CN112434056A - Method and device for inquiring detailed data - Google Patents
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Abstract
The invention discloses a method for inquiring detailed data, which comprises the following steps: responding to the aggregation query instruction, and sending an aggregation query request to an analytic database management system; acquiring an aggregation query result corresponding to the aggregation query request, wherein the aggregation query result is determined by the analytic database management system according to the aggregation query request; in response to a detail query instruction for the aggregated query result, sending a detail query request to an ad hoc query engine; and acquiring detail data corresponding to the detail query request, wherein the detail data is determined by the ad hoc query engine according to the detail query request. The method provided by the invention can improve the efficiency of data analysis and detail data query, reduce the query cost of the detail data and further improve the user experience of the detail data query.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for inquiring detailed data.
Background
Kylin is one of the widely used OLAP tools, has more than one billion levels of data query and analysis capability, can provide a standard SQL interface, and can perform interactive real-time data analysis, so that the Kylin performance is much higher than that of a common data warehouse (such as Hive). However, Kylin lacks a more critical detail query feature in application, and for this reason, when Kylin is adopted, users have to adopt various methods to make Kylin support detail query.
At present, a more common detail query scheme based on Kylin is generally adopted, in which one index is used for storing all detail data under a dimension, and when a user selects to view details under a specified dimension value, the detail data is queried in a general index query mode of Kylin. Specifically, because the Kylin is pre-calculated, in order to support the field detail query index, the aforesaid Kylin uses a dictionary in combination with a Bitmap to perform the detail data query.
However, because the detail field is typically very high radix, the bitmaps are computationally expensive. Therefore, the above prior art scheme will cause the detail data query to consume more bandwidth and computing resources and the expansion rate of the data is higher, that is, the above prior art scheme will cause the problems of longer time for constructing the Kylin data, slower detail query and higher proportion of detail data storage.
Disclosure of Invention
The invention provides a method and a device for inquiring detailed data, which can improve the efficiency of data analysis and detailed data inquiry, reduce the inquiry cost of the detailed data and further improve the user experience of the detailed data inquiry.
In a first aspect, the present invention provides a method for querying detail data, where the method includes:
responding to the aggregation query instruction, and sending an aggregation query request to an analytic database management system;
acquiring an aggregation query result corresponding to the aggregation query request, wherein the aggregation query result is determined by the analytic database management system according to the aggregation query request;
in response to a detail query instruction for the aggregated query result, sending a detail query request to the ad hoc query engine;
and acquiring detail data corresponding to the detail query request, wherein the detail data is determined by the ad hoc query engine according to the detail query request.
In a second aspect, the present invention provides an apparatus for querying detail data, the apparatus comprising:
the first sending unit is used for responding to the aggregation query instruction and sending an aggregation query request to the analysis type database management system;
a first obtaining unit, configured to obtain an aggregation query result corresponding to the aggregation query request, where the aggregation query result is determined by the analytic database management system according to the aggregation query request;
a second sending unit, configured to send a detail query request to the ad hoc query engine in response to a detail query instruction for the aggregated query result;
a second obtaining unit, configured to obtain detail data corresponding to the detail query request, where the detail data is determined by the ad hoc query engine according to the detail query request.
In a third aspect, the invention provides a readable medium comprising executable instructions, which when executed by a processor of an electronic device, perform the method according to any of the first aspect.
In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
According to the technical scheme, the method provided by the invention can respond to the aggregation query instruction, send the aggregation query request to the analysis type database management system and obtain the aggregation query result corresponding to the aggregation query request; and sending a detail query request to the ad hoc query engine in response to a detail query instruction for the aggregated query result, and acquiring detail data corresponding to the detail query request. Therefore, the method provided by the invention can separately process the aggregation query request and the detail query request, namely, the analysis type database management system and the ad hoc query engine can be used for separately processing the aggregation query request and the detail query request, so that the analysis type database management system (such as kylin) can improve the data construction efficiency of the analysis type database management system after removing the measurement of the detail query, and the ad hoc query engine has the functions of query optimization, index support and compression, so the ad hoc query engine can also improve the query speed of the detail data. Therefore, the detail data query mode combining the analytic database management system and the ad hoc query engine can avoid the problems of longer time for constructing the Kylin data, slower detail query, higher proportion for storing the detail data and higher cost caused by a detail data query scheme combining a dictionary and a Bitmap mode adopted by the Kylin, thereby improving the efficiency of data analysis and detail data query, reducing the query cost of the detail data and further improving the user experience of the detail data query.
Further effects of the above-mentioned unconventional preferred modes will be described below in conjunction with specific embodiments.
Drawings
In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a block diagram of an exemplary application scenario provided in an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method for querying detail data according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of another detail data query method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus for querying detail data according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.
In order to solve the problems that in the prior art, a dictionary adopted by Kylin is combined with a Bitmap mode to carry out detail data query, so that the construction time of Kylin data is longer, the detail query is slower, and the proportion of detail data storage is higher.
The invention provides a method for inquiring detail data, which can respond to an aggregation inquiry instruction, send an aggregation inquiry request to an analytic database management system and obtain an aggregation inquiry result corresponding to the aggregation inquiry request; and sending a detail query request to the ad hoc query engine in response to a detail query instruction for the aggregated query result, and acquiring detail data corresponding to the detail query request. Therefore, the method provided by the invention can separately process the aggregation query request and the detail query request, namely, the analysis type database management system and the ad hoc query engine can be used for separately processing the aggregation query request and the detail query request, so that the analysis type database management system (such as kylin) can improve the data construction efficiency of the analysis type database management system after removing the measurement of the detail query, and the ad hoc query engine has the functions of query optimization, index support and compression, so the ad hoc query engine can also improve the query speed of the detail data. Therefore, the detail data query mode combining the analytic database management system and the ad hoc query engine can avoid the problems of longer time for constructing the Kylin data, slower detail query, higher proportion for storing the detail data and higher cost caused by a detail data query scheme combining a dictionary and a Bitmap mode adopted by the Kylin, thereby improving the efficiency of data analysis and detail data query, reducing the query cost of the detail data and further improving the user experience of the detail data query.
For example, embodiments of the present invention may be applied to the detailed data query system architecture scenario shown in FIG. 1. In the system architecture scenario, the system includes a client 101 (such as various mobile terminals like a smart phone and a tablet computer), an analytic database management system 102, an ad hoc query engine 103, and a source database 104, where the client 101 is respectively in communication connection with the analytic database management system 102 and the ad hoc query engine 103, and the analytic database management system 102 and the ad hoc query engine 103 are respectively connected with the source database 104. Specifically, a user may input an aggregation query instruction through the client 101, and the client 101 may send an aggregation query request to the analytics database management system 102 in response to the aggregation query instruction; the analytic database management system 102 may determine an aggregate query result according to the aggregate query request and the source database 104, and return the aggregate query result to the client 101; the client 101 may send a detail query request to the ad hoc query engine 103 in response to a detail query instruction for the aggregated query result; the ad hoc query engine 103 may determine the detail data corresponding to the detail query request according to the detail query request and the source database 104, and return the detail data to the client 101, so that the client 101 displays the detail data to the user. Therefore, the efficiency of data analysis and detail data query can be improved, the query cost of the detail data is reduced, and the user experience of the detail data query is improved.
It is to be understood that the invention is not limited in its application to the details of execution, provided that the acts disclosed in the embodiments of the invention are performed. Therefore, the system architecture corresponding to the scheme provided by the invention is simpler, and the detailed data query mode combining the analytic database management system and the ad hoc query engine is adopted, so that the problems of longer time for constructing the Kylin data, slower detailed query, higher proportion for storing the detailed data and higher cost caused by the detailed data query scheme combining the dictionary adopted by the Kylin and the Bitmap can be avoided, the efficiency of data analysis and detailed data query can be improved, the query cost of the detailed data is reduced, and the user experience of the detailed data query is improved.
Various non-limiting embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Referring to fig. 2, a detail data query method in an embodiment of the present invention is shown, which may be applied to a detail data query system, which may include an analytic database management system and an ad hoc query engine. In this embodiment, the method may include, for example, the steps of:
s201: in response to the aggregate query instruction, an aggregate query request is sent to the analytical database management system.
In this embodiment, the aggregation query instruction may be understood as an instruction for triggering the client to generate the data query and data aggregation. In one possible implementation, the aggregation query instruction may include a field to be queried, a data table to be queried, and a field type to be aggregated; the field to be queried may be understood as a field to be filtered, for example, the field to be queried may be a medical-related field, such as a disease type, a gender, a time of visit, an age, and the like; the data table to be inquired can be understood as the range of data screening and filtering; the field type to be aggregated may be understood as an aggregation basis of data obtained by filtering and filtering the data, for example, if the field type to be aggregated is a disease type, the data obtained by filtering and filtering may be aggregated according to a specific disease type (such as hypertension, heart disease, etc.) to obtain statistical data corresponding to each specific disease type (such as the number of data corresponding to "hypertension" and the number of data corresponding to "heart disease").
As an example, the client may provide an aggregated query instruction input interface through which a user may input aggregated query instructions. After receiving the aggregation query instruction, the client can generate an aggregation query request according to the aggregation query instruction and send the aggregation query request to the analytic database management system.
The aggregate query request may be understood as an instruction for triggering the analytical database management system to perform data query and data aggregation. Wherein the aggregate query request may include a query dimension, an aggregate dimension, and a query data table identification. It should be noted that the query dimension in the aggregated query request can be understood as a field to be queried; aggregation dimension may be understood as the field basis for data aggregation statistics, such as "disease"; the query data table identification may be understood as the name or other type of identification information of the query data table. It is emphasized that dimension is a term of OLAP, meaning a way of classifying data, e.g., common dimensions include time, age, gender, school history, illness, etc.; the number of field values in a certain dimension may be referred to as a dimension cardinality, which is referred to as cardinality for short, the dimension is taken as gender for example, the field value corresponding to gender is male or female, the cardinality under the dimension is 2, the field value corresponding to diseases is taken as dimension diseases for example, the field value corresponding to diseases may include hypertension, diabetes, nephritis, gastritis and the like, and hundreds of thousands or millions of different names of diseases may be possible in medicine if the diseases are not classified according to standards, that is, the cardinality of the diseases is millions; the value of data in a dimension or a field value in a dimension (for example, the amount of data in the dimension or a field value in the dimension) may be referred to as a metric, and common metrics may be PV (game View), UV (uniform View, independent guest), order number, active user number, total amount of data, and the like.
It is emphasized that the query dimension in the aggregated query request may be determined according to the field to be queried in the aggregated query instruction, for example, the field to be queried in the aggregated query instruction may be used as the query dimension in the aggregated query request; the query data table identifier in the aggregated query request may be determined according to the data table to be queried in the aggregated query instruction, for example, the name of the data table to be queried in the aggregated query instruction may be used as the query data table identifier in the aggregated query request; the aggregation dimension in the aggregation query request may be determined according to the field type to be aggregated in the aggregation query instruction, for example, the field type to be aggregated in the aggregation query instruction may be used as the aggregation dimension in the aggregation query request.
In an implementation manner, the aggregated Query request may be a Structured Query Language (SQL) statement, for example, assuming that the aggregated Query instruction includes a field to be queried, which is "patient information" and "disease type", a data table to be queried, which is "hm _ outpatient _ fee", and a field to be aggregated, which is "disease type", an aggregated Query request is determined according to the aggregated Query instruction, which includes Query dimensions "patient _ information", "cd _ amount _ service", "code _ source _ fee group _ name", an aggregated dimension "disease _ name" and a Query data table identifier "hm _ interpatient _ fee", according to the data grouping manner.
S202: and acquiring an aggregation query result corresponding to the aggregation query request.
In this embodiment, after the analytic database management system receives the aggregation query request, the analytic database management system may determine, based on the source database, a query result corresponding to the aggregation query request. That is, in this embodiment, the aggregated query result is determined by the analytics database management system according to the aggregated query request. An analytical database management system may be understood as a structured data management system that provides decision support and online analysis of application data sources, for example, an analytical database management system may be a Kylin database management system. It is emphasized that in this embodiment, the source database may be stored in a data warehouse, for example, the source data of the source database may be stored in a data warehouse of a Hadoop Distributed File System (HDFS) based on the ORC format.
As an example, the analytic database management system may first screen, according to the aggregate query request, a data source corresponding to the aggregate query request from a data source library, and perform data analysis on the data source corresponding to the aggregate query request to obtain an aggregate query result. Then, the analytical database management system can load the data corresponding to the aggregated query result and form a wide table. Then, the analytical database management system may construct a data structure based on the broad table, for example, when the analytical database management system is a Kylin database management system, the Kylin database management system may construct a cube based on the broad table, wherein the cube is a data structure defined inside the Kylin database management system for representing a data cube. Next, the analytic database management system may return an aggregated query result corresponding to the aggregated query request to the client. It should be noted that, in an implementation manner, after the client acquires the aggregated query result corresponding to the aggregated query request, the aggregated query result may be displayed to the user in an icon form. For example, assuming that the aggregation dimension in the aggregation query request is a disease type, the aggregation query result corresponding to the aggregation query request may be statistical data (i.e., an index) corresponding to various disease types (i.e., various field values under the aggregation dimension), for example, the aggregation query result may include: indexes corresponding to disease type hypertension are 100 pieces of data, indexes corresponding to disease type heart disease are 3 pieces of data, and indexes corresponding to disease type myocardial infarction are 10 pieces of data; the client can generate a bar graph corresponding to the aggregation query result according to the aggregation query result so as to display the data quantity corresponding to each of the disease types of hypertension, heart disease and myocardial infarction, namely the data distribution condition corresponding to each disease type, to the user.
S203: sending a detail query request to the ad hoc query engine in response to a detail query instruction for the aggregated query result.
In this embodiment, the detail query instruction may be understood as an instruction for triggering the client to generate the detail data for the aggregated query result to query. It should be noted that the detail query instruction may include a field value of the detail data to be queried in the aggregated query result, a field to be queried, a data table to be queried, and a limit number of the detail data. It should be noted that the field value of the detail data to be queried in the aggregated query result may be understood as the field value selected by the user in the aggregated query result, and it should be noted that, in this embodiment, for convenience of description, the field value of the detail data to be queried in the aggregated query result may be referred to as a target field value. The field to be queried in the detail query instruction may be the same as the field to be queried in the aggregate query instruction, or of course, the two fields may be different. Because the aggregation query result is determined according to the aggregation query request, the source of the data in the aggregation query result is the data table corresponding to the query data table identifier in the aggregation query request; in order to ensure that the detail data corresponding to the field value in the aggregated query result can be accurately obtained, the detail data corresponding to the field value in the aggregated query result needs to be obtained from the same data table, and therefore, the data table to be queried in the detail query instruction is the same as the data table to be queried in the aggregated query instruction. The number of the detail data limit pieces may be understood as the number of the detail data corresponding to the target field value that the user needs to check, and it should be emphasized that the number of the detail data limit pieces may be set by the user or preset by the system, which is not limited in this embodiment.
In one possible implementation manner, the client may be provided with a detail data query entry, such as a query interface or a query button, for each field value in the aggregated query result; when the user needs to inquire the detail data of one or more field values in the aggregated query result, the user can input a detail query instruction aiming at the clicked field value to the client by clicking a detail data query entry of at least one field value in the query result. The client receives the detail query instruction, and can send a detail query request to the ad hoc query engine in response to the detail query instruction.
The detail query request may be understood as an instruction for triggering the ad hoc query engine to perform a detail data query on field values in the aggregated query result. Wherein the detail query request may include a query dimension, a query data table identifier, and constraint content; constraint content may be understood as a condition for screening the detail data of the target field value, and in one implementation, the constraint content may include a query condition field and a limit number of the detail data. The query dimension in the detail query request can be determined according to the query dimension in the detail query instruction, for example, the query field in the detail query instruction can be used as the query dimension in the detail query request; the query data table identifier in the detail query request may be determined according to the data table to be queried in the detail query instruction, for example, the name of the data table to be queried in the detail query instruction may be used as the query data table identifier in the detail query request; the constraint condition content in the detail query request may be determined according to the field value of the detail data to be queried in the aggregated query result in the detail query instruction and the number of the detail data limit pieces, for example, the field value of the detail data to be queried in the aggregated query result in the detail query instruction may be used as the query condition field in the constraint condition content, and the number of the detail data limit pieces in the detail query instruction may be used as the number of the detail data limit pieces in the constraint condition content.
In one implementation, the statement of the detail query request may be SQL. For example, assuming that the detail query request is an SQL query request, for example, assuming that the detail query instruction includes field values "high blood pressure", fields "patient _ id" and "release _ name" of the detail data to be queried in the aggregated query result, a data table "hm _ inpatient _ fe" to be queried, and a detail data limit number "10000", the detail query request "SELECT patient _ id from hm _ inpatient _ fe where _ name ═ high blood pressure 'limit 10000" may be determined according to the detail query instruction, where the detail query request may include query dimensions "patient _ id" and "release _ name", a query data table identifier "hm _ inpatient _ fe", and a constraint condition content (i.e., a query condition field "release _ name ═ high blood pressure'", and a data limit number "10000").
S204: and acquiring detail data corresponding to the detail query request.
In this embodiment, after the ad hoc query engine receives the detail query request, the ad hoc query engine may determine the detail data corresponding to the detail query request based on the source database. It can be understood that the detail data corresponding to the target field value are a plurality of specific data under the target field value, that is, specific data satisfying the target field value; for example, assuming that the target field value is "diabetes", the detail data corresponding to the target field value is a number of pieces of data which have the target field value "diabetes" in the query data table and meet other requirements in the detail query request. That is, in this embodiment, the detail data is determined by the ad hoc query engine according to the detail query request.
The ad hoc query engine may be understood as an engine that can generate a corresponding statistical result according to a query condition selected by a user, for example, the ad hoc query engine may be a presto query engine. It should be emphasized that, in this embodiment, in order to ensure that the detail data queried by the ad hoc query engine corresponds to the target field value in the aggregated query result, the source database queried by the ad hoc query engine and the source database queried by the analytic database management system are the same source database, or the two source databases are the same.
It should be noted that, the query engine conforms to a rapid deployment mode of a small private cloud cluster, the resource occupation is small, the query is fast, the community is active, the support for data storage in orc format is relatively perfect, more query optimizations are provided, and indexes and compression are supported, for example, in an implementation mode, the query engine can cache indexes of orc data blocks in a source database, so that an obvious optimization effect is provided for filtering data blocks at the initial stage of query. Accordingly, the source database in this embodiment may support indexing, and the ad hoc query engine may support querying the source database.
As an example, the ad hoc query engine may first screen a database corresponding to the detail query request from the database according to the detail query request, and perform data filtering on a data source corresponding to the detail query request to obtain detail data corresponding to the target field value; then, the ad hoc query engine may return the detailed data corresponding to the target field value to the client, and after the number of the detailed data received by the client satisfies the limited number of the detailed data corresponding to the detailed query request, the client does not receive the detailed data subsequently returned by the ad hoc query engine, so that if the detailed data of the previous returned batch satisfies the limited number of the detailed data, the client may directly ignore the batch of the detailed data returned by the subsequent ad hoc query engine, that is, the client does not need to wait for the completion of the query process of the whole detailed data, and only needs to wait until the detailed data satisfying the limited number of the detailed data is received, thereby shortening the query time of the whole detailed data query process, and further improving the efficiency of querying the detailed data. It should be noted that, in this embodiment, since the ad hoc query engine only needs to perform data query filtering processing, and does not need to perform data aggregation processing, after the ad hoc query engine queries the detail data corresponding to the target field value, the detail data is directly returned to the client, and it is not necessary to perform data summarization locally in the ad hoc query engine, and then return to the client.
According to the technical scheme, the method provided by the invention can respond to the aggregation query instruction, send the aggregation query request to the analysis type database management system and obtain the aggregation query result corresponding to the aggregation query request; and sending a detail query request to the ad hoc query engine in response to a detail query instruction for the aggregated query result, and acquiring detail data corresponding to the detail query request. Therefore, the method provided by the invention can separately process the aggregation query request and the detail query request, namely, the analysis type database management system and the ad hoc query engine can be used for separately processing the aggregation query request and the detail query request, so that the analysis type database management system (such as kylin) can improve the data construction efficiency of the analysis type database management system after removing the measurement of the detail query, and the ad hoc query engine has the functions of query optimization, index support and compression, so the ad hoc query engine can also improve the query speed of the detail data. Therefore, the detail data query mode combining the analytic database management system and the ad hoc query engine can avoid the problems of longer time for constructing the Kylin data, slower detail query, higher proportion for storing the detail data and higher cost caused by a detail data query scheme combining a dictionary and a Bitmap mode adopted by the Kylin, thereby improving the efficiency of data analysis and detail data query, reducing the query cost of the detail data and further improving the user experience of the detail data query.
Fig. 2 shows only a basic embodiment of the method of the present invention, and based on this, certain optimization and expansion can be performed, and other preferred embodiments of the method can also be obtained.
Fig. 3 shows another specific embodiment of the detail data query method according to the present invention. In this embodiment, S202, S203 and S204 are further explained on the basis of the corresponding embodiment shown in fig. 2, that is, S202 includes S302 to S304, S203 includes S305 to S306, and S204 includes S307 to S311. In this embodiment, the method specifically includes the following steps:
s301: the client responds to the aggregation query instruction and sends an aggregation query request to the analytic database management system;
in this embodiment, the aggregate query request includes a query dimension, an aggregate dimension, and a query data table identifier.
It should be noted that S301 in the present embodiment is the same as S201 in the corresponding embodiment of fig. 2. Therefore, in this embodiment, S301 is not described again, and reference may be specifically made to the description of S201.
S302: and the analytical database management system determines a data table corresponding to the query data table identifier in the source database according to the query data table identifier.
S303: the analytical database management system determines a number of pieces of data that conform to the query dimension based on the data table.
S304: and the analytic database management system counts the plurality of data conforming to the query dimension according to the aggregation dimension to obtain an aggregation query result, and returns the aggregation query result corresponding to the aggregation query request to the client.
After the analysis-type database management system receives the aggregated query request, the analysis-type database management system may determine, in the source database, a data table corresponding to the query data table identifier according to the query data table identifier in the aggregated query request.
The analytical database management system may then filter, in the data table, a number of pieces of data in the data table that meet the query dimension according to the query dimension. For example, when there is a value in a piece of data in the data table under the query dimension, the piece of data can be considered as data conforming to the query dimension.
Then, the analytic database management system may aggregate the plurality of pieces of data that conform to the query dimension according to the aggregation dimension of the aggregated query request, that is, count the plurality of pieces of data that conform to the query dimension according to the plurality of field values under the aggregation dimension to obtain indexes that respectively correspond to each field value under the aggregation dimension, and use the indexes that respectively correspond to each field value under the aggregation dimension as the aggregated query result that corresponds to the aggregated query request. That is, the aggregated query result includes indicators corresponding to respective field values under the aggregated dimension.
Next, the aggregate query request is illustrated as "select sum (equal) as s _ equal, count (discrete _ probability _ id) as cd _ probability from hm _ outer _ probability _ fee group by _ distance _ name". The analytic database management system may determine, in the source database, a data table corresponding to the query data table identifier according to the query data table identifier "hm _ outpatient _ fe" in the aggregated query request. Then, the analytical database management system may determine, in the data table, a number of pieces of data having numerical values under the query dimensions "probability _ id", "cd _ probability", and "distance _ name". Then, since the data grouping manner in the aggregation query request is "group by disease _ name", the analytic database management system may perform aggregation statistics on the pieces of data according to each field value (for example, "hypertension", "diabetes", and the like) in the aggregation dimension "disease _ name" to determine an index corresponding to each field value in the aggregation dimension "disease _ name" (for example, an index corresponding to the field value "hyperglycemia" is "7", and an index corresponding to the field value "diabetes" is "8"), and take the index corresponding to each field value in the aggregation dimension "disease _ name" as the aggregation query result corresponding to the aggregation query request.
S305: and the client responds to a detail query instruction aiming at a target field value, and generates a detail query request corresponding to the target field value.
S306: the client sends the detail query request to the ad hoc query engine.
And after receiving the aggregation query result corresponding to the aggregation query request, the client can display the aggregation query result. If the user needs to query detailed data corresponding to one or more field values in the aggregated query result, a detailed query instruction for the field values can be sent to the client by selecting the field values, such as clicking a key or a pattern corresponding to the field values. It should be noted that, in this embodiment, a field value to be queried for detailed data may be referred to as a target field value. That is, a field value that needs to query the detailed data and is selected by the user in the aggregated query result may be referred to as a target field value, it is understood that the target field value is at least one field value under the aggregated query result, and the detailed query instruction includes the target field value. And the target field value can be used as the field value of the detail data to be queried in the aggregated query result of the detail query instruction. For example, assuming that the field values in the aggregated query result include "hypertension" and "diabetes", if the client detects that the user clicks the field value "hypertension", a detail query instruction of a target field value "hypertension" may be input to the client, and the client may respond to the detail query instruction of the target field value "hypertension" and generate a detail query request corresponding to the target field value "hypertension", and send the detail query request to the ad hoc query engine.
After the client side obtains the detail query instruction, a detail query request corresponding to the target field value can be generated according to the detail query instruction, and the detail query request is sent to the ad hoc query engine, so that the ad hoc query engine can query the detail query request corresponding to the target field value based on the source database according to the detail query request. In one implementation, the constraint content may include a query condition field, which may be a target field value, and a limitation number of the detail data.
S307: and the ad hoc query engine determines a data table corresponding to the query data table identifier in the source database according to the query data table identifier.
S308: and the ad hoc query engine determines a plurality of pieces of data conforming to the target field value based on the data table, takes the plurality of pieces of data conforming to the target field value as detail data corresponding to the detail query request, and returns the detail data corresponding to the detail query request to the client.
After the ad hoc query engine receives the detail query request corresponding to the target field value, the ad hoc query engine may determine, according to the query data table identifier in the detail query request, a data table corresponding to the query data table identifier in the source database.
The ad hoc query engine may then filter, in the data table, a number of pieces of data in the data table that meet the query dimension in the detail query request according to the query dimension. For example, when a piece of data in the data table has a value in the query dimension, the piece of data can be considered as data conforming to the query dimension.
Then, the ad hoc query engine may perform data filtering processing on the pieces of data conforming to the query dimension according to the content of the constraint condition in the detail query request, that is, perform data filtering processing on the pieces of data conforming to the query dimension based on the query condition field (i.e., the target field value) in the content of the constraint condition and the number of the pieces of detail data limitation, to obtain the pieces of data conforming to the target field value, use the pieces of data conforming to the target field value as the detail data corresponding to the detail query request, and return the detail data corresponding to the detail query request to the client.
Next, the aggregate query request is illustrated as "SELECT candidate _ id from hm _ inactive _ fe _ where distance _ name ═ hypertension' limit 10000". The ad hoc query engine may determine, according to a query data table identifier "hm _ initial _ fe" in the detail query request, a data table corresponding to the query data table identifier "hm _ initial _ fe" in the source database, and determine a data table corresponding to the query data table identifier in the source database. Then, the ad hoc query engine may filter, in the data table, a number of pieces of data in the data table that meet the query dimension according to the query dimension "probability _ id" and "release _ name" in the detail query request. The ad hoc query engine may perform data filtering processing on a plurality of pieces of data conforming to the query dimensionality based on a query condition field "disease _ name ═ hypertension'" and a detail data limit number "limit 10000" in constraint condition content to obtain a plurality of pieces of data conforming to the target field value, take the plurality of pieces of data conforming to the target field value as detail data corresponding to the detail query request, and return the detail data corresponding to the detail query request to the client.
It should be noted that, in an implementation, the client may perform a detailed data query of the target field value through a Java Database connection (JDBC) of the ad hoc query engine (e.g., presto).
In particular, the ad hoc query engine may include a master node and a plurality of worker nodes. After receiving the detail query request, the master node may distribute the detail query request to the plurality of working nodes, for example, the master node may distribute a query task corresponding to the detail query request to the plurality of working nodes. Then, each working node may determine, based on the data table, a number of pieces of data that meet the target field value, and return the number of pieces of data that meet the target field value as the detail data, specifically, the working nodes may concurrently and asynchronously execute a query task according to the detail query request based on the data table, so as to query the detail data corresponding to the detail query request.
It should be noted that, because the query of the detail query request for data is to perform data filtering on the data in the data table, and data aggregation is not required, the detail data returned from each working node does not need to be summarized at the master node and then returned to the client, but each working node can directly return to the client after completing the respective detail data query. That is to say, the query mode of the multiple working nodes in this embodiment is an asynchronous query mode, and specifically, the multiple working nodes execute the query task in a concurrent and asynchronous manner, so that the detail data queried by each working node is also returned to the client by each working node in batches through the master node, that is, each working node queries the detail data simultaneously, but the time when each working node completes the query of the detail data and the time when each working node returns the detail data may be different.
S309: the client receives the detail data returned by a working node, and determines the number of the received detail data.
S310: and if the number of the received detailed data does not meet the limit number of the detailed data, the client continuously receives the detailed data returned by a working node and determines the number of the received detailed data.
S311: and if the number of the received detailed data meets the limit number of the detailed data, the client stops receiving the detailed data returned by the rest working nodes.
In this embodiment, the detail data queried by each working node in the ad hoc query engine is respectively returned to the client through the master node in batches by each working node, and the number of pieces of detail data returned by the ad hoc query engine received by the client is the limit number of pieces of detail data. Therefore, the client receives the detailed data returned by a working node, the number of the detailed data currently received by the client can be determined firstly, then, the client can judge whether the number of the currently received detailed data meets the limited number of the detailed data, and if the number of the received detailed data does not meet the limited number of the detailed data, the client can continue to execute the steps of receiving the detailed data returned by the working node and determining the number of the received detailed data; and if the number of the received detailed data meets the limit number of the detailed data, the client stops receiving the detailed data returned by the rest working nodes.
Specifically, if the number of pieces of detail data of a batch currently received by the client meets the requirement (that is, the number of pieces of detail data is limited), the master node may immediately terminate the query of each working node, and the master node may directly return the received data to the client as the detail data corresponding to the detail query request.
For example, suppose that the number of the detail data is limited to 3000, that is, the ad hoc query engine is provided with three working nodes, the query result return time of the first working node is 2s, the returned detail data is 1000, the query result return time of the second working node is 3s, the returned detail data is 2000, the query result return time of the third working node is 5s, and the returned detail data is 1000; the client receives the detailed data returned by the first working node firstly at the 2 nd s, and the client receives the detailed data returned by the second working node at the 3 rd s because the detailed data received by the client does not meet the limitation of the number of the detailed data, and the client stops receiving the detailed data returned by the rest working nodes (namely, the third working nodes) at the moment, so that the process of receiving the detailed data by the client only needs 3s, and the process of receiving the detailed data after waiting for the third working nodes to return the detailed data is not needed as in the prior art, namely, the process of receiving the detailed data by the client can shorten the time of 2s compared with the process of receiving the detailed data by the client in the prior art.
It can be seen that in this embodiment, after the master node terminates the query task of each working node, the asynchronous return data of each working node may be directly discarded; therefore, the client does not need to finish the detailed data query process after all the working nodes finish the query task, so that the query time of the detailed data of the client can be shortened, and the query efficiency of the detailed data is improved.
Therefore, the processing procedure of the query method of the detail data is realized by combining the specific application scene. Of course, the above scenario is only an exemplary scenario and is not intended to limit the method provided by the present invention. The method provided by the invention can be applied to the processing process of the detail data query method with the same other principles in an extensive way.
Fig. 4 shows a specific embodiment of the detail data query device according to the present invention. The apparatus of this embodiment is a physical apparatus for executing the method of the above embodiment. The technical solution is essentially the same as that in the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. The device in this embodiment includes:
a first sending unit 401, configured to send an aggregation query request to the analytics database management system in response to the aggregation query instruction;
a first obtaining unit 402, configured to obtain an aggregation query result corresponding to the aggregation query request, where the aggregation query result is determined by the analysis-type database management system according to the aggregation query request;
a second sending unit 403, configured to send a detail query request to the ad hoc query engine in response to a detail query instruction for the aggregated query result;
a second obtaining unit 404, configured to obtain detail data corresponding to the detail query request, where the detail data is determined by the ad hoc query engine according to the detail query request.
Optionally, the aggregate query request includes a query dimension, an aggregate dimension, and a query data table identifier; the first obtaining unit 402 is specifically configured to:
the analytical database management system determines a data table corresponding to the query data table identifier in a source database according to the query data table identifier;
the analytical database management system determines a number of pieces of data that conform to the query dimension based on the data table;
the analytical database management system carries out statistics on the plurality of data which accord with the query dimensionality according to the aggregation dimensionality to obtain an aggregation query result; and the aggregation query result comprises indexes corresponding to all field values under the aggregation dimension.
Optionally, the second sending unit 403 is specifically configured to:
responding to a detail query instruction aiming at a target field value, and generating a detail query request corresponding to the target field value; wherein the target field value is at least one field value under the aggregated query result, and the detailed query request includes the target field value;
sending the detail query request to the ad hoc query engine.
Optionally, the detail query request further includes the query data table identifier; the second obtaining unit 404 is specifically configured to:
the ad hoc query engine determines a data table corresponding to the query data table identifier in the source database according to the query data table identifier;
and the ad hoc query engine determines a plurality of pieces of data conforming to the target field value based on the data table, and takes the plurality of pieces of data conforming to the target field value as the detail data corresponding to the detail query request.
Optionally, the ad hoc query engine includes a master node and a plurality of work nodes; the second obtaining unit 404 is specifically configured to:
the master node distributes the detail inquiry requests to the plurality of working nodes respectively;
and each working node determines a plurality of pieces of data conforming to the target field value based on the data table respectively, and returns the plurality of pieces of data conforming to the target field value as detail data.
Optionally, the query mode of the plurality of working nodes is an asynchronous query mode; the detail query request comprises a detail data limit number; the second obtaining unit 404 is specifically configured to:
receiving detail data returned by a working node, and determining the number of the received detail data;
if the number of the received detailed data does not meet the limit number of the detailed data, continuing to receive the detailed data returned by a working node, and determining the number of the received detailed data;
and if the number of the received detailed data meets the limit number of the detailed data, stopping receiving the detailed data returned by the rest working nodes.
Optionally, the analytic database management system is a Kylin database management system, and the ad hoc query engine is a presto query engine.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry standard architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry standard architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 5, but this does not indicate only one bus or one type of bus.
And the memory is used for storing the execution instruction. In particular, a computer program that can be executed by executing instructions. The memory may include both memory and non-volatile storage and provides execution instructions and data to the processor.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory into the memory and then runs the execution instruction, and the corresponding execution instruction can also be obtained from other equipment, so as to form the inquiry device of the detail data on a logic level. The processor executes the execution instructions stored in the memory, so that the detail data query method provided by any embodiment of the invention is realized through the executed execution instructions.
The method performed by the detail data query device according to the embodiment of the invention shown in fig. 2 can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The embodiment of the present invention further provides a readable storage medium, where the readable storage medium stores an execution instruction, and when the stored execution instruction is executed by a processor of an electronic device, the electronic device can execute the method for querying detail data provided in any embodiment of the present invention, and is specifically configured to execute the method for querying the data.
The electronic device described in the foregoing embodiments may be a computer.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A method for querying detail data, the method comprising:
responding to the aggregation query instruction, and sending an aggregation query request to an analytic database management system;
acquiring an aggregation query result corresponding to the aggregation query request, wherein the aggregation query result is determined by the analytic database management system according to the aggregation query request;
in response to a detail query instruction for the aggregated query result, sending a detail query request to the ad hoc query engine;
and acquiring detail data corresponding to the detail query request, wherein the detail data is determined by the ad hoc query engine according to the detail query request.
2. The method of claim 1, wherein the aggregate query request comprises a query dimension, an aggregate dimension, and a query data table identification; the analytic database management system determines a manner of the aggregated query result according to the aggregated query request, including:
the analytical database management system determines a data table corresponding to the query data table identifier in a source database according to the query data table identifier;
the analytical database management system determines a number of pieces of data that conform to the query dimension based on the data table;
the analytical database management system carries out statistics on the plurality of data which accord with the query dimensionality according to the aggregation dimensionality to obtain an aggregation query result; and the aggregation query result comprises indexes corresponding to all field values under the aggregation dimension.
3. The method of claim 2, wherein sending a detail query request to the ad hoc query engine in response to a detail query instruction for the aggregated query result comprises:
responding to a detail query instruction aiming at a target field value, and generating a detail query request corresponding to the target field value; wherein the target field value is at least one field value under the aggregated query result, and the detailed query request includes the target field value;
sending the detail query request to the ad hoc query engine.
4. The method of claim 3, wherein the detail query request further comprises the query data table identification; the mode of determining the detail data by the ad hoc query engine according to the detail query request comprises the following steps:
the ad hoc query engine determines a data table corresponding to the query data table identifier in the source database according to the query data table identifier;
and the ad hoc query engine determines a plurality of pieces of data conforming to the target field value based on the data table, and takes the plurality of pieces of data conforming to the target field value as the detail data corresponding to the detail query request.
5. The method of claim 4, wherein the ad hoc query engine comprises a master node and a plurality of worker nodes; the chairman query engine determines a plurality of pieces of data conforming to the target field based on the data table, and takes the plurality of pieces of data conforming to the target field value as detail data corresponding to the detail query request, including:
the master node distributes the detail inquiry requests to the plurality of working nodes respectively;
and each working node determines a plurality of pieces of data conforming to the target field value based on the data table respectively, and returns the plurality of pieces of data conforming to the target field value as detail data.
6. The method according to claim 5, wherein the query mode of the plurality of working nodes is an asynchronous query mode; the detail query request comprises a detail data limit number; the obtaining of the detail data corresponding to the detail query request includes:
receiving detail data returned by a working node, and determining the number of the received detail data;
if the number of the received detailed data does not meet the limit number of the detailed data, continuing to receive the detailed data returned by a working node, and determining the number of the received detailed data;
and if the number of the received detailed data meets the limit number of the detailed data, stopping receiving the detailed data returned by the rest working nodes.
7. The method of any of claims 1-6, wherein the analytical database management system is a Kylin database management system and the ad hoc query engine is a presto query engine.
8. An apparatus for querying detail data, the apparatus comprising:
the first sending unit is used for responding to the aggregation query instruction and sending an aggregation query request to the analysis type database management system;
a first obtaining unit, configured to obtain an aggregation query result corresponding to the aggregation query request, where the aggregation query result is determined by the analytic database management system according to the aggregation query request;
a second sending unit, configured to send a detail query request to the ad hoc query engine in response to a detail query instruction for the aggregated query result;
a second obtaining unit, configured to obtain detail data corresponding to the detail query request, where the detail data is determined by the ad hoc query engine according to the detail query request.
9. A readable medium comprising executable instructions which, when executed by a processor of an electronic device, cause the electronic device to perform the method of any of claims 1 to 7.
10. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-7 when the processor executes the execution instructions stored by the memory.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112182028A (en) * | 2020-09-29 | 2021-01-05 | 北京人大金仓信息技术股份有限公司 | Data line number query method and device based on table of distributed database |
| CN113190578A (en) * | 2021-03-26 | 2021-07-30 | 有半岛(北京)信息科技有限公司 | Multi-source data query system, method, device, equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844415A (en) * | 2016-11-18 | 2017-06-13 | 北京奇虎科技有限公司 | A kind of data processing method and device in SparkSQL systems |
| CN108280082A (en) * | 2017-01-06 | 2018-07-13 | 北京京东尚科信息技术有限公司 | A kind of extemporaneous querying method and system of statistical data |
| CN108875042A (en) * | 2018-06-27 | 2018-11-23 | 中国农业银行股份有限公司 | A kind of mixing on-line analysing processing system and data query method |
| CN110489653A (en) * | 2019-08-23 | 2019-11-22 | 北京金堤科技有限公司 | Public feelings information querying method and device, system, electronic equipment, storage medium |
| CN110580255A (en) * | 2018-06-08 | 2019-12-17 | 深圳艾派网络科技股份有限公司 | method and system for storing and retrieving data |
| CN111125178A (en) * | 2018-10-30 | 2020-05-08 | 亿度慧达教育科技（北京）有限公司 | Data query method, device, terminal, presto query engine and storage medium |
| CN111367954A (en) * | 2018-12-26 | 2020-07-03 | 中兴通讯股份有限公司 | Data query processing method, device and system and computer readable storage medium |
-
2020
- 2020-10-12 CN CN202011084149.4A patent/CN112434056A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844415A (en) * | 2016-11-18 | 2017-06-13 | 北京奇虎科技有限公司 | A kind of data processing method and device in SparkSQL systems |
| CN108280082A (en) * | 2017-01-06 | 2018-07-13 | 北京京东尚科信息技术有限公司 | A kind of extemporaneous querying method and system of statistical data |
| CN110580255A (en) * | 2018-06-08 | 2019-12-17 | 深圳艾派网络科技股份有限公司 | method and system for storing and retrieving data |
| CN108875042A (en) * | 2018-06-27 | 2018-11-23 | 中国农业银行股份有限公司 | A kind of mixing on-line analysing processing system and data query method |
| CN111125178A (en) * | 2018-10-30 | 2020-05-08 | 亿度慧达教育科技（北京）有限公司 | Data query method, device, terminal, presto query engine and storage medium |
| CN111367954A (en) * | 2018-12-26 | 2020-07-03 | 中兴通讯股份有限公司 | Data query processing method, device and system and computer readable storage medium |
| CN110489653A (en) * | 2019-08-23 | 2019-11-22 | 北京金堤科技有限公司 | Public feelings information querying method and device, system, electronic equipment, storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112182028A (en) * | 2020-09-29 | 2021-01-05 | 北京人大金仓信息技术股份有限公司 | Data line number query method and device based on table of distributed database |
| CN113190578A (en) * | 2021-03-26 | 2021-07-30 | 有半岛(北京)信息科技有限公司 | Multi-source data query system, method, device, equipment and storage medium |
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