CN111241113A - Statistical graph data real-time computing system and method based on PaaS framework - Google Patents
Statistical graph data real-time computing system and method based on PaaS framework Download PDFInfo
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Abstract
The invention relates to a statistical chart data real-time computing system and method based on a PaaS framework, which carries out full computation on defined index rules through offline service and stores factors influencing index results in a database; the real-time service compares the detail of the user service data change with the factors influencing the index result, selects the field influenced by the service data change for incremental calculation, and updates the data in real time; full calculations were performed at fixed times daily to complete the analysis. The invention provides a PaaS-based architecture and a universal table design concept, a user can define the statistical index of any field of any object, and the method has good universality and flexible operation. The invention establishes an event-driven calculation model based on user data change, thereby reducing the calculation times and resource consumption. The updating time of the index data is in the minute level, and the user experience is optimized.
Description
Technical Field
The invention relates to the technical field of data statistical analysis, in particular to a statistical graph data real-time computing system and method based on a PaaS framework.
Background
Nowadays, more and more fields need big data analysis, for example, the financial industry needs credit wind control using big data system in combination with var (value at risk) or machine learning scheme, various IOT scenarios need big data system to continuously aggregate and analyze time series data, various major technology companies need to establish big data analysis middleboxes, and so on. Data analysis required by the scenes is supported, the analyzed data range spans real-time data and historical data, and both low-delay real-time data analysis and comprehensive data analysis on the historical data are required; at present, when an enterprise analyzes data by using a statistical chart, the following problems exist:
1. the definition of each index of the statistical chart is not flexible enough, the configured index is limited, and the custom object is difficult to support analysis.
2. Generally, the data updating of the statistical chart adopts a strategy of updating every other day, and the long time for updating the data can cause the lag of the data analysis work.
Disclosure of Invention
In order to solve the above problems, the invention provides a statistical graph data real-time computing system and method based on a PaaS architecture, a user defines statistical graph indexes to be analyzed through a page, an offline service collects indexes of an initialization state at night or in the morning of the next day to perform full computation, and the indexes after the full computation perform incremental real-time computation through the change of user business data, so that the updating frequency of the statistical graph data reaches the level of minutes.
In order to achieve the above object, an aspect of the present invention provides a statistical graph data real-time computing system based on PaaS architecture, which includes a configuration module, a field extraction module, a message generation module, a comparison module, an increment computation module, a full computation module, an update module, and a database;
the configuration module is used for providing an operation interface, and a user defines an index rule to be analyzed by an object through the operation interface; the index state to be analyzed is an initialization state;
the field extraction module is used for acquiring fields influencing the index rule according to the index rule;
the message generation module is used for acquiring behaviors generated at the bottom layer, generating messages containing behavior data, adding the messages into a message queue, consuming the message queue in real time and further acquiring change details of the behavior data;
the comparison module is used for comparing and judging the change details and the fields which have influence on the index rule to acquire the fields which are influenced by the change; acquiring an index rule influenced by the change;
the increment calculation module is used for carrying out increment calculation on the influenced index rule after the influenced index rule is changed;
the total calculation module is used for searching all index rules with the states as the initialization states from the database at fixed time every day and performing total calculation according to the index rules;
the updating module is used for updating the incremental calculation result to the field with the influence in the database; and updating the total calculation result to a corresponding field in the database to be used as basic data for incremental calculation of the next day.
Further, the configuration module is further configured to define a data aggregation mode.
The system further comprises a data table generating module for generating a storage table with a multi-column wide table structure, wherein the storage table slots are used for storing index calculation result data; generating a metadata table and storing the description information of the index rule; the metadata table and the multi-column-width table are associated according to the external key of the database.
Further, the updating module performs slot addressing through the metadata table according to each index related to the incremental calculation result or the full calculation result, finds a target slot of the corresponding index in the storage table, and inserts the calculation result into the target slot of the storage table to complete updating.
Further, the fixed time per day includes performing the current day's full-scale calculation after completing all incremental calculations per day or performing the previous day's full-scale calculation before beginning the incremental calculations per day.
The invention also provides a statistical graph data real-time computing method based on the PaaS framework, which comprises the following steps:
defining an index rule to be analyzed by an object by a user, wherein the index rule to be analyzed is in an initialization state; acquiring fields influencing the index rules according to the index rules to be analyzed;
when bottom-layer behaviors occur, generating messages of behavior data, adding the messages into a message queue, consuming the message queue in real time and further acquiring change details of the behavior data;
comparing and judging the change details and the fields with influence, selecting the index fields influenced by the change for incremental calculation, and updating the calculation result to a database;
and searching all indexes with initialized states from the database at fixed time every day, extracting index fields according to the extraction rule, then performing full calculation, and updating the calculation result to the database to serve as basic data of next day increment calculation.
Further, the method also comprises a mode of user-defined data aggregation.
Further, the database includes:
a storage table with a multi-column wide table structure is adopted, and the storage table slot position is used for storing index calculation result data;
a metadata table storing description information of the index field; the metadata table and the multi-column-width table are associated according to the external key of the database.
Further, updating the calculation results to the database includes: and each index related to the calculation result is subjected to slot addressing through the metadata table, a target slot position of the corresponding index in the storage table is found, and the calculation result is inserted into the target slot position of the storage table.
Further, the fixed time per day includes performing the current day's full-scale calculation after completing all incremental calculations per day or performing the previous day's full-scale calculation before beginning the incremental calculations per day.
The technical scheme of the invention comprises the following beneficial technical effects:
(1) the invention provides a PaaS-based architecture and a universal table design concept, a user can define the statistical index of any field of any object, and the method has good universality and flexible operation.
(2) On one hand, the statistical analysis defined by the user is completed through daily total calculation; on the other hand, the indexes after the full calculation are subjected to incremental real-time calculation through the change of user service data, and the real-time updating of the data is realized.
(3) The invention establishes an event-driven calculation model based on user data change, thereby reducing the calculation times and resource consumption. The updating time of the index data is in the minute level, and the user experience is optimized.
Drawings
FIG. 1 is a schematic view of a configuration interface;
FIG. 2 is a schematic data flow diagram;
fig. 3 is a schematic structural diagram of a statistical chart real-time computing system based on a PaaS architecture.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The invention provides a statistical graph data real-time computing system based on a PaaS framework, which comprises a configuration module based on the PaaS framework, a field extraction module, a message generation module, a comparison module, an increment computation module, a full computation module, an update module, a data table generation module and a database, wherein the configuration module, the field extraction module, the message generation module, the comparison module, the increment computation module, the full computation module, the update module, the data table generation module and the database are combined with a.
The configuration module is used for providing an operation interface, and a user defines an index rule to be analyzed by an object through the operation interface; the index rule state to be analyzed is initialized, and a data aggregation mode is defined. The index rule can be understood as the explanation of the indexes, which are in one-to-one correspondence with the indexes, and the corresponding fields are obtained according to the description of the indexes.
And the field extraction module is used for acquiring fields having influence on the index rule according to the index rule to be analyzed, wherein the fields having influence, namely the index rule, contain the fields to be analyzed.
Suppose that an index rule A is defined that counts the amount of orders for which the customer level is an important customer. Then the fields that affect metric rule a include: an order amount field, and a customer level field. In other words, if the amount of the order changes, the index rule A will change accordingly, and if the customer level changes, the index rule A will change accordingly.
The "field" is all fields of the database hierarchy, such as the customer level field and the order amount field, which are mentioned above, respectively, a field with a customer level on the database customer table, and its value includes "general customer, important customer, etc., an order amount field is in the order table, and the values under the fields are all the amount of the actual order, such as 100.32 yuan, 2000.51 yuan, etc.
And the message generation module is used for acquiring the behavior generated at the bottom layer, generating a message containing behavior data, adding the message into the message queue, consuming the message queue in real time and further acquiring the change details of the behavior data.
The comparison module is used for comparing and judging the change details and the fields which have influence on the index rule to acquire the fields which are influenced by the change; index rules affected by the change are obtained. Some fields do not need to be changed, such as remark fields, so the comparison module obtains the index rule affected by the change
The increment calculation module is used for carrying out increment calculation on the index rule after acquiring the index rule influenced by the change; if the index field with the affected change is not acquired, incremental calculation is not carried out, the field information is not considered to be changed, and processing is not carried out.
The total calculation module is used for searching all indexes with initialized states from the database at fixed time every day and performing total calculation according to the index rules; the daily fixed time comprises that the current day total amount calculation is carried out after all increment calculation is finished every day or the previous day total amount calculation is carried out before the increment calculation is started every day, and the calculation result is updated to a database to be used as basic data of the next day increment calculation.
The updating module is used for updating the incremental calculation result to the affected index field in the database; updating the total calculation result to a corresponding index field in the database; and according to each index related to the increment calculation result or the full calculation result, carrying out slot addressing through the metadata table, finding a target slot position of the corresponding index in the storage table, and inserting the calculation result into the target slot position of the storage table to complete updating.
The data table generating module is used for generating a storage table with a multi-column wide table structure, and the storage table slot position is used for storing index calculation result data; generating a metadata table and storing the description information of the index rule; the storage table and the metadata table are associated by a foreign key.
The database is used for storing the metadata table and the storage table, and for example, an SQL database can be adopted.
The system is developed based on a PaaS architecture, and can analyze indexes of any object and any type by utilizing the advantages brought by decoupling of PaaS architecture metadata and service data.
One aspect of the present invention provides a statistical graph data real-time calculation method based on PaaS architecture, which, with reference to fig. 2, includes the following steps:
s100, defining an index rule to be analyzed by an object by a user, wherein the state of the index rule to be analyzed is initialization; and acquiring fields having influence on the index rule according to the index rule to be analyzed.
With reference to fig. 1, the user defines the index rule to be analyzed through the interface.
After the user clicks and saves, a piece of detailed information of the index is saved in the database, and a slot position is allocated to the result of the index calculation in advance.
The invention designs a database table structure storage table of a multi-column wide table for supporting index analysis of any object, the storage table has 500 columns, each column is a free column for storing a calculation result, and column names are named by the rules of value1, value2, … … and value 500. The storage table stores index result data, a metadata table which stores field meaning information of the table coexists with the table, and the metadata table stores mapping relations between the field meaning information and the slot positions.
For example, there is an index a for counting the amount of an order, and when the index is defined, we will allocate a slot to the index, for example, called "value 10" (these information are all in the metadata table), and the storage table has 500 fields, respectively, value0, value1, … …, and value 500. The meaning is that the result of the calculation of the a index is placed under the field storing table value 10. The metadata table and the storage table are combined when the insertion and the index query result are made. And each index related to the calculation result is subjected to slot addressing through the metadata table, a target slot position of the corresponding index in the storage table is found, and the calculation result is inserted into the target slot position of the storage table.
S200, when the bottom layer behavior occurs, generating a message of behavior data to be added into a message queue, consuming the message queue in real time and further acquiring the change details of the behavior data.
And the user generates behavior data through bottom layer operation, acquires the behavior data and generates a message.
The change detail of the user business data is that the order amount of an order form of a database is changed from a database layer, for example, a form update message is sent, and the calculation service receives the change message and updates all index rules related to the order amount. And sending the message to a message queue, and consuming the message queue by a real-time service to acquire the change details of the service data.
S300, comparing and judging the change details and the index rules with influence, selecting the index fields influenced by the change for incremental calculation, and updating the calculation result to a database.
And comparing and judging through the change details and the index rule with influence, and selecting the field influenced by the change for incremental calculation. If the change details do not relate to an affected index field, then they are discarded and no incremental calculation is performed.
And after the incremental calculation result is updated to the database, updating the statistical chart according to a set aggregation mode.
S400, all index rules with initialized states are searched out from the database at fixed time every day, index fields are extracted according to the index rules, then total calculation is carried out, and calculation results are updated to the database and serve as basic data of incremental calculation of the next day. The user defines only the index rule like "count the amount of orders for which the customer level is an important customer".
The fixed time per day includes the current day's full volume calculation after all incremental calculations are done daily or the previous day's full volume calculation before the incremental calculations are started daily. And ensuring that the data increment calculation of the current day or the previous day is completed and completing data collection.
The user defines the metrics to be analyzed, and the state of the metrics is initialized. The off-line service can search all indexes with initialization states from a database at night or in the early morning to carry out full calculation, the calculation result firstly carries out slot addressing through the main key of each index, the slot of the corresponding index is found, and the calculation result is inserted into the target slot. And after the full amount calculation is completed, obtaining an updated statistical chart according to the selected aggregation mode.
The total calculation provides a data basis for real-time calculation, for example, the order amount is counted, the total calculation means that the amount of all orders of the user is counted, for example, the result is 100 ten thousand yuan, the total calculation is performed only once, if the user inserts an order with the order amount of 50 ten thousand yuan at this time, data updating is performed through the real-time calculation, and the order amount of the user is updated to 100 ten thousand +50 ten thousand to 150 ten thousand. All fields of all objects forming the index rule are considered to be factors influencing the index calculation result, so that the factors influencing the index result are stored in the database after the full calculation is finished to provide a data base for the real-time service.
In conclusion, the invention carries out full calculation on the defined index rule through the off-line service and stores the factors influencing the index result in the database; the real-time service compares the detail of the user service data change with the factors influencing the index result, selects the field influenced by the service data change for incremental calculation, and updates the data in real time; full calculations were performed at fixed times daily to complete the analysis. The invention provides a PaaS-based architecture and a universal table design concept, a user can define the statistical index of any field of any object, and the method has good universality and flexible operation. The invention establishes an event-driven calculation model based on user data change, thereby reducing the calculation times and resource consumption. The updating time of the index data is in the minute level, and the user experience is optimized.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (10)
1. A statistical graph data real-time computing system based on a PaaS framework is characterized by comprising a configuration module, a field extraction module, a message generation module, a comparison module, an increment computing module, a full-scale computing module, an updating module and a database;
the configuration module is used for providing an operation interface, and a user defines an index rule to be analyzed by an object through the operation interface; the index state to be analyzed is an initialization state;
the field extraction module is used for acquiring fields influencing the index rule according to the index rule;
the message generation module is used for acquiring behaviors generated at the bottom layer, generating messages containing behavior data, adding the messages into a message queue, consuming the message queue in real time and further acquiring change details of the behavior data;
the comparison module is used for comparing and judging the change details and the fields which have influence on the index rule to acquire the fields which are influenced by the change; acquiring an index rule influenced by the change;
the increment calculation module is used for carrying out increment calculation on the influenced index rule after the influenced index rule is changed;
the total calculation module is used for searching all index rules with the states as the initialization states from the database at fixed time every day and performing total calculation according to the index rules;
the updating module is used for updating the incremental calculation result to the field with the influence in the database; and updating the total calculation result to a corresponding field in the database to be used as basic data for incremental calculation of the next day.
2. The system according to claim 1, wherein the configuration module is further configured to define a data aggregation manner.
3. The statistical graph data real-time computing system based on the PaaS architecture as claimed in claim 1 or 2, further comprising a data table generating module for generating a plurality of rows of wide table structured storage tables, wherein the storage table slots are used for storing index computation result data; generating a metadata table and storing the description information of the index rule; the metadata table and the multi-column-width table are associated according to the external key of the database.
4. The system according to claim 3, wherein the update module performs slot addressing through the metadata table according to each index related to an incremental calculation result or a full calculation result, finds a target slot of a corresponding index in the storage table, and completes updating after inserting the calculation result into the target slot of the storage table.
5. The system for calculating the statistical graph data in real time based on the PaaS framework as claimed in claim 1 or 2, wherein the current total daily calculation is performed after all incremental calculations are completed at a fixed time per day or the previous total daily calculation is performed before the incremental calculations are started at the fixed time per day.
6. A statistical graph data real-time calculation method based on a PaaS framework is characterized by comprising the following steps:
defining an index rule to be analyzed by an object by a user, wherein the index rule to be analyzed is in an initialization state; acquiring fields influencing the index rules according to the index rules to be analyzed;
when bottom-layer behaviors occur, generating messages of behavior data, adding the messages into a message queue, consuming the message queue in real time and further acquiring change details of the behavior data;
comparing and judging the change details and the fields with influence, selecting the fields affected by the change for incremental calculation, and updating the calculation result to a database;
and searching all index rules with initialized states from the database at fixed time every day, extracting fields according to the index rules, performing full calculation, and updating the calculation results to the database to serve as basic data of next day increment calculation.
7. The method for calculating the statistical graph data in real time based on the PaaS framework according to claim 6, further comprising a user-defined data aggregation mode.
8. The method for calculating the statistical graph data in real time based on the PaaS framework as claimed in claim 6 or 7, wherein the database comprises:
a storage table with a multi-column wide table structure is adopted, and the storage table slot position is used for storing index calculation result data;
a metadata table storing description information of the index field; the metadata table and the multi-column-width table are associated according to the external key of the database.
9. The method of claim 8, wherein the updating the calculation result to the database comprises: and each index related to the calculation result is subjected to slot addressing through the metadata table, a target slot position of the corresponding index in the storage table is found, and the calculation result is inserted into the target slot position of the storage table.
10. The method for calculating the statistical graph data in real time based on the PaaS framework as claimed in claim 6 or 7, wherein the fixed time per day includes performing the current total daily calculation after completing all incremental calculations per day or performing the previous total daily calculation before starting the incremental calculations per day.
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