Mass data real-time query method and device, electronic equipment and storage medium

Abstract

The invention relates to a mass data real-time query method and device, electronic equipment and a storage medium. The mass data real-time query method comprises the following steps: establishing a data service platform for providing data query service and receiving and processing data; data service interface related contents are configured, configuration management is performed on data services, a pre-partitioning condition is obtained through a salting-out transcoding mode, and interface table information, interface table pre-partitioning and interface table permission are set; automatically creating an interface table according to data service configuration, inserting permission data into a permission table, and automatically refreshing permission cache; real-time data writing: inserting data in real time and writing the data into a data interface table by adopting a stream-batch integrated mode, and providing efficient query service of mass data; and monitoring operation, monitoring the abnormal condition of real-time written data by operation and maintenance, and automatically giving an email alarm. According to the mass data real-time query method, the data can be efficiently and quickly obtained, and mass data query can be automatically completed in real time.

Description

Technical Field

[0001] The present invention relates to the field of data service technology, and in particular to a method, device, electronic device and storage medium for real-time query of massive data based on configurable automation. Background Art

[0002] Traditional data query services are based on relational databases and provide queries through relational databases. They cannot support accurate millisecond queries on massive amounts of data. This method cannot meet the query timeliness when facing massive amounts of data.

[0003] At the same time, traditional data query services require manual configuration of data service authorization, requiring manual updates of authorization information through handwritten code, without a visual interface. Traditional NoSQL databases are manually created through code commands, using default configurations for trial use, which impacts data query efficiency. Currently, data query services are provided using manual, batch, and default configuration methods, which have the following drawbacks: Data is processed in batches, resulting in latency and the inability to query current data. Data is initialized using default configurations and is not pre-partitioned based on the actual query, resulting in low query efficiency. New queries are traditionally created through handwritten code, which is time-consuming, inefficient, and complex. Current relational database updates require manual processing, resulting in low efficiency. Operational monitoring requires significant manpower and is both time-consuming and labor-intensive. Furthermore, data services rely on manual command execution, and authorization is also achieved through code execution, resulting in low development agility.

[0004] Therefore, there is an urgent need to develop a method for real-time query of massive data that can automatically complete massive data query in real time. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that manually configured data query services cannot meet the query timeliness requirements and are inefficient.

[0006] To solve the above technical problems, according to one aspect of the present invention, a method for real-time query of massive data is provided, which includes the following steps: establishing a data service platform, which is established in a data center or cloud platform of an enterprise or organization to provide data query services, receive and process data; configuring data service interface-related content, configuring and managing the data service, setting interface table information, including pre-partitioning the interface table, and setting interface table permissions, wherein historical data of the existing first primary key is obtained through salting and transcoding (salting is a method of adding salt, generally referred to as salting or scattering), and is imported into a pre-partition configuration file based on the distribution of the first primary key data to obtain the pre-partition information; automatically creating an interface table based on the data service configuration, inserting permission data into the permission table, and automatically refreshing the permission cache; real-time data writing, using a unified stream and batch processing (unified stream and batch processing, which combines the advantages of stream processing and batch processing in a unified framework for data processing) method, inserting data in real time into the data interface table, and then obtaining data, thereby providing efficient query services for massive data; monitoring operations, and monitoring abnormalities in real-time data writing, and automatically sending email alerts.

[0007] According to an embodiment of the present invention, establishing a data service platform may include: data reception and processing, the data service center is used to receive data information including transaction data details, sensor data, and flow data; the data service center receives data from batch and real-time sources, pre-processes historical data, and improves query efficiency, wherein the data service platform is a platform for data query services, which is established on the cloud platform of an enterprise or organization; the data service center is an interface that provides data query and can realize fast point-checking of big data.

[0008] According to an embodiment of the present invention, when configuring the relevant content of the data service interface, the automatic configuration of the data service and the creation and authorization of the interface table are performed, which may include the following steps: reading the configuration file, including data service authorization information and data service interface information; salting out the historical data of the existing first primary key, and importing it into the pre-partition configuration file according to the distribution of the first primary key data to obtain the pre-partition situation; importing the pre-partition situation into the interface table configuration file to be created, and filling in the permissions to be configured according to the specifications; automatic data service generation, through Hbase (a distributed, column-oriented open source database) script program writing, generating table creation statements and completing table creation; after the table creation is completed, automatically inserting authorization information into the authorization table of the relational database through the permission code in the configuration file; generating a log to record the status, time, and operation status of data consumption.

[0009] According to an embodiment of the present invention, when an automated data service is generated, it is first possible to determine whether the configuration file of the interface table file meets the requirements, then read the configuration file, parse the fields and primary key information in the configuration file, place this information in the object, and then generate an Hbase table creation statement to complete the table creation; then confirm whether the metadata exists in the relational database. If so, delete the original metadata and re-insert the new metadata statement in the relational database. If not, directly insert the metadata.

[0010] According to an embodiment of the present invention, real-time data writing provides query services for real-time data insertion, which may include the following steps: data collection, pushing the collected data to a message queue; data query, writing a script program through Spark, writing data into a data service for query; monitoring abnormal situations in data processing and handling them.

[0011] According to an embodiment of the present invention, when a script program is written through Spark, the micro-batch time of the Spark configuration file is first read. Every time a configured time passes, the offset recorded by ZK (ZooKeeper) is read, and the data in Kafka (open source technology, used for big data processing and real-time data processing) is obtained according to the offset, thereby realizing data writing to the data service for query.

[0012] According to an embodiment of the present invention, the monitoring job is to automatically monitor data, which may include the following steps: monitoring data stream processing jobs, monitoring data stream processing status, whether there is data backlog, and sending an alarm by email if data backlog occurs; monitoring cluster resources, including monitoring data service storage capacity, usage rate, network, I / O, disk indicators, and failure conditions including media hardware failure and network failure; automatically handling abnormal conditions, when monitoring abnormal job failure, it can be automatically handled at intervals of five minutes to ensure data accuracy and reliability; generating reports, generating reports on the data service provision process, including the average response time of interface table queries, abnormal conditions and usage information, so as to evaluate and optimize the query performance of data services.

[0013] According to a second aspect of the present invention, a device for real-time query of massive data is provided, which includes: establishing a data service platform module for establishing a data service platform in a data center or cloud platform of an enterprise or organization, providing data query services, and receiving and processing data; configuring a data service interface related content module for configuring and managing data services, setting interface table information, including interface table pre-partitioning, and setting interface table permissions, wherein historical data of the existing first primary key is obtained by salting out and transcoding, and is imported into a pre-partition configuration file according to the distribution of the first primary key data to obtain the pre-partition situation; according to the data service configuration, the interface table is automatically created, the permission data is inserted into the permission table, and the permission cache is automatically refreshed; a real-time data writing module adopts a stream-batch integrated method for inserting data in real time, writing it into the data interface table, and then obtaining It retrieves data and provides efficient query services for massive data; the monitoring job module is used for operation and maintenance monitoring of abnormal situations in real-time data writing and automatic email alerts. Among them, the data service interface-related content modules are configured to automatically configure the data service and create and authorize the interface table. Script programs are written through Hbase to generate table creation statements and complete table creation to achieve automatic data service generation. Among them, when the automatic data service is generated, it is first determined whether the configuration file of the interface table file meets the requirements, and then the configuration file is read, the fields and primary key information in the configuration file are parsed, and this information is placed in the object, and then the Hbase table creation statement is generated to complete the table creation; then, it is confirmed in the relational database whether the metadata exists. If it exists, the original metadata is deleted and the new metadata statement is re-inserted into the relational database. If it does not exist, the metadata is directly inserted.

[0014] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory, a processor, and a massive data real-time query program stored in the memory and executable on the processor, wherein the massive data real-time query program implements the steps of the above-mentioned massive data real-time query method when executed by the processor.

[0015] According to a fourth aspect of the present invention, a computer storage medium is provided, wherein a massive data real-time query program is stored on the computer storage medium, and when the massive data real-time query program is executed by a processor, the steps of the above-mentioned massive data real-time query method are implemented.

[0016] Compared with the prior art, the technical solution provided by the embodiments of the present invention can achieve at least the following beneficial effects:

[0017] The real-time query method for massive data of the present invention realizes high availability of data query and ensures real-time query of data through automation, configuration and pre-processing combined with a stream-batch integrated data writing method, which has high technical advantages and commercial value.

[0018] According to the real-time query method for massive data of the present invention, automatic data preprocessing can improve efficiency. By using pre-partitioning technology plus salt-xi transcoding, data is pre-partitioned according to the distribution of the first primary key, and historical data is pre-processed and pre-imported into designated partitions according to the partitions, thereby improving query efficiency and solving the problem of low efficiency of inserted data query caused by inserting data using default configuration. By configuring relevant configuration information in the configuration file, creating an interface table and authorizing the interface table in an automated manner, millisecond-level query response for massive data is achieved.

[0019] The method and device for real-time query of massive data of the present invention adopts a stream-batch integrated approach to acquire data in real time, and writes data in real time, so that data can be acquired efficiently and quickly.

[0020] The method and device for real-time querying of massive amounts of data utilizes automated configuration of interface services, automatically creates interfaces, authorizes interface tables, and automatically updates interface permissions, ensuring the stability, reliability, and security of the query interface. Automated operation and maintenance, through job configuration, enables automated restart of jobs, reducing operation and maintenance workload.

[0021] The method and device for real-time query of massive data of the present invention are provided with a visual interface, and are configured in a visual manner, which is convenient and quick. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly introduced below. Obviously, the drawings in the following description only relate to some embodiments of the present invention, but are not intended to limit the present invention.

[0023] Figure 1 is a flow chart showing a method for real-time query of massive data according to an embodiment of the present invention;

[0024] Figure 2 FIG. 4 is a flowchart illustrating automatic data service generation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by persons of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in the present patent application specification and claims do not denote any order, quantity, or importance, but are merely used to distinguish different components. Similarly, terms such as "a" or "an" do not denote a limitation of quantity, but rather denote the presence of at least one.

[0027] Figure 1 The flowchart of the method for real-time query of massive data according to an embodiment of the present invention is shown.

[0028] like Figure 1 As shown, the method for real-time query of massive data includes the following steps:

[0029] Establish a data service platform in the data center or cloud platform of an enterprise or organization to provide data query services, receive and process data.

[0030] Configure data service interface-related content, perform configuration management on data services, set interface table information, including interface table pre-partitioning, and set interface table permissions. Obtain historical data of the existing first primary key through salting-out transcoding (salting-out is a way of adding salt, generally called adding salt and breaking up). Based on the distribution of the first primary key data, import it into the pre-partitioning configuration file to obtain the pre-partitioning status. Automatically create the interface table based on the data service configuration, insert the permission data into the permission table, and automatically refresh the permission cache.

[0031] Real-time data writing uses a stream-batch integrated approach to insert data in real time, write it into the data interface table, and then obtain the data, providing efficient query services for massive data.

[0032] Monitor jobs and operations, monitor abnormalities in real-time data writing, and send automated email alerts.

[0033] The real-time query method for massive data of the present invention realizes high availability of data query and ensures real-time query of data through automation, configuration and pre-processing combined with a stream-batch integrated data writing method, which has high technical advantages and commercial value.

[0034] According to the real-time query method for massive data of the present invention, automatic data preprocessing can improve efficiency. By using pre-partitioning technology plus salt-xi transcoding, data is pre-partitioned according to the distribution of the first primary key, and historical data is pre-processed and pre-imported into designated partitions according to the partitions, thereby improving query efficiency and solving the problem of low efficiency of inserted data query caused by inserting data using default configuration. By configuring relevant configuration information in the configuration file, creating an interface table and authorizing the interface table in an automated manner, millisecond-level query response for massive data is achieved.

[0035] According to one or some embodiments of the present invention, establishing a data service platform includes: data reception and processing, the data service center is used to receive data information including transaction data details, sensor data, and flow data; the data service center receives data from batch and real-time sources, pre-processes historical data, and improves query efficiency.

[0036] Among them, the data service platform is a platform for data query services, which is built on the cloud platform of the enterprise or organization; the data service center is an interface that provides data query and can realize rapid point-to-point query of big data.

[0037] According to one or some embodiments of the present invention, when configuring data service interface related content, performing automatic configuration of data services and creating and authorizing interface tables includes the following steps:

[0038] Read the configuration file, including data service authorization information and data service interface information.

[0039] Salting transcoding obtains the historical data of the existing first primary key, imports it into the pre-partitioning configuration file based on the distribution of the first primary key data, and obtains the pre-partitioning situation.

[0040] Import the pre-partitioning information into the interface table configuration file to be created, and fill in the permissions to be configured according to the specifications.

[0041] Automatically generate data services, write scripts through Hbase, generate table creation statements and complete table creation.

[0042] After the table is created, the authorization information is automatically inserted into the authorization table of the relational database through the permission code in the configuration file.

[0043] Generate logs to record the status, time, and operation of data consumption.

[0044] Figure 2 FIG. 4 is a flowchart illustrating automatic data service generation according to an embodiment of the present invention.

[0045] like Figure 2As shown in the figure, when the automatic data service is generated, it first determines whether the configuration file of the interface table file meets the requirements, then reads the configuration file, parses the fields and primary key information in the configuration file, puts this information in the object, and then generates the HBase table creation statement to complete the table creation; then confirms whether the metadata exists in the relational database. If it exists, delete the original metadata and re-insert the new metadata statement in the relational database. If it does not exist, directly insert the metadata.

[0046] According to one or some embodiments of the present invention, real-time data writing provides query services for real-time data insertion, including the following steps:

[0047] Data collection, pushing the collected data to the message queue.

[0048] Data queries are performed by writing scripts in Spark and writing data to the data service for querying. When writing scripts in Spark, the micro-batch time in the Spark configuration file is read. After each configured time, the offset recorded in ZK (ZooKeeper) is read. Based on the offset, data is retrieved from Kafka (an open source technology used for big data and real-time data processing). The data is then written to the data service for querying.

[0049] Monitor abnormal situations in data processing and handle them.

[0050] According to one or some embodiments of the present invention, the monitoring operation is to automatically monitor data, including the following steps:

[0051] Monitor data stream processing jobs, monitor data stream processing status, and check whether there is data backlog. If data backlog occurs, send an alert via email.

[0052] Monitor cluster resources, including data service storage capacity, usage, network, I / O, and disk metrics, as well as failure conditions including media hardware failures and network failures.

[0053] Automatically handle abnormal situations. When monitoring detects abnormal operation failure, it can automatically handle it every five minutes to ensure the accuracy and reliability of the data.

[0054] Generate reports on the data service provision process, including average response time, exceptions, and usage information for interface table queries, so as to evaluate and optimize the query performance of data services.

[0055] The method for real-time querying of massive data in this invention uses automated configuration of interface services, automatically creates interfaces, authorizes interface tables, and automatically updates interface permissions, ensuring the stability, reliability, and security of the query interface. Automated operation and maintenance, through job configuration, can automatically restart jobs, reducing operation and maintenance workload.

[0056] The real-time query method for massive data of the present invention can set a visual interface and perform configuration in a visual manner, which is convenient and fast.

[0057] The data service platform module is used to establish a data service platform in the data center or cloud platform of an enterprise or organization to provide data query services, receive and process data.

[0058] The module for configuring data service interface-related content is used to configure and manage data services, set interface table information, including interface table pre-partitioning, and set interface table permissions. The historical data of the existing first primary key is obtained through salting-out transcoding, and is imported into the pre-partitioning configuration file based on the distribution of the first primary key data to obtain the pre-partitioning situation. According to the data service configuration, the interface table is automatically created, the permission data is inserted into the permission table, and the permission cache is automatically refreshed.

[0059] The real-time data writing module adopts a stream-batch integrated approach to insert data in real time, write it into the data interface table, and then obtain the data to provide efficient query services for massive data.

[0060] The monitoring module is used for operation and maintenance monitoring of abnormal situations in real-time data writing and automatic email alerts.

[0061] Among them, the data service interface related content module is configured to automatically configure the data service and create and authorize the interface table. Script programs are written through Hbase to generate table creation statements and complete table creation to achieve automated data service generation.

[0062] Among them, when the automated data service is generated, it first determines whether the configuration file of the interface table file meets the requirements, then reads the configuration file, parses the fields and primary key information in the configuration file, puts this information in the object, and then generates the Hbase table creation statement to complete the table creation; then confirms whether the metadata exists in the relational database. If it exists, delete the original metadata and re-insert the new metadata statement in the relational database. If it does not exist, directly insert the metadata.

[0063] The method and device for real-time query of massive data of the present invention adopts a stream-batch integrated approach to acquire data in real time, and writes data in real time, so that data can be acquired efficiently and quickly.

[0064] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory, a processor, and a massive data real-time query program stored in the memory and executable on the processor, wherein the massive data real-time query program implements the steps of the above-mentioned massive data real-time query method when executed by the processor.

[0065] According to a fourth aspect of the present invention, a computer storage medium is provided, wherein a massive data real-time query program is stored on the computer storage medium, and when the massive data real-time query program is executed by a processor, the steps of the above-mentioned massive data real-time query method are implemented.

[0066] According to another aspect of the present invention, a device for real-time query of massive data is provided, comprising: a memory, a processor, and a real-time query program for massive data stored in the memory and executable on the processor, wherein the real-time query program for massive data implements the steps of the above-mentioned real-time query method for massive data when executed by the processor.

[0067] According to the present invention, a computer storage medium is also provided.

[0068] A massive data real-time query program is stored on the computer storage medium. When the massive data real-time query program is executed by the processor, the steps of the massive data real-time query method are implemented.

[0069] The method implemented when the massive data real-time query program running on the processor is executed can refer to the various embodiments of the massive data real-time query method of the present invention, and will not be described in detail here.

[0070] The present invention also provides a computer program product.

[0071] The computer program product of the present invention includes a massive data real-time query program, which implements the steps of the massive data real-time query method described above when executed by a processor.

[0072] The method implemented when the massive data real-time query program running on the processor is executed can refer to the various embodiments of the massive data real-time query method of the present invention, and will not be described in detail here.

[0073] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0074] The above description is merely an exemplary embodiment of the present invention and is not intended to limit the scope of protection of the present invention. The scope of protection of the present invention is determined by the appended claims.

Claims

1. A method for real-time query of massive data, comprising the following steps: Establish a data service platform in the enterprise or organization's data center or cloud platform to provide data query services, receive and process data; Configure data service interface-related content, manage data service configuration, set interface table information, including pre-partitioning the interface table and setting interface table permissions. This involves obtaining historical data of the existing first primary key through salting and transcoding. Based on the distribution of the first primary key data, this data is imported into the pre-partition configuration file to obtain the pre-partition information. Based on the data service configuration, the interface table is automatically created, permission data is inserted into the permission table, and the permission cache is automatically refreshed. Real-time data writing uses a stream-batch integration approach to insert data in real time, write it into the data interface table, and then retrieve the data, providing efficient query services for massive data; Monitor jobs and operations, monitor abnormalities in real-time data writing, and send automated email alerts.

2. The method for real-time query of massive data according to claim 1, wherein: Establishing a data service platform includes: Data reception and processing: The data service center is used to receive data information including transaction data details, sensor data, and flow data; the data service center receives data from batches and real-time, pre-processes historical data, and improves query efficiency. Among them, the data service platform is a platform for data query services, which is built on the cloud platform of the enterprise or organization; the data service center is an interface that provides data query and can realize rapid point-to-point query of big data.

3. The method for real-time query of massive data according to claim 1, wherein: When configuring data service interface related content, perform automatic configuration of data service and interface table creation and authorization, including the following steps: Read the configuration file, including data service authorization information and data service interface information; Salting transcoding obtains historical data of the existing first primary key and imports it into the pre-partition configuration file based on the first primary key data distribution to obtain the pre-partition status. Import the pre-partitioning information into the interface table configuration file to be created, and fill in the permissions to be configured according to the specifications; Automatic data service generation, script writing through Hbase, generating table creation statements and completing table creation; After the table is created, the authorization information is automatically inserted into the authorization table of the relational database using the permission code in the configuration file. Generate logs to record the status, time, and operation of data consumption.

4. The method for real-time query of massive data according to claim 3, wherein: When the automated data service is generated, it first determines whether the configuration file of the interface table file meets the requirements. Then it reads the configuration file, parses the fields and primary key information in the configuration file, places this information in the object, and then generates the HBase table creation statement to complete the table creation. Then confirm whether the metadata exists in the relational database. If it exists, delete the original metadata and re-insert the new metadata statement into the relational database. If it does not exist, directly insert the metadata.

5. The method for real-time query of massive data according to claim 1, wherein: Real-time data writing provides query services for real-time data insertion, including the following steps: Data collection, pushing the collected data to the message queue; Data query: write scripts through Spark and write data to the data service for query; Monitor abnormal situations in data processing and handle them.

6. The method for real-time query of massive data according to claim 5, wherein: When writing a script using Spark, first read the micro-batch time in the Spark configuration file. After each configured time, read the offset of the ZK record, obtain the data in Kafka based on the offset, and then write the data to the data service for query.

7. The method for real-time query of massive data according to claim 1, wherein: Monitoring operations are automated data monitoring operations and include the following steps: Monitor data stream processing jobs, monitor data stream processing status, and check whether there is data backlog. If data backlog occurs, send an alert via email. Monitor cluster resources, including data service storage capacity, usage, network, I / O, and disk metrics, as well as failure conditions such as media hardware failures and network failures. Automatically handle exceptions. When an abnormal operation failure is detected, it can be automatically processed every five minutes to ensure data accuracy and reliability. Generate reports on the data service provision process, including average response time, exceptions, and usage information for interface table queries, so as to evaluate and optimize the query performance of data services.

8. A device for real-time query of massive data, comprising: Establish a data service platform module to establish a data service platform on the data center or cloud platform of an enterprise or organization, provide data query services, and receive and process data; The data service interface-related content module is used to configure and manage data services, set interface table information, including interface table pre-partitioning and setting interface table permissions. The module obtains historical data of the existing first primary key through salting and transcoding. Based on the distribution of the first primary key data, it imports it into the pre-partition configuration file to obtain the pre-partition information. Based on the data service configuration, the module automatically creates the interface table, inserts permission data into the permission table, and automatically refreshes the permission cache. The real-time data writing module uses a stream-batch integrated approach to insert data in real time, write it into the data interface table, and then retrieve the data, providing efficient query services for massive data. The monitoring module is used for operation and maintenance monitoring of abnormal situations of real-time data writing and automatic email alerts. Among them, the data service interface related content modules are configured to automatically configure the data service and create and authorize the interface table. Scripts are written through Hbase to generate table creation statements and complete table creation to achieve automated data service generation. Among them, when the automated data service is generated, it first determines whether the configuration file of the interface table file meets the requirements, then reads the configuration file, parses the fields and primary key information in the configuration file, puts this information in the object, and then generates the Hbase table creation statement to complete the table creation; then confirms whether the metadata exists in the relational database. If it exists, delete the original metadata and re-insert the new metadata statement in the relational database. If it does not exist, directly insert the metadata.

9. An electronic device comprising: A memory, a processor, and a massive data real-time query program stored in the memory and executable on the processor, wherein the massive data real-time query program, when executed by the processor, implements the steps of the massive data real-time query method according to any one of claims 1 to 7.

10. A computer storage medium, wherein: The computer storage medium stores a massive data real-time query program, which, when executed by a processor, implements the steps of the massive data real-time query method according to any one of claims 1 to 7.

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