Instance-based database data consistency detection method and device

Abstract

This invention provides an instance-based database data consistency detection method and device. When the data of a certain IoT instance needs consistency detection, this invention generates an instruction to perform data consistency detection on the data of the IoT instance through the MDCC main service on the database master node, and sends the instruction to the MDCC sub-service on the data storage node. The MDCC sub-service assigns the detection tasks of the data shards with the same ID as the IoT instance on the data storage node where the IoT instance's data is located to the same MDCC thread. The MDCC thread performs consistency detection on the data shards with the same ID as the IoT instance based on the assigned detection tasks to obtain the detection results. This enables the cluster daemon to start the IoT instance-based data detection program to scan the entire database after the database cluster nodes recover from a crash, ensuring data recovery consistency, reducing the time when some cluster nodes are unavailable, and effectively improving the performance of database data consistency detection.

Description

Technical Field

[0001] This invention relates to an instance-based database data consistency detection method and device. Background Technology

[0002] With the development of the Internet of Things (IoT), distributed databases are being used more and more widely, and the requirements for data reliability are constantly increasing. In particular, after a database cluster node fails and recovers, data consistency testing becomes crucial, and the performance of this testing directly affects the service recovery time.

[0003] The current industry approach is to traverse the entire table from the root node of the index data to the leaf data nodes. The traversed object can be the entire table or a data partition.

[0004] Since the storage hierarchy of database systems in the industry is generally: Table -> Partition -> Page, the data consistency is generally based on the concurrent detection of the table and partition, which has low detection efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide an instance-based database data consistency detection method and device.

[0006] To address the above problems, this invention provides an instance-based database data consistency detection method, comprising:

[0007] The database storage hierarchy is set from largest to smallest as follows: data table, data partition, data shard, and data storage block. The data of each IoT instance is stored in one or more corresponding data shards. Each data shard is stored on the database's data storage node. Each data shard stores all or part of the data of only one corresponding IoT instance. The ID of each data shard is the ID of the corresponding IoT instance.

[0008] When a data shard needs data consistency testing, the MDCC main service on the database master node generates an instruction to perform data consistency testing on the data shard and sends the instruction to the MDCC sub-service on the corresponding data storage node. The MDCC sub-service assigns the testing task of the data shard with the same ID as the data shard that needs data consistency testing to the same MDCC thread on the corresponding data storage node.

[0009] The MDCC thread performs consistency checks on the data shards with the same ID that require consistency checks, based on the assigned detection task, to obtain the detection results.

[0010] The MDCC sub-service aggregates the detection results and sends them to the MDCC main service on the master node.

[0011] Furthermore, in the above method, when a data shard requires data consistency testing, the MDCC master service on the database master node generates an instruction to perform data consistency testing on that data shard, and sends the instruction to the MDCC sub-service on the corresponding data storage node, including:

[0012] The MDCC master service on the database master node accesses the master node's metadata to obtain the data shards that need to be checked for data consistency and the data storage nodes they reside on.

[0013] The MDCC main service on the database master node generates instructions to perform data consistency checks on the data shards that need to be checked and their respective data storage nodes, and sends the instructions to the MDCC sub-service on the data storage node where the data shards that need to be checked are located.

[0014] Furthermore, in the above method, the MDCC sub-service assigns the detection tasks of data shards with the same ID as the data shards that need to be checked for data consistency on the corresponding data storage node to the same MDCC thread, including:

[0015] The MDCC sub-service hashes the IDs of the data shards that need to be checked for data consistency on the corresponding data storage node to obtain the corresponding hash values.

[0016] The MDCC subservice assigns the detection tasks of data fragments with the same hash value to the same MDCC thread.

[0017] Furthermore, in the above method, the MDCC sub-service assigns the detection tasks of data shards with the same ID as the data shards that need to be checked for data consistency on the corresponding data storage node to the same MDCC thread, including:

[0018] The MDCC subservice hashes the ID of the data shard that needs to be checked for data consistency on the corresponding data storage node and the data partition to which the data shard belongs, to obtain the corresponding hash value.

[0019] The MDCC subservice assigns the detection tasks of data fragments with the same hash value to the same MDCC thread.

[0020] Furthermore, in the above method, the MDCC thread performs consistency checks on the data shards with the same ID that require consistency checks, based on the assigned detection task, to obtain the detection results, including:

[0021] Based on the assigned detection task, the MDCC thread performs a binary fast traversal on the data shards with the same ID that require consistency checks, according to the key value order, to obtain detection results including whether the key values ​​are ordered, whether the data is complete, and whether its timestamp is reasonable.

[0022] According to another aspect of the present invention, an instance-based database data consistency detection device is also provided, comprising:

[0023] The device is configured to set the storage hierarchy of the database from largest to smallest as follows: data table, data partition, data shard, and data storage block. The data of each IoT instance is stored in one or more corresponding data shards. Each data shard is stored on the data storage node of the database. Each data shard stores all or part of the data of only one corresponding IoT instance. The ID of each data shard is the ID of the corresponding IoT instance.

[0024] The MDCC master service on the master node is used to generate an instruction to perform a data consistency check on a data shard when a data shard needs a data consistency check, and send the instruction to the MDCC sub-service on the corresponding data storage node.

[0025] The MDCC sub-service is used to assign the detection tasks of data shards with the same ID as the data shards that need to be checked for data consistency on the corresponding data storage node to the same MDCC thread, and to summarize the detection results of the MDCC thread to the MDCC main service on the master node.

[0026] The MDCC thread is used to perform consistency checks on data shards with the same ID that require consistency checks, based on the assigned detection task, in order to obtain the detection results.

[0027] Furthermore, in the aforementioned device, the MDCC main service on the master node of the database is used to access the metadata of the master node to obtain the data shards that need to be tested for data consistency and their respective data storage nodes; based on the data shards that need to be tested for data consistency and their respective data storage nodes, it generates instructions to perform data consistency testing on the data shards that need to be tested for data consistency and sends the instructions to the MDCC sub-service on the data storage node where the data shards that need to be tested for data consistency are located.

[0028] Furthermore, in the aforementioned device, the MDCC sub-service is used to hash the IDs of the data shards that need to be checked for data consistency on the corresponding data storage node to obtain the corresponding hash values; and to assign the detection tasks of data shards with the same hash value to the same MDCC thread.

[0029] Furthermore, in the aforementioned device, the MDCC sub-service is used to hash the ID of the data shard that needs to be checked for data consistency on the corresponding data storage node and the data partition to which the data shard belongs, in order to obtain the corresponding hash value; and to assign the detection tasks of data shards with the same hash value to the same MDCC thread.

[0030] Furthermore, in the aforementioned device, the MDCC thread is used to perform a binary fast traversal detection on the data shards with the same ID that require consistency detection, based on the assigned detection task, according to the key value order, to obtain detection results including whether the key values ​​are ordered, whether the data is complete, and whether its timestamp is reasonable.

[0031] Compared with existing technologies, this invention sets the database storage hierarchy from largest to smallest as data tables, data partitions, data shards, and data storage blocks. Data for each IoT instance is stored in one or more corresponding data shards. Each data shard is stored on a database data storage node, and each data shard stores all or part of the data for a single IoT instance. The ID of each data shard is the ID of the corresponding IoT instance. When data consistency checks are required for a particular IoT instance, the MDCC master service on the database master node generates an instruction to perform a data consistency check on the data for that IoT instance and sends the instruction to the data storage node. The MDCC sub-service assigns the detection tasks of data shards with the same ID as the IoT instance on the data storage node where the IoT instance's data resides to the same MDCC thread. Based on the assigned detection tasks, the MDCC thread performs consistency checks on the data shards with the same ID as the IoT instance to obtain the detection results. This enables the cluster daemon to start a data detection program based on the IoT instance to scan the entire database after the database cluster nodes recover from a crash, ensuring data recovery consistency, reducing the time when some cluster nodes are unavailable, and effectively improving the performance of database data consistency detection. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of a distributed data storage system for an instance-based database data consistency detection method according to an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of a data storage block according to an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of data fragmentation according to an embodiment of the present invention;

[0035] Figure 4This is a schematic diagram of an IoT example according to an embodiment of the present invention. Detailed Implementation

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] like Figure 1 As shown, the present invention provides an instance-based database data consistency detection method, comprising:

[0038] Step S1: Set the storage hierarchy of the database from largest to smallest as data table, data partition, data shard, and data storage block. The data of each IoT instance is stored in one or more corresponding data shards (SliceTable). Each data shard is stored on the data storage node (Ranger) of the database. Each data shard (SliceTable) stores all or part of the data of the corresponding IoT instance. The ID of each data shard is the ID of the corresponding IoT instance.

[0039] Here, the data for an IoT instance may include: the IoT instance's ID, timestamp, and data value;

[0040] A data table can include multiple data partitions, a data partition can include multiple data shards, and a data shard can include multiple data storage blocks.

[0041] Step S2: When a data slice (SliceTable) needs data consistency testing, the MDCC master service on the database master node generates an instruction to perform data consistency testing on the data slice (SliceTable) and sends the instruction to the MDCC sub-service on the corresponding data storage node (Ranger). The MDCC sub-service assigns the testing tasks of the data slices with the same ID as the data slice (SliceTable) on the corresponding data storage node (Ranger) to the same MDCC thread.

[0042] Here, the data consistency checker service of the present invention (Maxtable Data Consistency Checker), hereinafter referred to as MDCC, has the MDCC main service running on the Master node and the MDCC sub-services running on each ranger node;

[0043] By using the data shard ID, which is also the IoT instance ID, detection tasks belonging to the same IoT instance can be grouped together and assigned to the same MDCC thread.

[0044] Step S3: Based on the assigned detection task, the MDCC thread performs consistency checks on the data in the data slices (SliceTable) with the same ID that require consistency checks, in order to obtain the detection results.

[0045] In step S4, the MDCC sub-service summarizes the detection results and sends them to the MDCC master service on the master node.

[0046] Maxtable, as described in this invention, is a distributed system for massive data storage and processing, addressing the database requirements of high concurrency, large data volumes, and low-latency read / write operations. It supports rich database-like query functions, including the creation and use of secondary indexes. It supports full-memory computation, resulting in fast data read / write response times, capable of handling a large number of concurrent requests and managing massive amounts of data. It offers excellent scalability; expansion only requires adding machines to the cluster, and the failure of any node will neither paralyze the system nor result in data loss.

[0047] Since the existing database system storage hierarchy is generally: Table -> Partition -> Page, its data consistency is generally based on the concurrent detection of data tables and data partitions, and the granularity of the data objects detected is relatively large.

[0048] The data storage hierarchy of Maxtable in this invention is: Table -> Tablet -> SliceTable -> Block.

[0049] The data storage block is the smallest basic unit of Maxtable's underlying storage. Its size can be configured to be 64KB, 128KB, 1MB, or other sizes depending on requirements. Each block stores multiple rows of data, sorted by row key. The format of a data storage block is as follows: Figure 2 As shown.

[0050] A data slice (SliceTable) consists of 64 storage blocks. The blocks can be linked based on metadata in their headers. The SliceTable is the smallest basic unit for load balancing, multi-replica fault tolerance, and disaster recovery among the Rangers (data storage nodes) in a Maxtable cluster. Its storage format is as follows: Figure 3 As shown.

[0051] The data table, data partition, and data block of this invention basically correspond to the concepts of data table, data partition, and data page in existing databases.

[0052] However, the data slice (SliceTable) of the present invention is different from the existing database page and partition concepts. From the perspective of storage size, the data slice (SliceTable) of the present invention is larger than the data page (Page) of the existing database and the data storage block (Block) of the present invention, but smaller than the data partition (Partition and Tablet) of the existing database.

[0053] From the perspective of data storage allocation logic, the data slice (SliceTable) of this invention is the basic unit for storage allocation based on IoT instances. One data slice (SliceTable) can only store the data of one IoT instance. The data of an IoT instance is stored by one or more data slices (SliceTables).

[0054] The data storage for IoT instances consists of a series of SliceTables indexed by the IoT instance's ID, with the basic structure as follows: Figure 4 As shown.

[0055] like Figure 1 As shown, the Maxtable system of this invention has two roles: Master (master node) and Ranger (data storage node). Figure 1 In this context, the Master-slave is a backup master node. When the Master fails, the Master-slave can continue to provide services as the Master.

[0056] The Master node is responsible for managing the Rangers (data storage nodes) and providing some global services, such as load balancing, schema management, and service recovery after a Ranger failure.

[0057] The Master node has two main responsibilities:

[0058] 1. Manage meta-operations, such as creating tables and changing table structures;

[0059] 2. Manage Rangers (data storage nodes), monitor their working status, adjust the data served by the Rangers to achieve load balancing, and migrate services when a Ranger fails.

[0060] A Ranger (data storage node) is the unit that provides services. Multiple Ranger services can be deployed on a single worker machine to fully utilize the processing power of a multi-core CPU. Each Ranger manages a certain range of SliceTable data from the user table, while also providing a memory cache to load the latest, most frequently accessed SliceTable data into memory and provide it to the public.

[0061] The Maxtable of this invention serves as a data storage system for IoT devices. Each IoT device connected to Maxtable is called an IoT instance, and each instance has a globally unique IoT instance ID. Each IoT device uploads instance status data at regular intervals. The IoT devices can be, for example, cameras, industrial robots, garbage trucks, etc. When the IoT device is a garbage truck, it uploads instance status data at regular intervals, such as vehicle speed, mileage, fuel consumption, etc.

[0062] This invention sets the database storage hierarchy from largest to smallest as data tables, data partitions, data shards, and data storage blocks. Data for each IoT instance is stored in one or more corresponding data shards. Each data shard is stored on a database data storage node, and each data shard stores all or part of the data for a specific IoT instance. The ID of each data shard is the ID of the corresponding IoT instance. When data consistency checks are required for a particular IoT instance, the MDCC master service on the database master node generates an instruction to perform a data consistency check on the IoT instance's data and sends the instruction to the MDC on the data storage node. The C sub-service, MDCC sub-service, assigns the detection tasks of data shards with the same ID as the IoT instance on the data storage node where the IoT instance's data resides to the same MDCC thread. Based on the assigned detection tasks, the MDCC thread performs consistency checks on the data shards with the same ID as the IoT instance to obtain the detection results. This enables the cluster daemon to start a data detection program based on the IoT instance to scan the entire database after the database cluster nodes recover from a crash, ensuring data recovery consistency, reducing the time when some cluster nodes are unavailable, and effectively improving the performance of database data consistency detection.

[0063] In one embodiment of the instance-based database data consistency detection method of the present invention, step S2, when a data shard (SliceTable) requires data consistency detection, the MDCC master service on the database master node generates an instruction to perform data consistency detection on the data shard (SliceTable), and sends the instruction to the MDCC sub-service on the corresponding data storage node (Ranger), including:

[0064] In step S211, the MDCC master service on the master node of the database accesses the metadata of the master node to obtain the data shards (SliceTable) that need to be checked for data consistency and the data storage nodes (Ranger) where they are located.

[0065] In step S212, the MDCC main service on the database master node generates an instruction to perform data consistency testing on the data shard (SliceTable) that needs to be tested for data consistency, based on the data shard (SliceTable) and the data storage node (Ranger) where it resides, and sends the instruction to the MDCC sub-service on the data storage node (Ranger) where the data shard (SliceTable) that needs to be tested for data consistency is located.

[0066] Here, by accessing the metadata of the master node through the MDCC main service, the data shards (SliceTable) that need to be checked for data consistency and their corresponding data storage nodes (Ranger) can be obtained. This allows for the accurate and efficient sending of instructions to the MDCC sub-service on the data storage node (Ranger) where the data shards (SliceTable) that need to be checked for data consistency are located.

[0067] In one embodiment of the instance-based database data consistency detection method of the present invention, step S2, the MDCC sub-service assigns the detection tasks of data shards on the corresponding data storage node (Ranger) with the same ID as the data shard (SliceTable) that needs to be tested for data consistency to the same MDCC thread, including:

[0068] Step S221: The MDCC sub-service hashes the IDs of the data slices (SliceTable) that need to be checked for data consistency on the corresponding data storage node (Ranger) to obtain the corresponding hash values.

[0069] In step S222, the MDCC sub-service assigns the detection tasks of data fragments with the same hash value to the same MDCC thread.

[0070] Here, the MDCC master service on the database master node sends a command to perform data consistency checks on the data shard to the MDCC sub-service on the data storage node (Ranger) where the data shard resides. The MDCC sub-service on the data storage node (Ranger) where the data shard resides performs hash classification based on the data shard ID recorded in the first block of the data shard, and assigns the detection tasks of data shards with the same hash value to the same MDCC thread. This makes the granularity of data processing and concurrency design of the MDCC thread very fine, ensuring high-performance and highly reliable online applications in large-scale IoT data storage scenarios.

[0071] In one embodiment of the instance-based database data consistency detection method of the present invention, step S2, the MDCC sub-service assigns the detection tasks of data shards on the corresponding data storage node (Ranger) with the same ID as the data shard (SliceTable) that needs to be tested for data consistency to the same MDCC thread, including:

[0072] Step S231: The MDCC sub-service hashes the ID of the data slice (SliceTable) that needs to be checked for data consistency on the corresponding data storage node (Ranger) and the data partition to which it belongs, in order to obtain the corresponding hash value.

[0073] In step S232, the MDCC sub-service assigns the detection tasks of data fragments with the same hash value to the same MDCC thread.

[0074] Here, when the master node needs to check the data consistency of each data storage node, the data shards to be checked can be categorized through a two-layer index of data partitioning and hashing, and then distributed to the corresponding threads within each node for parallel testing. Since SliceTable stores data shards on different Ranger nodes according to data partitions and IoT instance IDs, and its data shards can be set to hundreds of megabytes or even less than ten megabytes, the granularity of data processing and concurrency design of the MDCC thread is very fine, ensuring high-performance and highly reliable online applications in large-scale IoT data storage scenarios.

[0075] In one embodiment of the instance-based database data consistency detection method of the present invention, step S3, the MDCC thread performs consistency detection on the data in the data slice (SliceTable) with the same ID that needs consistency detection based on the assigned detection task, so as to obtain the detection result, including:

[0076] Based on the assigned detection task, the MDCC thread performs a binary fast traversal on the data in the data shard (SliceTable) with the same ID that needs consistency testing, according to the key value order, to obtain detection results including whether the key value is ordered, whether the data is complete, and whether its timestamp is reasonable.

[0077] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0078] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0079] Obviously, those skilled in the art can make various modifications and variations to the invention without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of the invention and their equivalents, the invention is also intended to include these modifications and variations.

Claims

1. An instance-based database data consistency detection method, characterized in that, include: The database storage hierarchy is set from largest to smallest as follows: data table, data partition, data shard, and data storage block. Data for each IoT instance is stored in one or more corresponding data shards. Each data shard is stored on a data storage node of the database, and each data shard stores all or part of the data for its corresponding IoT instance. The ID of each data shard is the ID of the corresponding IoT instance. A data table includes multiple data partitions, a data partition includes multiple data shards, and a data shard includes multiple data storage blocks. Each data storage block stores multiple rows of data, sorted by row key. Data storage blocks within a data shard are linked based on metadata in the Block header. When a data shard needs data consistency testing, the MDCC main service on the database master node generates an instruction to perform data consistency testing on the data shard and sends the instruction to the MDCC sub-service on the corresponding data storage node. The MDCC sub-service assigns the testing task of the data shard with the same ID as the data shard that needs data consistency testing to the same MDCC thread on the corresponding data storage node. The MDCC thread performs consistency checks on the data shards with the same ID that require consistency checks, based on the assigned detection task, to obtain the detection results. The MDCC sub-service aggregates the detection results and sends them to the MDCC main service on the master node. When a data shard requires data consistency testing, the MDCC master service on the database master node generates an instruction to perform data consistency testing on that data shard and sends the instruction to the MDCC sub-service on the corresponding data storage node, including: The MDCC master service on the database master node accesses the master node's metadata to obtain the data shards that need to be checked for data consistency and the data storage nodes they reside on. The MDCC main service on the database master node generates instructions to perform data consistency checks on the data shards that need to be checked for data consistency, based on the data shards that need to be checked for data consistency and the data storage nodes they reside on, and sends the instructions to the MDCC sub-service on the data storage nodes where the data shards that need to be checked for data consistency are located. The MDCC sub-service assigns the data shard detection tasks on the corresponding data storage node that have the same ID as the data shard requiring data consistency detection to the same MDCC thread, including: The MDCC sub-service hashes the IDs of the data shards that need to be checked for data consistency on the corresponding data storage node to obtain the corresponding hash values. The MDCC sub-service assigns the detection tasks of data fragments with the same hash value to the same MDCC thread; Alternatively, the MDCC sub-service may assign the detection tasks of data shards with the same ID as the data shards that need to be checked for data consistency on the corresponding data storage node to the same MDCC thread, including: The MDCC subservice hashes the ID of the data shard that needs to be checked for data consistency on the corresponding data storage node and the data partition to which the data shard belongs, to obtain the corresponding hash value. The MDCC subservice assigns the detection tasks of data fragments with the same hash value to the same MDCC thread.

2. The instance-based database data consistency detection method as described in claim 1, characterized in that, The MDCC thread performs consistency checks on data shards with the same ID that require consistency checks, based on the assigned detection task, to obtain the detection results, including: Based on the assigned detection task, the MDCC thread performs a binary fast traversal on the data shards with the same ID that require consistency checks, according to the key value order, to obtain detection results including whether the key values ​​are ordered, whether the data is complete, and whether its timestamp is reasonable.

3. An instance-based database data consistency detection device, characterized in that, include: The device is configured to set the database storage hierarchy from largest to smallest as follows: data table, data partition, data shard, and data storage block. Data of each IoT instance is stored in one or more corresponding data shards. Each data shard is stored on a data storage node of the database, and each data shard stores all or part of the data of a corresponding IoT instance. The ID of each data shard is the ID of the corresponding IoT instance. A data table includes multiple data partitions, a data partition includes multiple data shards, and a data shard includes multiple data storage blocks. Each data storage block stores multiple rows of data, sorted by row key value. Data storage blocks within a data shard are linked based on the metadata of the Blockheader. The MDCC master service on the master node is used to generate an instruction to perform a data consistency check on a data shard when a data shard needs a data consistency check, and send the instruction to the MDCC sub-service on the corresponding data storage node. The MDCC sub-service is used to assign the detection tasks of data shards with the same ID as the data shards that need to be checked for data consistency on the corresponding data storage node to the same MDCC thread, and to summarize the detection results of the MDCC thread to the MDCC main service on the master node. The MDCC thread is used to perform consistency checks on data shards with the same ID that require consistency checks, based on the assigned detection task, in order to obtain the detection results. The MDCC main service on the master node of the database is used to access the metadata of the master node to obtain the data shards that need to be tested for data consistency and their respective data storage nodes; based on the data shards that need to be tested for data consistency and their respective data storage nodes, it generates instructions to perform data consistency testing on the data shards that need to be tested for data consistency and sends the instructions to the MDCC sub-service on the data storage node where the data shards that need to be tested for data consistency are located; The MDCC sub-service is used to hash the IDs of the data shards that need to be checked for data consistency on the corresponding data storage node to obtain the corresponding hash values; and to assign the detection tasks of data shards with the same hash value to the same MDCC thread. Alternatively, the MDCC sub-service is used to hash the ID of the data shard that needs to be checked for data consistency on the corresponding data storage node and the data partition to which the data shard belongs, in order to obtain the corresponding hash value; and to assign the detection tasks of data shards with the same hash value to the same MDCC thread.

4. The instance-based database data consistency detection device as described in claim 3, characterized in that, The MDCC thread is used to perform a binary fast traversal test on the data shards with the same ID that need consistency testing, based on the assigned detection task, according to the key value order, to obtain detection results including whether the key value is ordered, whether the data is complete, and whether its timestamp is reasonable.

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