CN112115121B - Real-time data quality detection system for data management - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/14—Error detection or correction of the data by redundancy in operation
- G06F11/1402—Saving, restoring, recovering or retrying
- G06F11/1446—Point-in-time backing up or restoration of persistent data
- G06F11/1458—Management of the backup or restore process
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses a real-time data quality detection system for data management, which comprises an HBASE database, a configuration module, a Spark detection module, a statistics module and a storage module, wherein the HBASE database is used for initializing a data incremental backup table, the configuration module configures the detection task, inputs a task name, a table to be detected, an association rule and a task execution priority, the Spark detection module receives the detection task configured by the configuration module and detects the detection task, and the statistics module uses an accumulator mode to automatically count the detection result of data meeting the detection rule. The invention can process PB level data, has huge detection data quantity, adopts a cluster detection mode, has higher response speed, flexibly supports single field pair rule and single rule multi-detection by the rule, can also support user-defined detection by the built-in multiple detection rules, can continuously and automatically detect incremental data of configured tasks, and can provide a perfect data quality detection report.
Description
Technical Field
The invention relates to the field of data management, in particular to a real-time data quality detection system for data management.
Background
Data governance is a complete set of administrative actions in an organization involving the use of data, initiated and pursued by the enterprise data governance department, regarding how to formulate and implement a series of policies and procedures for business application and technical management of data throughout the enterprise, the definition given by the international data management association: data governance is an active set of data asset management exercise rights and controls, and real-time data quality detection is part of data governance, which is used to screen for useful data.
The existing data governance real-time data quality detection system has limited detection capability, small data volume which can be detected when aiming at PB level data detection, general response speed, single detection specification and incapability of continuously and automatically detecting incremental data of configured tasks, so that how to construct a novel data governance real-time data quality detection system is a problem to be solved urgently.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the existing real-time data quality detection system for data management has the defects that the detection capability is limited, the data quantity which can be detected when aiming at PB level data detection is small, the response speed is general, the detection specification is single, and the incremental data of the configured task cannot be continuously and automatically detected; the invention adopts the HBASE database to create the data incremental backup table, is matched with MYSQL, can be used for processing PB-level data, has huge detection data, meets the detection requirements of different real-time data, configures the detection tasks by adopting the configuration module, adopts the incremental backup table to detect the input task name, the table to be detected, the association rule and the priority of task execution of each detection task, and detects according to the priority of the task to be detected, improves the response speed of data detection by utilizing a cluster detection mode, can select the same field to be detected in the same task for multiple times by utilizing the association rule, can select the same rule in the same task for multiple times, flexibly supports single-field pair rule and single-rule multiple detection, embeds multiple detection rules, can also support user-defined detection, can continuously and automatically detect the incremental data of the configured tasks, and can provide a perfect data quality detection report.
The technical problems are solved by the following technical scheme, and the real-time data quality detection system for data management comprises an HBASE database, a configuration module, a Spark detection module, a statistics module and a storage module;
the HBASE database is used for initializing a data incremental backup table;
the configuration module configures the detection task, and inputs the task name, the table to be detected, the association rule and the priority of task execution;
the Spark detection module receives the detection task configured by the configuration module and detects the detection task;
the statistics module is used for automatically counting the detection results of the data meeting the detection rules in an accumulator mode, and updating the data state of the same batch of backup tables as in the process;
the storage module is used for storing the detection result counted by the counting module.
Preferably, the incremental backup table is used for storing one copy of the RowKey, the data status, the batch number batch id, the original table name, the original table RowKey/oldwkey of the incremental data for each detection task in the backup table, wherein:
the RowKey generation rule in the incremental backup table is a splicing mode of an original table RowKey, an original table name and a batch number;
the data states of the column status in the incremental backup table are divided into pending, processing neutral, and processed.
The list batch id in the incremental backup table is the batch number of the incremental data, and the batch number ensures that the same batch is the same and different batches are unique.
The incremental backup table is listed as the name of the original table, and the column oldRowKey is listed as the RowKey of the original table.
Preferably, the priority of task execution is marked as X 1 、X 2 ....N n Wherein X is 1 At an optimal level of X 1 And sequencing the optimal grades backwards in sequence.
Preferably, the configuration module includes a scheduling unit, the scheduling unit is configured to schedule the detection task according to the priority of task execution configured by the scheduling configuration module, and the configuration unit schedules the detection task to the Spark detection module according to the priority of task execution.
Preferably, after the configuration module configures the detection task, the detection task ends and stores the detection result in a report table in MYSQL, so that a user can check the detection report through the system and update the data state of the same batch of backup table to be processed.
Preferably, the scheduling unit starts the detection task to scan the backup table at regular time, detects the newly added data and gives an incremental detection report, and the incremental detection report is stored in a MYSQL report table, where the detection report includes: quality assessment score, detection data volume, problem data duty ratio, reference rule number, problem level, problem distribution condition and data quality rule statistics.
Preferably, the association rule includes: the detection indexes comprise fields, detection levels, rule types and rule names, and the association relation between the fields to be detected and the detection rules can be selected multiple times in the same task, and the same rule can be selected multiple times in the same task.
The quality evaluation score is the percentage of the sum of the problem data quantity and the sum of the detection data quantity in each association rule detected in the task;
detecting data quantity, namely detecting the data quantity of each association rule;
the problem data volume is that each association rule detects the data volume conforming to the rule;
the problem data duty ratio is that the data quantity which is detected to be in accordance with the rule by each association rule is divided by the data quantity percentage which is detected to be in accordance with the rule by each association rule;
the number of the quoting rules is that how many detecting rules are commonly quoted by the detecting task;
problem level, namely setting the problem level when a user sets a detection task;
the distribution of the problems, namely the respective percentages of urgent, important, secondary and general tasks;
and (5) counting data quality rules, namely counting the percentage of each detection rule.
Preferably, the system, when executed, specifically comprises the following steps:
s1: the HBASE system initializes a data incremental backup table, a user configures a quality detection task through a system interface configuration module, and a specified column in a specified table is detected by using a specified rule after the detection task is started;
s2: after the detection task is finished, storing the detection report in a designated MYSQL library;
s3: the new data of the detection target table stores the new RowKey in a data incremental backup table of the HBASE, a dispatching unit detection task acquires the RowKey of the new data of the batch from the backup table, and then the batch of RowKey is used for acquiring the complete data of the batch from the detection target table for detection;
s4: modifying the data state of the batch of backup tables with the end of detection in the detection completion modification process, and storing the detection report of the batch in MYSQL
Compared with the prior art, the invention has the following advantages: the real-time data quality detection system for data management adopts an HBASE database to create a data incremental backup table, is matched with MYSQL, can be used for processing PB-level data, has huge detection data, meets the detection requirements of different real-time data, configures detection tasks by adopting a configuration module, adopts an incremental backup table to detect the input task name, a table to be detected, an association rule and the priority of task execution of each detection task, improves the response speed of data detection by utilizing a cluster detection mode, utilizes the association rule, can select the same field to be detected in the same task for multiple times, can flexibly support single-field pair rule and single-rule multiple detection, can also support user-defined detection, can continuously and automatically detect the incremental data of the configured tasks, and can provide perfect data quality detection reports.
Drawings
Fig. 1 is a system block diagram of the present invention.
Fig. 2 is a flow chart of the present invention.
Detailed Description
The following describes in detail the examples of the present invention, which are implemented on the premise of the technical solution of the present invention, and detailed embodiments and specific operation procedures are given, but the scope of protection of the present invention is not limited to the following examples.
As shown in fig. 1-2, the present embodiment provides a technical solution: the real-time data quality detection system for data management comprises an HBASE database, a configuration module, a Spark detection module, a statistics module and a storage module;
the HBASE database is used for initializing the data incremental backup table;
the configuration module configures the detection task, and inputs the task name, the table to be detected, the association rule and the priority of task execution;
the Spark detection module receives the detection task configured by the configuration module and detects the detection task;
the statistics module is used for automatically counting the detection results of the data meeting the detection rules in an accumulator mode, and updating the data state of the same batch of backup tables in the process;
the storage module is used for storing the detection result counted by the counting module.
The incremental backup table is used for storing one copy of the RowKey, the data status, the batch number batch id, the original table name, the original table RowKey/oldwkey of the incremental data for each detection task in the backup table, wherein:
the RowKey generation rule in the incremental backup table is a splicing mode of an original table RowKey, an original table name and a batch number;
the data states of the column status in the incremental backup table are divided into pending, processing neutral, and processed.
The list batch id in the incremental backup table is the batch number of the incremental data, and the batch number ensures that the same batch is the same and different batches are unique.
The incremental backup table is listed as the name of the original table, and the column oldRowKey is listed as the RowKey of the original table.
Task execution priority is marked as X 1 、X 2 ....N n Wherein X is 1 At an optimal level of X 1 And sequencing the optimal grades backwards in sequence.
The configuration module comprises a scheduling unit, the scheduling unit is used for scheduling the detection task according to the priority of task execution configured by the scheduling configuration module, and the configuration unit schedules the detection task to the Spark detection module according to the priority of task execution.
After the configuration module configures the detection task, the detection task is finished, the detection result is stored in a MYSQL report table, so that a user can check the detection report through the system, and the data state of the same batch of backup tables is updated to be processed.
The scheduling unit starts a detection task to scan the backup table at regular time, detects the newly added data and gives an incremental detection report, and the incremental detection report is stored in a MYSQL report table, wherein the detection report comprises: quality assessment score, detection data volume, problem data duty ratio, reference rule number, problem level, problem distribution condition and data quality rule statistics.
The association rule includes: the detection indexes comprise fields, detection levels, rule types and rule names, and the association relation between the fields to be detected and the detection rules can be selected multiple times in the same task, and the same rule can be selected multiple times in the same task.
The quality evaluation score is the percentage of the sum of the problem data quantity and the sum of the detection data quantity in each association rule detected in the task;
detecting data quantity, namely detecting the data quantity of each association rule;
the problem data volume is that each association rule detects the data volume conforming to the rule;
the problem data duty ratio is that the data quantity which is detected to be in accordance with the rule by each association rule is divided by the data quantity percentage which is detected to be in accordance with the rule by each association rule;
the number of the quoting rules is that how many detecting rules are commonly quoted by the detecting task;
problem level, namely setting the problem level when a user sets a detection task;
the distribution of the problems, namely the respective percentages of urgent, important, secondary and general tasks;
and (5) counting data quality rules, namely counting the percentage of each detection rule.
In summary, when the invention is used, the HBASE system initializes a data incremental backup table, a user configures a quality detection task through a system interface, a specified column in the specified table is detected by using a specified rule after the detection task is started, a detection report is stored in a specified MYSQL library after the detection task is finished, MYSQL can be used for periodically inquiring the quality detection task to be executed, newly-added data of a detection target table is stored in the data incremental backup table of HBASE, a dispatching unit detection task acquires the new-added data RowKey of the batch from the backup table, then the whole data of the batch is acquired in the detection target table by using the RowKey of the batch for detection, the detection result is compared with large data clusters YARN and Spark, the data state of the batch is modified with the detection completion in the modification process, the data to be detected is read, the data to be detected is stored in the HBASE database, finally the quality detection report is generated, the detection report of the batch is stored in MYSQL, visual quality detection report is provided by MYSQL, and the detection task is finished.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (6)
1. The real-time data quality detection system for data management is characterized by comprising an HBASE database, a configuration module, a Spark detection module, a statistics module and a storage module;
the HBASE database is used for initializing a data incremental backup table;
the configuration module configures the detection task, and inputs the task name, the table to be detected, the association rule and the priority of task execution;
the Spark detection module receives the detection task configured by the configuration module and detects the detection task;
the statistics module is used for automatically counting the detection results of the data meeting the detection rules in an accumulator mode, and updating the data state of the same batch of backup tables as in the process;
the storage module is used for storing the detection result counted by the counting module;
the incremental backup table is used for storing one copy of the RowKey, the data state status, the batch number batch Id, the original table name and the original table RowKey of the incremental data of each detection task in the backup table, wherein:
the RowKey generation rule in the incremental backup table is a splicing mode of an original table RowKey, an original table name and a batch number;
the data state of the column status in the incremental backup table is divided into to-be-processed, processed-neutralization processed;
the batch id in the incremental backup table is the batch number of the incremental data, and the batch number ensures that the same batch is the same and different batches are unique;
the column tableName in the incremental backup table is the name of the original table, and the column oldRowKey is the RowKey of the original table;
the system comprises the following steps when being executed:
s1: the HBASE system initializes a data incremental backup table, a user configures a quality detection task through a system interface configuration module, and a specified column in a specified table is detected by using a specified rule after the detection task is started;
s2: after the detection task is finished, storing the detection report in a designated MYSQL library;
s3: the new data of the detection target table stores the new RowKey in a data incremental backup table of the HBASE, a dispatching unit detection task acquires the RowKey of the new data of the batch from the backup table, and then the batch of RowKey is used for acquiring the complete data of the batch from the detection target table for detection;
s4: and modifying the data state of the batch of backup tables with the end of detection in the detection completion modification process, and storing the detection report of the batch in MYSQL.
2. The data governance real-time data quality detection system of claim 1, wherein: the priority of task execution is marked as X 1 、X 2 ....X n Wherein X is 1 At an optimal level of X 1 And sequencing the optimal grades backwards in sequence.
3. The data governance real-time data quality detection system of claim 1, wherein: the configuration module comprises a scheduling unit, the scheduling unit is used for scheduling the detection task according to the priority of task execution configured by the scheduling configuration module, and the configuration unit schedules the detection task to the Spark detection module according to the priority of task execution.
4. The data governance real-time data quality detection system of claim 1, wherein: after the configuration module configures the detection task, the detection task is finished, the detection result is stored in a report table in MYSQL, so that a user can check the detection report through the system, and the data state of the same batch of backup tables is updated to be processed.
5. A data governance real-time data quality testing system according to claim 3 and wherein: the scheduling unit starts a detection task scanning backup table at fixed time, detects newly added data and gives an incremental detection report, and the incremental detection report is stored in a MYSQL report table, wherein the detection report comprises: quality assessment score, detection data volume, problem data duty ratio, reference rule number, problem level, problem distribution condition and data quality rule statistics.
6. The data governance real-time data quality detection system of claim 1, wherein: the association rule includes: the detection indexes comprise fields, detection levels, rule types and rule names, and the association relation between the fields to be detected and the detection rules can be selected multiple times in the same task, and the same rule can be selected multiple times in the same task.
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