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CN111209298A - Method, device, equipment and storage medium for querying database data - Google Patents

Method, device, equipment and storage medium for querying database data Download PDF

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CN111209298A
CN111209298A CN202010010711.2A CN202010010711A CN111209298A CN 111209298 A CN111209298 A CN 111209298A CN 202010010711 A CN202010010711 A CN 202010010711A CN 111209298 A CN111209298 A CN 111209298A
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data
query
operation instruction
grammar
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许璐
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Ping An Technology Shenzhen Co Ltd
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Priority to PCT/CN2020/131734 priority patent/WO2021139426A1/en
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    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24Querying
    • G06F16/245Query processing
    • G06F16/2452Query translation
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24Querying
    • G06F16/242Query formulation
    • G06F16/2433Query languages
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28Databases characterised by their database models, e.g. relational or object models
    • G06F16/283Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP

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Abstract

本发明涉及一种大数据技术领域,公开了一种查询数据库数据的方法、装置、设备和存储介质,通过利用Hive将各数据库匹配的查询指令转化成目标handle,通过结构化查询语言一种数据查询方式查询不同数据源的数据,避免中间表的产生,减少数据传输量以及传输时间,提高数据查询的效率。本发明方法包括:通过查询接口获取访问目标数据源的目标操作指令;根据目标操作指令,获取目标数据源的目标语法标识;根据预置数据仓库工具Hive和目标语法标识,将目标操作指令转化成目标钩子handle;根据目标handle,查询对应的目标数据源中的目标数据;反馈查询结果,查询结果用于指示是否查询到目标数据。

Figure 202010010711

The invention relates to the technical field of big data, and discloses a method, device, device and storage medium for querying database data. By using Hive, the query instructions matched by each database are converted into target handles, and a data query through structured query language is used. The query mode queries data from different data sources, avoids the generation of intermediate tables, reduces the amount of data transmission and transmission time, and improves the efficiency of data query. The method of the invention includes: obtaining the target operation instruction for accessing the target data source through the query interface; obtaining the target syntax identifier of the target data source according to the target operation instruction; according to the preset data warehouse tool Hive and the target syntax identifier, converting the target operation instruction into Target hook handle; according to the target handle, query the target data in the corresponding target data source; feedback the query result, which is used to indicate whether the target data is queried.

Figure 202010010711

Description

Method, device, equipment and storage medium for querying database data
Technical Field
The present invention relates to the field of big data technologies, and in particular, to a method, an apparatus, a device, and a storage medium for querying database data.
Background
As the value of data increases and storage costs decrease, more and more businesses keep data as assets for long periods of time. Due to different query scenarios and usage specifications, data is stored in different systems for use, such as traditional relational databases, distributed databases, streaming data storage, and time-series data storage. Different data storage components correspond to different query modes so as to meet the requirements of an upper-layer service system.
In the existing data acquisition scheme, data is stored in different systems, and the different systems correspond to different query modes, for example: the Hive is a data warehouse tool based on Hadoop, and can map a structured data file into a database table and provide a Structured Query Language (SQL) query function; HBase can be accessed by Java language, HBase is an open-source non-relational distributed database, and the programming language for realizing the HBase is Java.
When a server needs to query different databases, different query modes need to be developed, and query is performed by using different query statements, if there is data source query, operation needs to be performed through different intermediate tables, so that the data transmission amount is large, the execution instruction is complicated, and the data query efficiency is very low.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for querying database data, which are used for solving the problem of different query modes when database data are queried and improving the data query efficiency.
A first aspect of an embodiment of the present invention provides a method for querying database data, including: acquiring a target operation instruction for accessing a target data source through a query interface, wherein the query interface is a uniform interface for querying different types of data sources by a client, and the target operation instruction is an instruction described by a Structured Query Language (SQL); according to the target operation instruction, acquiring a target grammar identifier of the target data source, wherein the target grammar identifier is used for indicating the instruction grammar of the target data source; converting the target operation instruction into a target hook handle according to a preset data warehouse tool Hive and the target grammar identifier; inquiring corresponding target data in the target data source according to the target handle; feeding back a query result, wherein the query result is used for indicating whether the target data is queried or not.
Optionally, in a first implementation manner of the first aspect of the embodiment of the present application, a target language rule of the target operation instruction is read; converting the target language rule into a grammar tree; a parser to generate the target language rules; and analyzing the target operation instruction based on the syntax tree and the analyzer to obtain the target syntax identifier of the target data source, wherein the target syntax identifier is used for indicating the instruction syntax of the target data source.
Optionally, in a second implementation manner of the first aspect of the embodiment of the present invention, a syntax rule of the target syntax identifier is read from the preset data warehouse tool; analyzing the grammar rule to obtain an abstract grammar tree; converting the abstract syntax tree to obtain a query block; translating the query block to obtain an execution operation tree; and recompiling the target operation instruction by using the execution operation tree to obtain the target handle.
Optionally, in a third implementation manner of the first aspect of the embodiment of the present invention, an execution log of the target handle is obtained, and alarm information is generated in the execution log.
Optionally, in a fourth implementation manner of the first aspect of the embodiment of the present invention, an execution log of the target handle is obtained, and a target keyword is extracted, where the target keyword is a sensitive word in an execution process of the target handle, and a preset log sensitive table is set in the execution log; judging whether the target keyword is matched with the preset log sensitive table or not; and if the target keyword is matched with the preset log sensitive table, generating alarm information in the execution log.
Optionally, in a fifth implementation manner of the first aspect of the embodiment of the present invention, if the target data is not queried and the alarm information is generated, the alarm information is fed back through the query interface, where the alarm information is used to indicate that the target data is not queried; and if the target data is inquired, feeding back the target data through the inquiry interface.
Optionally, in a sixth implementation manner of the first aspect of the embodiment of the present invention, an audit log is added, where the audit log is an annotation in a data query process.
A second aspect of the present invention provides an apparatus for querying database data, comprising: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a target operation instruction for accessing a target data source through a query interface, the query interface is a uniform interface for a client to query different types of data sources, and the target operation instruction is an instruction described by Structured Query Language (SQL); a second obtaining module, configured to obtain, according to the target operation instruction, a target syntax identifier of the target data source, where the target syntax identifier is used to indicate instruction syntax of the target data source; the conversion module is used for converting the target operation instruction into a target hook handle according to a preset data warehouse tool Hive and the target grammar identifier; the query module is used for querying the corresponding target data in the target data source according to the target handle; and the feedback module is used for feeding back a query result, and the query result is used for indicating whether the target data is queried or not.
Optionally, in a first implementation manner of the second aspect of the embodiment of the present application, the second obtaining module is specifically configured to: reading a target language rule of the target operation instruction; converting the target language rule into a grammar tree; a parser to generate the target language rules; and analyzing the target operation instruction based on the syntax tree and the analyzer to obtain the target syntax identifier of the target data source, wherein the target syntax identifier is used for indicating the instruction syntax of the target data source.
Optionally, in a second implementation manner of the second aspect of the embodiment of the present application, the conversion module is specifically configured to: reading the grammar rule of the target grammar identifier in the preset data warehouse tool; analyzing the grammar rule to obtain an abstract grammar tree; converting the abstract syntax tree to obtain a query block; translating the query block to obtain an execution operation tree; and recompiling the target operation instruction by using the execution operation tree to obtain the target handle.
Optionally, in a third implementation manner of the second aspect of the embodiment of the present application, the apparatus for querying database data further includes: and the generating module is used for acquiring the execution log of the target handle and generating alarm information in the execution log.
Optionally, in a fourth implementation manner of the second aspect of the embodiment of the present application, the generating module is specifically configured to: acquiring an execution log of the target handle, and extracting a target keyword, wherein the target keyword is a sensitive word in the execution process of the target handle, and a preset log sensitive table is arranged in the execution log; judging whether the target keyword is matched with the preset log sensitive table or not; and if the target keyword is matched with the preset log sensitive table, generating alarm information in the execution log.
Optionally, in a fifth implementation manner of the second aspect of the embodiment of the present application, the feedback module is specifically configured to: if the target data is not inquired and the alarm information is generated, feeding back the alarm information through the inquiry interface, wherein the alarm information is used for indicating that the target data is not inquired; and if the target data is inquired, feeding back the target data through the inquiry interface.
Optionally, in a sixth implementation manner of the second aspect of the embodiment of the present application, the apparatus for querying database data further includes: and the adding module is used for adding an audit log, wherein the audit log is an annotation in the data query process.
A third aspect of the present invention provides an apparatus for querying database data, comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line; the at least one processor invokes the instructions in the memory to cause the apparatus for querying database data to perform the method of the first aspect.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein instructions which, when run on a computer, cause the computer to perform the method of the first aspect described above.
According to the technical scheme, the embodiment of the invention has the following advantages:
according to the embodiment of the invention, the query instruction matched with each database is converted into the target handle by using Hive, and the data of different data sources is queried in a data query mode of a structured query language, so that the generation of a middle table is avoided, the data transmission quantity and transmission time are reduced, and the data query efficiency is improved.
Drawings
FIG. 1 is a diagram of an embodiment of a method for querying database data according to an embodiment of the present invention;
FIG. 2 is a diagram of another embodiment of the method for querying database data according to the embodiment of the present invention;
FIG. 3 is a diagram of an embodiment of an apparatus for querying database data according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of another embodiment of an apparatus for querying database data according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an embodiment of an apparatus for querying database data in the embodiment of the present invention.
Detailed Description
The invention provides a method, a device, equipment and a storage medium for querying database data, which are used for solving the problem of different query modes when database data are queried and improving the data query efficiency.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of understanding, a detailed flow in the embodiment of the present invention is described below, and referring to fig. 1, an embodiment of the method for querying database data in the embodiment of the present invention includes:
101. and acquiring a target operation instruction for accessing a target data source through a query interface, wherein the query interface is a uniform interface for querying different types of data sources by a client, and the target operation instruction is an instruction described by a Structured Query Language (SQL).
The server obtains a target operation instruction for accessing a target data source through a query interface, the query interface is a uniform interface for querying different types of data sources by a client, and the target operation instruction is an instruction described by a Structured Query Language (SQL). In a large data processing system, because the types of data are various and the data amount is complicated, different types of data are stored in different databases, great convenience is provided for a client to query and call the data, but different query interfaces are required for the client to access different databases. For example: the Druid utilizes the REST interface to perform data query, is an efficient data query system and mainly solves the problem of performing aggregation query on a large amount of data based on time sequence; HBase utilizes a JavaAPI interface to perform data query, HBase is an open-source non-relational distributed database, and the programming language for realizing the HBase is Java. Therefore, the server obtains the operation instructions for different types of data sources, so as to facilitate data query, and the type of the target data source is not limited, so as to facilitate data query in multiple data sources. The uniform query interface for the client to access the database is a query interface of a preset data warehouse tool Hive, and because the access interfaces of different types of databases are different, the query interfaces are uniform. Hive has three main query interfaces: command-line interface (CLI), Client, and web product interface (WUI). The most common of them is the CLI, and the server will start a Hive copy at the same time when starting the CLI. The Client is the Client of the Hive, and when the Server starts the Client mode, the Client is connected to the node where the Hive Server is located, and the Hive Server is started at the node. WUI is the access to data by browser access Hive. In the embodiment of the present invention, the query interface of Hive is not limited.
It should be noted that the preset data warehouse tool in the embodiment of the present invention is Hive, and Hive is a data warehouse processing tool with Hadoop packaged at the bottom, and data query is implemented by using SQL-like language, where all Hive data is stored in a Hadoop-compatible file system. Hive does not modify data in the process of loading data, but moves the data to a directory set by Hive in a distributed file system (HDFS). The Hive comprises an interpreter, a compiler and an optimizer, and can complete lexical analysis, syntactic analysis, compilation and optimization of the query statement. The feature of Hive supports the analysis of the query commands of other different databases, so the data query is carried out on the basis of Hive in the invention.
It is further explained that, since SQL is widely used in data warehouses, the server uses the SQL language as the target operation instruction.
SQL is divided into two parts, a data operation language and a data definition language, and is syntax for executing queries, and also contains syntax for updating, inserting and deleting records enough for compilation of query programs. The following is a commonly used compilation statement for SQL:
for example, a database is created using SQL:
hive>create database financials;
hive>create database ifnot exists financials;
and querying the data in the table by using SQL:
hive>select*from student;
102. and acquiring a target grammar identifier of the target data source according to the target operation instruction, wherein the target grammar identifier is used for indicating the instruction grammar of the target data source.
And the server acquires a target grammar identifier of the target data source according to the target operation instruction, wherein the target grammar identifier is used for indicating the instruction grammar of the target data source. Specifically, the server reads a target language rule of a target operation instruction; the server converts the target language rule into a grammar tree; the server generates a parser of the target language rule; and the server analyzes the target operation instruction based on the syntax tree and the analyzer to obtain a target syntax identifier of the target data source, wherein the target syntax identifier is used for indicating the instruction syntax of the target data source.
When the client accesses different types of databases, different access languages are needed, the server converts and translates the target operation instruction to obtain a target grammar identifier of the target data source, the target grammar identifier is an instruction grammar used for indicating the target data source, and the server can access the corresponding database by using the converted target operation instruction. When extracting the target grammar identifier of the target data source, the server needs to analyze the target operation instruction, and the server acquires the target grammar identifier corresponding to the target data source by analyzing the target operation instruction.
It is further explained that, because the query language of Hive is SQL language, the server here obtains the language rules of SQL.
Taking access to the HBase database as an example: java is an instruction syntax for indicating the HBase database, so that the server accesses a target operation instruction of the HBase according to the description of the SQL language and obtains a target syntax identifier which needs to be converted into Java. In this process: the server reads the target language rule of SQL; the server converts the target language rule of the SQL into a syntax tree; the server generates a parser of the target language rule of the SQL, and the server parses the target operation instruction by using the syntax tree and the parser; and the server extracts a target grammar identifier of the HBase from the analyzed target operation instruction, wherein the target grammar identifier is the identifier of the Java.
103. And converting the target operation instruction into a target hook handle according to the preset data warehouse tool Hive and the target grammar identifier.
And the server converts the target operation instruction into a target hook handle according to the preset data warehouse tool Hive and the target grammar identifier. And the server converts the target operation instruction accessing the target data source into a target handle by using the preset Hive and the extracted target grammar identifier. Specifically, the server reads a grammar rule of a target grammar identifier in a preset data warehouse tool; the server analyzes the grammar rule to obtain an abstract grammar tree; the server converts the abstract syntax tree to obtain a query block; the server translates the query block to obtain an execution operation tree; and the server recompiles the target operation instruction by using the execution operation tree to obtain the target handle.
It is further described that, after the server extracts the target syntax identifier, in order to perform data query, the server needs to convert the target operation instruction into a query instruction of the target data source, that is, convert the target operation instruction into a target handle.
104. And inquiring the target data in the corresponding target data source according to the target handle.
And the server queries the target data in a target data source corresponding to the target handle according to the target handle.
All data in Hive is stored in the HDFS, and no special data storage format is provided, such as text format, sequence file format and parquetfile format are supported. Wherein Hive comprises the following data models: database (DB) appears in HDFS as $ { live. metastore. ware house. dir } directory next folder; an internal Table (Table) represents the next folder of the DB directory in the HDFS; an External Table (External Table) is similar to a Table, and the data storage location thereof can be in any specified path; partitions (partitions) appear as subdirectories under Table directories in the HDFS; buckets (buckets) appear in HDFS as multiple files under the same table entry after hash.
It is further understood that the data source can be abstracted into the External Table of Hive, and the server queries the External Table by using the target handle, i.e. can execute the instruction for accessing the database.
105. And feeding back a query result, wherein the query result is used for indicating whether the target data is queried or not.
And the server feeds back a query result, wherein the query result is used for indicating whether the target data is queried or not. And the server feeds back the query result according to whether the target data is queried or not, and if the target data is queried, the query result fed back by the server is the target data.
According to the embodiment of the invention, the query instruction matched with each database is converted into the target handle by using Hive, and the data of different data sources is queried in a data query mode of a structured query language, so that the generation of a middle table is avoided, the data transmission quantity and transmission time are reduced, and the data query efficiency is improved.
Referring to fig. 2, another embodiment of the method for querying database data according to the embodiment of the present invention includes:
201. and acquiring a target operation instruction for accessing a target data source through a query interface, wherein the query interface is a uniform interface for querying different types of data sources by a client, and the target operation instruction is an instruction described by a Structured Query Language (SQL).
The server obtains a target operation instruction for accessing a target data source through a query interface, the query interface is a uniform interface for querying different types of data sources by a client, and the target operation instruction is an instruction described by a Structured Query Language (SQL). In a large data processing system, because the types of data are various and the data amount is complicated, different types of data are stored in different databases, great convenience is provided for a client to query and call the data, but different query interfaces are required for the client to access different databases. For example: the Druid utilizes the REST interface to perform data query, is an efficient data query system and mainly solves the problem of performing aggregation query on a large amount of data based on time sequence; HBase utilizes a JavaAPI interface to perform data query, HBase is an open-source non-relational distributed database, and the programming language for realizing the HBase is Java. Therefore, the server obtains the operation instructions for different types of data sources, so as to facilitate data query, and the type of the target data source is not limited, so as to facilitate data query in multiple data sources. The uniform query interface for the client to access the database is a query interface of a preset data warehouse tool Hive, and because the access interfaces of different types of databases are different, the query interfaces are uniform. Hive has three main query interfaces: command-line interface (CLI), Client, and web product interface (WUI). The most common of them is the CLI, and the server will start a Hive copy at the same time when starting the CLI. The Client is the Client of the Hive, and when the Server starts the Client mode, the Client is connected to the node where the Hive Server is located, and the Hive Server is started at the node. WUI is the access to data by browser access Hive. In the embodiment of the present invention, the query interface of Hive is not limited.
It should be noted that the preset data warehouse tool in the embodiment of the present invention is Hive, and Hive is a data warehouse processing tool with Hadoop packaged at the bottom, and data query is implemented by using SQL-like language, where all Hive data is stored in a Hadoop-compatible file system. Hive does not modify data in the process of loading data, but moves the data to a directory set by Hive in a distributed file system (HDFS). The Hive comprises an interpreter, a compiler and an optimizer, and can complete lexical analysis, syntactic analysis, compilation and optimization of the query statement. The feature of Hive supports the analysis of the query commands of other different databases, so the data query is carried out on the basis of Hive in the invention.
It is further explained that, since SQL is widely used in data warehouses, the server uses the SQL language as the target operation instruction.
SQL is divided into two parts, a data operation language and a data definition language, and is syntax for executing queries, and also contains syntax for updating, inserting and deleting records enough for compilation of query programs. The following is a commonly used compilation statement for SQL:
for example, a database is created using SQL:
hive>create database financials;
hive>create database ifnot exists financials;
and querying the data in the table by using SQL:
hive>select*from student;
202. and acquiring a target grammar identifier of the target data source according to the target operation instruction, wherein the target grammar identifier is used for indicating the instruction grammar of the target data source.
And the server acquires a target grammar identifier of the target data source according to the target operation instruction, wherein the target grammar identifier is used for indicating the instruction grammar of the target data source. Specifically, the server reads a target language rule of a target operation instruction; the server converts the target language rule into a grammar tree; the server generates a parser of the target language rule; and the server analyzes the target operation instruction based on the syntax tree and the analyzer to obtain a target syntax identifier of the target data source, wherein the target syntax identifier is used for indicating the instruction syntax of the target data source.
When the client accesses different types of databases, different access languages are needed, the server converts and translates the target operation instruction to obtain a target grammar identifier of the target data source, the target grammar identifier is an instruction grammar used for indicating the target data source, and the server can access the corresponding database by using the converted target operation instruction. When extracting the target grammar identifier of the target data source, the server needs to analyze the target operation instruction, and the server acquires the target grammar identifier corresponding to the target data source by analyzing the target operation instruction.
It is further explained that, because the query language of Hive is SQL language, the server here obtains the language rules of SQL.
Taking access to the HBase database as an example: java is an instruction syntax for indicating the HBase database, so that the server accesses a target operation instruction of the HBase according to the description of the SQL language and obtains a target syntax identifier which needs to be converted into Java. In this process: the server reads the target language rule of SQL; the server converts the target language rule of the SQL into a syntax tree; the server generates a parser of the target language rule of the SQL, and the server parses the target operation instruction by using the syntax tree and the parser; and the server extracts a target grammar identifier of the HBase from the analyzed target operation instruction, wherein the target grammar identifier is Java.
203. Reading the grammar rules of the target grammar identifications in a preset data warehouse tool.
And the server reads the grammar rules of the target grammar identifications in the preset data warehouse tool. And after the server acquires the target grammar identification of the target data source, reading the grammar rule of the target grammar identification in a preset data warehouse tool.
The language rules are divided into lexical rules and grammatical rules, and the lexical rules define a mode of converting the code character string sequence into the mark sequence; the grammar rules define the way in which the sequence of tokens is converted into a grammar tree. Typically, the rule names of lexical rules are named in upper case letters, while the rule names of grammatical rules start in lower case letters.
For example, taking access to the HBase database as an example: and after the server acquires the target grammar identification Java of the HBase, reading a grammar rule of the Java in Hive.
204. And analyzing the grammar rule to obtain an abstract grammar tree.
And the server analyzes the grammar rule to obtain an abstract grammar tree. After the server analyzes the morphology and the grammar, if the expression needs to be further processed, the server converts the input sentence into a grammar tree while analyzing the grammar by using the abstract grammar tree grammar, and then completes the further processing when traversing the grammar tree.
205. And converting the abstract syntax tree to obtain a query block.
And the server converts the abstract syntax tree to obtain a query block. And traversing the abstract syntax tree by the server, and converting the abstract syntax tree into a query block. The query block is the most basic unit in an instruction, namely a sub-query, and comprises three parts: input sources, computing processes, and outputs.
The server generates a parser for the target language rules during the translation, the parser providing two mechanisms to traverse the generated syntax tree: listener and Visitor, when a server traverses a syntax tree using Listener mode, the inside of the server encounters different nodes in the process of traversing the nodes of the syntax tree, so the server calls different methods for providing Listener; when the server utilizes the Visitor mode, the server needs to manually designate access to a specific type of node. The server employs these two mechanisms to traverse the abstract syntax tree.
206. And translating the query block to obtain an execution operation tree.
And the server translates the query block to obtain an execution operation tree. And traversing the query block by the server, and recursively calling the sub-queries to obtain an execution operation tree.
The server translates the query block into instructions compiled using the grammar rules identified by the target grammar through the translated grammar tree and the generated parser, in which the server accesses the abstract grammar tree using Lister or Visitor.
207. And recompiling the target operation instruction by utilizing the execution operation tree to obtain the target handle.
And the server recompiles the target operation instruction by using the execution operation tree to obtain the target handle. And traversing the execution operation tree by the server, and recompiling the target operation instruction, wherein the compiled instruction is a target handle, and the target handle is an instruction for accessing the target data source.
For example, taking the case that a server accesses data in the HBase as an example, the server describes an operation of accessing the HBase database by using SQL, and in Hive, the server reads syntax rules of Java accessing the HBase; the server analyzes the grammar rule by using the interpreter and converts the grammar rule into an abstract grammar tree; the server converts the abstract syntax tree into query blocks using a logical plan generator (including an optimizer); the server translates the query block to obtain an execution operation tree; and the server recompiles the target operation instruction by using the execution operation tree to obtain an instruction for accessing HBase compiled by Java, wherein the instruction compiled by Java is the target handle.
208. And inquiring the target data in the corresponding target data source according to the target handle.
And the server queries the target data in a target data source corresponding to the target handle according to the target handle.
All data in Hive is stored in the HDFS, and no special data storage format is provided, such as text format, sequence file format and parquetfile format are supported. Wherein Hive comprises the following data models: database (DB) appears in HDFS as $ { live. metastore. ware house. dir } directory next folder; an internal Table (Table) represents the next folder of the DB directory in the HDFS; an External Table (External Table) is similar to a Table, and the data storage location thereof can be in any specified path; partitions (partitions) appear as subdirectories under Table directories in the HDFS; buckets (buckets) appear in HDFS as multiple files under the same table entry after hash.
It is further understood that the data source can be abstracted into the External Table of Hive, and the server queries the External Table by using the target handle, i.e. can execute the instruction for accessing the database.
209. And acquiring an execution log of the target handle, and generating alarm information in the execution log.
And the server acquires an execution log of the target handle and generates alarm information in the execution log. Specifically, the server acquires an execution log of the target handle and extracts a target keyword, wherein the target keyword is a sensitive word in the execution process of the target handle, and a preset log sensitive table is arranged in the execution log; the server judges whether the target keyword is matched with a preset log sensitive table or not; and if the target keyword is matched with the preset log sensitive table, the server generates alarm information in the execution log.
It is understood that the server extracts a target keyword, where the target keyword is a sensitive word generated during the process of executing the query by the handle, and where the target keyword is generated during the process of executing the query by the server. And when the server normally executes the query task and no error occurs, the target keyword is not matched with the preset log sensitive table, and no alarm information is generated in the execution log. And after judging whether the target keyword is matched with the preset log sensitive table or not, whether alarm information is generated in the execution log or not can be known, and if the alarm information is generated in the execution log, the error exists in the process of executing the query task.
The preset log sensitive table set by the server is abnormal information occurring in the process of executing the query by the handle, and the abnormal information causes that the server cannot continuously execute the query task. The setting of the log sensitive table is preset, so that the server can timely find out errors or non-compliant operations in execution, and timely feed back alarm information, thereby ensuring the accuracy and safety of server data transmission.
210. And feeding back a query result, wherein the query result is used for indicating whether the target data is queried or not.
And the server feeds back a query result, wherein the query result is used for indicating whether the target data is queried or not. The server judges whether the target data is inquired or not, if the target data is not inquired and the alarm information is generated, the server feeds back the alarm information through the inquiry interface, and the alarm information is used for indicating that the target data is not inquired; and if the target data is inquired, the server feeds back the target data through the inquiry interface.
If the server generates alarm information in the execution log, the server directly feeds back the alarm information through the query interface; if no alarm information is generated in the execution log, the server directly feeds back the inquired target data through the inquiry interface, which indicates that no error occurs in the execution process of the inquiry task. By the operation, the server further guarantees a good operating environment under the condition of conveniently monitoring, inquiring and auditing the execution logs.
In the process of executing the query task, the server can also add an audit log, wherein the audit log is an annotation in the process of querying data.
It should be noted that the server may add an audit log, which is an annotation during the process of executing the query task by the server. The server adds the audit log in the process of executing the query task, so that the user can conveniently examine the information in the executing process.
According to the embodiment of the invention, the query instruction matched with each database is converted into the target handle by using Hive, and the data of different data sources is queried in a data query mode of a structured query language, so that the generation of a middle table is avoided, the data transmission quantity and transmission time are reduced, and the data query efficiency is improved.
The above describes the method for querying database data in the embodiment of the present invention, and the following describes the apparatus for querying database data in the embodiment of the present invention, referring to fig. 3, the apparatus for querying database data in the embodiment of the present invention includes:
referring to fig. 3, an embodiment of the apparatus for querying database data according to the embodiment of the present invention includes:
a first obtaining module 301, configured to obtain a target operation instruction for accessing a target data source through a query interface, where the query interface is a unified interface for querying different types of data sources by a client, and the target operation instruction is an instruction described by structured query language SQL;
a second obtaining module 302, configured to obtain, according to the target operation instruction, a target syntax identifier of the target data source, where the target syntax identifier is used to indicate instruction syntax of the target data source;
the conversion module 303 is configured to convert the target operation instruction into a target hook handle according to a preset data warehouse tool Hive and the target syntax identifier;
the query module 304 is configured to query, according to the target handle, the corresponding target data in the target data source;
a feedback module 305, configured to feed back a query result, where the query result is used to indicate whether the target data is queried.
In the embodiment of the present invention, a first obtaining module 301 obtains a target operation instruction for accessing a target data source through a query interface, where the query interface is a unified interface for a client to query different types of data sources, and the target operation instruction is an instruction described by structured query language SQL; the second obtaining module 302 obtains a target syntax identifier of the target data source according to the target operation instruction, where the target syntax identifier is used to indicate instruction syntax of the target data source; the conversion module 303 converts the target operation instruction into a target hook handle according to a preset data warehouse tool Hive and the target grammar identifier; the query module 304 queries the corresponding target data in the target data source according to the target handle; the feedback module 305 feeds back a query result indicating whether the target data is queried.
According to the embodiment of the invention, the query instruction matched with each database is converted into the target handle by using Hive, and the data of different data sources is queried in a data query mode of a structured query language, so that the generation of a middle table is avoided, the data transmission quantity and transmission time are reduced, and the data query efficiency is improved.
Referring to fig. 4, another embodiment of the apparatus for querying database data according to the embodiment of the present invention includes:
a first obtaining module 301, configured to obtain a target operation instruction for accessing a target data source through a query interface, where the query interface is a unified interface for querying different types of data sources by a client, and the target operation instruction is an instruction described by structured query language SQL;
a second obtaining module 302, configured to obtain, according to the target operation instruction, a target syntax identifier of the target data source, where the target syntax identifier is used to indicate instruction syntax of the target data source;
the conversion module 303 is configured to convert the target operation instruction into a target hook handle according to a preset data warehouse tool Hive and the target syntax identifier;
the query module 304 is configured to query, according to the target handle, the corresponding target data in the target data source;
a feedback module 305, configured to feed back a query result, where the query result is used to indicate whether the target data is queried.
Optionally, the second obtaining module 302 is specifically configured to:
reading a target language rule of the target operation instruction;
converting the target language rule into a grammar tree;
a parser to generate the target language rules;
and analyzing the target operation instruction based on the syntax tree and the analyzer to obtain the target syntax identifier of the target data source, wherein the target syntax identifier is used for indicating the instruction syntax of the target data source.
Optionally, the conversion module 303 is specifically configured to:
reading the grammar rule of the target grammar identifier in the preset data warehouse tool;
analyzing the grammar rule to obtain an abstract grammar tree;
converting the abstract syntax tree to obtain a query block;
translating the query block to obtain an execution operation tree;
and recompiling the target operation instruction by using the execution operation tree to obtain the target handle.
Optionally, the apparatus for querying database data further includes:
the generating module 306 is configured to obtain an execution log of the target handle, and generate alarm information in the execution log.
Optionally, the generating module 306 is specifically configured to:
acquiring an execution log of the target handle, and extracting a target keyword, wherein the target keyword is a sensitive word in the execution process of the target handle, and a preset log sensitive table is arranged in the execution log;
judging whether the target keyword is matched with the preset log sensitive table or not;
and if the target keyword is matched with the preset log sensitive table, generating alarm information in the execution log.
Optionally, the feedback module 305 is specifically configured to:
if the target data is not inquired and the alarm information is generated, feeding back the alarm information through the inquiry interface, wherein the alarm information is used for indicating that the target data is not inquired;
and if the target data is inquired, feeding back the target data through the inquiry interface.
Optionally, the apparatus for querying database data further includes:
and an adding module 307, configured to add an audit log, where the audit log is an annotation in the data query process.
In the embodiment of the present invention, a first obtaining module 301 obtains a target operation instruction for accessing a target data source through a query interface, where the query interface is a unified interface for a client to query different types of data sources, and the target operation instruction is an instruction described by structured query language SQL; the second obtaining module 302 obtains a target syntax identifier of the target data source according to the target operation instruction, where the target syntax identifier is used to indicate instruction syntax of the target data source; the conversion module 303 converts the target operation instruction into a target hook handle according to a preset data warehouse tool Hive and the target grammar identifier; the query module 304 queries the corresponding target data in the target data source according to the target handle; a generating module 306, configured to obtain an execution log of the target handle, and generate alarm information in the execution log; the feedback module 305 feeds back a query result, wherein the query result is used for indicating whether the target data is queried or not; and an adding module 307, configured to add an audit log, where the audit log is an annotation in the data query process.
According to the embodiment of the invention, the query instruction matched with each database is converted into the target handle by using Hive, and the data of different data sources is queried in a data query mode of a structured query language, so that the generation of a middle table is avoided, the data transmission quantity and transmission time are reduced, and the data query efficiency is improved.
Fig. 3 to fig. 4 describe the database query apparatus in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the database query apparatus in the embodiment of the present invention is described in detail from the perspective of hardware processing.
Fig. 5 is a schematic structural diagram of an apparatus for querying database data according to an embodiment of the present invention, where the apparatus 500 for querying database data may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 501 (e.g., one or more processors) and a memory 509, and one or more storage media 508 (e.g., one or more mass storage devices) for storing applications 507 or data 506. Memory 509 and storage medium 508 may be, among other things, transient storage or persistent storage. The program stored on the storage medium 508 may include one or more modules (not shown), each of which may include a sequence of instructions operating on a query database data device. Still further, the processor 501 may be configured to communicate with the storage medium 508 to execute a series of instruction operations in the storage medium 508 on the device 500 for querying database data.
The apparatus 500 for querying database data may also include one or more power supplies 502, one or more wired or wireless network interfaces 503, one or more input-output interfaces 504, and/or one or more operating systems 505, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that the configuration of the apparatus for querying database data shown in FIG. 5 does not constitute a limitation of the apparatus for querying database data, and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
The following describes each component of the apparatus for querying database data in detail with reference to fig. 5:
the processor 501 is a control center of an apparatus for querying database data, and may perform processing according to a method for querying database data. The processor 501 is connected to various parts of the whole device for querying database data through various interfaces and lines, and by running or executing the data stored in the memory 509, the problem of different query modes when querying database data is solved, and the data query efficiency is improved. The storage medium 508 and the memory 509 are carriers for storing data, in the embodiment of the present invention, the storage medium 508 may be an internal memory with a small storage capacity but a high speed, and the memory 509 may be an external memory with a large storage capacity but a low storage speed.
The memory 509 may be used to store software programs and modules, and the processor 501 executes various functional applications and data processing of the apparatus 500 for querying database data by operating the software programs and modules stored in the memory 509. The memory 509 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from use of the query database data device, and the like. Further, the memory 509 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. The program for querying database data and the received data stream provided in the embodiment of the present invention are stored in the memory, and when they are needed to be used, the processor 501 calls from the memory 509.
When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, optical fiber, twisted pair) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., compact disk), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
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 embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, 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.
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 invention 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, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will 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 technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method of querying database data, comprising:
acquiring a target operation instruction for accessing a target data source through a query interface, wherein the query interface is a uniform interface for querying different types of data sources by a client, and the target operation instruction is an instruction described by a Structured Query Language (SQL);
according to the target operation instruction, acquiring a target grammar identifier of the target data source, wherein the target grammar identifier is used for indicating the instruction grammar of the target data source;
converting the target operation instruction into a target hook handle according to a preset data warehouse tool Hive and the target grammar identifier;
inquiring corresponding target data in the target data source according to the target handle;
feeding back a query result, wherein the query result is used for indicating whether the target data is queried or not.
2. The method of claim 1, wherein the obtaining, according to the target operation instruction, a target syntax identifier of the target data source, the target syntax identifier indicating an instruction syntax of the target data source comprises:
reading a target language rule of the target operation instruction;
converting the target language rule into a grammar tree;
a parser to generate the target language rules;
and analyzing the target operation instruction based on the syntax tree and the analyzer to obtain the target syntax identifier of the target data source, wherein the target syntax identifier is used for indicating the instruction syntax of the target data source.
3. The method of claim 1, wherein the converting the target operation instruction into a target hook handle according to a preset data warehouse tool Hive and the target grammar identifier comprises:
reading the grammar rule of the target grammar identifier in the preset data warehouse tool;
analyzing the grammar rule to obtain an abstract grammar tree;
converting the abstract syntax tree to obtain a query block;
translating the query block to obtain an execution operation tree;
and recompiling the target operation instruction by using the execution operation tree to obtain the target handle.
4. The method according to claim 1, wherein after the target data in the corresponding target data source is queried according to the target handle, the query result is fed back, and the query result is used to indicate whether the target data is queried or not, and the method further comprises:
and acquiring an execution log of the target handle, and generating alarm information in the execution log.
5. The method of claim 4, wherein obtaining the execution log of the target handle and generating the alarm information in the execution log comprises:
acquiring an execution log of the target handle, and extracting a target keyword, wherein the target keyword is a sensitive word in the execution process of the target handle, and a preset log sensitive table is arranged in the execution log;
judging whether the target keyword is matched with the preset log sensitive table or not;
and if the target keyword is matched with the preset log sensitive table, generating alarm information in the execution log.
6. The method of claim 4, wherein the feeding back the query result indicating whether the target data is queried comprises:
if the target data is not inquired and the alarm information is generated, feeding back the alarm information through the inquiry interface, wherein the alarm information is used for indicating that the target data is not inquired;
and if the target data is inquired, feeding back the target data through the inquiry interface.
7. The method according to any one of claims 1 to 6, wherein the query result is fed back, and the query result is used for indicating whether the target data is queried or not, and the method further comprises:
and adding an audit log which is an annotation in the data query process.
8. An apparatus for querying database data, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a target operation instruction for accessing a target data source through a query interface, the query interface is a uniform interface for a client to query different types of data sources, and the target operation instruction is an instruction described by Structured Query Language (SQL);
a second obtaining module, configured to obtain, according to the target operation instruction, a target syntax identifier of the target data source, where the target syntax identifier is used to indicate instruction syntax of the target data source;
the conversion module is used for converting the target operation instruction into a target hook handle according to a preset data warehouse tool Hive and the target grammar identifier;
the query module is used for querying the corresponding target data in the target data source according to the target handle;
and the feedback module is used for feeding back a query result, and the query result is used for indicating whether the target data is queried or not.
9. An apparatus for querying database data, comprising:
a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invokes the instructions in the memory to cause the device querying database data to perform the method of any of claims 1-7.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program realizing the steps of the method according to any one of claims 1-7 when executed by a processor.
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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111858644A (en) * 2020-07-08 2020-10-30 联思智云(北京)科技有限公司 Method, device and system for data fusion and query
CN112487275A (en) * 2020-12-11 2021-03-12 杭州安恒信息技术股份有限公司 Data retrieval method, system, equipment and readable storage medium
CN112579610A (en) * 2020-12-23 2021-03-30 安徽航天信息有限公司 Multi-data source structure analysis method, system, terminal device and storage medium
CN112910980A (en) * 2021-01-27 2021-06-04 中国银联股份有限公司 Database access system and method
WO2021139426A1 (en) * 2020-01-06 2021-07-15 平安科技(深圳)有限公司 Method, device and apparatus for querying data in database, and storage medium
CN113641700A (en) * 2021-08-30 2021-11-12 北京沃东天骏信息技术有限公司 Data processing method and device based on Spring boot frame
CN113821196A (en) * 2021-10-12 2021-12-21 上海驻云信息科技有限公司 Construction method of universal query language of multi-storage system
CN113868343A (en) * 2021-09-24 2021-12-31 珠海金山办公软件有限公司 Database operation method and device, electronic equipment and readable storage medium
CN114661751A (en) * 2022-03-22 2022-06-24 医渡云(北京)技术有限公司 Data production method, device, system, equipment and medium based on SQL (structured query language) knowledge base
CN114780584A (en) * 2022-06-22 2022-07-22 云账户技术(天津)有限公司 Multi-scene streaming data processing method, system, network equipment and storage medium

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115033575B (en) * 2022-06-29 2025-01-10 政采云有限公司 Data query method, device, equipment and storage medium

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106649630A (en) * 2016-12-07 2017-05-10 乐视控股(北京)有限公司 Data query method and device
CN108132991A (en) * 2017-12-20 2018-06-08 上海斐讯数据通信技术有限公司 A kind of H5 pages loading method and system

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8145655B2 (en) * 2007-06-22 2012-03-27 International Business Machines Corporation Generating information on database queries in source code into object code compiled from the source code
KR101917806B1 (en) * 2017-12-22 2018-11-12 주식회사 웨어밸리 Synchronization Error Detection AND Replication Method of Database Replication System Using SQL Packet Analysis
CN110032575A (en) * 2019-04-15 2019-07-19 网易(杭州)网络有限公司 Data query method, apparatus, equipment and storage medium
CN110399388A (en) * 2019-07-29 2019-11-01 中国工商银行股份有限公司 Data query method, system and equipment
CN111209298A (en) * 2020-01-06 2020-05-29 平安科技(深圳)有限公司 Method, device, equipment and storage medium for querying database data

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106649630A (en) * 2016-12-07 2017-05-10 乐视控股(北京)有限公司 Data query method and device
CN108132991A (en) * 2017-12-20 2018-06-08 上海斐讯数据通信技术有限公司 A kind of H5 pages loading method and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
张磊 等: "基于Spark的交互式数据预处理系统", 计算机系统应用, no. 11, 15 November 2016 (2016-11-15) *
魏芳芳 等: "面向在线教育的学习分析云平台的构建与应用――以国家开放大学为例", 河北广播电视大学学报, no. 05, 25 October 2018 (2018-10-25), pages 1 - 2 *

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021139426A1 (en) * 2020-01-06 2021-07-15 平安科技(深圳)有限公司 Method, device and apparatus for querying data in database, and storage medium
CN111858644A (en) * 2020-07-08 2020-10-30 联思智云(北京)科技有限公司 Method, device and system for data fusion and query
CN111858644B (en) * 2020-07-08 2022-11-18 联思智云(北京)科技有限公司 Method, device and system for data fusion and query
CN112487275A (en) * 2020-12-11 2021-03-12 杭州安恒信息技术股份有限公司 Data retrieval method, system, equipment and readable storage medium
CN112579610A (en) * 2020-12-23 2021-03-30 安徽航天信息有限公司 Multi-data source structure analysis method, system, terminal device and storage medium
CN112910980B (en) * 2021-01-27 2022-11-15 中国银联股份有限公司 Database access system and method
CN112910980A (en) * 2021-01-27 2021-06-04 中国银联股份有限公司 Database access system and method
CN113641700A (en) * 2021-08-30 2021-11-12 北京沃东天骏信息技术有限公司 Data processing method and device based on Spring boot frame
CN113868343A (en) * 2021-09-24 2021-12-31 珠海金山办公软件有限公司 Database operation method and device, electronic equipment and readable storage medium
CN113821196A (en) * 2021-10-12 2021-12-21 上海驻云信息科技有限公司 Construction method of universal query language of multi-storage system
CN114661751A (en) * 2022-03-22 2022-06-24 医渡云(北京)技术有限公司 Data production method, device, system, equipment and medium based on SQL (structured query language) knowledge base
CN114780584B (en) * 2022-06-22 2022-09-02 云账户技术(天津)有限公司 Multi-scene streaming data processing method, system, network equipment and storage medium
CN114780584A (en) * 2022-06-22 2022-07-22 云账户技术(天津)有限公司 Multi-scene streaming data processing method, system, network equipment and storage medium

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