CN115809248A - Data query method and device and storage medium - Google Patents
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Abstract
The embodiment of the application provides a data query method, a data query device and a storage medium, wherein the method is applied to a database, data stored in the database are configured into a plurality of continuous rows of data to form a logical data block, and each logical data block is configured to use an index of the first row of data in the current logical data block as a sparse index of the current logical data block, and the method comprises the following steps: acquiring a query condition input by a user; based on query conditions input by a user, if the query conditions contain sparse index items, corresponding sparse indexes are used for data retrieval, an interval matched with the query conditions is determined, multiple query condition fields can be used for combining into a sparse index, the problem of low query performance when multiple columns of fields are screened as the query conditions under mass data is solved, and the data content to be queried is rapidly retrieved in the mass data in a manner of constructing a logic block sparse index under mass data column storage.
Description
Technical Field
The present application relates to the field of data query technologies, and in particular, to a data query method and apparatus, and a storage medium.
Background
With the explosion of 5G technology, the characteristics of high speed, wide connection and low time delay are highly compatible with the numerous scenes of the internet of vehicles. Meanwhile, behavior data generated based on vehicles also show explosive growth, wherein the log data of the vehicles accessing the internet is more of the order of billion increments of data every day, and the data to be searched needs to be searched from mass stored data in a short time when the data storage period reaches a certain degree, such as half a year or more.
In some data query schemes, the distributed columnar storage database hbase is used as an index to improve query efficiency, and then query of conditions of multiple columns is realized through Phonenix, however, hbase can only be queried through rowkey, if query is performed through other columns as conditions, although Phoenix can be used, when query statements are point search and small-range scanning, phoenix can be well satisfied, and a scene that a large amount of scan type OLAP queries or query modes are flexible is not suitable.
Disclosure of Invention
The embodiment of the application provides a data query method, a data query device and a storage medium, by which multiple query condition fields can be combined into a sparse index, the problem of low query performance when multiple columns of fields are screened as query conditions under mass data is solved, and the content of data to be queried can be quickly retrieved in large data volume in a manner of constructing a logic block sparse index under mass data column storage.
In a first aspect, an embodiment of the present application provides a data query method, which is applied to a database, where data stored in the database is configured as multiple consecutive rows of data to form one logical data block, and each of the logical data blocks is configured to use an index of a first row of data in a current logical data block as a sparse index of the current logical data block, where the method includes: acquiring a query condition input by a user; based on the query condition input by the user, if the query condition contains a sparse index item, using a corresponding sparse index to perform data retrieval, and determining an interval matched with the query condition, wherein data contained in the interval matched with the query condition is target data.
Further, the continuous rows of data forming a logical data block includes continuous 1024 rows of data forming a logical data block.
Further, the query condition is a multi-column query condition, and the multi-column query condition contains a combination of multiple attributes.
Further, the determining the interval matching the query condition includes: determining the starting line number of the range of the logical data block corresponding to the index main key formed by the query condition; determining the ending row number of the range of the logical data block corresponding to the index main key formed by the query condition; and determining the interval matched with the query condition based on the starting line number and the ending line number.
Further, the determining the starting row number of the logical data block range corresponding to the index primary key formed by the query condition includes: and determining whether the sparse index main key meeting the query condition is found, if not, setting the row number corresponding to the sparse index of the last logical data block as a starting row number, if so, determining whether the found sparse index main key meeting the query condition is the same as the index main key consisting of the query condition, if so, setting the row number of the first sparse index main key meeting the query condition as the starting row number, and if not, setting the row number corresponding to the previous sparse index of the sparse index main key meeting the current condition as the starting row number.
Further, determining the ending row number of the logical data block range corresponding to the index primary key formed by the query condition includes: searching the row number of the first sparse index main key which is larger than the sparse index main key meeting the query condition; and determining whether the first sparse index primary key which is larger than the sparse index primary key meeting the query condition is found, if not, taking the row number of the last row of data in the last logic data block as an end row number, and if so, setting the row number of the sparse index primary key which meets the query condition at present as the end row number.
Further, determining the interval matching the query condition based on the start line number and the end line number comprises: the upper and lower bound row numbers are identified in the columnar index using a binary search algorithm.
In a second aspect, an embodiment of the present application further provides a data query apparatus, including: a processor and a memory, the memory being configured to store at least one instruction which, when loaded and executed by the processor, implements the data query method provided by the first aspect.
In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the data query method provided in the first aspect.
In a fourth aspect, an embodiment of the present application further provides a computer program product, which includes a computer program or instructions, and when the computer program or instructions are executed by a processor, the data query method provided in the first aspect is implemented.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a diagram of an index architecture according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a data query method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a data query apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In some current schemes for data query, data query is generally performed by the following methods:
the first scheme is as follows: the distributed column type storage database hbase takes rowkey as an index to improve the query efficiency, and then queries of conditions of multiple columns are realized through Phonenix.
The second scheme is as follows: the time sequence database InfluxDB realizes the multi-dimensional quick query of mass data in an inverted index mode.
However, the above scheme has some disadvantages, which are as follows:
the first scheme has the following defects: the hbase can only be queried through rowkey, if the query is performed through other columns as conditions, although Phoenix can be used, when the query statement is point search and small-range scanning, phoenix can be better satisfied, and a scene that a large amount of scan-type OLAP queries or query modes are flexible is not suitable.
The second scheme has the defects that: although the time sequence database can realize multidimensional query on massive data in a mode of inverted index and the like, the requirement on the attribute of the data is strict, namely the data must have a time attribute.
In order to overcome the above technical problem, embodiments of the present application provide a data query method, which is described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an index architecture according to an embodiment of the present application.
Referring to fig. 1, in the process of constructing a database, multiple lines of data may be constructed into one logical data block, and then multiple logical data blocks are constructed according to the data to be processed. Illustratively, every 1024 rows of data form a logical data block, the data to be processed includes 0 th row of data to n th row of data, where the 0 th row of data to the 1023 th row of data (1024 rows of data) form a logical data block, the 1024 th row of data to the 2047 th row of data form a logical data block, and so on until the last row of data, where it should be noted that the multiple rows of data included in the logical data block where the last row of data is located are less than or equal to 1024 rows of data.
Referring to fig. 1, on the basis of constructing the plurality of logical data blocks, a sparse index key (a primary key of a sparse index) may be configured for each logical data block, that is, each logical data block stores a corresponding sparse index key, wherein the sparse index entry is formed by multi-column splicing combination ordering as a filtering condition, that is, a sparse index key is constructed according to a condition to be queried, and then the constructed sparse index key is ordered. In one embodiment, the index of the first row of data in each logical data block may be used as the sparse index key of the corresponding logical data block. For example, the logical data blocks formed by the line 0 to the line 1023 may use the index of the line 0 as the sparse index key of the logical data block where the logical data block is located, i.e. key1 shown in fig. 1. The logical data block formed by the 1024 th row to the 2047 th row may use the index of the 1024 th row of data as the sparse index key of the logical data block where the logical data block is located, that is, key2 shown in fig. 1. And in the same way, taking the index of the first row of data in each logical data block as the sparse index key of the corresponding logical data block.
In one embodiment, where each logical data block is configured with a corresponding sparse index key, a column level index may also be configured for each row of data. In one embodiment, the column-level index uses the row number of the storage data as an index, each row number corresponds to the physical storage address of the data block of the current row, and each row number index entry consists of the starting row number, position and length information of the corresponding data block. And then, the row number index table is searched by using the row number of a certain row of data, the position of the data block corresponding to the row number can be obtained, and after the target data block is read, the data can be further searched.
In one embodiment, the column data block is stored in blocks of the same size for each column of data and then written to or read from the memory device as a whole.
After the database is constructed based on the above manner, data query can be performed from the mass data stored in the database by using the data query method provided by the embodiment of the application.
Fig. 2 is a schematic flowchart of a data query method according to an embodiment of the present application.
Referring to fig. 2, the data query method may include the steps of:
step 10: and acquiring a query condition input by a user.
When a user wants to query target data from mass data stored in the database, a query condition can be input for data retrieval, and exemplarily, the query condition can be a corresponding keyword key of the data, and the keyword key input by the user is obtained. In one embodiment, the keyword key may contain a sparse index entry.
Step 20: based on the query condition input by the user, if the query condition contains a sparse index item, the sparse index is used for data retrieval, and an interval matched with the query condition is determined.
In one embodiment, based on a query condition input by a user, if the query condition includes a sparse index item, performing data retrieval using a sparse index includes: and searching a first sparse index key from the sparse indexes according to the multi-column query conditions.
In one embodiment, the query condition input by the user may be a multi-column query condition, which is a combination of multiple attributes.
Watch 1
| IP information | Date information | Domain name information | Upstream flow | Downstream traffic |
| 10.26.72.231 | 20221119 | 2.android.pool.ntp.org | 32917 | 45678 |
| 10.26.72.231 | 20221119 | 3.android.pool.ntp.org | 3291 | 4548 |
| 10.26.72.231 | 20221119 | 4.android.pool.ntp.org | 1145 | 3267 |
| ...... | ||||
| 10.26.72.232 | 20221120 | 2.android.pool.ntp.org | 3213 | 45278 |
| ...... | ...... | ...... | ...... | ...... |
For example, referring to table one, the multi-column query condition of the multi-attribute combination may be a combination of IP information and date information, that is, a key obtained by splicing the IP information and the date information as a sparse index. It should be noted that, the rows of data with the same attribute shown in table one (i.e., the rows of data with IP information of (10.26.72.231) and date information of (20221119)) may construct one or more logical data blocks, and the number of rows in the last logical data block in the plurality of logical data blocks is less than or equal to 1024.
In one embodiment, the searching the first sparse index key from the sparse index according to the multi-column query condition may be: and searching a first sparse index key meeting the multi-column query condition from a sparse index table (containing all sparse indexes). For example, referring to table one, if the multi-column query condition is "(10.26.72.231) + (20221119)", and a first sparse index key satisfying the multi-column query condition is searched from the sparse index table, it needs to be noted that, if the sparse index keys of a plurality of consecutive logical data blocks are all the same, it needs to be determined to search the first sparse index key satisfying the multi-column query condition in the sparse index table, where the sparse index key satisfying the multi-column query condition is specifically a sparse index entry that is found from the logical data blocks according to keys composed of the query conditions (i.e., the multi-column query condition of the multi-attribute combination) and is first encoded equal to or greater than the sparse index key in the sparse index.
In one embodiment, the step 20 of "determining the interval matching the query condition" may be specifically implemented by the following steps:
step 201: and determining the starting line number of the key (index main key) corresponding to the logical data block range formed by the query condition.
Step 202: and determining the ending line number of the key corresponding to the logical data block range formed by the query conditions.
Step 203, determining the interval matched with the query condition based on the start line number and the end line number.
In an embodiment, step 201 may be specifically implemented by the following steps:
In step 20, the sparse index of each logical data block is traversed by "data retrieval using sparse index", and it is determined in step 201a that the sparse index that traverses all the logical blocks does not find a sparse index entry that is the same as the key composed of query conditions or greater than the sparse index key code. The first case is that indexes matched with keys composed of query conditions do not exist in the whole database; in the second case, the index matching the key composed of the query condition is the index of a certain row of data in the last logical data block. Illustratively, the key formed by the query condition is key (x), the sparse index key of the last logical data block in the database is (10.29.83.760) + (20221201), that is, the index of the first row data of the last logical data block is (10.29.83.760) + (20221201), and there may be other rows of data arranged after the first row data in the last logical data block, in other rows of data, the index of the partial data may be equal to the index of the first row data, and the index of the partial data may be larger than the index of the first row data, so in the sparse indexing process, it cannot be determined whether the indexes of other data in the last logical data block except for the first row data match the key formed by the query condition, and therefore, in the sparse indexing process, in the case of finding the sparse index key matching the key formed by the query condition, the last logical data block may be subjected to two-stage retrieval, wherein the row number corresponding to the last sparse index may be set as the start row number. It should be noted that, if the row data corresponding to the last sparse index key is multi-row data, the row number of the last row data may be the starting row number. Illustratively, the index of the first row data of the last logical data block is (10.29.83.760) + (20221201), and the index of the subsequent 14 rows of data is the same as the index of the first row data, i.e., the index of the first 15 rows of the last logical data block, in which case the row number of the 15 th row of data may be taken as the starting row number.
Step 201e: and setting the line number corresponding to the previous sparse index of the sparse index key meeting the current condition as a starting line number.
And searching a first sparse index item which is equal to or larger than sparse index key codes in the sparse index from the logic block according to keys formed by the query conditions. If an index entry meeting the condition is found and the sparse index entry is not the first sparse index entry, taking the line number corresponding to the previous sparse index entry of the sparse index entry as a starting line number; and if the index entry meeting the condition is found and is the first sparse index entry, taking the line number corresponding to the sparse index entry as the starting line number.
In an embodiment, step 202 may be specifically implemented by the following steps:
The principle is the same as that of step 201b, and is not described herein again.
And searching a first sparse index item which is larger than the sparse index key in the sparse index according to the key. If finding out the sparse index item meeting the condition, recording the line number corresponding to the index item as an end line number; and if the codes of which the sparse index entry is larger than the key are not found, taking the line number corresponding to a key subsequent to the sparse index as the ending line number.
In one embodiment, the specific implementation manner of step 203 includes:
step 203a: the upper and lower bound row numbers are identified in the columnar index using a binary search algorithm.
And searching the first line which is larger than the key code in the range of the column index line by bisection at the starting line number and the ending line number, wherein the line number is marked as an upper bound line number. Then, similarly, the corresponding lower bound line number is found from the starting line number and the upper bound line number, so that keys formed by the query conditions can be determined along with the range of all corresponding data lines, and the matched data content can be quickly retrieved.
For example, referring to table 1, when performing query according to keys composed of query conditions, first, find a sparse index entry key equal to or greater than that in the sparse index, for example, query IP information is 10.26.72.231, data with date information of 20221119 concatenates src _ IP + date _ id to form 10.26.72.23120221119, this find a sparse index entry equal to or first greater than 10.26.72.23120221119 in the sparse index, a key equal to the first row sparse index indicating that the index entry key is the first row of this logical block may be used as a start row number, and a key greater than that indicates that no matching sparse index entry key exists, and its corresponding key is in the previous logical block. Therefore, the line number corresponding to the previous sparse indexing item key is used as the starting line number, and in another case, if one sparse indexing item is not matched to be equal to or larger than the key, the line number corresponding to the last sparse indexing item key is recorded as the starting line number. Similarly, according to the keys formed by the query conditions, the codes corresponding to the first sparse index item larger than the sparse index key in the sparse index are searched for and taken as the ending line numbers, and if one code with the sparse index item larger than the key is not found, the line number corresponding to the last key in the sparse index is taken as the ending line number.
And searching the first line which is larger than the key code by bisection by taking the obtained starting line number and ending line number as a line range interval of the column-level index, and marking the line number as an upper boundary line number. And then, similarly, the first line which is equal to the key code is found from the starting line number and the upper bound line number and is used as the lower bound line number, so that the value range of the key formed by the query conditions can be determined, and the matched data content can be quickly retrieved.
By the data query method provided by the embodiment of the application, the data content to be queried can be rapidly retrieved in a large data volume in a mode of constructing the sparse index of the logic block under the condition of column-type storage of mass data, the data retrieval time can be shortened, the size of the retrieved data volume is reduced in the retrieval process, and the power consumption of equipment is reduced.
Fig. 3 is a schematic structural diagram of a data query apparatus according to an embodiment of the present application.
Referring to fig. 3, the data query apparatus may include a processor 301 and a memory 302, where the memory 302 is used to store at least one instruction, and the instruction is loaded by the processor 301 and executed to implement the data query method provided in any embodiment of the present application.
The embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the data query method provided in any embodiment of the present application.
Embodiments of the present application further provide a computer program product, which includes a computer program or an instruction, and when the computer program or the instruction is executed by a processor, the computer program or the instruction implements the data query method provided in any embodiment of the present application.
It should be noted that the terminal referred to in the embodiments of the present application may include, but is not limited to, a Personal Computer (PC), a Personal Digital Assistant (PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), a mobile phone, an MP3 player, an MP4 player, and the like.
It is to be understood that the application may be an application program (native app) installed on the terminal, or may also be a web page program (webApp) of a browser on the terminal, which is not limited in this embodiment of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the methods according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A data query method, applied to a database, wherein data stored in the database is configured as a logical data block by consecutive rows of data, and each of the logical data blocks is configured to use an index of a first row of data in a current logical data block as a sparse index of the current logical data block, the method comprising:
acquiring a query condition input by a user;
based on the query condition input by the user, if the query condition contains a sparse index item, using a corresponding sparse index to perform data retrieval, and determining an interval matched with the query condition, wherein the data contained in the interval matched with the query condition is target data.
2. The method of claim 1, wherein the plurality of consecutive rows of data forming a logical data block comprises a plurality of consecutive 1024 rows of data forming a logical data block.
3. The method of claim 1, wherein the query is a multi-column query comprising a combination of attributes.
4. The method of claim 1, wherein determining the interval matching the query condition comprises:
determining the starting line number of the range of the logical data block corresponding to the index main key formed by the query condition;
determining the ending row number of the range of the logical data block corresponding to the index main key formed by the query condition;
and determining the interval matched with the query condition based on the starting line number and the ending line number.
5. The method of claim 4, wherein determining the starting row number of the logical data block range corresponding to the index primary key formed by the query condition comprises:
and determining whether the sparse index main key meeting the query condition is found, if not, setting the row number corresponding to the sparse index of the last logical data block as a starting row number, if so, determining whether the found sparse index main key meeting the query condition is the same as the index main key consisting of the query condition, if so, setting the row number of the first sparse index main key meeting the query condition as the starting row number, and if not, setting the row number corresponding to the previous sparse index of the sparse index main key meeting the current condition as the starting row number.
6. The method of claim 4, wherein determining the end row number of the logical data block range corresponding to the index primary key formed by the query condition comprises:
searching the row number of the first sparse index main key which is larger than the sparse index main key meeting the query condition;
and determining whether the first sparse index primary key which is larger than the sparse index primary key meeting the query condition is found, if not, taking the row number of the last row of data in the last logic data block as an end row number, and if so, setting the row number of the sparse index primary key which meets the query condition at present as the end row number.
7. The method of any of claims 4-6, wherein determining the interval matching the query condition based on the start line number and the end line number comprises:
the upper and lower bound row numbers are identified in the columnar index using a binary search algorithm.
8. A data query apparatus, characterized in that the apparatus comprises:
a processor and a memory for storing at least one instruction which, when loaded and executed by the processor, implements the method of any of claims 1-6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-6.
10. A computer program product comprising a computer program or instructions, characterized in that the computer program or instructions, when executed by a processor, implement the method according to any of claims 1-6.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5758145A (en) * | 1995-02-24 | 1998-05-26 | International Business Machines Corporation | Method and apparatus for generating dynamic and hybrid sparse indices for workfiles used in SQL queries |
| US20120179698A1 (en) * | 2011-01-12 | 2012-07-12 | International Business Machines Corporation | Multiple Sparse Index Intelligent Table Organization |
| US20150106382A1 (en) * | 2013-10-11 | 2015-04-16 | Oracle International Corporation | Tables With Unlimited Number Of Sparse Columns And Techniques For An Efficient Implementation |
| WO2017096892A1 (en) * | 2015-12-07 | 2017-06-15 | 百度在线网络技术（北京）有限公司 | Index construction method, search method, and corresponding device, apparatus, and computer storage medium |
| CN107515882A (en) * | 2016-06-17 | 2017-12-26 | 阿里巴巴集团控股有限公司 | Data query method and device |
| US20180129691A1 (en) * | 2016-11-04 | 2018-05-10 | Salesforce.Com, Inc. | Dynamic creation and maintenance of multi-column custom indexes for efficient data management in an on-demand services environment |
| CN110019218A (en) * | 2017-12-08 | 2019-07-16 | 阿里巴巴集团控股有限公司 | Data storage and querying method and equipment |
| CN111611250A (en) * | 2020-07-03 | 2020-09-01 | 腾讯科技（深圳）有限公司 | Data storage device, data query method, data query device, server and storage medium |
| WO2021017422A1 (en) * | 2019-07-29 | 2021-02-04 | 创新先进技术有限公司 | Index creation method in block chain type account book, device and apparatus |
| CN113961580A (en) * | 2021-12-22 | 2022-01-21 | 联通智网科技股份有限公司 | Data query method, service system and electronic equipment |
| US20220149865A1 (en) * | 2019-07-15 | 2022-05-12 | Use-256 B.V. | Permutation-based coding for data storage and data transmission |
| CN115328950A (en) * | 2022-07-08 | 2022-11-11 | 厦门服云信息科技有限公司 | Secondary index-based hbase query method, terminal device and storage medium |
| CN115408384A (en) * | 2022-08-04 | 2022-11-29 | 阿里云计算有限公司 | Database access method, device and storage medium |
| US11514236B1 (en) * | 2020-09-30 | 2022-11-29 | Amazon Technologies, Inc. | Indexing in a spreadsheet based data store using hybrid datatypes |
-
2022
- 2022-12-30 CN CN202211722030.4A patent/CN115809248B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5758145A (en) * | 1995-02-24 | 1998-05-26 | International Business Machines Corporation | Method and apparatus for generating dynamic and hybrid sparse indices for workfiles used in SQL queries |
| US20120179698A1 (en) * | 2011-01-12 | 2012-07-12 | International Business Machines Corporation | Multiple Sparse Index Intelligent Table Organization |
| US20150106382A1 (en) * | 2013-10-11 | 2015-04-16 | Oracle International Corporation | Tables With Unlimited Number Of Sparse Columns And Techniques For An Efficient Implementation |
| WO2017096892A1 (en) * | 2015-12-07 | 2017-06-15 | 百度在线网络技术（北京）有限公司 | Index construction method, search method, and corresponding device, apparatus, and computer storage medium |
| CN107515882A (en) * | 2016-06-17 | 2017-12-26 | 阿里巴巴集团控股有限公司 | Data query method and device |
| US20180129691A1 (en) * | 2016-11-04 | 2018-05-10 | Salesforce.Com, Inc. | Dynamic creation and maintenance of multi-column custom indexes for efficient data management in an on-demand services environment |
| CN110019218A (en) * | 2017-12-08 | 2019-07-16 | 阿里巴巴集团控股有限公司 | Data storage and querying method and equipment |
| US20220149865A1 (en) * | 2019-07-15 | 2022-05-12 | Use-256 B.V. | Permutation-based coding for data storage and data transmission |
| WO2021017422A1 (en) * | 2019-07-29 | 2021-02-04 | 创新先进技术有限公司 | Index creation method in block chain type account book, device and apparatus |
| CN111611250A (en) * | 2020-07-03 | 2020-09-01 | 腾讯科技（深圳）有限公司 | Data storage device, data query method, data query device, server and storage medium |
| US11514236B1 (en) * | 2020-09-30 | 2022-11-29 | Amazon Technologies, Inc. | Indexing in a spreadsheet based data store using hybrid datatypes |
| CN113961580A (en) * | 2021-12-22 | 2022-01-21 | 联通智网科技股份有限公司 | Data query method, service system and electronic equipment |
| CN115328950A (en) * | 2022-07-08 | 2022-11-11 | 厦门服云信息科技有限公司 | Secondary index-based hbase query method, terminal device and storage medium |
| CN115408384A (en) * | 2022-08-04 | 2022-11-29 | 阿里云计算有限公司 | Database access method, device and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 无: "DorisDB系列文档（五）表设计详解", Retrieved from the Internet <URL:《https://z.itpub.net/article/detial/B197C1EF73B33178F8C05F3C7AFDF0FF》> * |
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