CN112131288B - Data source access processing method and device - Google Patents
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- 238000003672 processing method Methods 0.000 title claims abstract description 14
- 230000002776 aggregation Effects 0.000 claims abstract description 107
- 238000004220 aggregation Methods 0.000 claims abstract description 107
- 238000013507 mapping Methods 0.000 claims abstract description 33
- 238000000547 structure data Methods 0.000 claims abstract description 26
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- 238000013079 data visualisation Methods 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims description 13
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Abstract
The disclosure provides a data source access processing method and device, and relates to the field of data processing. Accessing a third party data source from a preset application program interface to obtain list structure data containing a plurality of pieces of mapping information; according to the set dimension parameters, each piece of mapping information is analyzed into a group of node sets with determined relevance; the nodes in each group of node sets are associated to the same root node so as to be spliced to form an aggregation tree corresponding to the list structure data; according to the set operator, performing corresponding aggregation operation on index parameters of nodes with the same names in the aggregation tree; and converting the aggregation tree into a front-end component to display a required style, so that the data visualization platform can support access of a third-party data source.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a method and apparatus for processing access to a data source.
Background
The data visualization platform can currently support mature databases, such as MySQL, oracle, SQLserver, presto, sparkSQL and the like, as data sources, and the databases can support aggregation operation of various dimensions so as to facilitate data analysis.
Disclosure of Invention
The inventor finds that the data visualization platform cannot support the access of the third party data source, and the user requirement is difficult to meet. The third party data source is, for example, POP (pctowap open platform, computer to wireless application protocol open platform) or the like.
In view of this, the embodiments of the present disclosure propose a data source access processing scheme, so that a data visualization platform can support access of a third party data source.
Some embodiments of the present disclosure provide a data source access processing method, including:
accessing a third party data source from a preset application program interface to obtain list structure data containing a plurality of pieces of mapping information;
according to the set dimension parameters, each piece of mapping information is analyzed into a group of node sets with determined relevance;
the nodes in each group of node sets are associated to the same root node so as to be spliced to form an aggregation tree corresponding to the list structure data;
according to the set operator, performing corresponding aggregation operation on index parameters of nodes with the same names in the aggregation tree;
the aggregation tree is converted into a front-end component to expose the required style.
In some embodiments, the parsing each piece of mapping information into a set of nodes of the association determination includes:
determining the number of dimension parameters as the depth of the aggregation tree;
and determining the position information of the corresponding node of the dimension parameter in the aggregation tree according to the level information of the dimension parameter in each piece of mapping information.
In some embodiments, if the level of the dimension parameter in each piece of mapping information is 1, the parent node of the corresponding node of the dimension parameter in the aggregation tree is the root node;
if the level of the dimension parameter in each piece of mapping information is i, the parent node of the corresponding node of the dimension parameter in the aggregation tree is the corresponding node of the dimension parameter of the i-1 level, and the integer i is more than or equal to 2.
In some embodiments, the performing a corresponding aggregation operation on index parameters of nodes with the same name in the aggregation tree includes:
aiming at a summation operator, superposing index values of nodes with the same name in the aggregation tree;
aiming at a maximum operator, taking the maximum value of index values of nodes with the same names in the aggregation tree;
aiming at a minimum operator, taking the minimum value of index values of nodes with the same names in the aggregation tree;
and aiming at an averaging operator, superposing index values of nodes with the same names in the aggregation tree, and dividing the superposed values by the number of the nodes with the same key names in the aggregation tree.
In some embodiments, the converting the aggregation tree to the front-end component exposes the desired style includes:
and selecting a corresponding component converter according to the set chart type, and converting the aggregation tree into a front-end component to display a required style.
In some embodiments, the corresponding third party data source is accessed from a pre-set application program interface using an access address and an authentication key, wherein the authentication key is generated or pre-defined based on the access address.
In some embodiments, at least one of the following is also included:
filtering the acquired list structure data according to the set filtering description information;
and ordering the acquired list structure data according to the set ordering description information.
Some embodiments of the present disclosure provide a data source access processing apparatus, including:
the data acquisition module is configured to access a third party data source from a preset application program interface to acquire list structure data containing a plurality of pieces of mapping information;
the analysis module is configured to analyze each piece of mapping information into a group of node sets with determined relevance according to the set dimension parameters;
the association module is configured to associate the nodes in each group of node sets to the same root node so as to splice and form an aggregation tree corresponding to the list structure data;
the aggregation operation module is configured to perform corresponding aggregation operation on index parameters of nodes with the same names in the aggregation tree according to the set operators;
and the display module is configured to select a corresponding component converter according to the set chart type and convert the aggregation tree into a front-end component display required style.
Some embodiments of the present disclosure provide a data source access processing apparatus, including:
a memory; and
a processor coupled to the memory, the processor configured to perform the data source access processing method of any of the embodiments based on instructions stored in the memory.
Some embodiments of the present disclosure propose a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the data source access processing method of any one of the embodiments.
Drawings
The drawings that are required for use in the description of the embodiments or the related art will be briefly described below. The present disclosure will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings,
it will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without inventive faculty.
Fig. 1 is a flow chart illustrating some embodiments of a data source access processing method of the present disclosure.
Fig. 2 is a flow chart illustrating other embodiments of the data source access processing method of the present disclosure.
Fig. 3 is a schematic diagram of a parametric model of some embodiments of the present disclosure.
Fig. 4 is a schematic diagram of a parametric model configuration of some embodiments of the present disclosure.
Fig. 5 is a schematic diagram of an aggregation tree construction process according to some embodiments of the present disclosure.
Fig. 6 is a schematic diagram of an aggregation tree in accordance with some embodiments of the present disclosure.
Fig. 7 is a schematic structural diagram of some embodiments of a data source access processing apparatus of the present disclosure.
Fig. 8 is a schematic structural diagram of another embodiment of a data source access processing device of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure.
Fig. 1 is a flow chart illustrating some embodiments of a data source access processing method of the present disclosure. As shown in fig. 1, the method of this embodiment includes:
and 11, accessing a third party data source from a preset application program interface to acquire list structure data containing a plurality of pieces of mapping information.
A third party data source may correspond to an application program interface through which the corresponding third party data source may be accessed. The third party data sources are, for example, other data sources outside of the mature database (e.g., mySQL, oracle, SQLserver, presto, sparkSQL, etc.), such as POP data sources.
The third party data source returns a result set, the result set can be described by a JSON (JavaScriptObject Notation, JS object numbered musical notation) format, and the result set can be analyzed into list structure data containing a plurality of pieces of mapping information in a unified format. Wherein "map" and "list" are two types of sets, "map" is a data structure of a key-name-value (key-value) type, "list" contains pieces of information that are sequentially repeatable, "list structure data containing pieces of map information" refers to pieces of map information that are sequentially repeatable. The following example is list structure data containing 3 pieces of mapping information.
name, zhang San, age 15, sex;
name is Lifour, age is 12, sex is female;
name: king five, age 11, sex: man.
In some embodiments, the access address is used to access the corresponding third party data source from the preset application program interface, and the access can carry an authentication key for verifying the access authority so as to improve the access security. The authentication key may be predefined by the user or may be generated by operating on the access address using a secure hash algorithm.
And step 12, according to the set dimension parameters, analyzing each piece of mapping information into a group of node sets with determined relevance.
In some embodiments, the number of the dimension parameters is determined as the depth of the aggregation tree, and the position information of the corresponding node of the dimension parameters in the aggregation tree is determined according to the level information of the dimension parameters in each piece of mapping information.
For example, if the level of the dimension parameter in each piece of mapping information is 1, the parent node of the corresponding node of the dimension parameter in the aggregation tree is the root node; if the level of the dimension parameter in each piece of mapping information is i, the parent node of the corresponding node of the dimension parameter in the aggregation tree is the corresponding node of the dimension parameter of the i-1 level, and the integer i is more than or equal to 2. Thus, the relevance of each node in the node set is clarified.
And 13, associating the nodes in each group of node sets to the same root node according to the relevance among the nodes in the node sets so as to splice and form an aggregation tree corresponding to the list structure data.
And 14, carrying out corresponding aggregation operation on index parameters of nodes with the same names in the aggregation tree according to the set operators.
Aiming at a summation operator, superposing index values of nodes with the same name in the aggregation tree; aiming at a maximum operator, taking the maximum value of index values of nodes with the same names in the aggregation tree; aiming at a minimum operator, taking the minimum value of index values of nodes with the same names in the aggregation tree; and aiming at an averaging operator, superposing index values of nodes with the same names in the aggregation tree, and dividing the superposed values by the number of the nodes with the same key names in the aggregation tree.
Step 15, converting the aggregation tree into a front-end component to display a required style.
In some embodiments, the corresponding component transformer is selected according to the set chart type, transforming the aggregate tree into the front-end component presentation required style. The chart type is, for example, a bar chart, a pie chart, a bar chart, a table, etc., but is not limited to the illustrated example.
The above-described embodiments enable a data visualization platform to support access to third party data sources.
Fig. 2 is a flow chart illustrating some embodiments of a data source access processing method of the present disclosure. As shown in fig. 2, the method of this embodiment includes:
and step 21, accessing a third party data source from a preset application program interface.
In some embodiments, the access address is used to access the corresponding third party data source from the preset application program interface, and the access can carry an authentication key for verifying the access authority so as to improve the access security. The authentication key may be predefined by the user or may be generated by operating on the access address using a secure hash algorithm.
In step 22, the third party data source returns a result set, which may be described in JSON format, for example, and the result set may be parsed into List structure data containing a plurality of pieces of mapping information in a unified format, which may be denoted as List < Map < key, value > >, abbreviated as ListMap, and reference is made to the foregoing symbol meaning and will not be repeated here.
An exemplary ListMap is:
{
[ "Primary department": department a, "Secondary department": department a1, "number of people": 30],
[ "Primary department": department a, "Secondary department": department a2, "number of people": 40],
[ "Primary department": department a, "Secondary department": department a3, "number of people": 50],
[ "Primary department": department b, "Secondary department": department b1, "number of people": 60],
[ "Primary department": department b, "Secondary department": department b2, "number of people": 70],}
The result set can be subjected to operations such as query, filtering, paging, sequencing, aggregation operation and the like. The calculation engine may first perform field screening, filtering, and sorting (if necessary) on the result set based on the dimension field, the index field, the filtering field, the sorting field, and the like in the parameter model, and then construct an aggregation tree based on the dimension field and the index field, and perform an aggregation operation. Described in detail below.
Step 23, the user builds a parameter model based on the data visualization platform and transmits the parameter model to the computing engine.
Referring to fig. 3, the parameter model includes, for example, parameter information such as a data model, a component data related configuration, component attribute information, a chart type, whether or not to page, and the like. The data model includes, for example, a data source identification (id), a data source type, and the like. The component data related configuration includes, for example, a dimension field, an index field, a filter descriptor, a sort descriptor, a number of displays (topN) descriptor. The dimension field and the index field include, for example, a field name, a field alias, a database table name, a table name id, whether it is a calculation field, a calculation field formula, a numeric formatting, an aggregation type, and the like. The filter descriptors include, for example, filter information for filtering data in the result set. The ranking descriptor includes, for example, a ranking type, such as from large to small, or from small to large, etc. The display quantity (topN) descriptor includes, for example, the number of display bars, the offset, and the like. The component attribute information includes, for example, a component name, a component refresh duration, and the like.
The user can build a parametric model in the front page of the data visualization platform by drag-and-drop operations on the fields in the result set. For example, referring to fig. 4, "primary department" and "secondary department" are set as dimension fields, and "number of persons" is set as index field.
In step 24, the computing engine builds an aggregation tree and performs an aggregation operation.
And if the filtering description information is set in the parameter model, filtering list structure data containing a plurality of pieces of mapping information. For example, the ListMap is packed into Stream, and is filtered by the Stream's filter method according to the field name (key value corresponding to map) and the field value (value corresponding to map) described by FilterDescriptor in the parameter model.
And if the ordering description information is set in the parameter model, ordering the list structure data containing a plurality of pieces of mapping information.
As shown in fig. 5, the aggregation tree construction process includes:
in step 241, the aggregation tree is started to be built, where the aggregation tree node TreeMapNode includes, for example, child node child, parent, tree depth level, node name, index value, and node repeated occurrence count (initial value defaults to 1).
In step 242, a root node is created.
Step 243, looping through the ListMap set to build an aggregation tree, comprising:
for each piece of Map information in the ListMap set, the following operations are performed:
step 2431, parsing a Map message into a set of node sets with determined relevance according to dimension and index fields.
Specifically, an aggregation tree with depth n is built based on the number dimensions of dimension () =n of dimension fields, the dimension fields correspond to nodes forming the aggregation tree, names of the nodes correspond to values of the dimension fields, index values of the nodes correspond to values of the index fields, position information (i.e. level information) of the nodes in the aggregation tree is determined according to level information of the dimension fields, and the level information of the dimension fields can be represented by subscripts of the dimension fields in a dimension set.
For example, a parent node of a node corresponding to a dimension field with a level of 1 in the aggregation tree is a root node, or, in other words, a parent node of a node corresponding to a dimension field with a subscript of 0 in the dimension set (i.e., dimensions. Get (0)) in the aggregation tree is a root node; the parent node of the corresponding node of the dimension field of the level i is the corresponding node of the dimension field of the level i-1 in the aggregation tree, or in other words, the parent node of the corresponding node of the dimension field (i.e. dimension. Get (i-1)) of the index i-1 in the dimension set is the corresponding node of the dimension field of the index i-2 in the aggregation tree, so that the position of each node in the aggregation tree is determined.
Taking the ListMap of the departments and the number of people as an example, 5 pieces of map information are shared, a first-level department (with the level of 1) and a second-level department (with the level of 2) are dimension fields, the number of people is an index field, the depth of the aggregation tree is 2, the node set corresponding to the first piece of map information comprises two nodes, namely a node corresponding to the department a and a node corresponding to the department a1, the father node of the node corresponding to the department a in the aggregation tree is a root node, and the father node of the node corresponding to the department a1 in the aggregation tree is a node corresponding to the department a. The analysis of the other map information is similar to the analysis of the first piece of map information.
Step 2432, associating the nodes in each group of node sets to the same root node according to the association between the nodes in the node sets so as to splice and form an aggregation tree, and performing corresponding aggregation operation on index parameters of nodes with the same name in the aggregation tree according to the set operators.
Taking the aforementioned ListMap of departments and numbers of people as an example, referring to fig. 6, the aggregation tree is formed as follows: the root node, the father node is two first level nodes of the root node, the father node is five second level nodes of the first level nodes, wherein the two first level nodes comprise a node corresponding to a department a and a node corresponding to a department b, the five second level nodes comprise three nodes corresponding to departments a1, a2 and a3 of which the father node is a node corresponding to the department a, and two nodes corresponding to departments b1 and b2 of which the father node is a node corresponding to the department b, and if summation aggregation operation is assumed, the head count index value of the node corresponding to the department a is 30+40+50=120, and the head count index value of the node corresponding to the department b is 60+70=130.
At step 244, building the aggregation tree ends.
Step 25, converting the aggregation tree into a front-end component exposes a desired style, such as a histogram, pie chart, bar chart, table, etc., but is not limited to the illustrated example.
The above-described embodiments enable a data visualization platform to support access to third party data sources.
Fig. 7 is a schematic structural diagram of some embodiments of a data source access processing apparatus of the present disclosure.
As shown in fig. 7, the apparatus 70 of this embodiment includes:
a data acquisition module 71 configured to acquire list structure data containing a plurality of pieces of mapping information from a preset application program interface access third party data source;
a parsing module 72 configured to parse each piece of mapping information into a set of node sets with determined relevance according to the set dimension parameters;
an association module 73, configured to associate the nodes in each group of node sets to the same root node, so as to splice and form an aggregation tree corresponding to the list structure data;
the aggregation operation module 74 is configured to perform corresponding aggregation operation on index parameters of nodes with the same names in the aggregation tree according to the set operators;
the presentation module 75 is configured to select a corresponding component converter according to the set chart type, and convert the aggregation tree into a front-end component presentation required style.
The modules 72-74 therein may be deployed in a computing engine.
In some embodiments, the data acquisition module 71 is configured to access the respective third party data source from a preset application program interface using an access address and an authentication key, wherein the authentication key is generated or predefined from the access address.
In some embodiments, the parsing module 72 is configured to determine the number of dimension parameters as the depth of the aggregate tree; and determining the position information of the corresponding node of the dimension parameter in the aggregation tree according to the level information of the dimension parameter in each piece of mapping information.
In some embodiments, the parsing module 72 is configured to, if the level of the dimension parameter in each piece of mapping information is 1, make the parent node of the corresponding node in the aggregation tree be the root node; if the level of the dimension parameter in each piece of mapping information is i, the parent node of the corresponding node of the dimension parameter in the aggregation tree is the corresponding node of the dimension parameter of the i-1 level, and the integer i is more than or equal to 2.
In some embodiments, the aggregation operation module 74 is configured to:
aiming at a summation operator, superposing index values of nodes with the same name in the aggregation tree;
aiming at a maximum operator, taking the maximum value of index values of nodes with the same names in the aggregation tree;
aiming at a minimum operator, taking the minimum value of index values of nodes with the same names in the aggregation tree;
and aiming at an averaging operator, superposing index values of nodes with the same names in the aggregation tree, and dividing the superposed values by the number of the nodes with the same key names in the aggregation tree.
In some embodiments, the presentation module 75 is configured to select a corresponding component converter according to the set chart type, converting the aggregate tree into a front-end component presentation required style.
In some embodiments, the apparatus 70 further comprises: and a filtering module 76 configured to filter the acquired list structure data according to the set filtering description information.
In some embodiments, the apparatus 70 further comprises: the sorting module 77 is configured to sort the acquired list structure data according to the set sorting description information.
Fig. 8 is a schematic structural diagram of some embodiments of a data source access processing apparatus of the present disclosure.
As shown in fig. 8, the apparatus 80 of this embodiment includes:
a memory 81; and
a processor 82 coupled to the memory, the processor being configured to perform the data source access processing method of any of the previous embodiments based on instructions stored in the memory.
The memory 81 may include, for example, a system memory, a fixed nonvolatile storage medium, and the like. The system memory stores, for example, an operating system, application programs, boot Loader (Boot Loader), and other programs.
It will be appreciated by those skilled in the art that embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The foregoing description of the preferred embodiments of the present disclosure is not intended to limit the disclosure, but rather to enable any modification, equivalent replacement, improvement or the like, which fall within the spirit and principles of the present disclosure.
Claims (9)
1. A data source access processing method, comprising:
accessing a third party data source which does not support a data visualization platform from a preset application program interface to obtain list structure data containing a plurality of pieces of mapping information;
according to the set dimension parameters, each piece of mapping information is resolved into a group of node sets with determined relevance, and the method comprises the following steps: determining the number of dimension parameters as the depth of the aggregation tree; determining the position information of the corresponding node of the dimension parameter in the aggregation tree according to the level information of the dimension parameter in each piece of mapping information;
the nodes in each group of node sets are associated to the same root node so as to be spliced to form an aggregation tree corresponding to the list structure data;
according to the set operator, performing corresponding aggregation operation on index parameters of nodes with the same names in the aggregation tree;
the aggregation tree is converted into a front-end component to expose the required style.
2. The method of claim 1, wherein,
if the level of the dimension parameter in each piece of mapping information is 1, the father node of the corresponding node of the dimension parameter in the aggregation tree is a root node;
if the level of the dimension parameter in each piece of mapping information is i, the parent node of the corresponding node of the dimension parameter in the aggregation tree is the corresponding node of the dimension parameter of the i-1 level, and the integer i is more than or equal to 2.
3. The method of claim 1, wherein performing the corresponding aggregation operation on index parameters of nodes with the same name in the aggregation tree comprises:
aiming at a summation operator, superposing index values of nodes with the same name in the aggregation tree;
aiming at a maximum operator, taking the maximum value of index values of nodes with the same names in the aggregation tree;
aiming at a minimum operator, taking the minimum value of index values of nodes with the same names in the aggregation tree;
and aiming at an averaging operator, superposing index values of nodes with the same names in the aggregation tree, and dividing the superposed values by the number of the nodes with the same key names in the aggregation tree.
4. The method of claim 1, wherein converting the aggregate tree into a front-end component exposing a desired style comprises:
and selecting a corresponding component converter according to the set chart type, and converting the aggregation tree into a front-end component to display a required style.
5. The method of claim 1, wherein,
and accessing the corresponding third party data source from a preset application program interface by using the access address and an authentication key, wherein the authentication key is generated or predefined according to the access address.
6. The method of claim 1, further comprising at least one of:
filtering the acquired list structure data according to the set filtering description information;
and ordering the acquired list structure data according to the set ordering description information.
7. A data source access processing apparatus, comprising:
the data acquisition module is configured to acquire list structure data containing a plurality of pieces of mapping information from a third party data source which does not support the data visualization platform through a preset application program interface access;
the analysis module is configured to analyze each piece of mapping information into a group of node sets with determined relevance according to the set dimension parameters, and comprises the following steps: determining the number of dimension parameters as the depth of the aggregation tree; determining the position information of the corresponding node of the dimension parameter in the aggregation tree according to the level information of the dimension parameter in each piece of mapping information;
the association module is configured to associate the nodes in each group of node sets to the same root node so as to splice and form an aggregation tree corresponding to the list structure data;
the aggregation operation module is configured to perform corresponding aggregation operation on index parameters of nodes with the same names in the aggregation tree according to the set operators;
and the display module is configured to select a corresponding component converter according to the set chart type and convert the aggregation tree into a front-end component display required style.
8. A data source access processing apparatus, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the data source access processing method of any of claims 1-6 based on instructions stored in the memory.
9. A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the data source access processing method according to any of claims 1-6.
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