Data query method and device, electronic equipment and readable storage medium

A data query and query request technology, applied in database indexing, electronic digital data processing, structured data retrieval, etc., can solve problems such as poor timeliness of data query, long MR task development cycle, and inability to meet rapid business iteration, etc., to meet The effect of rapid business iteration and improved timeliness

Pending Publication Date: 2020-09-08
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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AI-Extracted Technical Summary

Problems solved by technology

In this case, due to the long development cycle of MR tasks, etc., the timeliness o...
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Method used

2) pingo4 engine is the internal engine of some scenes, and different tasks share the spark queue resources exclusively, and the task query efficiency is high, and the resource usage is controlled by limiting the concurrent number of tasks, and the data output is at the minute level.
According to the technical scheme of the embodiment of the application, the query request parameter input by the user can be used to generate the target SQL statement, and the target SQL statement can be used to perform data query, so that the research and development personnel do not need to develop a special task to query the data, thereby improving the The timeliness of data query meets the needs of rapid business iteration.
Optionally, the above-mentioned filter condition information according to the target table name, the target field name and the query request parameter, the process of generating the inner layer SQL statement corresponding to the logical table may include: The target field name is stored in the first linked list, and the filter condition information in the query request parameter is stored in the second linked list; according to the first linked list, the second linked list and the target table name, generate The inner SQL statement. Wherein, the first linked list may be a selectColumnList linked list, and the second linked list may be a whereList linked list. In this way, through the linking order of the pointers in the linked list, the field element information can be spliced ​​simply and effectively, so as to obtain the required inner SQL statement.
The data query method in the embodiment of...
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Abstract

The invention discloses a data query method and device, electronic equipment and a readable storage medium, and relates to the technical field of big data. According to the specific implementation scheme, the method comprises the steps of: obtaining query request parameters input by a user; analyzing the query request parameters to obtain a plurality of logic tables required by the query and fieldinformation included in each logic table; generating a plurality of inner-layer SQL statements according to the plurality of logic tables and the field information included in each logic table; splicing the plurality of inner-layer SQL statements to obtain a target SQL statement; and performing data query by utilizing the target SQL statement. According to the scheme provided by the invention, the timeliness of data query can be improved, and the requirement of quick business iteration is met.

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  • Data query method and device, electronic equipment and readable storage medium
  • Data query method and device, electronic equipment and readable storage medium
  • Data query method and device, electronic equipment and readable storage medium

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Example Embodiment

[0033] Exemplary embodiments of the present application are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
[0034] The terms "first", "second" and the like in the specification and claims of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device comprising a sequence of steps or elements is not necessarily limited to the expressly listed instead, may include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus. "And/or" in the specification and claims means at least one of the connected objects.
[0035] In order to facilitate the understanding of the embodiments of the present application, the following contents are first described.
[0036] See figure 1 , figure 1 It is the overall architecture diagram of the data query system in the embodiment of this application. like figure 1 As shown, the data query system in the embodiment of the present application mainly involves the following parts:
[0037] Data Warehouse: Through data extraction (extract), transformation (transform), loading (load) process, namely ETL (Extract-Transform-Load) technology, business data, embedded logs, distributed file system (Hadoop) can be obtained from data sources Distributed File System, HDFS) data, File Transfer Protocol (File Transfer Protocol, FTP) data and/or other data. The type of data warehouse can be selected as but not limited to: Turing, UDW (large-scale parallel processing data warehouse), Alluxio (memory-centric virtual distributed storage system), Baidu data warehouse Palo, ES (Elasticsearch), etc.
[0038] Front-end interface: such as web page (web) user interface (User Interface, UI). Users can check the dimension and index selection boxes on the corresponding web UI for business topics (such as author topics, article topics, author portraits, etc.). Among them, the dimension represents the characteristics of the transaction phenomenon; for example, in the topic of Baijiahao articles, the dimensions can be: article identifier (id), article type, article title, article classification, etc. Indicators represent the units and methods for measuring the development of affairs, which are usually obtained through aggregation statistics such as summation and averaging; for example, in the topic of Baijiahao articles, the indicators can be selected as: reading volume, distribution volume, recommendation volume, comment volume, etc.
[0039] Server side (Server side): It can receive the parameter list in the query request input by the user through the web UI. The server side can realize the following management: 1) user management, such as permission verification through user input information; 2) association condition management and metadata management, mainly involving the SQL (Structured Query Language, Structured Query Language) construction process; 3) Task management (also called job management) mainly involves query task submission and query status polling. Optionally, after obtaining the list of query parameters input by the user, the server can perform the following processes: user permission verification, concurrency control, SQL construction, engine routing, engine adaptation based on user input information, query task submission (Submit), Query status polling and query result dumping, etc.
[0040] Execution engine: This system is designed with multiple engines, including but not limited to Turing engine, Zeppelin engine, Pingo4 engine, Presto engine, JDBC engine, etc. The server side can query (Query) the required data from the data warehouse through the adapted engine.
[0041] See figure 2 , figure 2 It is a schematic diagram of the hierarchical structure of the data query system in the embodiment of this application. like figure 2 As shown, the data query system in this application is mainly divided into the following four levels:
[0042] 1. Data layer: The data layer mainly includes modules such as user information, metadata information, task (job) information, and query result information. User information records the user's identity, including departments and teams, and provides data support for authority control. Metadata information records the subject information of the system query platform division, dependent business tables and other information. SQL construction is to translate the parameter fields entered by users into SQL statements based on metadata information. The job information records the entire life cycle of the query task from submission to completion, so that the source of the task can be traced. The query result information records the returned result of the query, and finally displays it to the user on the front end.
[0043] 2. Engine layer: For example, this system can cover 4 query engines, which can meet the needs of different scenarios such as offline query and ad hoc query. The engine is pluggable, and each query engine is individually packaged into a service, which has strong scalability. The four query engines can be selected as but not limited to:
[0044] 1) Zeppelin engine, all submitted tasks share the same spark queue resources, resource usage is controllable, and data output is at the minute level.
[0045] 2) The pingo4 engine is an internal engine for certain scenarios. Different tasks share spark queue resources exclusively. Task query efficiency is high. Resource usage is controlled by limiting the number of concurrent tasks, and data output is at the minute level.
[0046]3) The Turing engine is an internal engine for certain scenarios. The underlying storage has extremely high query efficiency due to its index and cache mechanism, and the data output is at the second level. The index can use an external index method, which is not intrusive to the file format. It supports two index types: B+Tree and BitMap. The index can be cached and cached in the off-heap memory of the Executor. The cache can be a column granular cache, supporting passive loading and active loading strategies.
[0047] 4) The palo engine, which supports the construction of data cubes, is suitable for statistical analysis scenarios in ad hoc queries, and can return query results within seconds.
[0048] 3. Service layer: This system can encapsulate public services and support external quick access. The service layer can implement the following functions:
[0049] 1) SQL splicing. After the user selects the required dimensions and indicators on the front-end interface and submits the task, the server side obtains the parameter list from the request parameters and performs SQL splicing. SQL splicing can adopt the divide-and-conquer idea, split it into one or more "inner splicing" and the final "outer splicing", and assemble it into an executable target SQL statement.
[0050] 2) Routing selection, select different query engines to query data according to conditions such as user identity information, query scenarios, and business table meta information.
[0051] 3) Permission management, for different user roles to perform permission management on business tables.
[0052] 4. Application layer: The system can open query application programming interface (Application Programming Interface, API), support to query data by submitting SQL statements, support direct sampling, random sampling, weighted sampling and other sampling requirements and Roll up, drill down and other statistical requirements.
[0053] In order to solve the problem of poor timeliness in existing data query methods, the embodiment of the present application provides a method for querying data by constructing an executable SQL statement, which is described as follows.
[0054] See image 3 , image 3 is a flow chart of a data query method provided in the embodiment of this application, which is applied to electronic devices, such as image 3 As shown, the method includes the following steps:
[0055] Step 301: Obtain query request parameters input by the user.
[0056] In this embodiment, the user can check the dimension and index selection boxes on the front-end page (or called: front-end interface) to input query request parameters. For different business themes, there can be different front-end pages, such as author theme, article theme, author portrait, etc. Each front-end page can include several dimension and metric selection boxes. Among them, the dimension represents the characteristics of the transaction phenomenon; for example, in the topic of Baijiahao articles, the dimensions can be: article identifier (id), article type, article title, article classification, etc. Indicators represent the units and methods for measuring the development of affairs, which are usually obtained through aggregation statistics such as summation and averaging; for example, in the topic of Baijiahao articles, the indicators can be selected as: reading volume, distribution volume, recommendation volume, comment volume, etc.
[0057] Step 302: Parse the query request parameters to obtain multiple logical tables required for this query and field information included in each logical table.
[0058] In this embodiment, the logic table can be preset, and generally represents the logical relationship of the query request parameters, and is related to the query request parameters input by the user. The field information refers to query request parameter fields, and the query request parameter fields include, for example, dimension fields and index fields. Both dimension fields and index fields can be used as display fields and filter fields.
[0059] For example, on the article topic page, if the user checks the selection boxes of article B-end ID, article C-end ID and article publishing time in the dimension field, and the posting time range is selected from 2020-05-04 to 2020-05-05, At the same time, check the reading volume and distribution volume selection boxes in the indicator field, and then query the request parameter field by parsing. For example, the parameter example is as follows {"k":"article_dim-publish_at","startTime":"2020-05-04", "endTime":"2020-05-5"}, the k value "article_dim-publish_at" represents the publish_at field in the article_dim logical table, and the values ​​of startTime and endTime represent the start and end time respectively. It can be determined that the corresponding logical table is the article_dim logical table.
[0060] Step 303: Generate multiple inner SQL statements according to the multiple logical tables and the field information included in each logical table.
[0061] In this embodiment, each logical table may correspond to an inner SQL statement, that is, corresponds to an inner SQL statement splicing task. If the query request parameters input by the user in the query scenario involve multiple logical tables, multiple inner SQL statements can be constructed through a for loop (that is, a specific number of cyclic repetitions). Each inner SQL statement can be concatenated based on the relevant field information in the corresponding logical table.
[0062] Step 304: Concatenate the multiple inner SQL statements to obtain a target SQL statement.
[0063] Optionally, in this embodiment, multiple inner SQL statements can be directly spliced ​​to obtain the required executable target SQL statement. The target SQL statement may be called an outer SQL statement, which is obtained by concatenating multiple inner SQL statements.
[0064] Step 305: Perform data query by using the target SQL statement.
[0065] The data query method in the embodiment of the present application can generate a target SQL statement based on the query request parameters input by the user, and use the target SQL statement to perform data query, so that there is no need for R&D personnel to develop special tasks to query data, thereby improving data query. The timeliness meets the needs of rapid business iteration. Furthermore, zero R&D costs and zero communication costs can be achieved, the data acquisition cycle can be shortened from days to minutes, and the timeliness of data is greatly improved.
[0066] In the embodiment of the present application, in order to meet different query requirements, there may be multiple query engines, and a target query engine is selected from them to query the required data. Optionally, a target query engine may be selected from multiple query engines according to user input information (such as user identity information, required service information, etc.). Further, the above-mentioned process of performing data query by using the target SQL statement may include: performing data query by using the target SQL statement through the target query engine. In this way, the accuracy of data query can be improved by using the selected target query engine to query data.
[0067] In the embodiment of the present application, each logical table in the foregoing multiple logical tables may correspond to an inner SQL statement. The process of generating multiple inner SQL statements in the above step 303 may include:
[0068] For each logical table and the field information included in the logical table, perform the following process respectively:
[0069] Associating the logical table with pre-stored metadata information to obtain an actual target table name, and associating the field information with the pre-stored metadata information to obtain an actual target field name;
[0070] An inner SQL statement corresponding to the logic table is generated according to the target table name, the target field name, and the filter condition information in the query request parameters.
[0071] Wherein, the pre-stored metadata information may be data layer metadata information, which records information such as pre-divided subject information and dependent service tables. The target field name can be understood as the field name to be displayed. In this way, with the help of pre-stored metadata information, the query request parameters input by the user can be translated into SQL statements, so as to obtain executable SQL statements that meet requirements.
[0072] Optionally, the process of generating the inner SQL statement corresponding to the logical table according to the target table name, the target field name, and the filter condition information in the query request parameters may include: adding the target field name into the first linked list, and store the filter condition information in the query request parameter into the second linked list; according to the first linked list, the second linked list and the target table name, generate the inner Layer SQL statement. Wherein, the first linked list may be a selectColumnList linked list, and the second linked list may be a whereList linked list. In this way, through the linking order of the pointers in the linked list, the field element information can be spliced ​​simply and effectively, so as to obtain the required inner SQL statement.
[0073] Optionally, the above-mentioned process of splicing the multiple inner SQL statements to obtain the target SQL statement may include: determining the association between each logical table according to the multiple logical tables and the pre-stored metadata information Condition information: splicing the plurality of inner SQL statements according to the target field name and the association condition information between each logical table to obtain the target SQL statement. Among them, when obtaining the target SQL statement, you can first associate and assemble the corresponding multiple inner SQL statements based on the association condition information between each logical table; then based on the target field name, that is, the field to be displayed, perform association The SQL statement is processed to obtain the final target SQL statement.
[0074] In one embodiment, when associating and assembling multiple inner SQL statements, Join on can be used. For example, if Figure 4 As shown, if three inner SQL statements are generated: SELECT a,...FROM A, SELECT b,...FROM B, SELECT c,...FROM C, the following Join expression can be used to obtain the outer SQL statement (i.e. the target SQL statement):
[0075] SELECT a.x,b.y,c.z
[0076] FROM
[0077] (SELECT a,...FROM A)a
[0078] JOIN
[0079] (SELECT b,...FROM B)b
[0080] ON a.x=b.x—this is the association condition of the first two inner SQL statements;
[0081] JOIN
[0082] (SELECT c,...FROM C)c
[0083] ON b.y=c.y—this is the association condition of the last two inner SQL statements.
[0084] In one embodiment, the process for the server side to obtain the outer SQL statement can be: first, the inner layer splicing process (such as Figure 5Shown): 1) The server side obtains the query request parameters input by the user based on the collectParam operation; 2) Analyzes the query request parameters to obtain multiple logical tables and field information used in this query, and each table corresponds to an inner SQL For splicing tasks, if the query scene involves multiple tables, multiple inner SQL statements will be constructed through a for loop; 3) For each logical table and its field information, associate with the metadata information of the data layer to obtain the actual data in the data warehouse Target table name and target field name; 4) Store the target field name in the selectColumnList linked list, and write it into the outerFieldNameBuild linked list for use in outer splicing; 5) Store the query filter condition information in the obtained query request parameters in the whereList linked list ; 6) Generate an SQL statement according to the selectColumnList linked list, whereList linked list and target table name; and store the result of this SQL splicing into the innerSQLList linked list. Second, the outer splicing process (such as Image 6 shown): 7) Obtain the inner splicing result from the innerSQLList linked list, which is the inner SQL statement; 8) Obtain the target field name from the outerFieldNameBuild linked list, which is the field to be displayed; 9) According to the metadata information of each logical table and data layer, Query the association condition information between each logical table; 10) Based on the fields to be displayed and the association condition information between each logic table, use JOIN ON to associate and assemble each inner SQL statement to generate the final query SQL.
[0085] The present application will be described in detail below in conjunction with specific embodiments.
[0086] In this specific embodiment, it is assumed that the reading volume and distribution volume of articles published from 2020-05-04 to 2020-05-05 are counted. The specific process of generating query SQL can be:
[0087] S1: The user checks the B-end ID, article C-end ID, and article publishing time selection boxes in the dimension field of the article topic page, and selects the posting time range from 2020-05-04 to 2020-05-05, in the indicator field Check the reading volume and distribution volume selection boxes in , and submit a query request. The request parameters can be as follows:
[0088]
[0089] S2: The server parses the request parameters. The parameter examples are as follows {"k":"article_dim-publish_at","startTime":"2020-05-04","endTime":"2020-05-5"}, then the k value "article_dim-publish_at" represents the publish_at field in the article_dim logical table, and the values ​​of startTime and endTime represent the start and end time respectively, so as to determine that the logical table of this query includes article_dim and article_fac, and the required fields include: bid, cid, publish_at, and view_count , click_pv.
[0090] S3: Group the fields involved in the query according to the logical table name. In this example, there are two logical tables article_dim and article_fac. Therefore, the article_dim logical table and the included rid, bid, cid, and publish_at fields are divided into one group, and the article_fac logical table and the included rid, reprint_view_count, and click_pv fields are divided into one group.
[0091] S4: Select the logical table article_dim and the fields rid, bid, cid, and publish_at to associate with the metadata information of the data layer to obtain the actual table name and field name in the data warehouse.
[0092] S5: Store the actual field name in the selectColumnList linked list, and write it into the outerFieldNameBuild linked list for outer splicing.
[0093] S6: Store the publish_at filter condition in the whereList linked list.
[0094] S7: Generate a sql query statement according to the selectColumnList linked list, whereList linked list and the actual table name, and store the result of this sql splicing into the innerSQLList linked list.
[0095] S8: Traversing all groups, repeating S5 to S7. At this point, the internal sql splicing is completed.
[0096] For example, the inner SQL splicing results are as follows:
[0097] (1)
[0098] SELECT rid, bid, cid, publish_at
[0099] FROM bjh_data.bjh_dim_essay_df
[0100] WHERE event_day=20200508
[0101] AND to_date(publish_at)>='2020-05-04'
[0102] AND to_date(publish_at)<='2020-05-05'
[0103] (2)
[0104] SELECT rid, reprint_view_count, click_pv
[0105] FROM bjh_data.bjh_essay_pre_aggregated
[0106] WHERE event_day=20200508
[0107] S9: Obtain the inner SQL from the innerSQLList linked list.
[0108] S10: Obtain the final fields to be displayed from outerFieldNameBuild, including: a.bid, a.cid, a.publish_at; b.reprint_view_count, b.click_pv.
[0109] S11: Query the association condition information of each logical table according to the inner SQL table and the metadata information of the data layer. In this example, the association condition is that the IDs are the same.
[0110] S12: Based on the fields that need to be displayed and the association condition information between each logical table, associate and assemble each inner SQL statement (for example, using JOIN ON expression) to generate the final query SQL.
[0111] For example, the outer query SQL in this example can be as follows:
[0112]
[0113] See Figure 7 , Figure 7 It is a schematic structural diagram of a data query device provided in the embodiment of this application, such as Figure 7 As shown, the data query device 70 includes:
[0114] An acquisition module 71, configured to acquire query request parameters input by the user;
[0115] The parsing module 72 is configured to parse the query request parameters to obtain a plurality of logical tables required for this query and the field information included in each of the logical tables;
[0116] A generating module 73, configured to generate a plurality of inner SQL statements according to the plurality of logical tables and the field information included in each of the logical tables;
[0117] The splicing module 74 is used for splicing the multiple inner layer SQL statements to obtain the target SQL statement;
[0118] A query module 75, configured to use the target SQL statement to perform data query.
[0119] Optionally, each logical table in the plurality of logical tables corresponds to an inner SQL statement; the generating module 73 includes:
[0120] The execution unit is configured to execute the following process for each logical table and the field information included in the logical table:
[0121] Associating the logical table with pre-stored metadata information to obtain an actual target table name, and associating the field information with the pre-stored metadata information to obtain an actual target field name;
[0122] An inner SQL statement corresponding to the logic table is generated according to the target table name, the target field name, and the filter condition information in the query request parameters.
[0123] Optionally, the execution unit includes:
[0124] The storing subunit is used to store the target field name in the first linked list, and store the filter condition information in the query request parameters in the second linked list;
[0125] A generating subunit is configured to generate the inner SQL statement according to the first linked list, the second linked list and the target table name.
[0126] Optionally, the splicing module 74 includes:
[0127] A determining unit, configured to determine association condition information between logical tables according to the plurality of logical tables and the pre-stored metadata information;
[0128] The splicing unit is configured to splice the plurality of inner SQL statements according to the target field name and the association condition information between each logical table to obtain the target SQL statement.
[0129] Optionally, the data query device 70 also includes:
[0130] A selection module, configured to select a target query engine from multiple query engines according to user input information;
[0131] The inquiry module 75 is specifically used for:
[0132] Through the target query engine, the target SQL statement is used to perform data query.
[0133] It can be understood that the data query device 70 in the embodiment of the present application can realize the above-mentioned image 3 The processes implemented in the illustrated method embodiments and the same beneficial effects are achieved, and will not be repeated here to avoid repetition.
[0134] According to the embodiments of the present application, the present application also provides an electronic device and a readable storage medium.
[0135] like Figure 8Shown is a block diagram of an electronic device according to the data query method of the embodiment of the present application. Electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are by way of example only, and are not intended to limit implementations of the applications described and/or claimed herein.
[0136] like Figure 8 As shown, the electronic device includes: one or more processors 801, a memory 802, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and can be mounted on a common motherboard or otherwise as desired. The processor may process instructions executed within the electronic device, including instructions stored in or on the memory, to display graphical information of a GUI on an external input/output device such as a display device coupled to an interface. In other implementations, multiple processors and/or multiple buses may be used with multiple memories and multiple memories, if desired. Likewise, multiple electronic devices may be connected, with each device providing some of the necessary operations (eg, as a server array, a set of blade servers, or a multi-processor system). Figure 8 A processor 801 is taken as an example.
[0137] The memory 802 is a non-transitory computer-readable storage medium provided in this application. Wherein, the memory stores instructions executable by at least one processor, so that the at least one processor executes the data query method provided in this application. The non-transitory computer-readable storage medium of the present application stores computer instructions, and the computer instructions are used to cause the computer to execute the data query method provided in the present application.
[0138] As a non-transitory computer-readable storage medium, the memory 802 can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as program instructions/modules corresponding to the data query method in the embodiment of the present application (for example, attached Figure 7 The shown acquisition module 71, analysis module 72, generation module 73, splicing module 74 and query module 75). The processor 801 executes various functional applications and data processing of the server by running non-transitory software programs, instructions and modules stored in the memory 802, that is, implements the data query method in the above-mentioned method embodiments.
[0139] The memory 802 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of the electronic device for data query, and the like. In addition, the memory 802 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the storage 802 may optionally include storages that are remotely located relative to the processor 801, and these remote storages may be connected to electronic devices for implementing the data query method through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
[0140] The electronic equipment used to implement the data query method may also include: an input device 803 and an output device 804 . The processor 801, the memory 802, the input device 803 and the output device 804 may be connected via a bus or in other ways, Figure 8 Take connection via bus as an example.
[0141] The input device 803 can receive input numbers or character information, and generate key signal input related to user settings and function control of electronic equipment that implements data query methods, such as touch screens, small keyboards, mice, trackpads, touchpads, and pointers , one or more mouse buttons, trackballs, joysticks, and other input devices. The output device 804 may include a display device, an auxiliary lighting device (eg, LED), a tactile feedback device (eg, a vibration motor), and the like. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
[0142] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuitry, integrated circuit systems, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor Can be special-purpose or general-purpose programmable processor, can receive data and instruction from storage system, at least one input device, and at least one output device, and transmit data and instruction to this storage system, this at least one input device, and this at least one output device an output device.
[0143] These computing programs (also referred to as programs, software, software applications, or codes) include machine instructions for a programmable processor and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine language calculation program. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or means for providing machine instructions and/or data to a programmable processor ( For example, magnetic disks, optical disks, memories, programmable logic devices (PLDs), including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
[0144] To provide for interaction with the user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user. ); and a keyboard and pointing device (eg, a mouse or a trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and can be in any form (including Acoustic input, speech input or, tactile input) to receive input from the user.
[0145] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., as a a user computer having a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system can be interconnected by any form or medium of digital data communication, eg, a communication network. Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN) and the Internet.
[0146] A computer system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other.
[0147] According to the technical solution of the embodiment of the present application, the target SQL statement can be generated based on the query request parameters input by the user, and the target SQL statement can be used for data query, so that there is no need for R&D personnel to develop special tasks to query data, thereby improving the efficiency of data query. Timeliness to meet the needs of rapid business iteration.
[0148] It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, the steps described in the present application may be executed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in the present application can be achieved, there is no limitation herein.
[0149] The above specific implementation methods are not intended to limit the protection scope of the present application. It should be apparent to those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application shall be included within the protection scope of this application.
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Description & Claims & Application Information

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the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
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