CN110222030A - The method of Database Dynamic dilatation, storage medium - Google Patents
The method of Database Dynamic dilatation, storage medium Download PDFInfo
- Publication number
- CN110222030A CN110222030A CN201910392783.5A CN201910392783A CN110222030A CN 110222030 A CN110222030 A CN 110222030A CN 201910392783 A CN201910392783 A CN 201910392783A CN 110222030 A CN110222030 A CN 110222030A
- Authority
- CN
- China
- Prior art keywords
- cluster
- hash
- mapping relationship
- hash mapping
- data
- 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.)
- Granted
Links
Classifications
-
- 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
- G06F16/21—Design, administration or maintenance of databases
- G06F16/214—Database migration support
-
- 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
- G06F16/2228—Indexing structures
- G06F16/2255—Hash tables
-
- 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
- G06F16/23—Updating
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present invention provides the method for Database Dynamic dilatation, storage medium, and method includes: that table is divided in point library for including in default cluster；Hash Agent layer writes data into corresponding cluster according to the first Hash mapping relationship；A cluster is replicated, a former cluster and a new cluster are obtained；Modifying and corresponding to the Hash mapping relationship of a cluster in the first Hash mapping relationship is to respectively correspond the Hash mapping relationship of described one former cluster and a new cluster.The present invention realizes dynamic capacity-expanding by two layers of hash, does not need point library in modification code not only and divides table regular, without progress Data Migration；Further, moreover it is possible to while solving the problems, such as single table upper limit and hot spot data occur；Finally, being also able to achieve automatic optimization of data library storage resource.
Description
Technical field
The present invention relates to field of data storage, and in particular to the method for Database Dynamic dilatation, storage medium.
Background technique
In present many systems or APP application, each APP has required a corresponding background server to mention
For interface service；Simultaneously as user's number of application is more, and the various businesses operation etc. of each user will generate number
It is believed that breath.Therefore, the system of each application requires to store hundreds of millions of user information and user behavior information.
Above- mentioned information are stored, very big pressure is brought to server database.So general now large-scale internet is public
Department all carries out the storage work of big data quantity in the form of table is divided in point library.I.e. by certain computation rule, using " hash
(key) % divides table quantity " as hash mode realize that table is divided in a point library.If general such use, it can pre-set point
The quantity of table is divided in library, if the later period has and encounters quantity and explode, when needing to carry out data-base capacity-enlarging, it is necessary to newly-increased number in advance
According to library；Then, old data are migrated and re-start hash division, different Data Migrations is into different correspondence library tables.Cause
This, each dilatation migration is all a painful process.
Summary of the invention
The technical problems to be solved by the present invention are: the method for Database Dynamic dilatation, storage medium are provided, it can be in number
According to not migrating, in the case where dividing table rule without change point library, the dynamic capacity-expanding of database is realized.
In order to solve the above-mentioned technical problem, the technical solution adopted by the present invention are as follows:
The method of Database Dynamic dilatation, comprising:
Table is divided in point library for including in default cluster；
Hash Agent layer writes data into corresponding cluster according to the first Hash mapping relationship；
A cluster is replicated, a former cluster and a new cluster are obtained；
Modify corresponded in the first Hash mapping relationship a cluster Hash mapping relationship be respectively correspond it is described
The Hash mapping relationship of one former cluster and a new cluster.
Another technical solution provided by the invention are as follows:
A kind of computer readable storage medium is stored thereon with computer program, described program when being executed by processor,
Be able to achieve above-mentioned Database Dynamic dilatation method it is included the step of.
The beneficial effects of the present invention are: it only needs before data are written, configures Hash Agent layer and its arrive cluster
First Hash mapping relationship；It can be in dilatation, by replicating cluster, and the collection in the first Hash mapping relationship of corresponding modification
Dynamic capacity-expanding can be realized in the Hash mapping relationship of group.Dilatation way of the invention does not need the migration for carrying out former data, not yet
Needing to change a point library divides table regular, i.e., the second Hash mapping relationship for dividing table point library is written in data；Former data still can foundation
Modified first Hash mapping relationship finds corresponding storage location.Therefore, dilatation way provided by the present application significantly improves
Practicability, and operation convenience.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of the method for Database Dynamic dilatation of the embodiment of the present invention；
Fig. 2 be using Fig. 1 method carry out dilatation after with the contrast schematic diagram before dilatation；
Fig. 3 is the flow diagram of the method for the Database Dynamic dilatation of the embodiment of the present invention two.
Specific embodiment
To explain the technical content, the achieved purpose and the effect of the present invention in detail, below in conjunction with embodiment and cooperate attached
Figure is explained.
The most critical design of the present invention is: the hash by Hash Agent layer maps which data explicit data corresponds to
Library cluster；Explicit data, which is mapped, by the hash of cluster internal corresponds to which library is which table；By replicating cluster, and corresponding modification
Dynamic capacity-expanding can be realized in the Hash mapping relationship of the cluster in the hash mapping relations of Hash Agent layer.
Explanation of technical terms of the present invention:
Fig. 1 is please referred to, the present invention provides the method for Database Dynamic dilatation, comprising:
Table is divided in point library for including in default cluster；
Hash Agent layer writes data into corresponding cluster according to the first Hash mapping relationship；
A cluster is replicated, a former cluster and a new cluster are obtained；
Modify corresponded in the first Hash mapping relationship a cluster Hash mapping relationship be respectively correspond it is described
The Hash mapping relationship of one former cluster and a new cluster.
Further, the Hash Agent layer writes data into corresponding cluster according to the first Hash mapping relationship, it
Afterwards, further includes:
According to the second Hash mapping relationship, writes data into corresponding point of library in cluster and divide table.
Seen from the above description, data are mapped to corresponding cluster by Hash Agent layer, then passes through second in cluster
Specifically a point library is divided in table in Hash mapping relationship map to cluster, and realize still can divide table to realize based on a point library carries out data
Storage, to improve database performance.
Further, the Hash mapping relationship that a cluster is corresponded in the modification the first Hash mapping relationship is
The Hash mapping relationship of described one former cluster and a new cluster is respectively corresponded, later, further includes:
According to modified first Hash mapping relationship, delete respectively in described one former cluster and a new cluster with from
The data of Hash mapping relationship are not present in body.
Seen from the above description, by deleting data extra in former cluster and new cluster, hash is avoided to occupy money
Source, to improve the validity of company-data.
Further, the quantity of the cluster is two or more.
Seen from the above description, Large Volume Data can be supported according to specific business demand, flexible configuration number of clusters
Storage.
Further, the Hash mapping relationship that a cluster is corresponded in the modification the first Hash mapping relationship is
The Hash mapping relationship of described one former cluster and a new cluster is respectively corresponded, specifically:
Modifying and corresponding to the Hash mapping relationship of a cluster in the first Hash mapping relationship is described in half is corresponding
One former cluster, the other half corresponds to a new cluster.
Seen from the above description, the data storage capacity for expanding a cluster is realized, and does not have to change data to specifically
The mapping ruler of table is divided in point library, without carrying out Data Migration.
Another technical solution provided by the invention are as follows:
A kind of computer readable storage medium is stored thereon with computer program, described program when being executed by processor,
Be able to achieve above-mentioned Database Dynamic dilatation method it is included the step of.
As can be seen from the above description, corresponding those of ordinary skill in the art will appreciate that realizing the whole in above-mentioned technical proposal
Or part process, relevant hardware can be instructed to realize by computer program, the program can be stored in one
In computer-readable storage medium, the program is when being executed, it may include such as the process of above-mentioned each method.Described program is being held
After row, the corresponding beneficial effect of same available above-mentioned each method process.
Wherein, the storage medium can be disk, optical disc, read-only memory (Read-Only Memory,
) or random access memory (Random Access Memory, RAM) etc. ROM.
Embodiment one
Referring to Fig.1 and 2, the present embodiment provides a kind of methods of Database Dynamic dilatation, and moved without carrying out data
Move or carry out the modification that table rule is divided in point library, thus practicability with higher and property easy to use.
The method of this implementation may include:
S1: table is divided in point library for including in default cluster.
Specifically, it is pre-configured the cluster that entire database includes at least one, comprising predetermined number in each cluster
Subdata base includes the tables of data of predetermined number in each subdata base.For example, including A cluster and B collection in configuration database
Group；It include 2 subdata bases in cluster A；It include 20 tables of data in each subdata base.Preferably, entire database includes
At least two cluster.
The form of table is directly divided in database point library by the present embodiment compared to the prior art, difference be a point library divide table it
Before also divide cluster, carry out a point library again in the cluster and divide table.It so is for subsequent configuration Hash Agent layer and dynamic
Dilatation provides service, and two can improve data query performance, play the role of the place cluster of quick lock in target data.
S2: Hash Agent layer writes data into corresponding cluster according to the first Hash mapping relationship.
Data storage rule used in the present embodiment divides table data storage rule consistent with existing point of library, herein not into
The detailed specific description of row.Difference is, increases Hash Agent layer by this step and carries out the mapping that data correspond to cluster, so
And used Hash mapping algorithm also divides the hash algorithm of table consistent with point library, difference is only that, in Hash calculation formula
The value (dividing table quantity) of mum is not according to depending on traffic needs, but according to the cluster number of configuration and all points of libraries point
Depending on table quantity, a biggish fixed numbers are preferably provided to, such as 1000-1 ten thousand.
Specifically, realizing the hash mapping of Hash Agent layer with the calculation formula of hash (key) %num.
Assuming that num is set as 1000, then the one of database to be written records data, will go first through Hash Agent layer according to above-mentioned meter
It calculates the numerical value corresponding with the record data that formula calculates and carries out map classification, i.e., this record data are mapped to correspondence
Cluster in.
Such as, it is assumed that database is that an account data manages database, then to be written one record data, will first
The calculating of Hash modulus is carried out with its unique corresponding account information, i.e. UID according to recording in record by Hash Agent layer,
Assuming that number 20 is calculated, then the calculated result according to " 0-500 " that is pre-configured corresponds to cluster A；The calculating of " 500-1000 "
As a result B cluster is corresponded to, writes direct this record data into cluster A.
S3: it according to the second Hash mapping relationship, writes data into corresponding point of library in cluster and divides table.
Specifically, the second Hash mapping relationship, i.e. a point library divide table regular.Pass through Hash Agent layer in previous step
After being mapped in corresponding cluster, the record data that table rule can be divided to be written into database according to existing point of library are written
Divide in table to a specific point library.
Equally based on it is above-mentioned " such as " case, then by this step, by again depending on the Hash calculation in cluster,
This record data being written in cluster A are specifically written to a certain number in some subdata base in cluster A again
According in table.
It should be noted that since second hash (the second Hash mapping relationship i.e. in cluster) has been according to some
Key divides table to carry out a point library, thus has been realized in the mean allocation function of data, thus solve single table upper limit and
There is the problem of hot spot data.
Above-mentioned is the data storage method increased after Hash Agent layer based on the application.
In the following, by the detailed process of database of descriptions dynamic capacity-expanding:
S4: one cluster of duplication obtains a former cluster and a new cluster.
When the data volume of subdata base reaches certain amount, it is necessary to carry out dilatation, specific dilatation amount is according to data
Library and actual pressure condition determine.
Specifically, if one of cluster it is necessary to carry out dilatation, it is assumed that the cluster be cluster A, then replicate cluster A,
It obtains an original cluster A1 and one is replicated obtained new cluster A2, point library in the two clusters divides table data to be just the same
's.
S5: the Hash mapping relationship of a cluster is corresponded to respectively correspond in modification the first Hash mapping relationship
State the Hash mapping relationship of former a cluster and a new cluster.
After the S4 is replicated, it is only necessary to carry out repairing for correspondence to the first Hash mapping relationship of Hash Agent layer
Change, and (hash (key) %num in Agent layer etc. is constant, the key to come in without the hash algorithm of modification Hash Agent layer
And num or as before), less spend modification the second Hash mapping relationship, i.e., a point library divide table rule, without
The migration for carrying out data, can realize dynamic capacity-expanding.
Specifically, the mode of modification are as follows: by the Hash calculation of the cluster A before duplication corresponding in the first Hash mapping relationship
As a result, splitting into two parts, a portion Hash calculation results modification is the new cluster obtained with duplication, i.e. cluster A2 is opposite
It answers；If the former cluster after duplication, i.e., the title of the described cluster A change, it is assumed that be changed to cluster A1, then by another part Hash
Calculated result is revised as corresponding with cluster A1.
One specifically with the cluster A2 and cluster A1 in example, obtained after duplication to the cluster A's before partly dividing duplication equally
Hash mapping relationship.Modifying and corresponding to the Hash mapping relationship of cluster A in the first Hash mapping relationship is the corresponding collection of half
Group A2, the other half corresponds to cluster A1.
Here, " calculated result of ' 0-500 ' corresponds to cluster A " of directly reference the example above is illustrated, then it can be according to
According to pre-configuration, the first Hash mapping relationship is modified, the calculated result of " 0-250 " is corresponded into cluster A1；By the calculating of " 251-500 "
As a result cluster A2 is corresponded to.So far, then it realizes after dilatation, by Hash Agent layer, will be counted according to the first Hash mapping relationship
According to being mapped in corresponding cluster；Then, still table can be divided to advise according to the second unmodified Hash mapping relationship, i.e. a point library
Then, data are then written in specific subdata base in specific tables of data.But but have been realized in cluster dilatation, i.e.,
Data store the double of total amount.
It can be seen from the above, the Database Dynamic expansion method of the present embodiment, only duplication has obtained a new cluster number
According to library, and the first Hash mapping relationship of Hash Agent layer is had modified, other make no modifications, and can realize automatically dynamic
Dilatation.Meanwhile old program and data still can be closed according to newest mapping relations, i.e., modified first Hash mapping
System and the second unmodified Hash mapping relationship find corresponding data information, that is, do not have to a modification point library and divide table regular, i.e., and second
Hash mapping relationship, without progress Data Migration.In addition, due to the clear cluster of Agent layer hash of the present embodiment, and cluster
Internal there are also hash to realize that table is divided in a point library, solves the problems, such as single table upper limit and hot spot data.
Embodiment two
Referring to figure 3., the present embodiment on the basis of example 1, further limits, and has it and is automatically deleted expansion
The function of hash after appearance, to optimize resource.
The present embodiment is the same as example 1 place and no longer repeats, and difference is, after the S5 step of embodiment one, also
Include:
S6: it according to modified first Hash mapping relationship, is deleted in described one former cluster and a new cluster respectively
The data of Hash mapping relationship are not present with itself.
Specifically, the content specifically deleted in the step according to the modified first Hash mapping relationship of step S5 and
It is fixed.To sum up, be automatically deleted in cluster A1 and cluster A2 respectively will not hash to own cluster data content.For example,
After dilatation, cluster A1 can only write the data information of 0-250, therefore directly delete the data information between wherein 251-500；And collect
The data information between 0-250 is then deleted in group A2.
It is preferred that the step can be realized by an independent program.
Embodiment three
The present embodiment corresponding embodiment one and embodiment two provide one specifically with scene:
Before dilatation, there are two data-base clusters (A, B), there are 2 databases in each cluster, deposited in each database
In 20 tables, hash computation rule: hash (key) %1000 is acted on behalf of by first layer (i.e. the Hash Agent layer of above-described embodiment)
Rule, judge 0-500 result set be directed toward A cluster；The result set of 501-1000 is directed toward B cluster, and (mapping ruler can be certainly
Row is configured)；And then secondary hash calculating is carried out, hash (key) %2 can navigate to which of cluster
It in database, can be navigated in which table according to hash (key) %20, be thus the process of hash positioning.
After dilatation, there are three data-base clusters (A1, A2, B), there are 2 databases, each databases in each cluster
Middle there are 20 tables, and by the first layer proxy hash computation rule, the rule of hash (key) %1000 judges the knot of 0-250
Fruit is directed toward A1 cluster, and the result of 251-500 is directed toward A2 cluster, and the result of 500-1000 is directed toward B cluster, and then carries out second
Secondary hash is calculated, which database hash (key) %2 can navigate in, can be navigated to according to hash (key) %20
It is thus the process of hash positioning in which table.
It should be noted that originally there was only 2 clusters, 2 libraries of each cluster, table is opened in each library 20；Through this embodiment
After dilatation, become 3 clusters, 2 libraries of each cluster, table is opened in each library 20, and total memory capacity is become by 80 original tables
120 tables, to realize data-base capacity-enlarging.And if dilatation at 4 clusters, reforms into 160 tables, double dilatation
?.
Importantly, not so specific dilatation all only needs to modify the hash mapping ruler of the first level at several clusters,
It can be directed toward multiple clusters, to realize dynamically dilatation.
Meanwhile after dilatation in A1 and A2 cluster data be all it is duplicate, it is subsequent that redundant digit is completed by asynchronous task
According to removing work.Using which, it is only necessary to which the mapping ruler in hash is acted on behalf of in modification, without modifying the second level
Hash rule, so that it may directly carry out the dilatation of database.
Example IV
The present embodiment corresponding embodiment one provides a kind of computer readable storage medium, is stored thereon with to embodiment three
Computer program, described program are able to achieve above-described embodiment one to any one implementation of embodiment three when being executed by processor
The step of method of Database Dynamic dilatation described in example is included.Specific step content without repeating, is asked in detail herein
Refering to embodiment one to the record of embodiment three.
It should be noted that on computer readable storage medium through this embodiment computer program execution, equally
It is able to achieve the dynamic capacity-expanding of database, and only needs to replicate hash points of cluster and modification the first level (i.e. Agent layer) in the process
With rule, it is not necessary to modify a point libraries to divide table regular, and there are no need to carry out Data Migration；It also can solve single table upper limit and appearance simultaneously
The problem of hot spot data.
In conclusion the method for Database Dynamic dilatation provided by the invention, storage medium, are realized by two layers of hash
Dynamic capacity-expanding does not need point library in modification code not only and divides table regular, without progress Data Migration；Further, moreover it is possible to
It solves the problems, such as single table upper limit simultaneously and hot spot data occurs；Finally, being also able to achieve automatic optimization of data library storage resource.
The above description is only an embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalents made by bright specification and accompanying drawing content are applied directly or indirectly in relevant technical field, similarly include
In scope of patent protection of the invention.
Claims (6)
1. the method for Database Dynamic dilatation characterized by comprising
Table is divided in point library for including in default cluster；
Hash Agent layer writes data into corresponding cluster according to the first Hash mapping relationship；
A cluster is replicated, a former cluster and a new cluster are obtained；
Modifying and corresponding to the Hash mapping relationship of a cluster in the first Hash mapping relationship is to respectively correspond an original
The Hash mapping relationship of cluster and a new cluster.
2. the method for Database Dynamic dilatation as described in claim 1, which is characterized in that the Hash Agent layer is according to first
Hash mapping relationship writes data into corresponding cluster, later, further includes:
According to the second Hash mapping relationship, writes data into corresponding point of library in cluster and divide table.
3. the method for Database Dynamic dilatation as described in claim 1, which is characterized in that modification first Hash reflects
Penetrating in relationship and corresponding to the Hash mapping relationship of a cluster is to respectively correspond the Kazakhstan of described one former cluster and a new cluster
Uncommon mapping relations, later, further includes:
According to modified first Hash mapping relationship, delete in described one former cluster and a new cluster respectively with itself not
There are the data of Hash mapping relationship.
4. the method for Database Dynamic dilatation as described in claim 1, which is characterized in that the quantity of the cluster be two with
On.
5. the method for Database Dynamic dilatation as described in claim 1, which is characterized in that modification first Hash reflects
Penetrating in relationship and corresponding to the Hash mapping relationship of a cluster is to respectively correspond the Kazakhstan of described one former cluster and a new cluster
Uncommon mapping relations, specifically:
Modifying and corresponding to the Hash mapping relationship of a cluster in the first Hash mapping relationship is that half corresponds to an original
Cluster, the other half corresponds to a new cluster.
6. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that described program is processed
Device execute when, be able to achieve Database Dynamic dilatation described in the claims 1-5 any one method it is included the step of.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910392783.5A CN110222030B (en) | 2019-05-13 | 2019-05-13 | Dynamic database capacity expansion method and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910392783.5A CN110222030B (en) | 2019-05-13 | 2019-05-13 | Dynamic database capacity expansion method and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110222030A true CN110222030A (en) | 2019-09-10 |
| CN110222030B CN110222030B (en) | 2021-08-06 |
Family
ID=67820916
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910392783.5A Active CN110222030B (en) | 2019-05-13 | 2019-05-13 | Dynamic database capacity expansion method and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110222030B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110750513A (en) * | 2019-09-17 | 2020-02-04 | 福建天泉教育科技有限公司 | Dynamic database capacity expansion method and storage medium |
| CN111400273A (en) * | 2019-11-19 | 2020-07-10 | 杭州海康威视***技术有限公司 | Database capacity expansion method and device, electronic equipment and machine-readable storage medium |
| CN112632342A (en) * | 2020-12-29 | 2021-04-09 | 北京大米科技有限公司 | Data query method and device, storage medium and electronic equipment |
| CN113507390A (en) * | 2021-07-08 | 2021-10-15 | 中国工商银行股份有限公司 | Distributed system cluster online fragmentation and capacity expansion method and device |
| CN113505177A (en) * | 2021-07-08 | 2021-10-15 | 中国工商银行股份有限公司 | Distributed system cluster online fragmentation and capacity expansion method and device |
| CN113704230A (en) * | 2021-09-02 | 2021-11-26 | 京东科技控股股份有限公司 | Database capacity expansion method and device, electronic equipment and computer storage medium |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6374251B1 (en) * | 1998-03-17 | 2002-04-16 | Microsoft Corporation | Scalable system for clustering of large databases |
| CN101510209A (en) * | 2009-03-30 | 2009-08-19 | 北京金山软件有限公司 | Method, system and server for implementing real time search |
| CN102033938A (en) * | 2010-12-10 | 2011-04-27 | 天津神舟通用数据技术有限公司 | Secondary mapping-based cluster dynamic expansion method |
| WO2012039990A2 (en) * | 2010-09-24 | 2012-03-29 | Hitachi Data Systems Corporation | System and method for managing scalability in a distributed database |
| US20160125012A1 (en) * | 2014-10-30 | 2016-05-05 | Bank Of America Corporation | Data cache architecture |
| CN105991769A (en) * | 2016-04-12 | 2016-10-05 | 上海帝联信息科技股份有限公司 | Data migration method and device and edge node server |
| CN106407308A (en) * | 2016-08-31 | 2017-02-15 | 天津南大通用数据技术股份有限公司 | Method and device for expanding capacity of distributed database |
| CN106682199A (en) * | 2016-12-29 | 2017-05-17 | 北京奇虎科技有限公司 | Method and device for achieving automatic expansion of Mongos cluster |
| CN107229688A (en) * | 2017-05-12 | 2017-10-03 | 上海前隆金融信息服务有限公司 | A kind of database level point storehouse point table method and system, server |
| CN107544848A (en) * | 2017-08-30 | 2018-01-05 | 深圳云天励飞技术有限公司 | Cluster expansion method, apparatus, electronic equipment and storage medium |
| CN107665246A (en) * | 2017-09-13 | 2018-02-06 | 北京知道未来信息技术有限公司 | Dynamic date migration method and chart database cluster based on chart database |
| CN108446308A (en) * | 2017-02-03 | 2018-08-24 | 数据捷豹有限公司 | The system and method for scalable distributed database |
| CN109040212A (en) * | 2018-07-24 | 2018-12-18 | 苏州科达科技股份有限公司 | Equipment access server cluster method, system, equipment and storage medium |
| CN109271376A (en) * | 2018-11-30 | 2019-01-25 | 北京锐安科技有限公司 | Database upgrade method, apparatus, equipment and storage medium |
-
2019
- 2019-05-13 CN CN201910392783.5A patent/CN110222030B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6374251B1 (en) * | 1998-03-17 | 2002-04-16 | Microsoft Corporation | Scalable system for clustering of large databases |
| CN101510209A (en) * | 2009-03-30 | 2009-08-19 | 北京金山软件有限公司 | Method, system and server for implementing real time search |
| WO2012039990A2 (en) * | 2010-09-24 | 2012-03-29 | Hitachi Data Systems Corporation | System and method for managing scalability in a distributed database |
| CN102033938A (en) * | 2010-12-10 | 2011-04-27 | 天津神舟通用数据技术有限公司 | Secondary mapping-based cluster dynamic expansion method |
| US20160125012A1 (en) * | 2014-10-30 | 2016-05-05 | Bank Of America Corporation | Data cache architecture |
| CN105991769A (en) * | 2016-04-12 | 2016-10-05 | 上海帝联信息科技股份有限公司 | Data migration method and device and edge node server |
| CN106407308A (en) * | 2016-08-31 | 2017-02-15 | 天津南大通用数据技术股份有限公司 | Method and device for expanding capacity of distributed database |
| CN106682199A (en) * | 2016-12-29 | 2017-05-17 | 北京奇虎科技有限公司 | Method and device for achieving automatic expansion of Mongos cluster |
| CN108446308A (en) * | 2017-02-03 | 2018-08-24 | 数据捷豹有限公司 | The system and method for scalable distributed database |
| CN107229688A (en) * | 2017-05-12 | 2017-10-03 | 上海前隆金融信息服务有限公司 | A kind of database level point storehouse point table method and system, server |
| CN107544848A (en) * | 2017-08-30 | 2018-01-05 | 深圳云天励飞技术有限公司 | Cluster expansion method, apparatus, electronic equipment and storage medium |
| CN107665246A (en) * | 2017-09-13 | 2018-02-06 | 北京知道未来信息技术有限公司 | Dynamic date migration method and chart database cluster based on chart database |
| CN109040212A (en) * | 2018-07-24 | 2018-12-18 | 苏州科达科技股份有限公司 | Equipment access server cluster method, system, equipment and storage medium |
| CN109271376A (en) * | 2018-11-30 | 2019-01-25 | 北京锐安科技有限公司 | Database upgrade method, apparatus, equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| ZHU HONG: "Formal Specification and Verification of an Extended Security Policy Model for Database Systems", 《IEEE》 * |
| 张伟宁: "大型DB2数据库表空间扩容方案及应用实践", 《金融科技时代》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110750513A (en) * | 2019-09-17 | 2020-02-04 | 福建天泉教育科技有限公司 | Dynamic database capacity expansion method and storage medium |
| CN111400273A (en) * | 2019-11-19 | 2020-07-10 | 杭州海康威视***技术有限公司 | Database capacity expansion method and device, electronic equipment and machine-readable storage medium |
| CN111400273B (en) * | 2019-11-19 | 2024-02-02 | 杭州海康威视***技术有限公司 | Database capacity expansion method and device, electronic equipment and machine-readable storage medium |
| CN112632342A (en) * | 2020-12-29 | 2021-04-09 | 北京大米科技有限公司 | Data query method and device, storage medium and electronic equipment |
| CN113507390A (en) * | 2021-07-08 | 2021-10-15 | 中国工商银行股份有限公司 | Distributed system cluster online fragmentation and capacity expansion method and device |
| CN113505177A (en) * | 2021-07-08 | 2021-10-15 | 中国工商银行股份有限公司 | Distributed system cluster online fragmentation and capacity expansion method and device |
| CN113704230A (en) * | 2021-09-02 | 2021-11-26 | 京东科技控股股份有限公司 | Database capacity expansion method and device, electronic equipment and computer storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110222030B (en) | 2021-08-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110222030A (en) | The method of Database Dynamic dilatation, storage medium | |
| US20240211461A1 (en) | Customer-requested partitioning of journal-based storage systems | |
| US11520670B2 (en) | Method and apparatus for restoring data from snapshots | |
| US7293154B1 (en) | System and method for optimizing storage operations by operating only on mapped blocks | |
| US10346434B1 (en) | Partitioned data materialization in journal-based storage systems | |
| US7660966B2 (en) | Location-independent RAID group virtual block management | |
| CN107423422B (en) | Spatial data distributed storage and search method and system based on grid | |
| US20160350302A1 (en) | Dynamically splitting a range of a node in a distributed hash table | |
| US20130110873A1 (en) | Method and system for data storage and management | |
| CN105468473A (en) | Data migration method and data migration apparatus | |
| CN103597440A (en) | Method for creating clone file, and file system adopting the same | |
| US11048678B2 (en) | Bulk-load for B-trees | |
| US20030056058A1 (en) | Logical volume data migration | |
| Wang et al. | A new reliability model in replication-based big data storage systems | |
| US10235407B1 (en) | Distributed storage system journal forking | |
| US20210349850A1 (en) | Managing snapshotting of a dataset using an ordered set of b+ trees | |
| CN113297171A (en) | Database migration method and device and database cluster | |
| JP6204753B2 (en) | Distributed query processing apparatus, processing method, and processing program | |
| CN107506466A (en) | A kind of small documents storage method and system | |
| Boehm et al. | EasyWinWin: a groupware-supported methodology for requirements negotiation | |
| CN106951338A (en) | A kind of high fault-tolerant distributed storage method based on GlusterFS | |
| CN110333970A (en) | A kind of data reconstruction method and device | |
| US8812453B2 (en) | Database archiving using clusters | |
| CN113204520B (en) | Remote sensing data rapid concurrent read-write method based on distributed file system | |
| Moise et al. | Improving the Hadoop map/reduce framework to support concurrent appends through the BlobSeer BLOB management system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |