CN115809248B - 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 and device and a storage medium, wherein the method is applied to a database, data stored in the database is configured to form a logic data block by continuous multi-line data, each logic data block is configured to take an index of first-line data in a current logic data block as a sparse index of the current logic data block, and the method comprises the following steps: 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, data retrieval is performed by using a corresponding sparse index to determine a section matched with the query condition, 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 condition under massive data is solved, and the data content to be queried is rapidly retrieved in the massive data through a mode of constructing a logic block sparse index under massive data column storage.
Description
Technical Field
The present disclosure 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 vigorous development of 5G technology, the characteristics of high speed, wide connection and low time delay are highly compatible with numerous scenes of the Internet of vehicles. At the same time, behavior data generated based on vehicles also shows explosive growth, wherein log data of the vehicles accessing the internet is more data magnitude reaching billions of increments per day, and when a data storage period reaches a certain degree, such as half a year or more, the data to be searched is required to be searched from mass stored data in a short time.
In some data query schemes, the database hbase is stored in a distributed column manner by taking a rowkey as an index to improve query efficiency, and then a multi-column condition query is realized through Phonenix, however, hbase can only be queried through the rowkey, if query is to be performed through other columns as conditions, although Phoenix can be used, when query sentences are point search and small-range scanning, phoenix can be better satisfied, and a large number of scan type OLAP queries, or a flexible mode of query 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 the method, 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 massive data is solved, and the data content to be queried is quickly searched in the massive data by constructing a logic block sparse index under massive data column storage.
In a first aspect, an embodiment of the present application provides a data query method, applied to a database, where data stored in the database is configured to form a logical data block from a plurality of consecutive rows of data, and each logical data block is configured to use an index of top row 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; and based on the query condition input by the user, if the query condition comprises a sparse index item, performing data retrieval by using a corresponding sparse index, and determining a section matched with the query condition, wherein data contained in the section matched with the query condition is target data.
Further, the consecutive rows of data constitute one logical data block, and the consecutive 1024 rows of data constitute one logical data block.
Further, the query condition is a multi-column query condition, and the multi-column query condition comprises a combination of multiple attributes.
Further, the determining the interval matching the query condition includes: determining a starting line number of a logical data block range corresponding to an index main key formed by the query conditions; determining the end line number of the range of the logical data block corresponding to the index main key formed by the query condition; and determining an interval matched with the query condition based on the starting line number and the ending line number.
Further, the determining the starting line number of the range of the logical data block corresponding to the index primary key formed by the query condition includes: determining whether a sparse index main key meeting the query condition is found, if not, setting a 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 an index main key formed by the query condition, if so, setting a row number of a first sparse index main key meeting the query condition as the starting row number, and if not, setting a 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 end line number of the range of the logical data block corresponding to the index primary key formed by the query condition includes: searching a first row number larger than the sparse index main key meeting the query condition; and determining whether the first sparse index key is greater than the sparse index key meeting the query condition is found, if not, taking the line number of the last line of data in the last logical data block as an end line number, and if so, setting the line number of the sparse index key currently meeting the query condition as the end line number.
Further, determining an interval matching the query condition based on the start line number and the end line number includes: the upper bound and lower bound numbers are validated in the column index using a binary search algorithm.
In a second aspect, an embodiment of the present application further provides a data query device, where the device includes: the system comprises a processor and a memory for storing at least one instruction which when loaded and executed by the processor implements the data query method provided in the first aspect.
In a third aspect, embodiments of the present application further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the data query method provided in the first aspect.
In a fourth aspect, embodiments of the present application also provide a computer program product comprising a computer program or instructions which, when executed by a processor, implement the data query method provided in the first aspect.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the prior art descriptions, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic diagram of an index architecture according to one embodiment of the present application;
fig. 2 is a flow chart of a data query method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a data query device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of 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 apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In some current schemes for data querying, the data querying is generally performed by the following ways:
the first scheme is as follows: the distributed column storage database hbase is used as an index to improve query efficiency, and then multi-column conditional query is realized through Phonenix.
The second scheme is as follows: the time sequence database InfluxDB realizes multidimensional quick query of mass data in an inverted index mode.
However, the above solution has some drawbacks, in particular the following:
first scheme defect: hbase can only query through rowkey, if query through other columns is to be used as a condition, although Phoenix can be used, when query statement is point search and small-range scanning, phoenix can be satisfied better, but OLAP query of a large number of scan types or scene with flexible query mode is not suitable.
Second scheme defect: although the time sequence database can realize multidimensional inquiry of massive data by means of inverted index and the like, the data has strict requirements on the attribute, namely the data must be provided with time attribute.
In order to overcome the technical problems, the embodiments of the present application provide a data query method, and the data query method provided by the embodiments of the present application 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 constructing a database, a plurality of lines of data may be formed into one logical data block, and a plurality of logical data blocks may be constructed according to data to be processed. For example, each 1024 rows of data forms a logical data block, the data to be processed includes 0 th row of data to nth row of data, wherein the 0 th row of data to 1023 rd row of data (1024 th row of data in total) forms a logical data block, the 1024 th row to 2047 th row of data forms a logical data block, and so on until the last row of data, wherein it is to be noted that the plurality of rows of data included in the logical data block where the last row is located is 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 may store a corresponding sparse index key, where the sparse index item is formed by sorting a plurality of columns of stitching combinations as filtering conditions, that is, a sparse index key is constructed according to the condition to be queried, and then sorting the constructed sparse index key. In one embodiment, the index of the top row data in each logical data block may be used as a sparse index key for the corresponding logical data block. For example, the index of the 0 th row data to the 1023 rd row data may be used as the sparse index key of the logical data block where the index of the 0 th row data is located, i.e. key1 shown in fig. 1. The indexes of the 1024 th row data can be used as the sparse index key of the logic data block where the 1024 th row data is located, namely key2 shown in fig. 1, of the logic data blocks formed by the 1024 th row to the 2047 th row. And by analogy, taking the index of the first line data in each logic data block as the sparse index key of the corresponding logic data block.
In one embodiment, a column level index may also be configured for each row of data, with each logical data block configured with a corresponding sparse index key. In one embodiment, the column-level index is indexed by a row number of the stored data, each row number corresponds to a physical storage address of the data block of the current row, and each row number index entry is formed by a start row number, a position and length information of the corresponding data block. And then the line number index table is searched by using the line number of certain line data, the position of the data block corresponding to the line number can be obtained, and after the target data block is read, the data can be further searched.
In one embodiment, column data blocks are stored in blocks of the same size per column of data and then written to or read from the storage device in whole.
After the database is constructed based on the mode, the data query method provided by the embodiment of the application can be used for querying the data from mass data stored in the database.
Fig. 2 is a flow chart 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 the query condition input by the user.
When a user wants to query target data from mass data stored in a database, a query condition can be input for data retrieval, and the query condition can be a corresponding keyword key of the data and acquire the keyword key input by the user. In one embodiment, the keyword key may contain a sparse index term.
Step 20: based on the query condition input by the user, if the query condition contains a sparse index item, performing data retrieval by using the sparse index, and determining a section matched with the query condition.
In one embodiment, based on a query condition input by a user, if the query condition includes a sparse index term, performing data retrieval using the sparse index includes: and searching a first sparse index key from the sparse indexes according to the multi-column query condition.
In one embodiment, the user-entered query criteria may be a multi-column query criteria that is a combination of multiple attributes.
List one
| IP information | Date information | Domain name information | Upstream flow rate | Downstream flow rate |
| 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, i.e., a key in which the IP information and the date information are spliced as a sparse index. It should be noted that, the plurality of rows of data having the same attribute (i.e., the plurality of rows of data having the IP information of (10.26.72.231) and the date information of (20221119)) shown in table one may construct one or more logic data blocks, and the number of rows in the last logic data block of the plurality of logic 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: the first sparse index key satisfying the multi-column query condition is looked up from a sparse index table (containing all the sparse indexes). For example, referring to table one, if the multi-column query condition is "(10.26.72.231) + (20221119)", and the first sparse index key satisfying the multi-column query condition is searched from the sparse index table, it should be noted that if the sparse index keys of multiple consecutive logical data blocks are identical, it should be determined that the sparse index table searches for the first sparse index key satisfying the multi-column query condition, where the sparse index key satisfying the multi-column query condition is specifically a key formed according to the query condition (i.e., the multi-column query condition of the multi-attribute combination) and searches for the first sparse index item equal to or greater than the sparse index key code from the logical data blocks in the sparse index.
In one embodiment, the "determining the interval matching the query condition" in step 20 may be specifically implemented by the following steps:
step 201: and determining the starting line number of the range of the corresponding logical data block of the key (index key) formed by the query condition.
Step 202: and determining the end line number of the range of the key corresponding to the logic data block formed by the query condition.
Step 203, determining an interval matched with the query condition based on the start line number and the end line number.
In one embodiment, step 201 may be specifically implemented by the following steps:
step 201a: it is determined whether the sparse index key is found from the sparse index table, if not found, step 201b is performed, and if found, step 201c is performed.
Step 201b: and setting the row number corresponding to the last sparse index key as a starting row number.
Wherein, since the sparse index of each logical data block is traversed by "data retrieval using sparse index" in step 20, and it is determined that the sparse index traversing all the logical blocks does not find a sparse index item that is the same as or greater than the sparse index key code composed of the query condition by step 201 a. Furthermore, the first case is that no index matched with the key formed by the query condition exists in the whole database; in the second case, the index matching the key formed by the query condition is the index of a certain line of data in the last logic data block. Illustratively, the key formed by the query condition is key (x), the sparse index key of the last logic data block in the database is (10.29.83.760) + (20221201), that is, the index of the first line data of the last logic data block is (10.29.83.760) + (20221201), and there may be other lines of data arranged after the first line data in the last logic data block, in the other lines of data, the index of part of the data may be equal to the index of the first line data, and the index of part of the data may be greater than the index of the first line data, so in the sparse indexing process, it cannot be determined whether the indexes of other data except the first line data in the last logic data block are matched with the key formed by the query condition, and therefore, in the sparse indexing process, in the case of finding a sparse index key matched with the key formed by the query condition, the last logic data block may be subjected to secondary retrieval, where the line number corresponding to the last sparse index key may be set as the start line number. It should be noted that, if the line data corresponding to the last sparse index key is a plurality of line data, the line number of the last line data may be the starting line number. Illustratively, the first line data of the last logical data block has an index of (10.29.83.760) + (20221201), and the index of the next 14 lines of data is the same as the index of the first line data, i.e., the index of the first 15 lines of the last logical data block is the same, in which case the line number of the 15 th line of data may be taken as the start line number.
Step 201c: if the found sparse index key is the same as the key composed of the query conditions, executing step 201d if yes, and executing step 201e if not.
Step 201d: setting a row number corresponding to the first sparse index key as a starting row number.
Step 201e: setting the row number corresponding to the previous sparse index of the sparse index key meeting the current condition as a starting row number.
And searching a first sparse index item which is equal to or larger than sparse index key codes in the sparse index slave logic block according to the keys formed by the query conditions. If an index item meeting the condition is found and the sparse index item is not the first sparse index item, taking the row number corresponding to the previous sparse index item of the sparse index item as a starting row number; if the index item meeting the condition is found and the index item is the first sparse index item, the row number corresponding to the sparse index item is used as the starting row number.
In one embodiment, step 202 may be specifically implemented by:
step 202a: the first row number greater than the sparse index key is found.
Step 202b: it is determined whether the first index key is greater than the sparse index key is found from the sparse index table, if not found, step 202c is performed, and if found, step 202d is performed.
Step 202c: and taking the line number of the last line of data in the last logic data block as an ending line number.
The principle is the same as that of step 201b, and will not be described again here.
Step 202d: and setting a row number corresponding to the current satisfied sparse index key as an ending row number.
And searching a first sparse index item larger than the sparse index key in the sparse index according to the key. If a sparse index item meeting the condition is found, recording a row number corresponding to the index item as an ending row number; if no code with a sparse index item larger than the key is found, the line number corresponding to the subsequent key of the sparse index is used as the ending line number.
In one embodiment, the specific implementation of step 203 includes:
step 203a: the upper bound and lower bound numbers are validated in the column index using a binary search algorithm.
And searching a first row larger than the key code in the range of the column index row by a dichotomy method at the beginning row number and the ending row number, wherein the row number is marked as an upper bound row number. And then the corresponding lower bound line number is found in the starting line number and the upper bound line number in the same way, so that the key formed by the query condition 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 a query is performed according to a key formed by query conditions, first, a sparse index key equal to or greater than the sparse index key is searched in the sparse index, for example, query IP information is 10.26.72.231, data with date information of 20221119 will splice src_ip+date_id to form 10.26.72.23120221119, this sparse index key equal to or greater than 10.26.72.23120221119 is searched in the sparse index, a key equal to the first row sparse index indicating that the sparse index key is the logical block can be used as a starting row number, and a key greater than the sparse index key indicating that the sparse index key is not matched to be consistent is in the previous logical block. And the line number corresponding to the previous sparse index key is used as a starting line number, and in the other case, the line number corresponding to the last sparse index key is recorded as the starting line number if one sparse index key is not matched to be equal to or larger than the key. Similarly, searching for a code corresponding to a sparse index item, which is larger than the sparse index key, in the sparse index according to the key formed by the query conditions, taking the code corresponding to the first sparse index item larger than the sparse index key as an ending row number, and taking the row number corresponding to the last key of the sparse index as the ending row number if no code, which is larger than the key, of the sparse index item is found.
The first row larger than the key code is searched by a dichotomy in a row range interval taking the starting row number and the ending row number obtained as column-level indexes, and the row number is marked as an upper-bound row number. And then the first line which is equal to the key code is found out from the starting line number and the upper line number to be used as the lower line number, so that the value range of the key formed by the query condition can be determined, and the matched data content can be quickly retrieved.
According to the data query method provided by the embodiment of the application, the data content to be queried can be quickly searched in a large data volume in a mode of constructing a logic block sparse index under the massive data column storage, the data search time can be reduced, the size of the searched data volume is reduced in the search process, and the power consumption of equipment is reduced.
Fig. 3 is a schematic structural diagram of a data query device according to an embodiment of the present application.
Referring to fig. 3, the data query device may include a processor 301 and a memory 302, where the memory 302 is configured to store at least one instruction, and the instruction is loaded and executed by the processor 301 to implement a data query method provided in any embodiment of the present application.
The embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the data query method provided by any of the embodiments of the present application.
Embodiments of the present application also provide a computer program product comprising a computer program or instructions which, when executed by a processor, implement the data query method provided in any of the embodiments of the present application.
It should be noted that, the terminals in the embodiments of the present application may include, but are not limited to, a personal Computer (Personal Computer, PC), a personal digital assistant (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 may 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 will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above 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, etc.) or a Processor (Processor) to perform part of the steps of the methods described in 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 (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description of the preferred embodiments of the present invention is not intended to limit the invention to the precise form disclosed, and any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention are intended to be included within the scope of the present invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution 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 scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
Claims (6)
1. A data query method, applied to a database, wherein data stored in the database is configured to form a logical data block by a plurality of continuous rows of data, and each logical data block is configured to use an index of first row data in a current logical data block as a sparse index of the current logical data block, the sparse index including query conditions composed of a plurality of columns of fields, the method comprising:
acquiring a query condition input by a user;
based on the query condition input by a user, if the query condition comprises a sparse index item, performing data retrieval by using a corresponding sparse index, and determining a section matched with the query condition, wherein data contained in the section matched with the query condition is target data;
the determining the interval matched with the query condition comprises:
determining a starting line number of a logical data block range corresponding to an index main key formed by the query conditions;
determining the end line number of the range of the logical data block corresponding to the index main key formed by the query condition;
determining an interval matched with the query condition based on the starting line number and the ending line number;
wherein, the determining the starting line number of the range of the logical data block corresponding to the index primary key formed by the query condition includes:
determining whether a sparse index main key meeting the query condition is found, if not, setting a 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 an index main key formed by the query condition, if so, setting a row number of a first sparse index main key meeting the query condition as the starting row number, and if not, setting a row number corresponding to the previous sparse index of the sparse index main key meeting the current condition as the starting row number;
determining the end line number of the range of the logical data block corresponding to the index main key formed by the query condition comprises the following steps:
searching a first row number larger than the sparse index main key meeting the query condition;
and determining whether the first sparse index key is greater than the sparse index key meeting the query condition is found, if not, taking the line number of the last line of data in the last logical data block as an end line number, and if so, setting the line number of the sparse index key currently meeting the query condition as the end line number.
2. The method of claim 1, wherein the consecutive rows of data comprise a logical data block and wherein the consecutive 1024 rows of data comprise a logical data block.
3. The method of claim 1, wherein the query criteria is a multi-column query criteria comprising a combination of attributes.
4. A method according to any of claims 1-3, wherein determining an interval matching a query condition based on the start line number and the end line number comprises:
the upper bound and lower bound numbers are validated in the column index using a binary search algorithm.
5. A data querying device, the device comprising:
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 1-4.
6. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-4.
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