs of document data bank
The data of customer relationship carry out polymerization and Querying by group, local scheduling service carry out summarizing tune by way of setup time interval
Degree service, what local relevant database reception local scheduling service transmission returned summarizes data.
Local relevant database is MicroSoft Sql Server in the present invention.
The local scheduling service that local server provides, according to the time interval being locally configured, timing to cloud server terminal
Cloud Web API send Http request, the collecting index data of return are inserted into local relevant database.Local clothes
Cipher mode to be worth based on timestamp and salt figure, is used as by the abstract that MD5 hashes 16 systems of return and is judged whether by business device
The foundation of safety, cloud server terminal judged according to the salt figure transmitted with hashed value, if the hash code of cloud server terminal generation and
Local server is transmitted through the hash code come always, then message safety.Local scheduling service is based on open source scheduling storehouse Quartz and realizes
Customized scheduler task.Scheduling storehouse Quartz increase income according to the time interval being locally configured, the Web to cloud server terminal of timing
API sends Http request, and by the collecting index data of return, relevant index is inserted into local relevant database.
The achievement data that cloud server terminal provides is PC number, abnormal number and active users, and received parameter is period ginseng
Several and pointer type parameter.Document data bank ElasticSearch passes through polymerization and date to PV details, the abnormal details of user
Grouping obtains the PV number, abnormal number and active users of user's needs.
The implementation method of big data aggregates dispatch service of the invention, the packet aggregation inquiry including big data, cloud Web
API and local scheduling service are aggregated into local relevant database, specifically includes the following steps:
S1, local server start local scheduling service, periodically send Http to the cloud Web API of cloud server terminal and ask
It asks.The parameter of addition salt figure and MD5 hashed value is safe for identification in Http request.
The safety certification of Web API.The essential information of its HTTP message body includes a salt value parameter, a MD5 digest
Message parameter, an achievement data list parameter.Salt value parameter in message be by by current time stamp, a random number and
Two sections of public tab character strings synthesize, and the MD5 digest message in message is led to after being merged by public tab character string with salt figure
Cross 16 binary message data of MD5 digest generation.Achievement data list in message is the specific targets data (PV of user's request
Number, abnormal number or number of users).
Local server starts local scheduling service, between the mainly flexible acquisition time for freely configuring acquisition achievement data
Every, pass through Quartz component obtain the customized time interval configuration section of user.Relative to common timer component, Quartz
More complicated time interval configuration can be completed, such as daily operation peak period interval N can be defined according to user demand
Minute collects once, is spaced 4N minutes and is collected once during inoperative.Quartz can be convenient with Springboot is integrated answers
With, therefore this dispatch service establishes the form of micro services publication in production.Method is by Springboot and Quartz group
It is packaged into Docker mirror image after part integration, Docker mirror image is issued into Docker container on local host, is realized micro-
The deployment of service.
The transmission Http request operation code of the process is as follows:
It is as follows to send Http request operation code:
S2, it is requested according to Http, cloud server terminal carries out safe judgement to the data of return, after confirming safety, fetches.
S3, according to step S2 safety determine and Web API request data category, cloud server terminal call document data bank
ElasticSearch carries out polymerization to the data that user is concerned about and looks into grouping.
It is required according to polymerization and access agreement is grouped polymerization to the data summarized, polymerization requires to include the time point
Group, enterprise's grouping or user grouping, the agreement of access include PV number, abnormal number or number of users.
The code of the communication decryption of Handshake Protocol is as follows:
S4, cloud server terminal return to achievement data.
S5, local server receive the achievement data that cloud server terminal returns and store collecting index data.
Local server receives the achievement data that cloud server terminal returns, and is deserialized as needing by the JSON data of return
The data object list for storing local relevant database is inserted into corresponding achievement data to relevant database.
Embodiment described above, the only present invention more preferably specific embodiment, those skilled in the art is at this
The usual variations and alternatives carried out within the scope of inventive technique scheme should be all included within the scope of the present invention.
Claims (9)
1. a kind of realization system of big data aggregates dispatch service, it is characterised in that: including cloud server terminal and local server, institute
Stating cloud server terminal offer Hash adds the cloud Web API of salt encryption and document data bank ElasticSearch, local server to mention
For local scheduling service and local relevant database, cloud server terminal calls document data bank ElasticSearch to close user
The data of system carry out polymerization and Querying by group, local scheduling service carry out aggregates dispatch clothes by way of setup time interval
Business, what local relevant database reception local scheduling service transmission returned summarizes data.
2. the realization system of big data aggregates dispatch service according to claim 1, it is characterised in that: local server mentions
The local scheduling service of confession, according to the time interval being locally configured, timing sends Http to the cloud WebAPI of cloud server terminal
The collecting index data of return are inserted into local relevant database by request.
3. the realization system of big data aggregates dispatch service according to claim 2, it is characterised in that: cloud server terminal provides
Achievement data be PC number, abnormal number and active users, received parameter is period parameter and pointer type parameter.
4. the realization system of big data aggregates dispatch service according to claim 3, it is characterised in that: the file data
Library ElasticSearch is grouped the PV details of user, abnormal details by polymerization and date, obtain user's needs PV number,
Abnormal number and active users.
5. the realization system of big data aggregates dispatch service according to claim 4, it is characterised in that: the local scheduling
Service realizes customized scheduler task based on open source scheduling storehouse Quartz.
6. a kind of implementation method of big data aggregates dispatch service, it is characterised in that: this method includes the packet aggregation of big data
Inquiry, cloud Web API and local scheduling service are aggregated into local relevant database, specifically includes the following steps:
S1, local server start local scheduling service, periodically send Http request to the cloud Web API of cloud server terminal；
S2, it is requested according to Http, cloud server terminal carries out safe judgement to the data of return, after confirming safety, fetches；
S3, according to step S2 safety determine and Web API request data category, cloud server terminal call document data bank
ElasticSearch carries out polymerization to the data that user is concerned about and looks into grouping；
S4, cloud server terminal return to achievement data；
S5, local server receive the achievement data that cloud server terminal returns and store collecting index data.
7. the implementation method of big data aggregates dispatch service according to claim 6, it is characterised in that: in step S1,
The parameter of addition salt figure and MD5 hashed value is safe for identification in Http request.
8. the implementation method of big data aggregates dispatch service according to claim 7, it is characterised in that: basis in step S3
Polymerization requires and access agreement is grouped polymerization to the data summarized, polymerization require to include time grouping, enterprise is grouped or
User grouping, the agreement of access include PV number, abnormal number or number of users.
9. the implementation method of big data aggregates dispatch service according to claim 8, it is characterised in that: in step S5, this
Ground server receives the achievement data that cloud server terminal returns, and is deserialized as needing to store to local by the JSON data of return
The data object list of relevant database is inserted into corresponding achievement data to relevant database.
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