CN110222030B - Dynamic database capacity expansion method and storage medium - Google Patents
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Abstract
The invention provides a method for dynamically expanding a database and a storage medium, wherein the method comprises the following steps: presetting a sub-library and sub-table contained in a cluster; the Hash agent layer writes the data into the corresponding cluster according to the first Hash mapping relation; copying a cluster to obtain an original cluster and a new cluster; and modifying the hash mapping relation corresponding to the cluster in the first hash mapping relation into the hash mapping relations corresponding to the original cluster and the new cluster respectively. The dynamic capacity expansion is realized through two layers of hash, so that not only is the database and table division rule in the code not required to be modified, but also data migration is not required; furthermore, the problems of single table upper limit and hot spot data can be solved at the same time; finally, automatic optimization of database storage resources can also be achieved.
Description
Technical Field
The invention relates to the field of data storage, in particular to a method and a storage medium for dynamically expanding a database.
Background
In many systems or APP applications at present, each APP needs to have a corresponding background server to provide interface services; meanwhile, the number of the applied users is large, and various business operations of each user and the like generate data information. Therefore, the system of each application needs to store hundreds of millions of user information as well as user behavior information.
The information is stored, and great pressure is brought to a server-side database. Therefore, large internet companies now generally use a form of database and table to store large data volumes. Namely, the database and table division is realized by a hash mode of hash (key) percent of the number of tables through a certain calculation rule. Generally, if the quantity of the sub-databases and the sub-tables is suddenly increased in the later period, the database needs to be newly added when the capacity of the database needs to be expanded; and then, migrating the old data and carrying out hash division again, and migrating different data into different corresponding table lists. Therefore, each dilatation migration is a painful process.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method and the storage medium for dynamically expanding the database can realize the dynamic expansion of the database under the conditions that data is not migrated and the rules of the database division and the table division are not changed.
In order to solve the technical problems, the invention adopts the technical scheme that:
the method for dynamically expanding the database comprises the following steps:
presetting a sub-library and sub-table contained in a cluster;
the Hash agent layer writes the data into the corresponding cluster according to the first Hash mapping relation;
copying a cluster to obtain an original cluster and a new cluster;
and modifying the hash mapping relation corresponding to the cluster in the first hash mapping relation into the hash mapping relations corresponding to the original cluster and the new cluster respectively.
The invention provides another technical scheme as follows:
a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, is capable of implementing the steps included in the above method for dynamically expanding a database.
The invention has the beneficial effects that: only before data is written, a Hash agent layer and a first Hash mapping relation from the Hash agent layer to a cluster are configured; during capacity expansion, the dynamic capacity expansion can be realized by copying the cluster and correspondingly modifying the hash mapping relation of the cluster in the first hash mapping relation. The capacity expansion mode of the invention does not need to migrate the original data and change the rules of the tables and the tables of the sub-database, namely, the data is written into the second Hash mapping relation of the tables and the tables; the original data can still find the corresponding storage position according to the modified first hash mapping relation. Therefore, the expansion mode provided by the application obviously improves the practicability and the operation convenience.
Drawings
Fig. 1 is a schematic flowchart illustrating a method for dynamically expanding a database according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating comparison between a post-expansion process and a pre-expansion process performed by the method of FIG. 1;
fig. 3 is a flowchart illustrating a method for dynamically expanding a database according to a second embodiment of the present invention.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
The most key concept of the invention is as follows: determining which database cluster the data corresponds to through hash mapping of a hash proxy layer; determining which table of the corresponding database of the data is determined through hash mapping inside the cluster; dynamic capacity expansion can be realized by copying the cluster and correspondingly modifying the hash mapping relation of the cluster in the hash mapping relation of the hash proxy layer.
The technical terms related to the invention are explained as follows:
referring to fig. 1, the present invention provides a method for dynamically expanding a database, including:
presetting a sub-library and sub-table contained in a cluster;
the Hash agent layer writes the data into the corresponding cluster according to the first Hash mapping relation;
copying a cluster to obtain an original cluster and a new cluster;
and modifying the hash mapping relation corresponding to the cluster in the first hash mapping relation into the hash mapping relations corresponding to the original cluster and the new cluster respectively.
Further, the hash agent layer writes the data into the corresponding cluster according to the first hash mapping relationship, and then, the method further includes:
and writing the data into the corresponding sub-base sub-table in the cluster according to the second Hash mapping relation.
As can be seen from the above description, the data is mapped to the corresponding cluster by the hash proxy layer, and then mapped to the specific sub-database table in the cluster by the second hash mapping relationship in the cluster, so that the data storage can still be realized based on the sub-database table, thereby improving the database performance.
Further, the modifying the hash mapping relationship corresponding to the cluster in the first hash mapping relationship is the hash mapping relationship corresponding to the original cluster and the new cluster, and then the method further includes:
and deleting the data which do not have the Hash mapping relation with the original cluster and the new cluster according to the modified first Hash mapping relation.
According to the description, the redundant data in the original cluster and the new cluster are deleted, so that the useless data are prevented from occupying resources, and the effectiveness of the cluster data is improved.
Further, the number of the clusters is more than two.
As can be seen from the above description, the number of clusters can be flexibly configured according to specific service requirements, and large-capacity data storage is supported.
Further, the modifying the hash mapping relationship corresponding to the cluster in the first hash mapping relationship is to respectively correspond to the hash mapping relationships of the original cluster and the new cluster, and specifically includes:
and modifying the hash mapping relation corresponding to the cluster in the first hash mapping relation into half corresponding to the original cluster and the other half corresponding to the new cluster.
As can be seen from the above description, the data storage capacity of the cluster is expanded, and the mapping rule of the data to the specific sub-base and sub-table is not changed, and the data migration is not performed.
The invention provides another technical scheme as follows:
a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, is capable of implementing the steps included in the above method for dynamically expanding a database.
As can be understood from the above description, those skilled in the art can understand that all or part of the processes in the above technical solutions can be implemented by instructing related hardware through a computer program, where the program can be stored in a computer-readable storage medium, and when executed, the program can include the processes of the above methods. After the program is executed, the beneficial effects corresponding to the above method flows can be obtained.
The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Example one
Referring to fig. 1 and fig. 2, the present embodiment provides a method for dynamically expanding a database without data migration or modification of a rule of database partitioning and table partitioning, so that the method has high practicability and convenience in use.
The method of the present implementation may include:
s1: and presetting a sub-library and sub-table contained in the cluster.
Specifically, the whole database is preconfigured to include at least one cluster, each cluster includes a preset number of sub-databases, and each sub-database includes a preset number of data tables. For example, the configuration database includes a cluster a and a cluster B; the cluster A comprises 2 sub databases; each sub-database contains 20 data tables. Preferably, the entire database comprises at least two clusters.
Compared with the prior art that the database is directly divided into the databases and the tables, the method is characterized in that the clusters are further divided before the database division and the table division are performed in the clusters. Therefore, the service is provided for the subsequent configuration of the hash agent layer and the dynamic capacity expansion, the data query performance can be improved, and the function of quickly locking the cluster of the target data is achieved.
S2: and the Hash agent layer writes the data into the corresponding cluster according to the first Hash mapping relation.
The data storage rule used in this embodiment is consistent with the existing database and table data storage rule, and will not be specifically described here. The difference is that the hash agent layer is added to map the clusters corresponding to the data in this step, however, the used hash mapping algorithm is also consistent with the hash algorithm of the sub-pool sub-table, and the difference is that the value of mum (i.e. the number of sub-tables) in the hash calculation formula is not determined by the traffic demand, but by the number of configured clusters and the number of all sub-pool sub-tables, and is preferably set to a larger fixed value, such as 1000-1 ten thousand.
Specifically, a hash (key)% num calculation formula is used to realize the hash mapping of the hash proxy layer. Assuming that num is set to 1000, a piece of recorded data to be written into the database is mapped and classified by the hash agent layer according to the value corresponding to the recorded data calculated by the above calculation formula, that is, the piece of recorded data is mapped into the corresponding cluster.
For example, assuming that the database is an account data management database, a piece of recorded data to be written is subjected to Hash modulo calculation by a Hash proxy layer according to account information, namely UID, recorded in a record and uniquely corresponding to the recorded account information, and assuming that the calculation result is a number 20, the cluster a corresponds to the calculation result of "0 to 500" which is pre-configured; the calculation result of "500-" 1000 "corresponds to the cluster B, and the record data is directly written into the cluster a.
S3: and writing the data into the corresponding sub-base sub-table in the cluster according to the second Hash mapping relation.
Specifically, the second hash mapping relationship is a library and table splitting rule. After the last step is mapped into the corresponding cluster through the Hash agent layer, the record data to be written into the database can be written into a specific sub-library sub-table according to the existing sub-library sub-table rule.
Also based on the case of "say", through this step, the record data that has been written into the cluster a is further specifically written into a certain data table in a certain sub-database in the cluster a according to the hash calculation in the cluster again.
It should be noted that, since the second hash (i.e., the second hash mapping relationship in the cluster) is already performed with library splitting and table splitting according to a certain key, the function of average distribution of data is already implemented, and thus the problems of single table upper limit and hot data occurrence are solved.
The above is a data storage mode after a hash proxy layer is added based on the application.
The following will explain the specific process of dynamic capacity expansion of the database:
s4: a cluster is replicated to obtain an original cluster and a new cluster.
When the data volume of the sub-database reaches a certain limit, the capacity expansion is necessary, and the specific capacity expansion amount is determined according to the database and the actual pressure condition.
Specifically, if one of the clusters needs to be expanded, assuming that the cluster is the cluster a, the cluster a is copied to obtain an original cluster a1 and a new cluster a2, and the data of the sub-pools and the sub-tables in the two clusters are identical.
S5: and modifying the hash mapping relation corresponding to the cluster in the first hash mapping relation into the hash mapping relations corresponding to the original cluster and the new cluster respectively.
After the S4 is copied, only the first hash mapping relationship of the hash proxy layer needs to be modified correspondingly, but the hash algorithm of the hash proxy layer does not need to be modified (the hash (key)% num and the like in the proxy layer are not changed, and the incoming key and num are the same as those before), and the second hash mapping relationship, that is, the database and table partitioning rule, and the data migration do not need to be modified, so that dynamic capacity expansion can be realized.
Specifically, the modification is as follows: splitting the hash calculation result of the cluster A before replication in the first hash mapping relation into two parts, wherein one part of the hash calculation result is modified to correspond to a new cluster obtained by replication, namely the cluster A2; if the name of the original cluster after replication, namely the cluster a, is changed, assuming that the name of the original cluster after replication is changed to the cluster a1, the other part of the hash calculation result is modified to correspond to the cluster a 1.
In a specific example, the hash mapping relationship between the cluster a2 obtained after replication and the cluster a1 before halving replication is used. That is, the hash mapping relationship of the corresponding cluster a in the first hash mapping relationship is modified to be half of the corresponding cluster a2, and the other half of the corresponding cluster a 1.
Here, the above example is directly referred to that the calculation result of "0 to 500" corresponds to the cluster a "for explanation, and then the first hash mapping relationship may be modified according to the pre-configuration, so that the calculation result of" 0 to 250 "corresponds to the cluster a 1; the result of the calculation of "251-" 500 "is corresponding to the cluster a 2. After capacity expansion is achieved, data are mapped into the corresponding clusters through the Hash agent layer according to the first Hash mapping relation; and then, the data can still be written into a specific data table in a specific sub-database according to the unmodified second hash mapping relation, namely the database-dividing and table-dividing rule. However, cluster expansion, i.e., doubling the total amount of data storage, has been achieved.
As can be seen from the above, the dynamic database capacity expansion method of this embodiment only obtains a new cluster database by copying, modifies the first hash mapping relationship of the hash proxy layer, and can implement automatic dynamic capacity expansion without any modification. Meanwhile, the old program and the data can still find corresponding data information according to the latest mapping relation, namely the modified first Hash mapping relation and the unmodified second Hash mapping relation, namely the database and table splitting rule, namely the second Hash mapping relation, is not required to be modified, and data migration is not required. In addition, the proxy layer hash of the embodiment defines the cluster, and the hash is also arranged in the cluster to realize database division and table division, so that the problems of single table upper limit and hot spot data are solved.
Example two
Referring to fig. 3, the present embodiment is further limited based on the first embodiment, so that the present embodiment has a function of automatically deleting the useless data after expansion, thereby optimizing the resources.
The same points of this embodiment as those of the first embodiment will not be repeated, except that after the step of S5 of the first embodiment, the method further includes:
s6: and deleting the data which do not have the Hash mapping relation with the original cluster and the new cluster according to the modified first Hash mapping relation.
Specifically, the content deleted in this step depends on the modified first hash mapping relationship in step S5. In summary, data content in cluster a1 and cluster a2 that will not hash to itself is automatically deleted, respectively. For example, after capacity expansion, the cluster a1 will only write data information 0-250, and therefore directly delete the data information 251-500 therein; whereas in cluster a2 the data information between 0-250 is deleted.
Preferably this step can be implemented by a separate program.
EXAMPLE III
This embodiment corresponds to the first and second embodiments, and provides a specific application scenario:
before capacity expansion, two database clusters (a, B) exist, 2 databases exist in each cluster, 20 tables exist in each database, and the hash calculation rule is proxied through the first layer (namely, the hash proxy layer in the above embodiment): hash (key)% 1000 rule, judge that 0-500 result set points to A cluster; 501-1000 result set points to the B cluster (the mapping rule can be configured by itself); and then, performing a second hash calculation, wherein hash (key)% 2 can be positioned in which database in the cluster, and can be positioned in which table according to hash (key)% 20, so that the process of hash positioning is performed once.
After capacity expansion, three database clusters (A1, A2 and B) exist, each cluster has 2 databases, each database has 20 tables, the results of 0-250 point to the A1 cluster, the results of 251 and 500 and 1000 point to the A2 cluster and the results of 500 and 1000 point to the B cluster are judged according to the first layer proxy hash calculation rule and the hash (key) 1000 rule, and then the hash (key) 2 can be positioned in which database, and can be positioned in which table according to the hash (key) 20, so that the process of one-time hash positioning is realized.
It should be noted that, originally, there are only 2 clusters, each cluster has 2 banks, and each bank has 20 tables; after the capacity expansion is performed by the embodiment, the total storage capacity is changed into 120 tables from 80 tables in the original way, namely, 3 clusters, 2 banks in each cluster, 20 tables in each bank, and the capacity expansion of the database is realized. If the capacity is expanded into 4 clusters, the capacity becomes 160 sheets and the capacity is doubled.
More importantly, otherwise, specific capacity expansion into several clusters can be realized by only modifying the hash mapping rule of the first level to point to a plurality of clusters, thereby realizing dynamic capacity expansion.
Meanwhile, after capacity expansion, the data in the A1 cluster and the data in the A2 cluster are the same, and then the cleaning work of redundant data is finished by an asynchronous task. By adopting the mode, the capacity of the database can be directly expanded only by modifying the mapping rule in the proxy hash without modifying the hash rule of the second level.
Example four
Corresponding to the first to third embodiments, this embodiment provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when being executed by a processor, can implement the steps included in the method for dynamically expanding the capacity of the database according to any one of the first to third embodiments. The detailed steps are not repeated here, and refer to the descriptions of the first to third embodiments in detail.
It should be noted that, by executing the computer program on the computer-readable storage medium of this embodiment, dynamic capacity expansion of the database can also be achieved, and in the process, only the cluster needs to be copied and the hash allocation rule of the first layer (i.e., the proxy layer) needs to be modified, and the database and table partitioning rule does not need to be modified, and even data migration does not need to be performed; meanwhile, the problems of single table upper limit and hot data can be solved.
In summary, the dynamic capacity expansion method and the storage medium for the database provided by the invention realize dynamic capacity expansion through two layers of hash, and do not need to modify the rules of database division and table division in the code, nor perform data migration; furthermore, the problems of single table upper limit and hot spot data can be solved at the same time; finally, automatic optimization of database storage resources can also be achieved.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (5)
1. The method for dynamically expanding the database is characterized by comprising the following steps:
presetting a sub-library and sub-table contained in a cluster;
the Hash agent layer writes the data into the corresponding cluster according to the first Hash mapping relation;
writing the data into corresponding sub-base sub-tables in the cluster according to the second Hash mapping relation;
copying a cluster to obtain an original cluster and a new cluster;
modifying the hash mapping relation corresponding to the cluster in the first hash mapping relation into the hash mapping relations corresponding to the original cluster and the new cluster respectively;
and finding corresponding data information according to the modified first Hash mapping relation and the unmodified second Hash mapping relation.
2. A method for dynamically expanding databases as claimed in claim 1, wherein the modifying the hash mapping relationship corresponding to the one cluster in the first hash mapping relationship is a hash mapping relationship corresponding to the original cluster and the new cluster respectively, and thereafter further comprises:
and deleting the data which do not have the Hash mapping relation with the original cluster and the new cluster according to the modified first Hash mapping relation.
3. A method for dynamically expanding a database as recited in claim 1, wherein the number of said clusters is two or more.
4. A method for dynamically expanding a database as claimed in claim 1, wherein the modifying the hash mapping relationship corresponding to the one cluster in the first hash mapping relationship is to respectively correspond to the hash mapping relationships of the original cluster and the new cluster, which specifically comprises:
and modifying the hash mapping relation corresponding to the cluster in the first hash mapping relation into half corresponding to the original cluster and the other half corresponding to the new cluster.
5. A computer-readable storage medium, on which a computer program is stored, the program being adapted to perform the steps of the method for dynamically expanding a database according to any of claims 1-4 when executed by a processor.
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| 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 |
| CN111400273B (en) * | 2019-11-19 | 2024-02-02 | 杭州海康威视系统技术有限公司 | 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 |
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